


"""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,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
   import torch


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Any    =		['pixel_values']


     def __init__(      self    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = PILImageResampling.BILINEAR    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = 1 / 255    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          super().__init__(**__UpperCAmelCase							)
          _a    	=				size if size is not None else {'''shortest_edge''': 256}
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          _a    	=				crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          _a    	=				get_size_dict(__UpperCAmelCase    ,				param_name='''crop_size'''							)
          _a    	=				do_resize
          _a    	=				size
          _a    	=				resample
          _a    	=				do_center_crop
          _a    	=				crop_size
          _a    	=				do_rescale
          _a    	=				rescale_factor
          _a    	=				do_normalize
          _a    	=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _a    	=				image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = PILImageResampling.BICUBIC    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          if "shortest_edge" not in size:
               raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'							)
          _a    	=				get_resize_output_image_size(__UpperCAmelCase    ,				size=size['''shortest_edge''']    ,				default_to_square=__UpperCAmelCase							)
          return resize(__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				resample=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          _a    	=				get_size_dict(__UpperCAmelCase							)
          if "height" not in size or "width" not in size:
               raise ValueError(F'The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}'							)
          return center_crop(__UpperCAmelCase    ,				size=(size['''height'''], size['''width'''])    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase							)       ->							np.ndarray:
          return rescale(__UpperCAmelCase    ,				scale=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          return normalize(__UpperCAmelCase    ,				mean=__UpperCAmelCase    ,				std=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = ChannelDimension.FIRST    ,				**__UpperCAmelCase    ,				)       ->							Dict:
          _a    	=				do_resize if do_resize is not None else self.do_resize
          _a    	=				size if size is not None else self.size
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          _a    	=				resample if resample is not None else self.resample
          _a    	=				do_center_crop if do_center_crop is not None else self.do_center_crop
          _a    	=				crop_size if crop_size is not None else self.crop_size
          _a    	=				get_size_dict(__UpperCAmelCase    ,				param_name='''crop_size'''							)
          _a    	=				do_rescale if do_rescale is not None else self.do_rescale
          _a    	=				rescale_factor if rescale_factor is not None else self.rescale_factor
          _a    	=				do_normalize if do_normalize is not None else self.do_normalize
          _a    	=				image_mean if image_mean is not None else self.image_mean
          _a    	=				image_std if image_std is not None else self.image_std

          _a    	=				make_list_of_images(__UpperCAmelCase							)

          if not valid_images(__UpperCAmelCase							):
               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_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.'''							)

          # All transformations expect numpy arrays.
          _a    	=				[to_numpy_array(__UpperCAmelCase							) for image in images]

          if do_resize:
               _a    	=				[self.resize(image=__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				resample=__UpperCAmelCase							) for image in images]

          if do_center_crop:
               _a    	=				[self.center_crop(image=__UpperCAmelCase    ,				size=__UpperCAmelCase							) for image in images]

          if do_rescale:
               _a    	=				[self.rescale(image=__UpperCAmelCase    ,				scale=__UpperCAmelCase							) for image in images]

          if do_normalize:
               _a    	=				[self.normalize(image=__UpperCAmelCase    ,				mean=__UpperCAmelCase    ,				std=__UpperCAmelCase							) for image in images]

          _a    	=				[to_channel_dimension_format(__UpperCAmelCase    ,				__UpperCAmelCase							) for image in images]

          _a    	=				{'''pixel_values''': images}
          return BatchFeature(data=__UpperCAmelCase    ,				tensor_type=__UpperCAmelCase							)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							int:
          _a    	=				outputs.logits

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

               if is_torch_tensor(__UpperCAmelCase							):
                    _a    	=				target_sizes.numpy()

               _a    	=				[]

               for idx in range(len(__UpperCAmelCase							)							):
                    _a    	=				torch.nn.functional.interpolate(
                        logits[idx].unsqueeze(dim=0							)    ,				size=target_sizes[idx]    ,				mode='''bilinear'''    ,				align_corners=__UpperCAmelCase							)
                    _a    	=				resized_logits[0].argmax(dim=0							)
                    semantic_segmentation.append(__UpperCAmelCase							)
          else:
               _a    	=				logits.argmax(dim=1							)
               _a    	=				[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       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Union[str, Any]       ):






     """simple docstring"""

     _a    	=				BertConfig.from_json_file(_lowerCAmelCase       )
     print(f'Building PyTorch model from configuration: {config}'       )
     _a    	=				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__":
   __snake_case		=       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.'''
   )
   __snake_case		=       parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)