



"""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 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_vision_available, logging


if is_vision_available():
     import PIL


_UpperCamelCase: Any										=     logging.get_logger(__name__)


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase )   ->	Tuple:






      '''simple docstring'''


      lowercase     :					Union[str, Any] 		=      b.T
      lowercase     :					Union[str, Any] 		=      np.sum(np.square(UpperCamelCase__ )		,					axis=1 )
      lowercase     :					Dict 		=      np.sum(np.square(UpperCamelCase__ )		,					axis=0 )
      lowercase     :					Dict 		=      np.matmul(UpperCamelCase__		,					UpperCamelCase__ )
      lowercase     :					Union[str, Any] 		=      aa[:, None] - 2 * ab + ba[None, :]
      return d


def      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase )   ->	Dict:






      '''simple docstring'''


      lowercase     :					Optional[int] 		=      x.reshape(-1		,					3 )
      lowercase     :					int 		=      squared_euclidean_distance(UpperCamelCase__		,					UpperCamelCase__ )
      return np.argmin(UpperCamelCase__		,					axis=1 )





class      a__						(  UpperCamelCase__   ):
   _lowerCamelCase    							= ['pixel_values']


   def __init__(							self					:							Any,   lowerCAmelCase					:							Optional[Any] = None,   lowerCAmelCase					:							Union[str, Any] = True,   lowerCAmelCase					:							Any = None,   lowerCAmelCase					:							Dict = PILImageResampling.BILINEAR,   lowerCAmelCase					:							Optional[int] = True,   lowerCAmelCase					:							int = True,   **lowerCAmelCase					:							List[str],   )			->				Optional[int]:
         super().__init__(**__lowerCamelCase	)
         lowercase     :					Optional[int] 		=      size if size is not None else {'height': 256, 'width': 256}
         lowercase     :					Optional[Any] 		=      get_size_dict(__lowerCamelCase	)
         lowercase     :					Dict 		=      np.array(__lowerCamelCase	) if clusters is not None else None
         lowercase     :					Dict 		=      do_resize
         lowercase     :					Dict 		=      size
         lowercase     :					Tuple 		=      resample
         lowercase     :					Optional[Any] 		=      do_normalize
         lowercase     :					str 		=      do_color_quantize


   def 				lowercase (							self					:							Dict,   lowerCAmelCase					:							Dict,   lowerCAmelCase					:							Optional[int],   lowerCAmelCase					:							Optional[Any] = PILImageResampling.BILINEAR,   lowerCAmelCase					:							Union[str, Any] = None,   **lowerCAmelCase					:							int,   )			->				Any:
         lowercase     :					Dict 		=      get_size_dict(__lowerCamelCase	)
         if "height" not in size or "width" not in size:
               raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''	)
         return resize(
             __lowerCamelCase,   size=(size['height'], size['width']),   resample=__lowerCamelCase,   data_format=__lowerCamelCase,   **__lowerCamelCase	)


   def 				lowercase (							self					:							Optional[Any],   lowerCAmelCase					:							Union[str, Any],   lowerCAmelCase					:							List[str] = None,   )			->				Optional[Any]:
         lowercase     :					List[Any] 		=      rescale(image=__lowerCamelCase,   scale=1 / 127.5,   data_format=__lowerCamelCase	)
         lowercase     :					Union[str, Any] 		=      image - 1
         return image






   def 				lowercase (							self					:							str,   lowerCAmelCase					:							Optional[int],   lowerCAmelCase					:							Optional[int] = None,   lowerCAmelCase					:							List[str] = None,   lowerCAmelCase					:							Dict = None,   lowerCAmelCase					:							Tuple = None,   lowerCAmelCase					:							str = None,   lowerCAmelCase					:							List[Any] = None,   lowerCAmelCase					:							str = None,   lowerCAmelCase					:							str = ChannelDimension.FIRST,   **lowerCAmelCase					:							Any,   )			->				List[Any]:
         lowercase     :					str 		=      do_resize if do_resize is not None else self.do_resize
         lowercase     :					str 		=      size if size is not None else self.size
         lowercase     :					Optional[int] 		=      get_size_dict(__lowerCamelCase	)
         lowercase     :					int 		=      resample if resample is not None else self.resample
         lowercase     :					Any 		=      do_normalize if do_normalize is not None else self.do_normalize
         lowercase     :					str 		=      do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         lowercase     :					Union[str, Any] 		=      clusters if clusters is not None else self.clusters
         lowercase     :					List[Any] 		=      np.array(__lowerCamelCase	)

         lowercase     :					Optional[int] 		=      make_list_of_images(__lowerCamelCase	)

         if not valid_images(__lowerCamelCase	):
               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_color_quantize and clusters is None:
               raise ValueError('Clusters must be specified if do_color_quantize is True.'	)

         # All transformations expect numpy arrays.
         lowercase     :					Dict 		=      [to_numpy_array(__lowerCamelCase	) for image in images]

         if do_resize:
               lowercase     :					List[str] 		=      [self.resize(image=__lowerCamelCase,   size=__lowerCamelCase,   resample=__lowerCamelCase	) for image in images]

         if do_normalize:
               lowercase     :					Optional[int] 		=      [self.normalize(image=__lowerCamelCase	) for image in images]

         if do_color_quantize:
               lowercase     :					List[Any] 		=      [to_channel_dimension_format(__lowerCamelCase,   ChannelDimension.LAST	) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               lowercase     :					Optional[Any] 		=      np.array(__lowerCamelCase	)
               lowercase     :					str 		=      color_quantize(__lowerCamelCase,   __lowerCamelCase	).reshape(images.shape[:-1]	)

               # flatten to (batch_size, height*width)
               lowercase     :					Tuple 		=      images.shape[0]
               lowercase     :					str 		=      images.reshape(__lowerCamelCase,   -1	)

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               lowercase     :					Any 		=      list(__lowerCamelCase	)
         else:
               lowercase     :					str 		=      [to_channel_dimension_format(__lowerCamelCase,   __lowerCamelCase	) for image in images]

         lowercase     :					Optional[Any] 		=      {'input_ids': images}
         return BatchFeature(data=__lowerCamelCase,   tensor_type=__lowerCamelCase	)







import argparse

import torch

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


logging.set_verbosity_info()


def 	UpperCamelCase__(       UpperCamelCase__			:       Any ,     UpperCamelCase__			:       int ,     UpperCamelCase__			:       int  )->int:
       # Initialise PyTorch model
       A__											=						BertConfig.from_json_file(UpperCamelCase__  )
       print(f"Building PyTorch model from configuration: {config}"  )
       A__											=						BertForPreTraining(UpperCamelCase__  )

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

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


if __name__ == "__main__":
       a__:      str									=		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__:      Any									=		parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





