code: 
  
  """simple docstring"""
  # Copyright 2021 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.
  
  import json
  import os
  from dataclasses import dataclass
  from enum import Enum
  from typing import List, Optional, Union
  
  import yaml
  
  from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
  from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION
  
  
  _A						=     os.path.expanduser(
      os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
  )
  _A						=     os.path.join(hf_cache_home, """accelerate""")
  _A						=     os.path.join(cache_dir, """default_config.yaml""")
  _A						=     os.path.join(cache_dir, """default_config.yaml""")
  
  # For backward compatibility: the default config is the json one if it's the only existing file.
  if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
      _A						=     default_yaml_config_file
  else:
      _A						=     default_json_config_file
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   Any:
        if config_file is not None:
              if not os.path.isfile(lowerCAmelCase						):
                    raise FileNotFoundError(
                        F"""The passed configuration file `{config_file}` does not exist. """
                        """Please pass an existing file to `accelerate launch`, or use the the default one """
                        """created through `accelerate config` and run `accelerate launch` """
                        """without the `--config_file` argument."""						)
        else:
              UpperCAmelCase__ :       Union[str, Any]						=						default_config_file
        with open(lowerCAmelCase ,  """r""" ,  encoding="""utf-8"""						) as f:
              if config_file.endswith(""".json"""						):
                    if (
                        json.load(lowerCAmelCase						).get("""compute_environment""" ,  ComputeEnvironment.LOCAL_MACHINE						)
                        == ComputeEnvironment.LOCAL_MACHINE
                    ):
                          UpperCAmelCase__ :       List[Any]						=						ClusterConfig
                    else:
                          UpperCAmelCase__ :       Tuple						=						SageMakerConfig
                    return config_class.from_json_file(json_file=lowerCAmelCase						)
              else:
                    if (
                        yaml.safe_load(lowerCAmelCase						).get("""compute_environment""" ,  ComputeEnvironment.LOCAL_MACHINE						)
                        == ComputeEnvironment.LOCAL_MACHINE
                    ):
                          UpperCAmelCase__ :       str						=						ClusterConfig
                    else:
                          UpperCAmelCase__ :       Union[str, Any]						=						SageMakerConfig
                    return config_class.from_yaml_file(yaml_file=lowerCAmelCase						)
  
  @dataclass
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     42
    SCREAMING_SNAKE_CASE							=     42
    SCREAMING_SNAKE_CASE							=     42
    SCREAMING_SNAKE_CASE							=     42
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						self.__dict__
          # For serialization, it's best to convert Enums to strings (or their underlying value type).
          for key, value in result.items():
                if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ):
                      UpperCAmelCase__ :       List[str]						=						value.value
                if isinstance(_lowerCamelCase       ,					_lowerCamelCase  ) and not bool(_lowerCamelCase  ):
                      UpperCAmelCase__ :       str						=						None
          UpperCAmelCase__ :       Union[str, Any]						=						{k: v for k, v in result.items() if v is not None}
          return result
  
  
  
    @classmethod
    def    _a			(cls       ,					_lowerCamelCase=None  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						default_json_config_file if json_file is None else json_file
          with open(_lowerCamelCase       ,					"""r"""       ,					encoding="""utf-8"""  ) as f:
                UpperCAmelCase__ :       Any						=						json.load(_lowerCamelCase  )
          if "compute_environment" not in config_dict:
                UpperCAmelCase__ :       int						=						ComputeEnvironment.LOCAL_MACHINE
          if "mixed_precision" not in config_dict:
                UpperCAmelCase__ :       Tuple						=						"""fp16""" if ("""fp16""" in config_dict and config_dict["""fp16"""]) else None
          if "fp16" in config_dict:  # Convert the config to the new format.
                del config_dict["fp16"]
          if "dynamo_backend" in config_dict:  # Convert the config to the new format.
                UpperCAmelCase__ :       Optional[Any]						=						config_dict.pop("""dynamo_backend"""  )
                UpperCAmelCase__ :       List[str]						=						{} if dynamo_backend == """NO""" else {"""dynamo_backend""": dynamo_backend}
          if "use_cpu" not in config_dict:
                UpperCAmelCase__ :       Optional[int]						=						False
          return cls(**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          with open(_lowerCamelCase       ,					"""w"""       ,					encoding="""utf-8"""  ) as f:
                UpperCAmelCase__ :       List[Any]						=						json.dumps(self.to_dict()       ,					indent=2       ,					sort_keys=_lowerCamelCase  ) + """\n"""
                f.write(_lowerCamelCase  )
  
  
  
    @classmethod
    def    _a			(cls       ,					_lowerCamelCase=None  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Any						=						default_yaml_config_file if yaml_file is None else yaml_file
          with open(_lowerCamelCase       ,					"""r"""       ,					encoding="""utf-8"""  ) as f:
                UpperCAmelCase__ :       Any						=						yaml.safe_load(_lowerCamelCase  )
          if "compute_environment" not in config_dict:
                UpperCAmelCase__ :       Tuple						=						ComputeEnvironment.LOCAL_MACHINE
  
          if "mixed_precision" not in config_dict:
                UpperCAmelCase__ :       Dict						=						"""fp16""" if ("""fp16""" in config_dict and config_dict["""fp16"""]) else None
          if isinstance(config_dict["""mixed_precision"""]       ,					_lowerCamelCase  ) and not config_dict["mixed_precision"]:
                UpperCAmelCase__ :       List[str]						=						"""no"""
          if "fp16" in config_dict:  # Convert the config to the new format.
                del config_dict["fp16"]
          if "dynamo_backend" in config_dict:  # Convert the config to the new format.
                UpperCAmelCase__ :       List[str]						=						config_dict.pop("""dynamo_backend"""  )
                UpperCAmelCase__ :       List[Any]						=						{} if dynamo_backend == """NO""" else {"""dynamo_backend""": dynamo_backend}
          if "use_cpu" not in config_dict:
                UpperCAmelCase__ :       Union[str, Any]						=						False
          return cls(**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          with open(_lowerCamelCase       ,					"""w"""       ,					encoding="""utf-8"""  ) as f:
                yaml.safe_dump(self.to_dict()       ,					_lowerCamelCase  )
  
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          if isinstance(self.compute_environment       ,					_lowerCamelCase  ):
                UpperCAmelCase__ :       List[str]						=						ComputeEnvironment(self.compute_environment  )
          if isinstance(self.distributed_type       ,					_lowerCamelCase  ):
                if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
                      UpperCAmelCase__ :       Optional[int]						=						SageMakerDistributedType(self.distributed_type  )
                else:
                      UpperCAmelCase__ :       Union[str, Any]						=						DistributedType(self.distributed_type  )
          if self.dynamo_config is None:
                UpperCAmelCase__ :       Optional[Any]						=						{}
  
  
  
  
  @dataclass
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     42
    SCREAMING_SNAKE_CASE							=     0
    SCREAMING_SNAKE_CASE							=     1
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     "static"
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     "main"
  
    # args for deepspeed_plugin
    SCREAMING_SNAKE_CASE							=     None
    # args for fsdp
    SCREAMING_SNAKE_CASE							=     None
    # args for megatron_lm
    SCREAMING_SNAKE_CASE							=     None
    # args for ipex
    SCREAMING_SNAKE_CASE							=     None
    # args for TPU
    SCREAMING_SNAKE_CASE							=     False
  
    # args for TPU pods
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     False
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
  
    # args for dynamo
    SCREAMING_SNAKE_CASE							=     None
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          if self.deepspeed_config is None:
                UpperCAmelCase__ :       str						=						{}
          if self.fsdp_config is None:
                UpperCAmelCase__ :       Any						=						{}
          if self.megatron_lm_config is None:
                UpperCAmelCase__ :       Union[str, Any]						=						{}
          if self.ipex_config is None:
                UpperCAmelCase__ :       Optional[int]						=						{}
          return super().__post_init__()
  
  
  
  
  @dataclass
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     42
    SCREAMING_SNAKE_CASE							=     42
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     "us-east-1"
    SCREAMING_SNAKE_CASE							=     1
    SCREAMING_SNAKE_CASE							=     "all"
    SCREAMING_SNAKE_CASE							=     F"accelerate-sagemaker-{num_machines}"
    SCREAMING_SNAKE_CASE							=     SAGEMAKER_PYTORCH_VERSION
    SCREAMING_SNAKE_CASE							=     SAGEMAKER_TRANSFORMERS_VERSION
    SCREAMING_SNAKE_CASE							=     SAGEMAKER_PYTHON_VERSION
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
    SCREAMING_SNAKE_CASE							=     None
  
  
style_context: 
  
  """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 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
        # Initialise PyTorch model
        UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
        UpperCAmelCase__ :       Any						=						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}"""						)
        UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        # Save pytorch-model
        print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
        model.save_pretrained(lowerCAmelCase						)
  
  
  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)
  
  