



"""simple docstring"""

from typing import Dict, List, Optional, Tuple, Union

import numpy as np

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


if is_vision_available():
							import PIL

if is_torch_available():
							import torch


A_						:    Union[str, Any]				=      logging.get_logger(__name__)
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= ['''pixel_values''']





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





					"""simple docstring"""


					super().__init__(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     size if size is not None else {'shortest_edge': 224}
					SCREAMING_SNAKE_CASE						:							int							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       default_to_square=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     crop_size if crop_size is not None else {'height': 256, 'width': 256}
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       param_name='crop_size'    )

					SCREAMING_SNAKE_CASE						:							List[str]							=     do_resize
					SCREAMING_SNAKE_CASE						:							Tuple							=     size
					SCREAMING_SNAKE_CASE						:							str							=     resample
					SCREAMING_SNAKE_CASE						:							List[str]							=     do_rescale
					SCREAMING_SNAKE_CASE						:							List[str]							=     rescale_factor
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     do_center_crop
					SCREAMING_SNAKE_CASE						:							str							=     crop_size
					SCREAMING_SNAKE_CASE						:							List[str]							=     do_flip_channel_order





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       np.ndarray     ,       _SCREAMING_SNAKE_CASE				:       Dict[str, int]     ,       _SCREAMING_SNAKE_CASE				:       PILImageResampling = PIL.Image.BILINEAR     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[str, ChannelDimension]] = None     ,       **_SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       )						->	np.ndarray:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       default_to_square=_SCREAMING_SNAKE_CASE    )
					if "shortest_edge" not in size:
									raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""    )
					SCREAMING_SNAKE_CASE						:							Dict							=     get_resize_output_image_size(_SCREAMING_SNAKE_CASE     ,       size=size['shortest_edge']     ,       default_to_square=_SCREAMING_SNAKE_CASE    )
					return resize(_SCREAMING_SNAKE_CASE     ,       size=_SCREAMING_SNAKE_CASE     ,       resample=_SCREAMING_SNAKE_CASE     ,       data_format=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     get_size_dict(_SCREAMING_SNAKE_CASE    )
					if "height" not in size or "width" not in size:
									raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""    )
					return center_crop(_SCREAMING_SNAKE_CASE     ,       size=(size['height'], size['width'])     ,       data_format=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       np.ndarray     ,       _SCREAMING_SNAKE_CASE				:       Union[int, float]     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[str, ChannelDimension]] = None     ,       **_SCREAMING_SNAKE_CASE				:       Any     ,       )						->	Optional[int]:





					"""simple docstring"""


					return rescale(_SCREAMING_SNAKE_CASE     ,       scale=_SCREAMING_SNAKE_CASE     ,       data_format=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       np.ndarray     ,       _SCREAMING_SNAKE_CASE				:       Optional[Union[str, ChannelDimension]] = None    )						->	np.ndarray:





					"""simple docstring"""


					return flip_channel_order(_SCREAMING_SNAKE_CASE     ,       data_format=_SCREAMING_SNAKE_CASE    )





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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     do_resize if do_resize is not None else self.do_resize
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     resample if resample is not None else self.resample
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     do_rescale if do_rescale is not None else self.do_rescale
					SCREAMING_SNAKE_CASE						:							List[str]							=     rescale_factor if rescale_factor is not None else self.rescale_factor
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     do_center_crop if do_center_crop is not None else self.do_center_crop
					SCREAMING_SNAKE_CASE						:							str							=     (
					    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
					)

					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     size if size is not None else self.size
					SCREAMING_SNAKE_CASE						:							int							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       default_to_square=_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     crop_size if crop_size is not None else self.crop_size
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     get_size_dict(_SCREAMING_SNAKE_CASE     ,       param_name='crop_size'    )

					SCREAMING_SNAKE_CASE						:							Dict							=     make_list_of_images(_SCREAMING_SNAKE_CASE    )

					if not valid_images(_SCREAMING_SNAKE_CASE    ):
									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:
									raise ValueError('Size must be specified if do_resize is True.'    )

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

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

					# All transformations expect numpy arrays.
					SCREAMING_SNAKE_CASE						:							int							=     [to_numpy_array(_SCREAMING_SNAKE_CASE    ) for image in images]

					if do_resize:
									SCREAMING_SNAKE_CASE						:							Tuple							=     [self.resize(image=_SCREAMING_SNAKE_CASE     ,       size=_SCREAMING_SNAKE_CASE     ,       resample=_SCREAMING_SNAKE_CASE    ) for image in images]

					if do_center_crop:
									SCREAMING_SNAKE_CASE						:							Dict							=     [self.center_crop(image=_SCREAMING_SNAKE_CASE     ,       size=_SCREAMING_SNAKE_CASE    ) for image in images]

					if do_rescale:
									SCREAMING_SNAKE_CASE						:							List[str]							=     [self.rescale(image=_SCREAMING_SNAKE_CASE     ,       scale=_SCREAMING_SNAKE_CASE    ) for image in images]

					# the pretrained checkpoints assume images are BGR, not RGB
					if do_flip_channel_order:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     [self.flip_channel_order(image=_SCREAMING_SNAKE_CASE    ) for image in images]

					SCREAMING_SNAKE_CASE						:							List[str]							=     [to_channel_dimension_format(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) for image in images]

					SCREAMING_SNAKE_CASE						:							Any							=     {'pixel_values': images}
					return BatchFeature(data=_SCREAMING_SNAKE_CASE     ,       tensor_type=_SCREAMING_SNAKE_CASE    )







	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       List[Tuple] = None    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[Any]							=     outputs.logits

					# Resize logits and compute semantic segmentation maps
					if target_sizes is not None:
									if len(_SCREAMING_SNAKE_CASE    ) != len(_SCREAMING_SNAKE_CASE    ):
													raise ValueError(
													    'Make sure that you pass in as many target sizes as the batch dimension of the logits'    )

									if is_torch_tensor(_SCREAMING_SNAKE_CASE    ):
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     target_sizes.numpy()

									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     []

									for idx in range(len(_SCREAMING_SNAKE_CASE    )    ):
													SCREAMING_SNAKE_CASE						:							int							=     torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0    )     ,       size=target_sizes[idx]     ,       mode='bilinear'     ,       align_corners=_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Tuple							=     resized_logits[0].argmax(dim=0    )
													semantic_segmentation.append(_SCREAMING_SNAKE_CASE    )
					else:
									SCREAMING_SNAKE_CASE						:							str							=     logits.argmax(dim=1    )
									SCREAMING_SNAKE_CASE						:							List[str]							=     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]    )]

					return semantic_segmentation










"""simple docstring"""

import argparse

import torch

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


logging.set_verbosity_info()
def  __snake_case							(	__A      :						Union[str, Any]							,		__A      :						Any							,		__A      :						Dict     )     ->       Tuple:






				'''simple docstring'''
				# Initialise PyTorch model
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     BertConfig.from_json_file(__A     )
				print(F"""Building PyTorch model from configuration: {config}"""     )
				SCREAMING_SNAKE_CASE						:							List[Any]							=     BertForPreTraining(__A     )

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

				# Save pytorch-model
				print(F"""Save PyTorch model to {pytorch_dump_path}"""     )
				torch.save(model.state_dict()							,		__A     )


if __name__ == "__main__":
							A_						:    int				=      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.'
							)
							A_						:    str				=      parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





