

"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    str 	=			('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

	__lowerCamelCase    :    Optional[int] 	=			(
	    ('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(lowerCamelCase__      ):
		os.makedirs(lowerCamelCase__      )

	__lowerCamelCase    :    List[Any] 	=			model.state_dict()

	def to_tf_var_name(lowerCamelCase__      ):
		for patt, repl in iter(lowerCamelCase__      ):
			__lowerCamelCase    :    List[str] 	=			name.replace(lowerCamelCase__     ,			lowerCamelCase__      )
		return F"bert/{name}"

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

	tf.reset_default_graph()
	with tf.Session() as session:
		for var_name in state_dict:
			__lowerCamelCase    :    Optional[Any] 	=			to_tf_var_name(lowerCamelCase__      )
			__lowerCamelCase    :    Union[str, Any] 	=			state_dict[var_name].numpy()
			if any(x in var_name for x in tensors_to_transpose      ):
				__lowerCamelCase    :    Dict 	=			torch_tensor.T
			__lowerCamelCase    :    Union[str, Any] 	=			create_tf_var(tensor=lowerCamelCase__     ,			name=lowerCamelCase__     ,			session=lowerCamelCase__      )
			tf.keras.backend.set_value(lowerCamelCase__     ,			lowerCamelCase__      )
			__lowerCamelCase    :    List[Any] 	=			session.run(lowerCamelCase__      )
			print(F"Successfully created {tf_name}: {np.allclose(lowerCamelCase__     ,			lowerCamelCase__      )}"      )

		__lowerCamelCase    :    str 	=			tf.train.Saver(tf.trainable_variables()      )
		saver.save(lowerCamelCase__     ,			os.path.join(lowerCamelCase__     ,			model_name.replace('-'     ,			'_'      ) + '.ckpt'      )      )






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

	__lowerCamelCase    :    Union[str, Any] 	=			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=lowerCamelCase__     ,			ckpt_dir=args.tf_cache_dir     ,			model_name=args.model_name      )


if __name__ == "__main__":
					main()







from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		=logging.get_logger(__name__)

a		={
    """sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  str										=				'''vit_msn'''
							def __init__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int=7_6_8       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_2       ,SCREAMING_SNAKE_CASE__		:      Tuple=3_0_7_2       ,SCREAMING_SNAKE_CASE__		:      str="gelu"       ,SCREAMING_SNAKE_CASE__		:      Any=0.0       ,SCREAMING_SNAKE_CASE__		:      str=0.0       ,SCREAMING_SNAKE_CASE__		:      Any=0.02       ,SCREAMING_SNAKE_CASE__		:      int=1E-06       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=2_2_4       ,SCREAMING_SNAKE_CASE__		:      Dict=1_6       ,SCREAMING_SNAKE_CASE__		:      int=3       ,SCREAMING_SNAKE_CASE__		:      List[str]=True       ,**SCREAMING_SNAKE_CASE__		:      str       ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    List[Any] 	=			hidden_size
								__lowerCamelCase    :    Tuple 	=			num_hidden_layers
								__lowerCamelCase    :    Union[str, Any] 	=			num_attention_heads
								__lowerCamelCase    :    Dict 	=			intermediate_size
								__lowerCamelCase    :    List[Any] 	=			hidden_act
								__lowerCamelCase    :    Optional[Any] 	=			hidden_dropout_prob
								__lowerCamelCase    :    str 	=			attention_probs_dropout_prob
								__lowerCamelCase    :    List[str] 	=			initializer_range
								__lowerCamelCase    :    Optional[int] 	=			layer_norm_eps
								__lowerCamelCase    :    Tuple 	=			image_size
								__lowerCamelCase    :    Union[str, Any] 	=			patch_size
								__lowerCamelCase    :    Optional[Any] 	=			num_channels
								__lowerCamelCase    :    List[str] 	=			qkv_bias




