



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 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, logging


A_		:  Optional[int]										=  logging.get_logger(__name__)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      ['''pixel_values''']

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


    """simple docstring"""






    super().__init__(**__lowerCAmelCase				)
    a							=		size if size is not None else {"height": 224, "width": 224}
    a							=		get_size_dict(__lowerCAmelCase				)
    a							=		do_resize
    a							=		do_rescale
    a							=		do_normalize
    a							=		size
    a							=		resample
    a							=		rescale_factor
    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 		A				(   self      :							Tuple				, __lowerCAmelCase      :							np.ndarray				, __lowerCAmelCase      :							Dict[str, int]				, __lowerCAmelCase      :							PILImageResampling = PILImageResampling.BILINEAR				, __lowerCAmelCase      :							Optional[Union[str, ChannelDimension]] = None				, **__lowerCAmelCase      :							int				, )							->					np.ndarray:


    """simple docstring"""






    a							=		get_size_dict(__lowerCAmelCase				)
    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 resize(
        __lowerCAmelCase				, size=(size["height"], size["width"])				, resample=__lowerCAmelCase				, data_format=__lowerCAmelCase				, **__lowerCAmelCase				)

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							np.ndarray				, __lowerCAmelCase      :							float				, __lowerCAmelCase      :							Optional[Union[str, ChannelDimension]] = None				, **__lowerCAmelCase      :							Tuple				)							->					np.ndarray:


    """simple docstring"""






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

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


    """simple docstring"""






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




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


    """simple docstring"""






    a							=		do_resize if do_resize is not None else self.do_resize
    a							=		do_rescale if do_rescale is not None else self.do_rescale
    a							=		do_normalize if do_normalize is not None else self.do_normalize
    a							=		resample if resample is not None else self.resample
    a							=		rescale_factor if rescale_factor is not None else self.rescale_factor
    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							=		size if size is not None else self.size
    a							=		get_size_dict(__lowerCAmelCase				)

    a							=		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:
     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."				)

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

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

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

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

    a							=		[to_channel_dimension_format(__lowerCAmelCase				, __lowerCAmelCase				) for image in images]

    a							=		{"pixel_values": images}
    return BatchFeature(data=__lowerCAmelCase				, tensor_type=__lowerCAmelCase				)





'''simple docstring'''







import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							RoFormerConfig.from_json_file(__A       )
      print(F'''Building PyTorch model from configuration: {config}'''       )
      UpperCamelCase__      =							RoFormerForMaskedLM(__A       )

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

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


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__    :   Dict								=			parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





