"""simple docstring"""







# Copyright 2022 The HuggingFace Team. 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.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   '''Update an existing config file with the latest defaults while maintaining the old configuration.'''



def       A__    (	lowercase_     :	Dict						)  ->							Any:
   __lowerCamelCase    		=					args.config_file
   if config_file is None and Path(lowercase_						).exists():
      __lowerCamelCase    		=					default_config_file
   elif not Path(lowercase_						).exists():
      raise ValueError(F'''The passed config file located at {config_file} doesn\'t exist.'''						)
   __lowerCamelCase    		=					load_config_from_file(lowercase_						)

   if config_file.endswith('.json'						):
      config.to_json_file(lowercase_						)
   else:
      config.to_yaml_file(lowercase_						)
   return config_file



def       A__    (	lowercase_     :	Optional[Any]      , lowercase_     :	Any						)  ->							Optional[int]:
   __lowerCamelCase    		=					parser.add_parser('update'      , parents=lowercase_      , help=lowercase_      , formatter_class=lowercase_						)
   parser.add_argument(
       '--config_file'      , default=lowercase_      , help=(
           'The path to the config file to update. Will default to a file named default_config.yaml in the cache '
           'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
           'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
           'with \'huggingface\'.'
       )      , )

   parser.set_defaults(func=lowercase_						)
   return parser



def       A__    (	lowercase_     :	str						)  ->							List[Any]:
   __lowerCamelCase    		=					update_config(lowercase_						)
   print(F'''Sucessfully updated the configuration file at {config_file}.'''						)








'''simple docstring'''
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    json.load(open(__SCREAMING_SNAKE_CASE,		"r"							)							)
    snake_case_			:     Optional[int]        =    GPTNeoConfig(
        hidden_size=config_json["n_embd"],		num_layers=config_json["n_layer"],		num_heads=config_json["n_head"],		attention_types=config_json["attention_types"],		max_position_embeddings=config_json["n_positions"],		resid_dropout=config_json["res_dropout"],		embed_dropout=config_json["embed_dropout"],		attention_dropout=config_json["attn_dropout"],		)
    print(f'Building PyTorch model from configuration: {config}'							)
    snake_case_			:     Optional[Any]        =    GPTNeoForCausalLM(__SCREAMING_SNAKE_CASE							)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'							)
    model.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
  a_           =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained mesh-tf model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  a_           =    parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




