


"""simple docstring"""




import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
__UpperCamelCase						=      logging.get_logger('''transformers.models.encodec''')

__UpperCamelCase						=      {
    '''quantizer.vq.layers.*._codebook.inited''': '''quantizer.layers.*.codebook.inited''',
    '''quantizer.vq.layers.*._codebook.cluster_size''': '''quantizer.layers.*.codebook.cluster_size''',
    '''quantizer.vq.layers.*._codebook.embed''': '''quantizer.layers.*.codebook.embed''',
    '''quantizer.vq.layers.*._codebook.embed_avg''': '''quantizer.layers.*.codebook.embed_avg''',
}
__UpperCamelCase						=      {
    '''encoder.model.0.conv.conv''': '''encoder.layers.0.conv''',
    '''encoder.model.1.block.1.conv.conv''': '''encoder.layers.1.block.1.conv''',
    '''encoder.model.1.block.3.conv.conv''': '''encoder.layers.1.block.3.conv''',
    '''encoder.model.1.shortcut.conv.conv''': '''encoder.layers.1.shortcut.conv''',
    '''encoder.model.3.conv.conv''': '''encoder.layers.3.conv''',
    '''encoder.model.4.block.1.conv.conv''': '''encoder.layers.4.block.1.conv''',
    '''encoder.model.4.block.3.conv.conv''': '''encoder.layers.4.block.3.conv''',
    '''encoder.model.4.shortcut.conv.conv''': '''encoder.layers.4.shortcut.conv''',
    '''encoder.model.6.conv.conv''': '''encoder.layers.6.conv''',
    '''encoder.model.7.block.1.conv.conv''': '''encoder.layers.7.block.1.conv''',
    '''encoder.model.7.block.3.conv.conv''': '''encoder.layers.7.block.3.conv''',
    '''encoder.model.7.shortcut.conv.conv''': '''encoder.layers.7.shortcut.conv''',
    '''encoder.model.9.conv.conv''': '''encoder.layers.9.conv''',
    '''encoder.model.10.block.1.conv.conv''': '''encoder.layers.10.block.1.conv''',
    '''encoder.model.10.block.3.conv.conv''': '''encoder.layers.10.block.3.conv''',
    '''encoder.model.10.shortcut.conv.conv''': '''encoder.layers.10.shortcut.conv''',
    '''encoder.model.12.conv.conv''': '''encoder.layers.12.conv''',
    '''encoder.model.13.lstm''': '''encoder.layers.13.lstm''',
    '''encoder.model.15.conv.conv''': '''encoder.layers.15.conv''',
}
__UpperCamelCase						=      {
    '''encoder.model.0.conv.norm''': '''encoder.layers.0.norm''',
    '''encoder.model.1.block.1.conv.norm''': '''encoder.layers.1.block.1.norm''',
    '''encoder.model.1.block.3.conv.norm''': '''encoder.layers.1.block.3.norm''',
    '''encoder.model.1.shortcut.conv.norm''': '''encoder.layers.1.shortcut.norm''',
    '''encoder.model.3.conv.norm''': '''encoder.layers.3.norm''',
    '''encoder.model.4.block.1.conv.norm''': '''encoder.layers.4.block.1.norm''',
    '''encoder.model.4.block.3.conv.norm''': '''encoder.layers.4.block.3.norm''',
    '''encoder.model.4.shortcut.conv.norm''': '''encoder.layers.4.shortcut.norm''',
    '''encoder.model.6.conv.norm''': '''encoder.layers.6.norm''',
    '''encoder.model.7.block.1.conv.norm''': '''encoder.layers.7.block.1.norm''',
    '''encoder.model.7.block.3.conv.norm''': '''encoder.layers.7.block.3.norm''',
    '''encoder.model.7.shortcut.conv.norm''': '''encoder.layers.7.shortcut.norm''',
    '''encoder.model.9.conv.norm''': '''encoder.layers.9.norm''',
    '''encoder.model.10.block.1.conv.norm''': '''encoder.layers.10.block.1.norm''',
    '''encoder.model.10.block.3.conv.norm''': '''encoder.layers.10.block.3.norm''',
    '''encoder.model.10.shortcut.conv.norm''': '''encoder.layers.10.shortcut.norm''',
    '''encoder.model.12.conv.norm''': '''encoder.layers.12.norm''',
    '''encoder.model.15.conv.norm''': '''encoder.layers.15.norm''',
}
__UpperCamelCase						=      {
    '''decoder.model.0.conv.conv''': '''decoder.layers.0.conv''',
    '''decoder.model.1.lstm''': '''decoder.layers.1.lstm''',
    '''decoder.model.3.convtr.convtr''': '''decoder.layers.3.conv''',
    '''decoder.model.4.block.1.conv.conv''': '''decoder.layers.4.block.1.conv''',
    '''decoder.model.4.block.3.conv.conv''': '''decoder.layers.4.block.3.conv''',
    '''decoder.model.4.shortcut.conv.conv''': '''decoder.layers.4.shortcut.conv''',
    '''decoder.model.6.convtr.convtr''': '''decoder.layers.6.conv''',
    '''decoder.model.7.block.1.conv.conv''': '''decoder.layers.7.block.1.conv''',
    '''decoder.model.7.block.3.conv.conv''': '''decoder.layers.7.block.3.conv''',
    '''decoder.model.7.shortcut.conv.conv''': '''decoder.layers.7.shortcut.conv''',
    '''decoder.model.9.convtr.convtr''': '''decoder.layers.9.conv''',
    '''decoder.model.10.block.1.conv.conv''': '''decoder.layers.10.block.1.conv''',
    '''decoder.model.10.block.3.conv.conv''': '''decoder.layers.10.block.3.conv''',
    '''decoder.model.10.shortcut.conv.conv''': '''decoder.layers.10.shortcut.conv''',
    '''decoder.model.12.convtr.convtr''': '''decoder.layers.12.conv''',
    '''decoder.model.13.block.1.conv.conv''': '''decoder.layers.13.block.1.conv''',
    '''decoder.model.13.block.3.conv.conv''': '''decoder.layers.13.block.3.conv''',
    '''decoder.model.13.shortcut.conv.conv''': '''decoder.layers.13.shortcut.conv''',
    '''decoder.model.15.conv.conv''': '''decoder.layers.15.conv''',
}
__UpperCamelCase						=      {
    '''decoder.model.0.conv.norm''': '''decoder.layers.0.norm''',
    '''decoder.model.3.convtr.norm''': '''decoder.layers.3.norm''',
    '''decoder.model.4.block.1.conv.norm''': '''decoder.layers.4.block.1.norm''',
    '''decoder.model.4.block.3.conv.norm''': '''decoder.layers.4.block.3.norm''',
    '''decoder.model.4.shortcut.conv.norm''': '''decoder.layers.4.shortcut.norm''',
    '''decoder.model.6.convtr.norm''': '''decoder.layers.6.norm''',
    '''decoder.model.7.block.1.conv.norm''': '''decoder.layers.7.block.1.norm''',
    '''decoder.model.7.block.3.conv.norm''': '''decoder.layers.7.block.3.norm''',
    '''decoder.model.7.shortcut.conv.norm''': '''decoder.layers.7.shortcut.norm''',
    '''decoder.model.9.convtr.norm''': '''decoder.layers.9.norm''',
    '''decoder.model.10.block.1.conv.norm''': '''decoder.layers.10.block.1.norm''',
    '''decoder.model.10.block.3.conv.norm''': '''decoder.layers.10.block.3.norm''',
    '''decoder.model.10.shortcut.conv.norm''': '''decoder.layers.10.shortcut.norm''',
    '''decoder.model.12.convtr.norm''': '''decoder.layers.12.norm''',
    '''decoder.model.13.block.1.conv.norm''': '''decoder.layers.13.block.1.norm''',
    '''decoder.model.13.block.3.conv.norm''': '''decoder.layers.13.block.3.norm''',
    '''decoder.model.13.shortcut.conv.norm''': '''decoder.layers.13.shortcut.norm''',
    '''decoder.model.15.conv.norm''': '''decoder.layers.15.norm''',
}
__UpperCamelCase						=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
__UpperCamelCase						=      {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
__UpperCamelCase						=      []
__UpperCamelCase						=      []


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				List[Any]  )   ->						Tuple:
						for attribute in key.split('.'  ):
												SCREAMING_SNAKE_CASE													=					getattr(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )

						if weight_type is not None:
												SCREAMING_SNAKE_CASE													=					getattr(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ).shape
						else:
												SCREAMING_SNAKE_CASE													=					hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
												    F' {value.shape} for {full_name}'  )

						if weight_type == "weight":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "weight_g":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "weight_v":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "bias":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "running_mean":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "running_var":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "num_batches_tracked":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "weight_ih_l0":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "weight_hh_l0":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "bias_ih_l0":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "bias_hh_l0":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "weight_ih_l1":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "weight_hh_l1":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "bias_ih_l1":
												SCREAMING_SNAKE_CASE													=					value
						elif weight_type == "bias_hh_l1":
												SCREAMING_SNAKE_CASE													=					value
						else:
												SCREAMING_SNAKE_CASE													=					value

						logger.info(F'{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.'  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Optional[Any] ,			SCREAMING_SNAKE_CASE_		:				Tuple  )   ->						Union[str, Any]:
						for key in ignore_keys:
												if key.endswith('.*'  ):
																		if name.startswith(key[:-1]  ):
																								return True
												elif ".*." in key:
																		SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					key.split('.*.'  )
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					[]

						if model_name == "encodec_24khz" or "encodec_32khz":
												SCREAMING_SNAKE_CASE													=					MAPPING_24K
						elif model_name == "encodec_48khz":
												SCREAMING_SNAKE_CASE													=					MAPPING_48K
						else:
												raise ValueError(F'Unsupported model: {model_name}'  )

						for name, value in orig_dict.items():
												if should_ignore(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ):
																		logger.info(F'{name} was ignored'  )
																		continue

												SCREAMING_SNAKE_CASE													=					False
												for key, mapped_key in MAPPING.items():
																		if "*" in key:
																								SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					key.split('.*.'  )
																								if prefix in name and suffix in name:
																														SCREAMING_SNAKE_CASE													=					suffix

																		if key in name:
																								# HACK otherwise .embed gets initialized with .embed_avg too
																								if key.endswith('embed'  ) and name.endswith('embed_avg'  ):
																														continue

																								SCREAMING_SNAKE_CASE													=					True
																								if "*" in mapped_key:
																														SCREAMING_SNAKE_CASE													=					name.split(SCREAMING_SNAKE_CASE_  )[0].split('.'  )[-2]
																														SCREAMING_SNAKE_CASE													=					mapped_key.replace('*' ,			SCREAMING_SNAKE_CASE_  )
																								if "weight_g" in name:
																														SCREAMING_SNAKE_CASE													=					'weight_g'
																								elif "weight_v" in name:
																														SCREAMING_SNAKE_CASE													=					'weight_v'
																								elif "weight_ih_l0" in name:
																														SCREAMING_SNAKE_CASE													=					'weight_ih_l0'
																								elif "weight_hh_l0" in name:
																														SCREAMING_SNAKE_CASE													=					'weight_hh_l0'
																								elif "bias_ih_l0" in name:
																														SCREAMING_SNAKE_CASE													=					'bias_ih_l0'
																								elif "bias_hh_l0" in name:
																														SCREAMING_SNAKE_CASE													=					'bias_hh_l0'
																								elif "weight_ih_l1" in name:
																														SCREAMING_SNAKE_CASE													=					'weight_ih_l1'
																								elif "weight_hh_l1" in name:
																														SCREAMING_SNAKE_CASE													=					'weight_hh_l1'
																								elif "bias_ih_l1" in name:
																														SCREAMING_SNAKE_CASE													=					'bias_ih_l1'
																								elif "bias_hh_l1" in name:
																														SCREAMING_SNAKE_CASE													=					'bias_hh_l1'
																								elif "bias" in name:
																														SCREAMING_SNAKE_CASE													=					'bias'
																								elif "weight" in name:
																														SCREAMING_SNAKE_CASE													=					'weight'
																								elif "running_mean" in name:
																														SCREAMING_SNAKE_CASE													=					'running_mean'
																								elif "running_var" in name:
																														SCREAMING_SNAKE_CASE													=					'running_var'
																								elif "num_batches_tracked" in name:
																														SCREAMING_SNAKE_CASE													=					'num_batches_tracked'
																								else:
																														SCREAMING_SNAKE_CASE													=					None
																								set_recursively(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		continue
												if not is_used:
																		unused_weights.append(SCREAMING_SNAKE_CASE_  )

						logger.warning(F'Unused weights: {unused_weights}'  )


@torch.no_grad()
def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Optional[int] ,			SCREAMING_SNAKE_CASE_		:				int ,			SCREAMING_SNAKE_CASE_		:				Optional[int]=None ,			SCREAMING_SNAKE_CASE_		:				int=None ,			)   ->						Optional[int]:
						if config_path is not None:
												SCREAMING_SNAKE_CASE													=					EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
						else:
												SCREAMING_SNAKE_CASE													=					EncodecConfig()

						if model_name == "encodec_24khz":
												pass  # config is already correct
						elif model_name == "encodec_32khz":
												SCREAMING_SNAKE_CASE													=					[8, 5, 4, 4]
												SCREAMING_SNAKE_CASE													=					[2.2]
												SCREAMING_SNAKE_CASE													=					64
												SCREAMING_SNAKE_CASE													=					3_20_00
												SCREAMING_SNAKE_CASE													=					20_48
												SCREAMING_SNAKE_CASE													=					False
												SCREAMING_SNAKE_CASE													=					False
												SCREAMING_SNAKE_CASE													=					False
						elif model_name == "encodec_48khz":
												SCREAMING_SNAKE_CASE													=					[8, 5, 4, 2]
												SCREAMING_SNAKE_CASE													=					[3.0, 6.0, 12.0, 24.0]
												SCREAMING_SNAKE_CASE													=					4_80_00
												SCREAMING_SNAKE_CASE													=					2
												SCREAMING_SNAKE_CASE													=					False
												SCREAMING_SNAKE_CASE													=					'time_group_norm'
												SCREAMING_SNAKE_CASE													=					True
												SCREAMING_SNAKE_CASE													=					1.0
												SCREAMING_SNAKE_CASE													=					0.01
						else:
												raise ValueError(F'Unknown model name: {model_name}'  )

						SCREAMING_SNAKE_CASE													=					EncodecModel(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					EncodecFeatureExtractor(
						    feature_size=config.audio_channels ,			sampling_rate=config.sampling_rate ,			chunk_length_s=config.chunk_length_s ,			overlap=config.overlap ,			)
						feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					torch.load(SCREAMING_SNAKE_CASE_  )
						if "best_state" in original_checkpoint:
												# we might have a training state saved, in which case discard the yaml results and just retain the weights
												SCREAMING_SNAKE_CASE													=					original_checkpoint['best_state']
						recursively_load_weights(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
						model.save_pretrained(SCREAMING_SNAKE_CASE_  )

						if repo_id:
												print('Pushing to the hub...'  )
												feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE_  )
												model.push_to_hub(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
	__UpperCamelCase						=      argparse.ArgumentParser()
	parser.add_argument(
	    '''--model''',
	    default='''encodec_24khz''',
	    type=str,
	    help='''The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.''',
	)
	parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
	parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
	)

	__UpperCamelCase						=      parser.parse_args()
	convert_checkpoint(
	    args.model,
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.push_to_hub,
	)






"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

						def create_tf_var(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				tf.Session  ):
												SCREAMING_SNAKE_CASE													=					tf.dtypes.as_dtype(tensor.dtype  )
												SCREAMING_SNAKE_CASE													=					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ ,			shape=tensor.shape ,			name=SCREAMING_SNAKE_CASE_ ,			initializer=tf.zeros_initializer()  )
												session.run(tf.variables_initializer([tf_var]  )  )
												session.run(SCREAMING_SNAKE_CASE_  )
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		SCREAMING_SNAKE_CASE													=					to_tf_var_name(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose  ):
																								SCREAMING_SNAKE_CASE													=					torch_tensor.T
																		SCREAMING_SNAKE_CASE													=					create_tf_var(tensor=SCREAMING_SNAKE_CASE_ ,			name=SCREAMING_SNAKE_CASE_ ,			session=SCREAMING_SNAKE_CASE_  )
																		tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					session.run(SCREAMING_SNAKE_CASE_  )
																		print(F'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )}'  )

												SCREAMING_SNAKE_CASE													=					tf.train.Saver(tf.trainable_variables()  )
												saver.save(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			model_name.replace('-' ,			'_'  ) + '.ckpt'  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('--model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='model name e.g. bert-base-uncased'  )
						parser.add_argument(
						    '--cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory containing pytorch model'  )
						parser.add_argument('--pytorch_model_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='/path/to/<pytorch-model-name>.bin'  )
						parser.add_argument('--tf_cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory in which to save tensorflow model'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path  ) ,			cache_dir=args.cache_dir ,			)

						convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


