






import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



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

						_UpperCAmelCase									=			(
						    ('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	)

						_UpperCAmelCase									=			model.state_dict()

						def to_tf_var_name(__lowerCAmelCase	):
												for patt, repl in iter(__lowerCAmelCase	):
																		_UpperCAmelCase									=			name.replace(__lowerCAmelCase ,		__lowerCAmelCase	)
												return F"""bert/{name}"""

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

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


def __A    (      __lowerCAmelCase=None	)->  Any:





						"""simple docstring"""



						_UpperCAmelCase									=			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'	)
						_UpperCAmelCase									=			parser.parse_args(__lowerCAmelCase	)

						_UpperCAmelCase									=			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()





'''simple docstring'''





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


_lowerCAmelCase      						=	logging.get_logger(__name__)

_lowerCAmelCase      						=	{
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}






class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      Optional[Any]				=      '''focalnet'''




  def __init__(    self  ,__UpperCAmelCase=224  ,__UpperCAmelCase=4  ,__UpperCAmelCase=3  ,__UpperCAmelCase=96  ,__UpperCAmelCase=False  ,__UpperCAmelCase=[192, 384, 768, 768]  ,__UpperCAmelCase=[2, 2, 6, 2]  ,__UpperCAmelCase=[2, 2, 2, 2]  ,__UpperCAmelCase=[3, 3, 3, 3]  ,__UpperCAmelCase="gelu"  ,__UpperCAmelCase=4.0  ,__UpperCAmelCase=0.0  ,__UpperCAmelCase=0.1  ,__UpperCAmelCase=False  ,__UpperCAmelCase=1E-4  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=False  ,__UpperCAmelCase=0.0_2  ,__UpperCAmelCase=1E-5  ,__UpperCAmelCase=32  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase  ,)    ->		Optional[Any]:
      super().__init__(**__UpperCAmelCase      )

      lowerCAmelCase__ :  Dict							      =					image_size
      lowerCAmelCase__ :  int							      =					patch_size
      lowerCAmelCase__ :  str							      =					num_channels
      lowerCAmelCase__ :  Dict							      =					embed_dim
      lowerCAmelCase__ :  List[str]							      =					use_conv_embed
      lowerCAmelCase__ :  List[Any]							      =					hidden_sizes
      lowerCAmelCase__ :  Dict							      =					depths
      lowerCAmelCase__ :  List[str]							      =					focal_levels
      lowerCAmelCase__ :  List[str]							      =					focal_windows
      lowerCAmelCase__ :  Dict							      =					hidden_act
      lowerCAmelCase__ :  Dict							      =					mlp_ratio
      lowerCAmelCase__ :  Tuple							      =					hidden_dropout_prob
      lowerCAmelCase__ :  Tuple							      =					drop_path_rate
      lowerCAmelCase__ :  Dict							      =					use_layerscale
      lowerCAmelCase__ :  Optional[Any]							      =					layerscale_value
      lowerCAmelCase__ :  str							      =					use_post_layernorm
      lowerCAmelCase__ :  Union[str, Any]							      =					use_post_layernorm_in_modulation
      lowerCAmelCase__ :  int							      =					normalize_modulator
      lowerCAmelCase__ :  Optional[Any]							      =					initializer_range
      lowerCAmelCase__ :  List[str]							      =					layer_norm_eps
      lowerCAmelCase__ :  List[Any]							      =					encoder_stride
      lowerCAmelCase__ :  Dict							      =					["""stem"""] + [F"""stage{idx}""" for idx in range(1  ,len(self.depths      ) + 1      )]
      lowerCAmelCase__      ,   lowerCAmelCase__ :  Any							      =					get_aligned_output_features_output_indices(
          out_features=__UpperCAmelCase  ,out_indices=__UpperCAmelCase  ,stage_names=self.stage_names      )


