"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Dict		,			_SCREAMING_SNAKE_CASE	:Optional[int]		,			_SCREAMING_SNAKE_CASE	:Dict  )				-> List[str]:
						# Initialise PyTorch model
						a_							:			Optional[Any]     		=					RoFormerConfig.from_json_file(_SCREAMING_SNAKE_CASE  )
						print(F'''Building PyTorch model from configuration: {config}'''  )
						a_							:			List[Any]     		=					RoFormerForMaskedLM(_SCREAMING_SNAKE_CASE  )

						# Load weights from tf checkpoint
						load_tf_weights_in_roformer(_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE		,			_SCREAMING_SNAKE_CASE  )

						# Save pytorch-model
						print(F'''Save PyTorch model to {pytorch_dump_path}'''  )
						torch.save(model.state_dict()		,			_SCREAMING_SNAKE_CASE		,			_use_new_zipfile_serialization=_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
				UpperCamelCase     					=     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(
				    '--bert_config_file',
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        'The config json file corresponding to the pre-trained BERT 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.'
				)
				UpperCamelCase     					=     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase     					=     {'configuration_levit': ['LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LevitConfig', 'LevitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['LevitFeatureExtractor']
				UpperCamelCase     					=     ['LevitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LevitForImageClassification',
				    'LevitForImageClassificationWithTeacher',
				    'LevitModel',
				    'LevitPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_levit import LevitFeatureExtractor
								from .image_processing_levit import LevitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_levit import (
								    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LevitForImageClassification,
								    LevitForImageClassificationWithTeacher,
								    LevitModel,
								    LevitPreTrainedModel,
								)
else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




