





'''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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    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


a__			:					List[Any]       						=						logging.get_logger(__name__)






class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								=["pixel_values"]

       def __init__( self					:				Tuple       ,     a__					:				bool = True       ,     a__					:				Dict[str, int] = None       ,     a__					:				PILImageResampling = PILImageResampling.BICUBIC       ,     a__					:				bool = True       ,     a__					:				Union[int, float] = 1 / 255       ,     a__					:				bool = True       ,     a__					:				Optional[Union[float, List[float]]] = None       ,     a__					:				Optional[Union[float, List[float]]] = None       ,     a__					:				bool = True       ,     **a__					:				Optional[int]       ,     ):
              super().__init__(**a__						)
              UpperCAmelCase  =						size if size is not None else {'''height''': 384, '''width''': 384}
              UpperCAmelCase  =						get_size_dict(a__       ,     default_to_square=a__						)

              UpperCAmelCase  =						do_resize
              UpperCAmelCase  =						size
              UpperCAmelCase  =						resample
              UpperCAmelCase  =						do_rescale
              UpperCAmelCase  =						rescale_factor
              UpperCAmelCase  =						do_normalize
              UpperCAmelCase  =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
              UpperCAmelCase  =						image_std if image_std is not None else OPENAI_CLIP_STD
              UpperCAmelCase  =						do_convert_rgb

       def 			__snake_case      ( self					:				Tuple       ,     a__					:				np.ndarray       ,     a__					:				Dict[str, int]       ,     a__					:				PILImageResampling = PILImageResampling.BICUBIC       ,     a__					:				Optional[Union[str, ChannelDimension]] = None       ,     **a__					:				Optional[Any]       ,     ):
              UpperCAmelCase  =						get_size_dict(a__       ,     default_to_square=a__						)
              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()}"						)
              UpperCAmelCase  =						(size['''height'''], size['''width'''])
              return resize(a__       ,     size=a__       ,     resample=a__       ,     data_format=a__       ,     **a__						)

       def 			__snake_case      ( self					:				Optional[Any]       ,     a__					:				np.ndarray       ,     a__					:				Union[int, float]       ,     a__					:				Optional[Union[str, ChannelDimension]] = None       ,     **a__					:				str       ,     ):
              return rescale(a__       ,     scale=a__       ,     data_format=a__       ,     **a__						)

       def 			__snake_case      ( self					:				Any       ,     a__					:				np.ndarray       ,     a__					:				Union[float, List[float]]       ,     a__					:				Union[float, List[float]]       ,     a__					:				Optional[Union[str, ChannelDimension]] = None       ,     **a__					:				int       ,     ):
              return normalize(a__       ,     mean=a__       ,     std=a__       ,     data_format=a__       ,     **a__						)



       def 			__snake_case      ( self					:				List[Any]       ,     a__					:				ImageInput       ,     a__					:				Optional[bool] = None       ,     a__					:				Optional[Dict[str, int]] = None       ,     a__					:				PILImageResampling = None       ,     a__					:				Optional[bool] = None       ,     a__					:				Optional[float] = None       ,     a__					:				Optional[bool] = None       ,     a__					:				Optional[Union[float, List[float]]] = None       ,     a__					:				Optional[Union[float, List[float]]] = None       ,     a__					:				Optional[Union[str, TensorType]] = None       ,     a__					:				bool = None       ,     a__					:				ChannelDimension = ChannelDimension.FIRST       ,     **a__					:				Dict       ,     ):
              UpperCAmelCase  =						do_resize if do_resize is not None else self.do_resize
              UpperCAmelCase  =						resample if resample is not None else self.resample
              UpperCAmelCase  =						do_rescale if do_rescale is not None else self.do_rescale
              UpperCAmelCase  =						rescale_factor if rescale_factor is not None else self.rescale_factor
              UpperCAmelCase  =						do_normalize if do_normalize is not None else self.do_normalize
              UpperCAmelCase  =						image_mean if image_mean is not None else self.image_mean
              UpperCAmelCase  =						image_std if image_std is not None else self.image_std
              UpperCAmelCase  =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

              UpperCAmelCase  =						size if size is not None else self.size
              UpperCAmelCase  =						get_size_dict(a__       ,     default_to_square=a__						)

              UpperCAmelCase  =						make_list_of_images(a__						)

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

              # PIL RGBA images are converted to RGB
              if do_convert_rgb:
                     UpperCAmelCase  =						[convert_to_rgb(a__						) for image in images]

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

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

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

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

              UpperCAmelCase  =						[to_channel_dimension_format(a__       ,     a__						) for image in images]

              UpperCAmelCase  =						BatchFeature(data={'''pixel_values''': images}       ,     tensor_type=a__						)

              return encoded_outputs








import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :			Optional[Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				RobertaPreLayerNormConfig.from_pretrained(
	    __lowerCamelCase				,     architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

	# convert state_dict
	a									=				torch.load(hf_hub_download(repo_id=__lowerCamelCase				,     filename="""pytorch_model.bin"""       )       )
	a									=				{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""       ):
			a									=				"""roberta_prelayernorm.""" + tensor_key[len("""roberta."""       ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""       ) or tensor_key.endswith(""".self.LayerNorm.bias"""       ):
			continue

		a									=				tensor_value

	a									=				RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=__lowerCamelCase				,     config=__lowerCamelCase				,     state_dict=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )

	# convert tokenizer
	a									=				AutoTokenizer.from_pretrained(__lowerCamelCase       )
	tokenizer.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Union[str, Any]          = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--checkpoint-repo",
		    default=None,
		    type=str,
		    required=True,
		    help="Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.",
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		__UpperCamelCase   :			Any          = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

