






'''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 (
    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, logging


lowerCAmelCase_           = logging.get_logger(__name__)
class      UpperCamelCase (				_lowerCamelCase ):







		"""simple docstring"""



		snake_case    =   ["pixel_values"]
		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       str = True						,_SCREAMING_SNAKE_CASE		:       int = None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any] = PILImageResampling.BILINEAR						,_SCREAMING_SNAKE_CASE		:       List[str] = True						,_SCREAMING_SNAKE_CASE		:       Optional[int] = None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any] = True						,_SCREAMING_SNAKE_CASE		:       List[Any] = 1 / 2_5_5						,_SCREAMING_SNAKE_CASE		:       List[str] = True						,_SCREAMING_SNAKE_CASE		:       Any = None						,_SCREAMING_SNAKE_CASE		:       List[Any] = None						,**_SCREAMING_SNAKE_CASE		:       Tuple						,)    ->   None:


						'''simple docstring'''
						super().__init__(**A__		)
						A	 =      size if size is not None else {'shortest_edge': 2_5_6}
						A	 =      get_size_dict(A__						,default_to_square=A__		)
						A	 =      crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
						A	 =      get_size_dict(A__		)
						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       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       str = PILImageResampling.BICUBIC						,_SCREAMING_SNAKE_CASE		:       Dict = None						,**_SCREAMING_SNAKE_CASE		:       str						,)    ->   np.ndarray:


						'''simple docstring'''
						A	 =      get_size_dict(A__						,default_to_square=A__		)
						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(A__						,size=size['shortest_edge']						,default_to_square=A__		)
						return resize(A__						,size=A__						,resample=A__						,data_format=A__						,**A__		)
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       List[str] = None						,**_SCREAMING_SNAKE_CASE		:       Dict						,)    ->   np.ndarray:


						'''simple docstring'''
						A	 =      get_size_dict(A__		)
						return center_crop(A__						,size=(size['height'], size['width'])						,data_format=A__						,**A__		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Tuple = None						,**_SCREAMING_SNAKE_CASE		:       str		)    ->   np.ndarray:


						'''simple docstring'''
						return rescale(A__						,scale=A__						,data_format=A__						,**A__		)
		def       A(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Any						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Dict = None						,**_SCREAMING_SNAKE_CASE		:       List[Any]						,)    ->   np.ndarray:


						'''simple docstring'''
						return normalize(A__						,mean=A__						,std=A__						,data_format=A__						,**A__		)







		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       int = None						,_SCREAMING_SNAKE_CASE		:       List[Any] = None						,_SCREAMING_SNAKE_CASE		:       str = None						,_SCREAMING_SNAKE_CASE		:       Tuple = None						,_SCREAMING_SNAKE_CASE		:       Union[str, Any] = None						,_SCREAMING_SNAKE_CASE		:       List[Any] = None						,_SCREAMING_SNAKE_CASE		:       Optional[Any] = None						,_SCREAMING_SNAKE_CASE		:       List[Any] = None						,_SCREAMING_SNAKE_CASE		:       List[Any] = None						,_SCREAMING_SNAKE_CASE		:       int = None						,_SCREAMING_SNAKE_CASE		:       int = None						,_SCREAMING_SNAKE_CASE		:       Tuple = ChannelDimension.FIRST						,**_SCREAMING_SNAKE_CASE		:       List[Any]						,)    ->   Tuple:


						'''simple docstring'''
						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(A__						,default_to_square=A__		)
						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(A__		)
						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(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:
										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(A__		) for image in images]

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

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

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

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

						A	 =      [to_channel_dimension_format(A__						,A__		) for image in images]

						A	 =      {'pixel_values': images}
						return BatchFeature(data=A__						,tensor_type=A__		)













import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





