


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def 	lowerCAmelCase__			(      a__:		BertModel      ,  a__:		str      ,  a__:		str	)  ->		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(a__	):
														os.makedirs(a__	)

							_UpperCAmelCase					=      model.state_dict()

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

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

							tf.reset_default_graph()
							with tf.Session() as session:
														for var_name in state_dict:
																					_UpperCAmelCase					=      to_tf_var_name(a__	)
																					_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=a__      ,  name=a__      ,  session=a__	)
																					tf.keras.backend.set_value(a__      ,  a__	)
																					_UpperCAmelCase					=      session.run(a__	)
																					print(F'''Successfully created {tf_name}: {np.allclose(a__      ,  a__	)}'''	)

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

def 	lowerCAmelCase__			(      a__:		int=None	)  ->		List[Any]:


							'''simple docstring'''





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

							_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=a__      ,  ckpt_dir=args.tf_cache_dir      ,  model_name=args.model_name	)


if __name__ == "__main__":
		main()




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__  :Tuple             =							{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							['''BeitFeatureExtractor''']
		lowerCAmelCase__  :Optional[Any]             =							['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :int             =							[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Tuple             =							[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__  :Dict             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
