



'''simple docstring'''

import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


UpperCAmelCase      						=       logging.get_logger(__name__)

UpperCAmelCase      						=       {'vocab_file': 'vocab.txt'}

UpperCAmelCase      						=       {
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

UpperCAmelCase      						=       {
    'facebook/esm2_t6_8M_UR50D': 1024,
    'facebook/esm2_t12_35M_UR50D': 1024,
}






def 						_snake_case						(   _SCREAMING_SNAKE_CASE	:		int				)     ->		Tuple:







	"""simple docstring"""





	with open(SCREAMING_SNAKE_CASE__				,						"""r"""				) as f:
		lowerCAmelCase       =      f.read().splitlines()
		return [l.strip() for l in lines]





class __snake_case( UpperCamelCase_   ):


						'''simple docstring'''



						UpperCAmelCase				:       str 	=						VOCAB_FILES_NAMES
						UpperCAmelCase				:       Any 	=						PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase				:       Union[str, Any] 	=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase				:       Tuple 	=						['input_ids', 'attention_mask']


						def __init__(   self      ,			A_      ,			A_="<unk>"      ,			A_="<cls>"      ,			A_="<pad>"      ,			A_="<mask>"      ,			A_="<eos>"      ,			**A_      ,			)      ->      str:
							super().__init__(**lowercase_ )
							lowerCAmelCase       =      load_vocab_file(lowercase_ )
							lowerCAmelCase       =      dict(enumerate(self.all_tokens ) )
							lowerCAmelCase       =      {tok: ind for ind, tok in enumerate(self.all_tokens )}
							lowerCAmelCase       =      unk_token
							lowerCAmelCase       =      cls_token
							lowerCAmelCase       =      pad_token
							lowerCAmelCase       =      mask_token
							lowerCAmelCase       =      eos_token
							lowerCAmelCase       =      self.all_tokens
							self._create_trie(self.unique_no_split_tokens )


						def 							__snake_case       (   self      ,			A_ )      ->      str:
							return self._id_to_token.get(lowercase_      ,			self.unk_token )


						def 							__snake_case       (   self      ,			A_ )      ->      int:
							return self._token_to_id.get(lowercase_      ,			self._token_to_id.get(self.unk_token ) )


						def 							__snake_case       (   self      ,			A_      ,			**A_ )      ->      Dict:
							return text.split()


						def 							__snake_case       (   self      ,			A_=False )      ->      Any:
							return len(self._id_to_token )


						def 							__snake_case       (   self )      ->      List[str]:
							return {token: i for i, token in enumerate(self.all_tokens )}


						def 							__snake_case       (   self      ,			A_ )      ->      int:
							return self._token_to_id.get(lowercase_      ,			self._token_to_id.get(self.unk_token ) )


						def 							__snake_case       (   self      ,			A_ )      ->      str:
							return self._id_to_token.get(lowercase_      ,			self.unk_token )


						def 							__snake_case       (   self      ,			A_      ,			A_ = None )      ->      List[int]:
							lowerCAmelCase       =      [self.cls_token_id]
							lowerCAmelCase       =      [self.eos_token_id]  # No sep token in ESM vocabulary
							if token_ids_a is None:
								if self.eos_token_id is None:
									return cls + token_ids_a
								else:
									return cls + token_ids_a + sep
							elif self.eos_token_id is None:
								raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!""" )
							return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token


						def 							__snake_case       (   self      ,			A_      ,			A_ = None      ,			A_ = False )      ->      List[int]:




							if already_has_special_tokens:
								if token_ids_a is not None:
									raise ValueError(
									    """You should not supply a second sequence if the provided sequence of """
									    """ids is already formatted with special tokens for the model.""" )

								return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
							lowerCAmelCase       =      [1] + ([0] * len(lowercase_ )) + [1]
							if token_ids_a is not None:
								mask += [0] * len(lowercase_ ) + [1]
							return mask


						def 							__snake_case       (   self      ,			A_      ,			A_ )      ->      Tuple:
							lowerCAmelCase       =      os.path.join(lowercase_      ,			(filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt""" )
							with open(lowercase_      ,			"""w""" ) as f:
								f.write("""\n""".join(self.all_tokens ) )
							return (vocab_file,)


						@property
						def 							__snake_case       (   self )      ->      int:
							return self.get_vocab_size(with_added_tokens=lowercase_ )




						def 							__snake_case       (   self      ,			A_      ,			A_ = False )      ->      int:
							return super()._add_tokens(lowercase_      ,			special_tokens=lowercase_ )



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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


lowerCamelCase		   =			logging.get_logger(__name__)


if is_vision_available():
 import PIL




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =['pixel_values']






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




             super().__init__(**lowercase_					)
             _lowerCamelCase			:						Optional[int]            =size if size is not None else {'shortest_edge': 224}
             _lowerCamelCase			:						List[Any]            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =crop_size if crop_size is not None else {'height': 224, 'width': 224}
             _lowerCamelCase			:						str            =get_size_dict(lowercase_     ,  default_to_square=lowercase_     ,  param_name='crop_size'					)

             _lowerCamelCase			:						Dict            =do_resize
             _lowerCamelCase			:						int            =size
             _lowerCamelCase			:						Optional[Any]            =resample
             _lowerCamelCase			:						Optional[int]            =do_center_crop
             _lowerCamelCase			:						Tuple            =crop_size
             _lowerCamelCase			:						Optional[int]            =do_rescale
             _lowerCamelCase			:						Optional[Any]            =rescale_factor
             _lowerCamelCase			:						Union[str, Any]            =do_normalize
             _lowerCamelCase			:						Optional[int]            =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             _lowerCamelCase			:						Any            =image_std if image_std is not None else OPENAI_CLIP_STD
             _lowerCamelCase			:						Any            =do_convert_rgb






      def 			lowerCamelCase			(   self		:	int     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	PILImageResampling = PILImageResampling.BICUBIC     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Any     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						int            =get_size_dict(lowercase_     ,  default_to_square=lowercase_					)
             if "shortest_edge" not in size:
                    raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''					)
             _lowerCamelCase			:						Union[str, Any]            =get_resize_output_image_size(lowercase_     ,  size=size['shortest_edge']     ,  default_to_square=lowercase_					)
             return resize(lowercase_     ,  size=lowercase_     ,  resample=lowercase_     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Dict[str, int]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =get_size_dict(lowercase_					)
             if "height" not in size or "width" not in size:
                    raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''					)
             return center_crop(lowercase_     ,  size=(size['height'], size['width'])     ,  data_format=lowercase_     ,  **lowercase_					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[int, float]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	int     ,  )   ->		str:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	np.ndarray     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Union[float, List[float]]     ,  lowercase_		:	Optional[Union[str, ChannelDimension]] = None     ,  **lowercase_		:	Tuple     ,  )   ->		np.ndarray:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	List[str]     ,  lowercase_		:	ImageInput     ,  lowercase_		:	bool = None     ,  lowercase_		:	Dict[str, int] = None     ,  lowercase_		:	PILImageResampling = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	int = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	float = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	Optional[Union[float, List[float]]] = None     ,  lowercase_		:	bool = None     ,  lowercase_		:	Optional[Union[str, TensorType]] = None     ,  lowercase_		:	Optional[ChannelDimension] = ChannelDimension.FIRST     ,  **lowercase_		:	Union[str, Any]     ,  )   ->		PIL.Image.Image:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =do_resize if do_resize is not None else self.do_resize
             _lowerCamelCase			:						List[str]            =size if size is not None else self.size
             _lowerCamelCase			:						Any            =get_size_dict(lowercase_     ,  param_name='size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =resample if resample is not None else self.resample
             _lowerCamelCase			:						List[Any]            =do_center_crop if do_center_crop is not None else self.do_center_crop
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else self.crop_size
             _lowerCamelCase			:						Dict            =get_size_dict(lowercase_     ,  param_name='crop_size'     ,  default_to_square=lowercase_					)
             _lowerCamelCase			:						str            =do_rescale if do_rescale is not None else self.do_rescale
             _lowerCamelCase			:						Tuple            =rescale_factor if rescale_factor is not None else self.rescale_factor
             _lowerCamelCase			:						List[str]            =do_normalize if do_normalize is not None else self.do_normalize
             _lowerCamelCase			:						Tuple            =image_mean if image_mean is not None else self.image_mean
             _lowerCamelCase			:						int            =image_std if image_std is not None else self.image_std
             _lowerCamelCase			:						Tuple            =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             _lowerCamelCase			:						Any            =make_list_of_images(lowercase_					)

             if not valid_images(lowercase_					):
                    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.'					)

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

             # All transformations expect numpy arrays.
             _lowerCamelCase			:						Optional[Any]            =[to_numpy_array(lowercase_					) for image in images]

             if do_resize:
                    _lowerCamelCase			:						Optional[Any]            =[self.resize(image=lowercase_     ,  size=lowercase_     ,  resample=lowercase_					) for image in images]

             if do_center_crop:
                    _lowerCamelCase			:						Optional[int]            =[self.center_crop(image=lowercase_     ,  size=lowercase_					) for image in images]

             if do_rescale:
                    _lowerCamelCase			:						Optional[Any]            =[self.rescale(image=lowercase_     ,  scale=lowercase_					) for image in images]

             if do_normalize:
                    _lowerCamelCase			:						List[Any]            =[self.normalize(image=lowercase_     ,  mean=lowercase_     ,  std=lowercase_					) for image in images]

             _lowerCamelCase			:						List[str]            =[to_channel_dimension_format(lowercase_     ,  lowercase_					) for image in images]

             _lowerCamelCase			:						Tuple            ={'pixel_values': images}
             return BatchFeature(data=lowercase_     ,  tensor_type=lowercase_					)


