/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/python/framework/python_op_gen_internal.h"

#include <float.h>
#include <stdio.h>

#include <iomanip>
#include <sstream>
#include <unordered_map>

#include "absl/strings/escaping.h"
#include "tensorflow/core/framework/api_def.pb.h"
#include "tensorflow/core/framework/attr_value.pb.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_def.pb_text.h"
#include "tensorflow/core/framework/op_def.pb.h"
#include "tensorflow/core/framework/op_def_util.h"
#include "tensorflow/core/framework/op_gen_lib.h"
#include "tensorflow/core/framework/tensor.pb_text.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/lib/gtl/map_util.h"
#include "tensorflow/core/lib/gtl/stl_util.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/lib/strings/strcat.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/types.h"

namespace tensorflow {
namespace python_op_gen_internal {

const int kRightMargin = 78;
// Names specified in tf_export decorators are exported to
// TensorFlow 2.0 by default.
const int kLatestAPIExportVersion = 2;

bool IsPythonReserved(const string& s) {
  static const std::set<string>* const kPythonReserved = new std::set<string>(
      {// Keywords in Python, from:
       //   import keyword
       //   print keyword.kwlist
       "and", "as", "assert", "break", "class", "continue", "def", "del",
       "elif", "else", "except", "exec", "finally", "for", "from", "global",
       "if", "import", "in", "is", "lambda", "not", "or", "pass", "print",
       "raise", "return", "try", "while", "with", "yield",
       // Built-in functions and types in Python, from:
       //   [x for x in dir(__builtins__) if not x[0].islower()]
       "ArithmeticError", "AssertionError", "AttributeError", "BaseException",
       "BufferError", "BytesWarning", "DeprecationWarning", "EOFError",
       "Ellipsis", "EnvironmentError", "Exception", "False",
       "FloatingPointError", "FutureWarning", "GeneratorExit", "IOError",
       "ImportError", "ImportWarning", "IndentationError", "IndexError",
       "KeyError", "KeyboardInterrupt", "LookupError", "MemoryError",
       "NameError", "None", "NotImplemented", "NotImplementedError", "OSError",
       "OverflowError", "PendingDeprecationWarning", "ReferenceError",
       "RuntimeError", "RuntimeWarning", "StandardError", "StopIteration",
       "SyntaxError", "SyntaxWarning", "SystemError", "SystemExit", "TabError",
       "True", "TypeError", "UnboundLocalError", "UnicodeDecodeError",
       "UnicodeEncodeError", "UnicodeError", "UnicodeTranslateError",
       "UnicodeWarning", "UserWarning", "ValueError", "Warning",
       "ZeroDivisionError", "__debug__", "__doc__", "__import__", "__name__",
       "__package__"});

  return kPythonReserved->count(s) > 0;
}

bool IsOpWithUnderscorePrefix(const string& s) {
  static const std::set<string>* const kUnderscoreOps = new std::set<string>(
      {// Lowercase built-in functions and types in Python, from:
       // [x for x in dir(__builtins__) if x[0].islower()] except "round".
       // These need to be excluded so they don't conflict with actual built-in
       // functions since we use '*' imports.
       "abs", "all", "any", "apply", "bin", "bool", "buffer", "bytearray",
       "bytes", "callable", "chr", "classmethod", "cmp", "coerce", "compile",
       "complex", "copyright", "credits", "delattr", "dict", "dir", "divmod",
       "enumerate", "eval", "execfile", "exit", "file", "filter", "float",
       "format", "frozenset", "getattr", "globals", "hasattr", "hash", "help",
       "hex", "id", "input", "int", "intern", "isinstance", "issubclass",
       "iter", "len", "license", "list", "locals", "long", "map", "max",
       "memoryview", "min", "next", "object", "oct", "open", "ord", "pow",
       "print", "property", "quit", "range", "raw_input", "reduce", "reload",
       "repr", "reversed", "set", "setattr", "slice", "sorted", "staticmethod",
       "str", "sum", "super", "tuple", "type", "unichr", "unicode", "vars",
       "xrange", "zip",
       // These have the same name as ops defined in Python and might be used
       // incorrectly depending on order of '*' imports.
       // TODO(annarev): reduce usage of '*' imports and remove these from the
       // list.
       "fused_batch_norm", "histogram_fixed_width", "stack",
       "batch_norm_with_global_normalization", "clip_by_value"});
  return kUnderscoreOps->count(s) > 0;
}

string AvoidPythonReserved(const string& s) {
  if (IsPythonReserved(s)) return strings::StrCat(s, "_");
  return s;
}

// Indent the first line by "initial" spaces and all following lines
// by "rest" spaces.
string Indent(int initial, int rest, StringPiece in) {
  // TODO(josh11b): Also word-wrapping?
  string copy(in.data(), in.size());
  absl::StripTrailingAsciiWhitespace(&copy);
  std::vector<string> v = str_util::Split(copy, '\n');

  string result;
  bool first = true;
  for (const string& line : v) {
    if (first) {
      result = strings::StrCat(Spaces(initial), line, "\n");
      first = false;
    } else {
      if (line.empty()) {
        strings::StrAppend(&result, "\n");
      } else {
        strings::StrAppend(&result, Spaces(rest), line, "\n");
      }
    }
  }
  return result;
}

// Adds append to *dest, with a space if the first line will be <= width,
// or a newline otherwise.
void AppendWithinWidth(string* dest, StringPiece append, int width) {
  auto first_line = append.find('\n');
  if (first_line == string::npos) first_line = append.size();
  if (dest->size() + first_line + 1 /* space */ > static_cast<size_t>(width)) {
    strings::StrAppend(dest, "\n", append);
  } else {
    strings::StrAppend(dest, " ", append);
  }
}

// Like DataTypeString() but uses the Python names for the
// float types.
string PythonDataTypeString(DataType dtype) {
  switch (dtype) {
    case DT_FLOAT:
      return "float32";
    case DT_DOUBLE:
      return "float64";
    default:
      return DataTypeString(dtype);
  }
}

string TypeString(DataType dtype, bool ref) {
  if (ref) {
    return strings::StrCat("mutable `", PythonDataTypeString(dtype), "`");
  } else {
    return strings::StrCat("`", PythonDataTypeString(dtype), "`");
  }
}

string TypeListString(const AttrValue& value) {
  string ret;
  for (int t : value.list().type()) {
    if (!ret.empty()) strings::StrAppend(&ret, ", ");
    DataType dtype = static_cast<DataType>(t);
    if (IsRefType(dtype)) {
      strings::StrAppend(&ret, PythonDataTypeString(RemoveRefType(dtype)),
                         " mutable");
    } else {
      strings::StrAppend(&ret, "`", PythonDataTypeString(dtype), "`");
    }
  }
  return ret;
}

string SingleTensorName(DataType dtype, bool is_ref) {
  const string type_str = TypeString(dtype, is_ref);
  return strings::StrCat("A `Tensor` of type ", type_str, ".");
}

const char kUnknownTensorType[] = {"A `Tensor`."};

string ArgTypeName(const OpDef& op_def, const OpDef::ArgDef& arg,
                   const std::unordered_map<string, string>& inferred_attrs,
                   bool is_output) {
  if (!arg.number_attr().empty()) {
    // N Tensors with the same type
    const string* original_arg =
        gtl::FindOrNull(inferred_attrs, arg.number_attr());
    string prefix;
    if (original_arg == nullptr) {
      prefix = strings::StrCat("A list of `", arg.number_attr(), "`");
    } else if (*original_arg == arg.name()) {
      const OpDef::AttrDef* attr = FindAttr(arg.number_attr(), op_def);
      if (attr->has_minimum() && attr->minimum() > 0) {
        prefix = strings::StrCat("A list of at least ", attr->minimum());
      } else {
        prefix = "A list of";
      }
    } else {
      prefix = strings::StrCat("A list with the same length as `",
                               AvoidPythonReserved(*original_arg), "` of");
    }

    if (arg.type() != DT_INVALID) {
      return strings::StrCat(prefix, " `Tensor` objects with type ",
                             TypeString(arg.type(), arg.is_ref()), ".");
    } else {
      original_arg = gtl::FindOrNull(inferred_attrs, arg.type_attr());
      if (arg.is_ref()) {
        strings::StrAppend(&prefix, " mutable");
      }
      if (original_arg == nullptr) {
        return strings::StrCat(prefix, " `Tensor` objects with type `",
                               arg.type_attr(), "`.");
      } else if (*original_arg == arg.name()) {
        const OpDef::AttrDef* attr = FindAttr(arg.type_attr(), op_def);
        if (attr->has_allowed_values()) {
          return strings::StrCat(prefix,
                                 " `Tensor` objects with the same type in: ",
                                 TypeListString(attr->allowed_values()), ".");
        } else {
          return strings::StrCat(prefix,
                                 " `Tensor` objects with the same type.");
        }
      } else {
        return strings::StrCat(prefix,
                               " `Tensor` objects with the same type as `",
                               AvoidPythonReserved(*original_arg), "`.");
      }
    }
  } else if (!arg.type_attr().empty() || !arg.type_list_attr().empty()) {
    const bool is_list = !arg.type_list_attr().empty();
    const string attr_name = is_list ? arg.type_list_attr() : arg.type_attr();
    const OpDef::AttrDef* attr = FindAttr(attr_name, op_def);
    const string mutable_str = arg.is_ref() ? "mutable " : "";
    const string prefix =
        is_list ? strings::StrCat("A list of ", mutable_str, "`Tensor` objects")
                : strings::StrCat("A ", mutable_str, "`Tensor`");
    const string* original_arg = gtl::FindOrNull(inferred_attrs, attr_name);
    if (original_arg == nullptr) {
      return strings::StrCat(prefix, " of type `", attr_name, "`.");
    } else if (*original_arg == arg.name()) {
      if (attr->has_allowed_values()) {
        if (is_list) {
          return strings::StrCat(prefix, " with types from: ",
                                 TypeListString(attr->allowed_values()), ".");
        } else {
          return strings::StrCat(
              prefix, is_output ? ". Has one of the following types: "
                                : ". Must be one of the following types: ",
              TypeListString(attr->allowed_values()), ".");
        }
      } else {
        return strings::StrCat(prefix, ".");
      }
    } else {
      return strings::StrCat(prefix,
                             is_output ? ". Has the same type as `"
                                       : ". Must have the same type as `",
                             AvoidPythonReserved(*original_arg), "`.");
    }
  } else {
    return SingleTensorName(arg.type(), arg.is_ref());
  }
}

string GetReturns(const OpDef& op_def,
                  const std::vector<string>& output_type_string) {
  string result;
  DCHECK_EQ(op_def.output_arg_size(), output_type_string.size());
  const int num_outs = op_def.output_arg_size();
  strings::StrAppend(&result, "\n  Returns:\n");
  if (num_outs == 0) {
    strings::StrAppend(&result, "    The created Operation.\n");
  } else {
    if (num_outs == 1) {
      StringPiece description = op_def.output_arg(0).description();
      if (ConsumeEquals(&description)) {  // Skip the generated type info.
        strings::StrAppend(&result, Indent(4, 4, description));
      } else {
        // Special case of one output, don't use the name of the output unless
        // there is no description.
        string desc = output_type_string.empty() ? kUnknownTensorType
                                                 : output_type_string[0];
        if (desc == kUnknownTensorType) {
          // Special case where we don't understand how the output tensor type
          // depends on the input tensor types, just use the output arg
          // description if we can.
          if (!description.empty()) {
            desc = op_def.output_arg(0).description();
          } else if (!op_def.output_arg(0).name().empty()) {
            desc = strings::StrCat(" The ", op_def.output_arg(0).name(),
                                   " `Tensor`.");
          }
        } else if (!description.empty()) {
          AppendWithinWidth(&desc, description, kRightMargin - 4 /* indent */);
        }
        strings::StrAppend(&result, Indent(4, 4, desc));
      }
    } else {
      std::vector<string> out_names(num_outs);
      for (int i = 0; i < num_outs; ++i) {
        if (!op_def.output_arg(i).name().empty()) {
          out_names[i] = op_def.output_arg(i).name();
        } else {
          out_names[i] = strings::StrCat("output", i);
        }
      }
      strings::StrAppend(&result, "    A tuple of `Tensor` objects (",
                         absl::StrJoin(out_names, ", "), ").\n\n");
      for (int i = 0; i < num_outs; ++i) {
        string desc = strings::StrCat(out_names[i], ": ");
        StringPiece description = op_def.output_arg(i).description();
        if (ConsumeEquals(&description)) {  // Skip the generated type info.
          strings::StrAppend(&desc, description);
        } else {
          const string type = static_cast<size_t>(i) < output_type_string.size()
                                  ? output_type_string[i]
                                  : kUnknownTensorType;
          if (!description.empty()) {
            if (type == kUnknownTensorType) {
              // Special case where we don't understand how the output tensor
              // type depends on the input tensor types, so we just use the
              // output arg description.
              strings::StrAppend(&desc, description);
            } else {
              strings::StrAppend(&desc, type, " ", description);
            }
          } else {
            strings::StrAppend(&desc, type);
          }
        }
        strings::StrAppend(&result, Indent(4, 6, desc));
      }
    }
  }
  return result;
}

string StringToPython(const string& str) {
  return strings::StrCat("\"", absl::CEscape(str), "\"");
}

string DataTypeToPython(DataType dtype, const string& dtype_module) {
  return strings::StrCat(dtype_module, PythonDataTypeString(dtype));
}

string ShapeToPython(const TensorShapeProto& shape) {
  if (shape.unknown_rank()) {
    return "None";
  }
  string python = "[";
  for (const auto& dim : shape.dim()) {
    if (python.size() > 1) strings::StrAppend(&python, ", ");
    if (!dim.name().empty()) {
      strings::StrAppend(&python, "(", StringToPython(dim.name()), ", ",
                         dim.size(), ")");
    } else {
      strings::StrAppend(&python, dim.size());
    }
  }
  strings::StrAppend(&python, "]");
  return python;
}

string TensorToPython(const TensorProto& proto) {
  return ProtoShortDebugString(proto);
}

string AttrListToPython(const AttrValue& value,
                        const string& dtype_module = "tf.") {
  string ret;
  if (value.list().s_size() > 0) {
    for (int i = 0; i < value.list().s_size(); ++i) {
      if (i > 0) strings::StrAppend(&ret, ", ");
      strings::StrAppend(&ret, StringToPython(value.list().s(i)));
    }
  } else if (value.list().i_size() > 0) {
    for (int i = 0; i < value.list().i_size(); ++i) {
      if (i > 0) strings::StrAppend(&ret, ", ");
      strings::StrAppend(&ret, value.list().i(i));
    }
  } else if (value.list().f_size() > 0) {
    for (int i = 0; i < value.list().f_size(); ++i) {
      if (i > 0) strings::StrAppend(&ret, ", ");
      strings::StrAppend(&ret, value.list().f(i));
    }
  } else if (value.list().b_size() > 0) {
    for (int i = 0; i < value.list().b_size(); ++i) {
      if (i > 0) strings::StrAppend(&ret, ", ");
      strings::StrAppend(&ret, value.list().b(i) ? "True" : "False");
    }
  } else if (value.list().type_size() > 0) {
    for (int i = 0; i < value.list().type_size(); ++i) {
      if (i > 0) strings::StrAppend(&ret, ", ");
      strings::StrAppend(&ret,
                         DataTypeToPython(value.list().type(i), dtype_module));
    }
  } else if (value.list().shape_size() > 0) {
    for (int i = 0; i < value.list().shape_size(); ++i) {
      if (i > 0) strings::StrAppend(&ret, ", ");
      strings::StrAppend(&ret, ShapeToPython(value.list().shape(i)));
    }
  } else if (value.list().tensor_size() > 0) {
    for (int i = 0; i < value.list().tensor_size(); ++i) {
      if (i > 0) strings::StrAppend(&ret, ", ");
      strings::StrAppend(&ret, TensorToPython(value.list().tensor(i)));
    }
  } else if (value.list().func_size() > 0) {
    for (int i = 0; i < value.list().func_size(); ++i) {
      if (i > 0) strings::StrAppend(&ret, ", ");
      strings::StrAppend(&ret, StringToPython(value.list().func(i).name()));
    }
  }
  return ret;
}

// NOTE: The return value may contain spaces (for example, it could be
// a string "foo bar" with an embedded space) and is not safe to pass
// to WordWrap().
string AttrValueToPython(const string& type, const AttrValue& value,
                         const string& dtype_module) {
  if (type == "string") {
    return StringToPython(value.s());
  } else if (type == "int") {
    return strings::StrCat(value.i());
  } else if (type == "float") {
    if (std::isnan(value.f()) || std::isinf(value.f())) {
      return strings::StrCat("float('", value.f(), "')");
    } else {
      // Use locale-independent conversion.
      static_assert(FLT_DIG < 10, "FLT_DIG is too big");
      std::ostringstream s;
      s.imbue(std::locale::classic());
      s << std::setprecision(FLT_DIG) << value.f();
      return s.str();
    }
  } else if (type == "bool") {
    return value.b() ? "True" : "False";
  } else if (type == "type") {
    return DataTypeToPython(value.type(), dtype_module);
  } else if (type == "shape") {
    return ShapeToPython(value.shape());
  } else if (type == "tensor") {
    return TensorToPython(value.tensor());
  } else if (type == "func") {
    return StringToPython(value.func().name());
  } else if (absl::StartsWith(type, "list(")) {
    return strings::StrCat("[", AttrListToPython(value, dtype_module), "]");
  } else {
    return "?";
  }
}

void GenerateLowerCaseOpName(const string& str, string* result) {
  const char joiner = '_';
  const int last_index = str.size() - 1;
  for (int i = 0; i <= last_index; ++i) {
    const char c = str[i];
    // Convert namespace separators ('>' characters) to joiners
    if (c == '>') {
      result->push_back(joiner);
      continue;
    }

    // Emit a joiner only if a previous-lower-to-now-upper or a
    // now-upper-to-next-lower transition happens.
    if (isupper(c) && (i > 0)) {
      if (islower(str[i - 1]) || ((i < last_index) && islower(str[i + 1]))) {
        result->push_back(joiner);
      }
    }
    result->push_back(tolower(c));
  }
}

static void AddDelimiter(string* append_to, const string& delim) {
  if (!append_to->empty()) strings::StrAppend(append_to, delim);
}

const ApiDef::Attr* FindAttr(StringPiece name, const ApiDef& api_def) {
  for (int i = 0; i < api_def.attr_size(); ++i) {
    if (api_def.attr(i).name() == name) {
      return &api_def.attr(i);
    }
  }
  return nullptr;
}

GenPythonOp::GenPythonOp(const OpDef& op_def, const ApiDef& api_def,
                         const string& function_name)
    : op_def_(op_def),
      api_def_(api_def),
      function_name_(function_name),
      num_outs_(op_def.output_arg_size()) {}

GenPythonOp::~GenPythonOp() {}

string GenPythonOp::Code() {
  // This has all the input args followed by those attrs that don't have
  // defaults.
  std::vector<ParamNames> params_no_default;
  // The parameters with defaults (these have to be listed after those without).
  // No input args are included, just attrs.
  std::vector<ParamNames> params_with_default;

  for (int i = 0; i < api_def_.arg_order_size(); ++i) {
    const auto& arg = *FindInputArg(api_def_.arg_order(i), op_def_);
    const auto& api_def_arg = *FindInputArg(api_def_.arg_order(i), api_def_);
    params_no_default.emplace_back(api_def_arg.name(), api_def_arg.rename_to());
    if (!arg.type_attr().empty()) {
      gtl::InsertIfNotPresent(&inferred_attrs_, arg.type_attr(), arg.name());
    } else if (!arg.type_list_attr().empty()) {
      gtl::InsertIfNotPresent(&inferred_attrs_, arg.type_list_attr(),
                              arg.name());
    }
    if (!arg.number_attr().empty()) {
      gtl::InsertIfNotPresent(&inferred_attrs_, arg.number_attr(), arg.name());
    }
  }
  for (int i = 0; i < api_def_.attr_size(); ++i) {
    const auto& attr(api_def_.attr(i));
    // Do not add inferred attrs to the Python function signature.
    if (inferred_attrs_.find(attr.name()) == inferred_attrs_.end()) {
      if (attr.has_default_value()) {
        params_with_default.emplace_back(attr.name(), attr.rename_to());
      } else {
        params_no_default.emplace_back(attr.name(), attr.rename_to());
      }
    }
  }

  // Save the list of attr parameters (attrs that won't be inferred),
  // those with defaults go at the end.
  // Get the attrs in the order we want by taking the attrs without defaults
  // from the end of args_no_default, and adding args_no_default.
  attrs_.reserve(params_no_default.size() - op_def_.input_arg_size() +
                 params_with_default.size());
  for (int i = op_def_.input_arg_size(); i < params_no_default.size(); ++i) {
    attrs_.push_back(params_no_default[i].GetName());
  }
  for (int i = 0; i < params_with_default.size(); ++i) {
    attrs_.push_back(params_with_default[i].GetName());
  }

  param_names_.reserve(params_no_default.size() + params_with_default.size());
  param_names_.insert(param_names_.begin(), params_no_default.begin(),
                      params_no_default.end());
  for (const auto& param : params_with_default) {
    param_names_.push_back(param);
  }

  string parameters;
  for (const auto& param : params_no_default) {
    AddDelimiter(&parameters, ", ");
    strings::StrAppend(&parameters, param.GetRenameTo());
  }
  for (const auto& param_and_default : params_with_default) {
    AddDelimiter(&parameters, ", ");
    strings::StrAppend(&parameters, param_and_default.GetRenameTo(), "=None");
  }
  AddDelimiter(&parameters, ", ");
  strings::StrAppend(&parameters, "name=None");

  AddExport();
  AddDefLine(parameters);
  AddDocStringDescription();
  AddDocStringArgs();
  AddDocStringInputs();
  AddDocStringAttrs();
  AddDocStringNameArg();
  AddOutputGlobals();
  AddDocStringOutputs();
  strings::StrAppend(&result_, "  \"\"\"\n");
  AddBody("  ");
  strings::StrAppend(&result_, "\n\n");

  return prelude_ + result_;
}

void GenPythonOp::AddExport() {
  if (api_def_.visibility() != ApiDef::VISIBLE) {
    return;
  }
  // Whether op should be available in latest export version.
  bool op_available_in_latest =
      !api_def_.deprecation_version() ||
      api_def_.deprecation_version() > kLatestAPIExportVersion;

  string names;
  string names_v1;
  string deprecated_endpoints;

  for (const auto& endpoint : api_def_.endpoint()) {
    string endpoint_name;
    python_op_gen_internal::GenerateLowerCaseOpName(endpoint.name(),
                                                    &endpoint_name);
    if (endpoint.deprecated() || endpoint.deprecation_version() > 0) {
      AddDelimiter(&deprecated_endpoints, ", ");
      strings::StrAppend(&deprecated_endpoints, "'", endpoint_name, "'");
    }
    // Add all endpoints to TensorFlow 1.* API.
    AddDelimiter(&names_v1, ", ");
    strings::StrAppend(&names_v1, "'", endpoint_name, "'");
    // Add non-deprecated endpoints to TensorFlow 2.* API.
    if (op_available_in_latest &&
        (!endpoint.deprecation_version() ||
         endpoint.deprecation_version() > kLatestAPIExportVersion)) {
      AddDelimiter(&names, ", ");
      strings::StrAppend(&names, "'", endpoint_name, "'");
    }
  }

  // tf_export decorator has the following format:
  // @tf_export(v2_name, v2_name, v1=[v1_name, v1_name])
  if (names != names_v1) {
    AddDelimiter(&names, ", ");
    strings::StrAppend(&names, "v1=[", names_v1, "]");
  }
  strings::StrAppend(&result_, "@tf_export(", names, ")\n");

  // If all endpoints are deprecated, add @deprecated decorator.
  if (!api_def_.deprecation_message().empty()) {
    const string instructions = api_def_.deprecation_message();
    strings::StrAppend(&result_, "@deprecated(None, '", instructions, "')\n");
  }
  // Add @deprecated_endpoints decorator.
  if (!deprecated_endpoints.empty()) {
    strings::StrAppend(&result_, "@deprecated_endpoints(", deprecated_endpoints,
                       ")\n");
  }
}

void GenPythonOp::AddDefLine(const string& function_name,
                             const string& parameters) {
  strings::StrAppend(&result_, "def ", function_name, "(", parameters, "):\n");
}

void GenPythonOp::AddDefLine(const string& parameters) {
  AddDefLine(function_name_, parameters);
}

void GenPythonOp::AddDocStringDescription() {
  string comment;
  if (api_def_.summary().empty()) {
    comment = "TODO: add doc.\n";
  } else {
    comment = strings::StrCat(api_def_.summary(), "\n");
    if (!api_def_.description().empty()) {
      strings::StrAppend(&comment, "\n", Indent(2, 2, api_def_.description()));
    }
  }
  strings::StrAppend(&result_, "  r\"\"\"", comment, "\n");
}

void GenPythonOp::AddDocStringArgs() {
  strings::StrAppend(&result_, "  Args:\n");
}

void GenPythonOp::AddDocStringInputs() {
  for (int i = 0; i < api_def_.arg_order_size(); ++i) {
    const auto& arg = *FindInputArg(api_def_.arg_order(i), op_def_);
    const auto& api_def_arg = *FindInputArg(api_def_.arg_order(i), api_def_);
    StringPiece description = api_def_arg.description();
    string desc;
    if (ConsumeEquals(&description)) {  // Skip the generated type info.
      desc = strings::StrCat(param_names_[i].GetRenameTo(), ": ");
    } else {
      desc = strings::StrCat(param_names_[i].GetRenameTo(), ": ",
                             ArgTypeName(op_def_, arg, inferred_attrs_, false));
    }
    if (!description.empty()) {
      AppendWithinWidth(&desc, description, kRightMargin - 4 /* indent */);
    }
    strings::StrAppend(&result_, Indent(4, 6, desc));
  }
}

void GenPythonOp::AddDocStringAttrs() {
  for (const string& name : attrs_) {
    const auto& attr = *FindAttr(name, op_def_);
    const auto& api_def_attr = *FindAttr(name, api_def_);
    string desc =
        strings::StrCat(AvoidPythonReserved(api_def_attr.rename_to()), ": ");

    static const char* const kAttrTypeName[][2] = {
        {"string", "`string`"},
        {"list(string)", "list of `strings`"},
        {"int", "`int`"},
        {"list(int)", "list of `ints`"},
        {"float", "`float`"},
        {"list(float)", "list of `floats`"},
        {"bool", "`bool`"},
        {"list(bool)", "list of `bools`"},
        {"type", "`tf.DType`"},
        {"list(type)", "list of `tf.DTypes`"},
        {"shape", "`tf.TensorShape` or list of `ints`"},
        {"list(shape)",
         "list of shapes (each a `tf.TensorShape` or list of `ints`)"},
        {"tensor", "`tf.TensorProto`"},
        {"list(tensor)", "list of `tf.TensorProto` objects"},
        {"func", "function decorated with @Defun"},
        {"list(func)", "list of functions decorated with @Defun"},
    };
    for (size_t i = 0; i < TF_ARRAYSIZE(kAttrTypeName); ++i) {
      if (attr.type() == kAttrTypeName[i][0]) {
        string s;
        if (api_def_attr.has_default_value()) {
          s = strings::StrCat("optional ", kAttrTypeName[i][1]);
        } else {
          s = kAttrTypeName[i][1];
        }
        if (s[0] == 'o' || (s[0] == '`' && (s[1] == 'i' || s[1] == 'o'))) {
          strings::StrAppend(&desc, "An ", s);
        } else {
          strings::StrAppend(&desc, "A ", s);
        }
        break;
      }
    }

    if (attr.has_allowed_values()) {
      strings::StrAppend(&desc, " from: `",
                         AttrListToPython(attr.allowed_values()), "`");
    }

    if (attr.has_minimum()) {
      if (attr.type() == "int") {
        strings::StrAppend(&desc, " that is `>= ", attr.minimum(), "`");
      } else if (attr.minimum() > 0) {
        strings::StrAppend(&desc, " that has length `>= ", attr.minimum(), "`");
      }
    }

    strings::StrAppend(&desc, ".");

    if (api_def_attr.has_default_value()) {
      strings::StrAppend(
          &desc, " Defaults to `",
          AttrValueToPython(attr.type(), api_def_attr.default_value()), "`.");
    }
    if (!api_def_attr.description().empty()) {
      AppendWithinWidth(&desc, api_def_attr.description(),
                        kRightMargin - 4 /* indent */);
    }
    strings::StrAppend(&result_, Indent(4, 6, desc));
  }
}

void GenPythonOp::AddDocStringNameArg() {
  strings::StrAppend(&result_,
                     "    name: A name for the operation (optional).\n");
}

void GenPythonOp::AddOutputGlobals() {
  // Prepare a NamedTuple type to hold the outputs, if there are multiple
  if (num_outs_ > 1) {
    // Prepare the list of output names
    std::vector<string> out_names(num_outs_);
    for (int i = 0; i < num_outs_; ++i) {
      if (!api_def_.out_arg(i).rename_to().empty()) {
        out_names[i] = api_def_.out_arg(i).rename_to();
      } else {
        out_names[i] = strings::StrCat("output", i);
      }
    }
    string out_names_list =
        strings::StrCat("[\"", absl::StrJoin(out_names, "\", \""), "\"]");

    // Provide the output names as a Python list
    string lower_op_name_outputs =
        strings::StrCat("_", function_name_, "_outputs");
    const string outputs_prefix = strings::StrCat(lower_op_name_outputs, " = ");
    strings::StrAppend(&prelude_, "\n",
                       WordWrap(outputs_prefix, out_names_list, kRightMargin),
                       "\n");

    strings::StrAppend(&prelude_, "_", op_def_.name(),
                       "Output = _collections.namedtuple(\n");
    const string tuple_type_prefix = "    ";
    const string tuple_type_suffix = strings::StrCat(
        "\"", op_def_.name(), "\", ", lower_op_name_outputs, ")");
    strings::StrAppend(
        &prelude_, WordWrap(tuple_type_prefix, tuple_type_suffix, kRightMargin),
        "\n\n");
  }
  strings::StrAppend(&prelude_, "\n");
}

void GenPythonOp::AddDocStringOutputs() {
  std::vector<string> output_type_string;
  output_type_string.reserve(num_outs_);
  for (int i = 0; i < num_outs_; ++i) {
    output_type_string.push_back(
        ArgTypeName(op_def_, op_def_.output_arg(i), inferred_attrs_, true));
  }
  strings::StrAppend(&result_, GetReturns(op_def_, output_type_string));
}

void GenPythonOp::AddBody(const string& prefix) {
  const string apply_prefix = strings::StrCat(
      prefix, "_result = _op_def_lib.apply_op(\"", op_def_.name(), "\", ");
  AddBodyNoReturn(apply_prefix);
  if (num_outs_ > 1) {
    strings::StrAppend(&result_, prefix, "_result = _", op_def_.name(),
                       "Output._make(_result)\n");
  }
  strings::StrAppend(&result_, prefix, "return _result\n");
}

void GenPythonOp::AddBodyNoReturn(const string& apply_prefix) {
  string args;
  for (size_t i = 0; i < param_names_.size(); ++i) {
    strings::StrAppend(&args, AvoidPythonReserved(param_names_[i].GetName()),
                       "=", param_names_[i].GetRenameTo(), ", ");
  }
  strings::StrAppend(&args, "name=name)");

  strings::StrAppend(&result_,
                     // Wrap the arguments, and indent to the (.
                     WordWrap(apply_prefix, args, kRightMargin), "\n");
}

}  // namespace python_op_gen_internal
}  // namespace tensorflow
