




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				     =						logging.get_logger(__name__)


class 				_lowerCAmelCase		(  a__						):
				lowerCamelCase__          =     ["""pixel_values"""]




				def __init__(			self		,		snake_case_ = True		,		snake_case_ = None		,		snake_case_ = PILImageResampling.BICUBIC		,		snake_case_ = True		,		snake_case_ = 1 / 255		,		snake_case_ = True		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = True		,		**snake_case_		,		)     ->   None:
											super().__init__(**lowerCAmelCase__				)
											SCREAMING_SNAKE_CASE      :			List[str]						  =size if size is not None else {"height": 384, "width": 384}
											SCREAMING_SNAKE_CASE      :			List[Any]						  =get_size_dict(lowerCAmelCase__		,		default_to_square=lowerCAmelCase__				)

											SCREAMING_SNAKE_CASE      :			List[str]						  =do_resize
											SCREAMING_SNAKE_CASE      :			List[str]						  =size
											SCREAMING_SNAKE_CASE      :			int						  =resample
											SCREAMING_SNAKE_CASE      :			List[Any]						  =do_rescale
											SCREAMING_SNAKE_CASE      :			str						  =rescale_factor
											SCREAMING_SNAKE_CASE      :			List[Any]						  =do_normalize
											SCREAMING_SNAKE_CASE      :			List[str]						  =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											SCREAMING_SNAKE_CASE      :			Dict						  =image_std if image_std is not None else OPENAI_CLIP_STD
											SCREAMING_SNAKE_CASE      :			str						  =do_convert_rgb




				def      __a		(			self		,		snake_case_		,		snake_case_		,		snake_case_ = PILImageResampling.BICUBIC		,		snake_case_ = None		,		**snake_case_		,		)     ->   np.ndarray:
											SCREAMING_SNAKE_CASE      :			List[Any]						  =get_size_dict(lowerCAmelCase__		,		default_to_square=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()}'				)
											SCREAMING_SNAKE_CASE      :			Union[str, Any]						  =(size["height"], size["width"])
											return resize(lowerCAmelCase__		,		size=lowerCAmelCase__		,		resample=lowerCAmelCase__		,		data_format=lowerCAmelCase__		,		**lowerCAmelCase__				)




				def      __a		(			self		,		snake_case_		,		snake_case_		,		snake_case_ = None		,		**snake_case_		,		)     ->   Union[str, Any]:
											return rescale(lowerCAmelCase__		,		scale=lowerCAmelCase__		,		data_format=lowerCAmelCase__		,		**lowerCAmelCase__				)




				def      __a		(			self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_ = None		,		**snake_case_		,		)     ->   np.ndarray:
											return normalize(lowerCAmelCase__		,		mean=lowerCAmelCase__		,		std=lowerCAmelCase__		,		data_format=lowerCAmelCase__		,		**lowerCAmelCase__				)







				def      __a		(			self		,		snake_case_		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = None		,		snake_case_ = ChannelDimension.FIRST		,		**snake_case_		,		)     ->   PIL.Image.Image:
											SCREAMING_SNAKE_CASE      :			Tuple						  =do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE      :			str						  =resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE      :			Dict						  =do_rescale if do_rescale is not None else self.do_rescale
											SCREAMING_SNAKE_CASE      :			Any						  =rescale_factor if rescale_factor is not None else self.rescale_factor
											SCREAMING_SNAKE_CASE      :			int						  =do_normalize if do_normalize is not None else self.do_normalize
											SCREAMING_SNAKE_CASE      :			str						  =image_mean if image_mean is not None else self.image_mean
											SCREAMING_SNAKE_CASE      :			List[Any]						  =image_std if image_std is not None else self.image_std
											SCREAMING_SNAKE_CASE      :			List[str]						  =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											SCREAMING_SNAKE_CASE      :			Tuple						  =size if size is not None else self.size
											SCREAMING_SNAKE_CASE      :			List[Any]						  =get_size_dict(lowerCAmelCase__		,		default_to_square=lowerCAmelCase__				)

											SCREAMING_SNAKE_CASE      :			Optional[Any]						  =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_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:
																		SCREAMING_SNAKE_CASE      :			Optional[Any]						  =[convert_to_rgb(lowerCAmelCase__				) for image in images]

											# All transformations expect numpy arrays.
											SCREAMING_SNAKE_CASE      :			List[Any]						  =[to_numpy_array(lowerCAmelCase__				) for image in images]

											if do_resize:
																		SCREAMING_SNAKE_CASE      :			List[Any]						  =[self.resize(image=lowerCAmelCase__		,		size=lowerCAmelCase__		,		resample=lowerCAmelCase__				) for image in images]

											if do_rescale:
																		SCREAMING_SNAKE_CASE      :			int						  =[self.rescale(image=lowerCAmelCase__		,		scale=lowerCAmelCase__				) for image in images]

											if do_normalize:
																		SCREAMING_SNAKE_CASE      :			Optional[Any]						  =[self.normalize(image=lowerCAmelCase__		,		mean=lowerCAmelCase__		,		std=lowerCAmelCase__				) for image in images]

											SCREAMING_SNAKE_CASE      :			int						  =[to_channel_dimension_format(lowerCAmelCase__		,		lowerCAmelCase__				) for image in images]

											SCREAMING_SNAKE_CASE      :			Dict						  =BatchFeature(data={'''pixel_values''': images}		,		tensor_type=lowerCAmelCase__				)

											return encoded_outputs













"""simple docstring"""




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






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	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 :						List[Any]      =							"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 :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					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.'''
		)
		a		:					Tuple	      =     parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


