


"""simple docstring"""


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		int:







		"""simple docstring"""





		A					       = ('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

		A					       = (
		    ('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(UpperCamelCase__	):
				os.makedirs(UpperCamelCase__	)

		A					       = model.state_dict()

		def to_tf_var_name(UpperCamelCase__	):
				for patt, repl in iter(UpperCamelCase__	):
						A					       = name.replace(UpperCamelCase__ ,			UpperCamelCase__	)
				return f'bert/{name}'

		def create_tf_var(UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	):
				A					       = tf.dtypes.as_dtype(tensor.dtype	)
				A					       = tf.get_variable(dtype=UpperCamelCase__ ,			shape=tensor.shape ,			name=UpperCamelCase__ ,			initializer=tf.zeros_initializer()	)
				session.run(tf.variables_initializer([tf_var]	)	)
				session.run(UpperCamelCase__	)
				return tf_var

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

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







def  __snake_case       (   UpperCamelCase__=None	)      ->		Dict:







		"""simple docstring"""





		A					       = argparse.ArgumentParser()
		parser.add_argument('--model_name' ,			type=UpperCamelCase__ ,			required=UpperCamelCase__ ,			help='model name e.g. bert-base-uncased'	)
		parser.add_argument(
		    '--cache_dir' ,			type=UpperCamelCase__ ,			default=UpperCamelCase__ ,			required=UpperCamelCase__ ,			help='Directory containing pytorch model'	)
		parser.add_argument('--pytorch_model_path' ,			type=UpperCamelCase__ ,			required=UpperCamelCase__ ,			help='/path/to/<pytorch-model-name>.bin'	)
		parser.add_argument('--tf_cache_dir' ,			type=UpperCamelCase__ ,			required=UpperCamelCase__ ,			help='Directory in which to save tensorflow model'	)
		A					       = parser.parse_args(UpperCamelCase__	)

		A					       = 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=UpperCamelCase__ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name	)


if __name__ == "__main__":
	main()









"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase						:				int             =      logging.get_logger(__name__)

UpperCamelCase						:				Optional[int]             =      {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}



class 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_			):
						lowerCAmelCase       =     """bit"""
						lowerCAmelCase       =     ["""preactivation""", """bottleneck"""]
						lowerCAmelCase       =     ["""SAME""", """VALID"""]







						def __init__(							self   :    List[str]			,			_lowercase   :    int=3			,			_lowercase   :    List[str]=64			,			_lowercase   :    Optional[int]=[256, 512, 1_024, 2_048]			,			_lowercase   :    Optional[int]=[3, 4, 6, 3]			,			_lowercase   :    int="preactivation"			,			_lowercase   :    Union[str, Any]="relu"			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Tuple=32			,			_lowercase   :    Optional[int]=0.0			,			_lowercase   :    Union[str, Any]=False			,			_lowercase   :    Dict=32			,			_lowercase   :    Union[str, Any]=1			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Any=None			,			**_lowercase   :    str			,			):
								super().__init__(**_lowercase		)
								if layer_type not in self.layer_types:
										raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
								if global_padding is not None:
										if global_padding.upper() in self.supported_padding:
												A					       = global_padding.upper()
										else:
												raise ValueError(f'Padding strategy {global_padding} not supported'		)
								A					       = num_channels
								A					       = embedding_size
								A					       = hidden_sizes
								A					       = depths
								A					       = layer_type
								A					       = hidden_act
								A					       = global_padding
								A					       = num_groups
								A					       = drop_path_rate
								A					       = embedding_dynamic_padding
								A					       = output_stride
								A					       = width_factor

								A					       = ['stem'] + [f'stage{idx}' for idx in range(1			,			len(_lowercase		) + 1		)]
								A       , A					       = get_aligned_output_features_output_indices(
								    out_features=_lowercase			,			out_indices=_lowercase			,			stage_names=self.stage_names		)





