


'''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,
    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


__UpperCAmelCase       							=				logging.get_logger(__name__)


if is_vision_available():
		import PIL




class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  List[str]      		= ["pixel_values"]







							def __init__(						self   ,       lowerCamelCase_ = True   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = PILImageResampling.BICUBIC   ,       lowerCamelCase_ = True   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = True   ,       lowerCamelCase_ = 1 / 2_55   ,       lowerCamelCase_ = True   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = True   ,       **lowerCamelCase_   ,       )			->				None:
														super().__init__(**lowerCamelCase_  )
														lowerCAmelCase__			  =      size if size is not None else {'''shortest_edge''': 2_24}
														lowerCAmelCase__			  =      get_size_dict(lowerCamelCase_   ,       default_to_square=lowerCamelCase_  )
														lowerCAmelCase__			  =      crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
														lowerCAmelCase__			  =      get_size_dict(lowerCamelCase_   ,       default_to_square=lowerCamelCase_   ,       param_name='''crop_size'''  )

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







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_ = PILImageResampling.BICUBIC   ,       lowerCamelCase_ = None   ,       **lowerCamelCase_   ,       )			->				np.ndarray:
														lowerCAmelCase__			  =      get_size_dict(lowerCamelCase_   ,       default_to_square=lowerCamelCase_  )
														if "shortest_edge" not in size:
																					raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
														lowerCAmelCase__			  =      get_resize_output_image_size(lowerCamelCase_   ,       size=size['''shortest_edge''']   ,       default_to_square=lowerCamelCase_  )
														return resize(lowerCamelCase_   ,       size=lowerCamelCase_   ,       resample=lowerCamelCase_   ,       data_format=lowerCamelCase_   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       **lowerCamelCase_   ,       )			->				np.ndarray:
														lowerCAmelCase__			  =      get_size_dict(lowerCamelCase_  )
														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(lowerCamelCase_   ,       size=(size['''height'''], size['''width'''])   ,       data_format=lowerCamelCase_   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       **lowerCamelCase_   ,       )			->				str:
														return rescale(lowerCamelCase_   ,       scale=lowerCamelCase_   ,       data_format=lowerCamelCase_   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       **lowerCamelCase_   ,       )			->				np.ndarray:
														return normalize(lowerCamelCase_   ,       mean=lowerCamelCase_   ,       std=lowerCamelCase_   ,       data_format=lowerCamelCase_   ,       **lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = ChannelDimension.FIRST   ,       **lowerCamelCase_   ,       )			->				PIL.Image.Image:
														lowerCAmelCase__			  =      do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase__			  =      size if size is not None else self.size
														lowerCAmelCase__			  =      get_size_dict(lowerCamelCase_   ,       param_name='''size'''   ,       default_to_square=lowerCamelCase_  )
														lowerCAmelCase__			  =      resample if resample is not None else self.resample
														lowerCAmelCase__			  =      do_center_crop if do_center_crop is not None else self.do_center_crop
														lowerCAmelCase__			  =      crop_size if crop_size is not None else self.crop_size
														lowerCAmelCase__			  =      get_size_dict(lowerCamelCase_   ,       param_name='''crop_size'''   ,       default_to_square=lowerCamelCase_  )
														lowerCAmelCase__			  =      do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase__			  =      rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase__			  =      do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase__			  =      image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase__			  =      image_std if image_std is not None else self.image_std
														lowerCAmelCase__			  =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														lowerCAmelCase__			  =      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_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__			  =      [convert_to_rgb(lowerCamelCase_  ) for image in images]

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

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

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

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

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

														lowerCAmelCase__			  =      [to_channel_dimension_format(lowerCamelCase_   ,       lowerCamelCase_  ) for image in images]

														lowerCAmelCase__			  =      {'''pixel_values''': images}
														return BatchFeature(data=lowerCamelCase_   ,       tensor_type=lowerCamelCase_  )



'''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''': 1_024,
    '''facebook/esm2_t12_35M_UR50D''': 1_024,
}


def    _snake_case  (	A			)		->       Optional[Any]:
							with open(A					,       '''r'''			) as f:
														lowerCAmelCase__			  =      f.read().splitlines()
														return [l.strip() for l in lines]






class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  Optional[Any]      		= VOCAB_FILES_NAMES
							lowercase__     :  List[Any]      		= PRETRAINED_VOCAB_FILES_MAP
							lowercase__     :  Union[str, Any]      		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__     :  Union[str, Any]      		= ["input_ids", "attention_mask"]







							def __init__(						self   ,       lowerCamelCase_   ,       lowerCamelCase_="<unk>"   ,       lowerCamelCase_="<cls>"   ,       lowerCamelCase_="<pad>"   ,       lowerCamelCase_="<mask>"   ,       lowerCamelCase_="<eos>"   ,       **lowerCamelCase_   ,       )			->				Tuple:
														super().__init__(**lowerCamelCase_  )
														lowerCAmelCase__			  =      load_vocab_file(lowerCamelCase_  )
														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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				str:
														return self._id_to_token.get(lowerCamelCase_   ,       self.unk_token  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return self._token_to_id.get(lowerCamelCase_   ,       self._token_to_id.get(self.unk_token  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       **lowerCamelCase_  )			->				Union[str, Any]:
														return text.split()







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=False  )			->				Dict:
														return len(self._id_to_token  )







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return {token: i for i, token in enumerate(self.all_tokens  )}







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				int:
														return self._token_to_id.get(lowerCamelCase_   ,       self._token_to_id.get(self.unk_token  )  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_  )			->				str:
														return self._id_to_token.get(lowerCamelCase_   ,       self.unk_token  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = 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     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = None   ,       lowerCamelCase_ = 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(lowerCamelCase_  )) + [1]
														if token_ids_a is not None:
																					mask += [0] * len(lowerCamelCase_  ) + [1]
														return mask







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_  )			->				Union[str, Any]:
														lowerCAmelCase__			  =      os.path.join(lowerCamelCase_   ,       (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''  )
														with open(lowerCamelCase_   ,       '''w'''  ) as f:
																					f.write('''\n'''.join(self.all_tokens  )  )
														return (vocab_file,)







							@property
							def     __SCREAMING_SNAKE_CASE				(						self  )			->				int:
														return self.get_vocab_size(with_added_tokens=lowerCamelCase_  )







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_   ,       lowerCamelCase_ = False  )			->				int:
														return super()._add_tokens(lowerCamelCase_   ,       special_tokens=lowerCamelCase_  )