"""simple docstring"""
import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def 				lowerCAmelCase__      (     _UpperCamelCase    :			BertModel	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			str   )   ->			int:





						"""simple docstring"""






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

						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(_UpperCamelCase   ):
												os.makedirs(_UpperCamelCase   )

						snake_case        =     model.state_dict()

						def to_tf_var_name(_UpperCamelCase    :			str   ):
												for patt, repl in iter(_UpperCamelCase   ):
																		snake_case        =     name.replace(_UpperCamelCase	,     _UpperCamelCase   )
												return f"""bert/{name}"""

						def create_tf_var(_UpperCamelCase    :			np.ndarray	,     _UpperCamelCase    :			str	,     _UpperCamelCase    :			tf.Session   ):
												snake_case        =     tf.dtypes.as_dtype(tensor.dtype   )
												snake_case        =     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:
																		snake_case        =     to_tf_var_name(_UpperCamelCase   )
																		snake_case        =     state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose   ):
																								snake_case        =     torch_tensor.T
																		snake_case        =     create_tf_var(tensor=_UpperCamelCase	,     name=_UpperCamelCase	,     session=_UpperCamelCase   )
																		tf.keras.backend.set_value(_UpperCamelCase	,     _UpperCamelCase   )
																		snake_case        =     session.run(_UpperCamelCase   )
																		print(f"""Successfully created {tf_name}: {np.allclose(_UpperCamelCase	,     _UpperCamelCase   )}"""   )

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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]=None   )   ->			List[str]:





						"""simple docstring"""






						snake_case        =     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'   )
						snake_case        =     parser.parse_args(_UpperCamelCase   )

						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=_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


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Tuple     					=  """swinv2"""

				_lowerCAmelCase       :      Any     					=  {
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}

				def __init__(			self						,      lowerCAmelCase=2_24						,      lowerCAmelCase=4						,      lowerCAmelCase=3						,      lowerCAmelCase=96						,      lowerCAmelCase=[2, 2, 6, 2]						,      lowerCAmelCase=[3, 6, 12, 24]						,      lowerCAmelCase=7						,      lowerCAmelCase=4.0						,      lowerCAmelCase=True						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.0						,      lowerCAmelCase=0.1						,      lowerCAmelCase="gelu"						,      lowerCAmelCase=False						,      lowerCAmelCase=0.02						,      lowerCAmelCase=1E-5						,      lowerCAmelCase=32						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase      )

										snake_case        =     image_size
										snake_case        =     patch_size
										snake_case        =     num_channels
										snake_case        =     embed_dim
										snake_case        =     depths
										snake_case        =     len(lowerCAmelCase      )
										snake_case        =     num_heads
										snake_case        =     window_size
										snake_case        =     mlp_ratio
										snake_case        =     qkv_bias
										snake_case        =     hidden_dropout_prob
										snake_case        =     attention_probs_dropout_prob
										snake_case        =     drop_path_rate
										snake_case        =     hidden_act
										snake_case        =     use_absolute_embeddings
										snake_case        =     layer_norm_eps
										snake_case        =     initializer_range
										snake_case        =     encoder_stride
										# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
										# this indicates the channel dimension after the last stage of the model
										snake_case        =     int(embed_dim * 2 ** (len(lowerCAmelCase      ) - 1)      )
										snake_case        =     (0, 0, 0, 0)


