






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()
def       snake_case_		(     lowerCAmelCase_       :						Optional[int]       ,       lowerCAmelCase_       :						List[Any]       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						Optional[int]      ):
							# Initialise PyTorch model
							__lowercase      :							Any				=     FunnelConfig.from_json_file(lowerCAmelCase_      )
							print(F"Building PyTorch model from configuration: {config}"      )
							__lowercase      :							Optional[int]				=     FunnelBaseModel(lowerCAmelCase_      ) if base_model else FunnelModel(lowerCAmelCase_      )

							# Load weights from tf checkpoint
							load_tf_weights_in_funnel(lowerCAmelCase_       ,       lowerCAmelCase_       ,       lowerCAmelCase_      )

							# Save pytorch-model
							print(F"Save PyTorch model to {pytorch_dump_path}"      )
							torch.save(model.state_dict()       ,       lowerCAmelCase_      )


if __name__ == "__main__":
	lowerCamelCase : Tuple						   =       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 model. \nThis specifies the model architecture.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	parser.add_argument(
	    '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
	)
	lowerCamelCase : Any						   =       parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
	)







# 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 argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


lowerCamelCase : int						   =       '''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''
def       snake_case_		(     ):
							__lowercase      :							List[Any]				=     _ask_options(
							    """In which compute environment are you running?"""       ,       ["""This machine""", """AWS (Amazon SageMaker)"""]       ,       _convert_compute_environment       ,       )
							if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
														__lowercase      :							Union[str, Any]				=     get_sagemaker_input()
							else:
														__lowercase      :							str				=     get_cluster_input()
							return config
def       snake_case_		(     lowerCAmelCase_       :						List[str]=None      ):
							if subparsers is not None:
														__lowercase      :							Optional[int]				=     subparsers.add_parser("""config"""       ,       description=lowerCAmelCase_      )
							else:
														__lowercase      :							List[str]				=     argparse.ArgumentParser("""Accelerate config command"""       ,       description=lowerCAmelCase_      )

							parser.add_argument(
							    """--config_file"""       ,       default=lowerCAmelCase_       ,       help=(
							        """The path to use to store the config file. 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'."""
							    )       ,       )

							if subparsers is not None:
														parser.set_defaults(func=lowerCAmelCase_      )
							return parser
def       snake_case_		(     lowerCAmelCase_       :						Tuple      ):
							__lowercase      :							Union[str, Any]				=     get_user_input()
							if args.config_file is not None:
														__lowercase      :							List[Any]				=     args.config_file
							else:
														if not os.path.isdir(lowerCAmelCase_      ):
																					os.makedirs(lowerCAmelCase_      )
														__lowercase      :							Any				=     default_yaml_config_file

							if config_file.endswith(""".json"""      ):
														config.to_json_file(lowerCAmelCase_      )
							else:
														config.to_yaml_file(lowerCAmelCase_      )
							print(F"accelerate configuration saved at {config_file}"      )
def       snake_case_		(     ):
							__lowercase      :							str				=     config_command_parser()
							__lowercase      :							str				=     parser.parse_args()
							config_command(lowerCAmelCase_      )


if __name__ == "__main__":
	main()