




"""simple docstring"""





import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Tuple:


			"""simple docstring"""
			__UpperCAmelCase						:			int												=       ("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

			__UpperCAmelCase						:			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(UpperCamelCase				):
						os.makedirs(UpperCamelCase				)

			__UpperCAmelCase						:			Optional[Any]												=       model.state_dict()

			def to_tf_var_name(UpperCamelCase				):
						for patt, repl in iter(UpperCamelCase				):
									__UpperCAmelCase						:			Any												=       name.replace(UpperCamelCase					,							UpperCamelCase				)
						return f"bert/{name}"

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

						__UpperCAmelCase						:			Optional[int]												=       tf.train.Saver(tf.trainable_variables()				)
						saver.save(UpperCamelCase					,							os.path.join(UpperCamelCase					,							model_name.replace("-"					,							"_"				) + ".ckpt"				)				)

def        _UpperCamelCase     (      UpperCamelCase=None				)      ->      int:


			"""simple docstring"""
			__UpperCAmelCase						:			List[Any]												=       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"				)
			__UpperCAmelCase						:			List[Any]												=       parser.parse_args(UpperCamelCase				)

			__UpperCAmelCase						:			List[str]												=       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 typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


_UpperCamelCase					      =   logging.get_logger(__name__)
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							str									=		["""pixel_values"""]




				def __init__(  self	:str    ,     __lowercase	:bool = True    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = PILImageResampling.BILINEAR    ,     __lowercase	:bool = True    ,     __lowercase	:Union[int, float] = 1 / 255    ,     __lowercase	:bool = True    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     **__lowercase	:Tuple    ,     ):
											super().__init__(**__lowercase      )
											__lowerCamelCase   :   int       =size if size is not None else {'''shortest_edge''': 384}
											__lowerCamelCase   :   List[str]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Any       =do_resize
											__lowerCamelCase   :   List[str]       =size
											# Default value set here for backwards compatibility where the value in config is None
											__lowerCamelCase   :   Tuple       =crop_pct if crop_pct is not None else 224 / 256
											__lowerCamelCase   :   Any       =resample
											__lowerCamelCase   :   List[str]       =do_rescale
											__lowerCamelCase   :   Dict       =rescale_factor
											__lowerCamelCase   :   Any       =do_normalize
											__lowerCamelCase   :   int       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											__lowerCamelCase   :   List[Any]       =image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        __lowercase	(  self	:Any    ,     __lowercase	:np.ndarray    ,     __lowercase	:Dict[str, int]    ,     __lowercase	:float    ,     __lowercase	:PILImageResampling = PILImageResampling.BICUBIC    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:int    ,     ):
											__lowerCamelCase   :   int       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )
											if "shortest_edge" not in size:
																		raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'      )
											__lowerCamelCase   :   Optional[int]       =size['''shortest_edge''']

											if shortest_edge < 384:
																		# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																		__lowerCamelCase   :   int       =int(shortest_edge / crop_pct      )
																		__lowerCamelCase   :   str       =get_resize_output_image_size(__lowercase    ,     size=__lowercase    ,     default_to_square=__lowercase      )
																		__lowerCamelCase   :   str       =resize(image=__lowercase    ,     size=__lowercase    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )
																		# then crop to (shortest_edge, shortest_edge)
																		return center_crop(image=__lowercase    ,     size=(shortest_edge, shortest_edge)    ,     data_format=__lowercase    ,     **__lowercase      )
											else:
																		# warping (no cropping) when evaluated at 384 or larger
																		return resize(
																		    __lowercase    ,     size=(shortest_edge, shortest_edge)    ,     resample=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:int    ,     __lowercase	:np.ndarray    ,     __lowercase	:Union[int, float]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:List[Any]    ,     ):
											return rescale(__lowercase    ,     scale=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:str    ,     __lowercase	:np.ndarray    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Union[float, List[float]]    ,     __lowercase	:Optional[Union[str, ChannelDimension]] = None    ,     **__lowercase	:Optional[int]    ,     ):
											return normalize(__lowercase    ,     mean=__lowercase    ,     std=__lowercase    ,     data_format=__lowercase    ,     **__lowercase      )




				def        __lowercase	(  self	:int    ,     __lowercase	:ImageInput    ,     __lowercase	:bool = None    ,     __lowercase	:Dict[str, int] = None    ,     __lowercase	:float = None    ,     __lowercase	:PILImageResampling = None    ,     __lowercase	:bool = None    ,     __lowercase	:float = None    ,     __lowercase	:bool = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[float, List[float]]] = None    ,     __lowercase	:Optional[Union[str, TensorType]] = None    ,     __lowercase	:ChannelDimension = ChannelDimension.FIRST    ,     **__lowercase	:Optional[Any]    ,     ):
											__lowerCamelCase   :   Optional[Any]       =do_resize if do_resize is not None else self.do_resize
											__lowerCamelCase   :   int       =crop_pct if crop_pct is not None else self.crop_pct
											__lowerCamelCase   :   List[Any]       =resample if resample is not None else self.resample
											__lowerCamelCase   :   List[Any]       =do_rescale if do_rescale is not None else self.do_rescale
											__lowerCamelCase   :   List[Any]       =rescale_factor if rescale_factor is not None else self.rescale_factor
											__lowerCamelCase   :   List[Any]       =do_normalize if do_normalize is not None else self.do_normalize
											__lowerCamelCase   :   Optional[Any]       =image_mean if image_mean is not None else self.image_mean
											__lowerCamelCase   :   str       =image_std if image_std is not None else self.image_std

											__lowerCamelCase   :   str       =size if size is not None else self.size
											__lowerCamelCase   :   Optional[int]       =get_size_dict(__lowercase    ,     default_to_square=__lowercase      )

											__lowerCamelCase   :   Optional[int]       =make_list_of_images(__lowercase      )

											if not valid_images(__lowercase      ):
																		raise ValueError(
																		    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																		    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

											if do_resize and size is None or resample is None:
																		raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

											if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
																		raise ValueError('''crop_pct must be specified if size < 384.'''      )

											if do_rescale and rescale_factor is None:
																		raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

											if do_normalize and (image_mean is None or image_std is None):
																		raise ValueError('''Image mean and std must be specified if do_normalize is True.'''      )

											# All transformations expect numpy arrays.
											__lowerCamelCase   :   List[Any]       =[to_numpy_array(__lowercase      ) for image in images]

											if do_resize:
																		__lowerCamelCase   :   Union[str, Any]       =[self.resize(image=__lowercase    ,     size=__lowercase    ,     crop_pct=__lowercase    ,     resample=__lowercase      ) for image in images]

											if do_rescale:
																		__lowerCamelCase   :   int       =[self.rescale(image=__lowercase    ,     scale=__lowercase      ) for image in images]

											if do_normalize:
																		__lowerCamelCase   :   Union[str, Any]       =[self.normalize(image=__lowercase    ,     mean=__lowercase    ,     std=__lowercase      ) for image in images]

											__lowerCamelCase   :   Union[str, Any]       =[to_channel_dimension_format(__lowercase    ,     __lowercase      ) for image in images]

											__lowerCamelCase   :   Tuple       ={'''pixel_values''': images}
											return BatchFeature(data=__lowercase    ,     tensor_type=__lowercase      )




