


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

A       :		Any    =   logging.get_logger(__name__)






def  UpperCamelCase					(	__magic_name__     :							Union[str, Any]     )   ->  Tuple:
		"""simple docstring"""




		if isinstance(_lowerCamelCase    ,    (list, tuple)     ) and isinstance(videos[0]    ,    (list, tuple)     ) and is_valid_image(videos[0][0]     ):
				return videos

		elif isinstance(_lowerCamelCase    ,    (list, tuple)     ) and is_valid_image(videos[0]     ):
				return [videos]

		elif is_valid_image(_lowerCamelCase     ):
				return [[videos]]

		raise ValueError(f'''Could not make batched video from {videos}'''     )







class 							A    (       __A							):
					'''simple docstring'''






					A__			= ["pixel_values"]

					def __init__(self    :						Union[str, Any]      ,					_UpperCAmelCase    :						bool = True      ,					_UpperCAmelCase    :						Dict[str, int] = None      ,					_UpperCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR      ,					_UpperCAmelCase    :						bool = True      ,					_UpperCAmelCase    :						Dict[str, int] = None      ,					_UpperCAmelCase    :						bool = True      ,					_UpperCAmelCase    :						Union[int, float] = 1 / 255      ,					_UpperCAmelCase    :						bool = True      ,					_UpperCAmelCase    :						bool = True      ,					_UpperCAmelCase    :						Optional[Union[float, List[float]]] = None      ,					_UpperCAmelCase    :						Optional[Union[float, List[float]]] = None      ,					**_UpperCAmelCase    :						Union[str, Any]      ,					)						->   None:




							"""simple docstring"""


							super().__init__(**lowercase_		)
							lowercase__     			=		size if size is not None else {"shortest_edge": 256}
							lowercase__     			=		get_size_dict(lowercase_      ,					default_to_square=lowercase_		)
							lowercase__     			=		crop_size if crop_size is not None else {"height": 224, "width": 224}
							lowercase__     			=		get_size_dict(lowercase_      ,					param_name="""crop_size"""		)

							lowercase__     			=		do_resize
							lowercase__     			=		size
							lowercase__     			=		do_center_crop
							lowercase__     			=		crop_size
							lowercase__     			=		resample
							lowercase__     			=		do_rescale
							lowercase__     			=		rescale_factor
							lowercase__     			=		offset
							lowercase__     			=		do_normalize
							lowercase__     			=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowercase__     			=		image_std if image_std is not None else IMAGENET_STANDARD_STD

					def   lowerCamelCase__     (self    :						Dict      ,					_UpperCAmelCase    :						np.ndarray      ,					_UpperCAmelCase    :						Dict[str, int]      ,					_UpperCAmelCase    :						PILImageResampling = PILImageResampling.BILINEAR      ,					_UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None      ,					**_UpperCAmelCase    :						List[str]      ,					)						->   np.ndarray:




							"""simple docstring"""


							lowercase__     			=		get_size_dict(lowercase_      ,					default_to_square=lowercase_		)
							if "shortest_edge" in size:
									lowercase__     			=		get_resize_output_image_size(lowercase_      ,					size["""shortest_edge"""]      ,					default_to_square=lowercase_		)
							elif "height" in size and "width" in size:
									lowercase__     			=		(size["height"], size["width"])
							else:
									raise ValueError(f'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''		)
							return resize(lowercase_      ,					size=lowercase_      ,					resample=lowercase_      ,					data_format=lowercase_      ,					**lowercase_		)

					def   lowerCamelCase__     (self    :						Dict      ,					_UpperCAmelCase    :						np.ndarray      ,					_UpperCAmelCase    :						Dict[str, int]      ,					_UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None      ,					**_UpperCAmelCase    :						Dict      ,					)						->   np.ndarray:




							"""simple docstring"""


							lowercase__     			=		get_size_dict(lowercase_		)
							if "height" not in size or "width" not in size:
									raise ValueError(f'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}'''		)
							return center_crop(lowercase_      ,					size=(size["""height"""], size["""width"""])      ,					data_format=lowercase_      ,					**lowercase_		)

					def   lowerCamelCase__     (self    :						Optional[int]      ,					_UpperCAmelCase    :						np.ndarray      ,					_UpperCAmelCase    :						Union[int, float]      ,					_UpperCAmelCase    :						bool = True      ,					_UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None      ,					**_UpperCAmelCase    :						str      ,					)						->   Any:




							"""simple docstring"""


							lowercase__     			=		image.astype(np.floataa		)
							if offset:
									lowercase__     			=		image - (scale / 2)
							return rescale(lowercase_      ,					scale=lowercase_      ,					data_format=lowercase_      ,					**lowercase_		)

					def   lowerCamelCase__     (self    :						Optional[Any]      ,					_UpperCAmelCase    :						np.ndarray      ,					_UpperCAmelCase    :						Union[float, List[float]]      ,					_UpperCAmelCase    :						Union[float, List[float]]      ,					_UpperCAmelCase    :						Optional[Union[str, ChannelDimension]] = None      ,					**_UpperCAmelCase    :						List[str]      ,					)						->   np.ndarray:




							"""simple docstring"""


							return normalize(lowercase_      ,					mean=lowercase_      ,					std=lowercase_      ,					data_format=lowercase_      ,					**lowercase_		)

					def   lowerCamelCase__     (self    :						Tuple      ,					_UpperCAmelCase    :						ImageInput      ,					_UpperCAmelCase    :						bool = None      ,					_UpperCAmelCase    :						Dict[str, int] = None      ,					_UpperCAmelCase    :						PILImageResampling = None      ,					_UpperCAmelCase    :						bool = None      ,					_UpperCAmelCase    :						Dict[str, int] = None      ,					_UpperCAmelCase    :						bool = None      ,					_UpperCAmelCase    :						float = None      ,					_UpperCAmelCase    :						bool = None      ,					_UpperCAmelCase    :						bool = None      ,					_UpperCAmelCase    :						Optional[Union[float, List[float]]] = None      ,					_UpperCAmelCase    :						Optional[Union[float, List[float]]] = None      ,					_UpperCAmelCase    :						Optional[ChannelDimension] = ChannelDimension.FIRST      ,					)						->   np.ndarray:




							"""simple docstring"""


							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_center_crop and crop_size is None:
									raise ValueError("""Crop size must be specified if do_center_crop is True."""		)

							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."""		)

							if offset and not do_rescale:
									raise ValueError("""For offset, do_rescale must also be set to True."""		)

							# All transformations expect numpy arrays.
							lowercase__     			=		to_numpy_array(lowercase_		)

							if do_resize:
									lowercase__     			=		self.resize(image=lowercase_      ,					size=lowercase_      ,					resample=lowercase_		)

							if do_center_crop:
									lowercase__     			=		self.center_crop(lowercase_      ,					size=lowercase_		)

							if do_rescale:
									lowercase__     			=		self.rescale(image=lowercase_      ,					scale=lowercase_      ,					offset=lowercase_		)

							if do_normalize:
									lowercase__     			=		self.normalize(image=lowercase_      ,					mean=lowercase_      ,					std=lowercase_		)

							lowercase__     			=		to_channel_dimension_format(lowercase_      ,					lowercase_		)
							return image


					def   lowerCamelCase__     (self    :						List[Any]      ,					_UpperCAmelCase    :						ImageInput      ,					_UpperCAmelCase    :						bool = None      ,					_UpperCAmelCase    :						Dict[str, int] = None      ,					_UpperCAmelCase    :						PILImageResampling = None      ,					_UpperCAmelCase    :						bool = None      ,					_UpperCAmelCase    :						Dict[str, int] = None      ,					_UpperCAmelCase    :						bool = None      ,					_UpperCAmelCase    :						float = None      ,					_UpperCAmelCase    :						bool = None      ,					_UpperCAmelCase    :						bool = None      ,					_UpperCAmelCase    :						Optional[Union[float, List[float]]] = None      ,					_UpperCAmelCase    :						Optional[Union[float, List[float]]] = None      ,					_UpperCAmelCase    :						Optional[Union[str, TensorType]] = None      ,					_UpperCAmelCase    :						ChannelDimension = ChannelDimension.FIRST      ,					**_UpperCAmelCase    :						int      ,					)						->   PIL.Image.Image:




							"""simple docstring"""


							lowercase__     			=		do_resize if do_resize is not None else self.do_resize
							lowercase__     			=		resample if resample is not None else self.resample
							lowercase__     			=		do_center_crop if do_center_crop is not None else self.do_center_crop
							lowercase__     			=		do_rescale if do_rescale is not None else self.do_rescale
							lowercase__     			=		rescale_factor if rescale_factor is not None else self.rescale_factor
							lowercase__     			=		offset if offset is not None else self.offset
							lowercase__     			=		do_normalize if do_normalize is not None else self.do_normalize
							lowercase__     			=		image_mean if image_mean is not None else self.image_mean
							lowercase__     			=		image_std if image_std is not None else self.image_std

							lowercase__     			=		size if size is not None else self.size
							lowercase__     			=		get_size_dict(lowercase_      ,					default_to_square=lowercase_		)
							lowercase__     			=		crop_size if crop_size is not None else self.crop_size
							lowercase__     			=		get_size_dict(lowercase_      ,					param_name="""crop_size"""		)

							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."""		)

							lowercase__     			=		make_batched(lowercase_		)

							lowercase__     			=		[
							    [
							        self._preprocess_image(
							            image=lowercase_      ,					do_resize=lowercase_      ,					size=lowercase_      ,					resample=lowercase_      ,					do_center_crop=lowercase_      ,					crop_size=lowercase_      ,					do_rescale=lowercase_      ,					rescale_factor=lowercase_      ,					offset=lowercase_      ,					do_normalize=lowercase_      ,					image_mean=lowercase_      ,					image_std=lowercase_      ,					data_format=lowercase_      ,					)
							        for img in video
							    ]
							    for video in videos
							]

							lowercase__     			=		{"pixel_values": videos}
							return BatchFeature(data=lowercase_      ,					tensor_type=lowercase_		)


import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def 			lowercase_				(			_lowerCamelCase							:       str		,	_lowerCamelCase							:       List[Any]		,	_lowerCamelCase							:       Dict):
    # Initialise PyTorch model
    lowercase__ :   List[str]    					=				BertConfig.from_json_file(_lowerCamelCase)
    print(f'''Building PyTorch model from configuration: {config}''')
    lowercase__ :   Optional[Any]    					=				BertForPreTraining(_lowerCamelCase)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert(_lowerCamelCase		,	_lowerCamelCase		,	_lowerCamelCase)

    # Save pytorch-model
    print(f'''Save PyTorch model to {pytorch_dump_path}''')
    torch.save(model.state_dict()		,	_lowerCamelCase)


if __name__ == "__main__":
      UpperCamelCase  							=						argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--bert_config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained BERT model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      UpperCamelCase  							=						parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



