




'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case   :   int     							=	logging.get_logger(__name__)

_snake_case   :   Dict     							=	{
    'google/mobilenet_v1_1.0_224': 'https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json',
    'google/mobilenet_v1_0.75_192': 'https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class     A					(	_a					):
							lowercase_           =       'mobilenet_v1'







							def __init__(						self :						Union[str, Any]							,    lowerCAmelCase_ :						Union[str, Any]=3							,    lowerCAmelCase_ :						int=2_24							,    lowerCAmelCase_ :						List[str]=1.0							,    lowerCAmelCase_ :						Optional[int]=8							,    lowerCAmelCase_ :						int="relu6"							,    lowerCAmelCase_ :						List[Any]=True							,    lowerCAmelCase_ :						Optional[Any]=0.9_9_9							,    lowerCAmelCase_ :						Optional[Any]=0.0_2							,    lowerCAmelCase_ :						str=0.0_0_1							,    **lowerCAmelCase_ :						Any							,    )    ->							Dict:


														"""simple docstring"""


														super().__init__(**lowerCAmelCase_      )

														if depth_multiplier <= 0:
																					raise ValueError('''depth_multiplier must be greater than zero.'''      )

														_a			  =       num_channels
														_a			  =       image_size
														_a			  =       depth_multiplier
														_a			  =       min_depth
														_a			  =       hidden_act
														_a			  =       tf_padding
														_a			  =       classifier_dropout_prob
														_a			  =       initializer_range
														_a			  =       layer_norm_eps



class     A					(	_a					):
							lowercase_           =       version.parse('1.11'					)







							@property
							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


														return OrderedDict([('''pixel_values''', {0: '''batch'''})]      )







							@property
							def      __lowerCAmelCase    (						self :						int      )    ->							Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


														if self.task == "image-classification":
																					return OrderedDict([('''logits''', {0: '''batch'''})]      )
														else:
																					return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]      )







							@property
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							float:


														"""simple docstring"""


														return 1e-4







'''simple docstring'''


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel
def 	snake_case_					(UpperCamelCase :						BertModel				,		UpperCamelCase :						str				,		UpperCamelCase :						str   ):





							'''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 :						str   ):
														for patt, repl in iter(UpperCamelCase   ):
																					_a			  =       name.replace(UpperCamelCase				,		UpperCamelCase   )
														return f'bert/{name}'

							def create_tf_var(UpperCamelCase :						np.ndarray				,		UpperCamelCase :						str				,		UpperCamelCase :						tf.Session   ):
														_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 :						Tuple=None   ):





							'''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()

