





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


__lowercase				    =      logging.get_logger(__name__)


__lowercase				    =      {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowercase				    =      {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

__lowercase				    =      {
    """RUCAIBox/mvp""": 1_024,
}


@lru_cache()
def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					(
      list(range(ord('''!''')  ,				ord('''~''') + 1)) + list(range(ord('''¡''')  ,				ord('''¬''') + 1)) + list(range(ord('''®''')  ,				ord('''ÿ''') + 1))
  )
  __lowerCAmelCase								=					bs[:]
  __lowerCAmelCase								=					0
  for b in range(2**8):
    if b not in bs:
      bs.append(__lowercase)
      cs.append(2**8 + n)
      n += 1
  __lowerCAmelCase								=					[chr(__lowercase) for n in cs]
  return dict(zip(__lowercase  ,				__lowercase))


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[str]):



  '''simple docstring'''






  __lowerCAmelCase								=					set()
  __lowerCAmelCase								=					word[0]
  for char in word[1:]:
    pairs.add((prev_char, char))
    __lowerCAmelCase								=					char
  return pairs




class 	__snake_case					(      _lowerCAmelCase ):






    '''simple docstring'''
    _lowerCAmelCase  =						VOCAB_FILES_NAMES
    _lowerCAmelCase  =						PRETRAINED_VOCAB_FILES_MAP
    _lowerCAmelCase  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _lowerCAmelCase  =						["input_ids", "attention_mask"]





    def __init__( self    :		Optional[Any]      ,    snake_case    :		List[str]      ,    snake_case    :		Tuple      ,    snake_case    :		Optional[int]="replace"      ,    snake_case    :		Any="<s>"      ,    snake_case    :		str="</s>"      ,    snake_case    :		Optional[Any]="</s>"      ,    snake_case    :		Optional[Any]="<s>"      ,    snake_case    :		int="<unk>"      ,    snake_case    :		Any="<pad>"      ,    snake_case    :		Union[str, Any]="<mask>"      ,    snake_case    :		Union[str, Any]=False      ,    **snake_case    :		Dict      ,    ):

      """simple docstring"""





      __lowerCAmelCase								=					AddedToken(A__      ,    lstrip=A__      ,    rstrip=A__				) if isinstance(A__      ,    A__				) else bos_token
      __lowerCAmelCase								=					AddedToken(A__      ,    lstrip=A__      ,    rstrip=A__				) if isinstance(A__      ,    A__				) else eos_token
      __lowerCAmelCase								=					AddedToken(A__      ,    lstrip=A__      ,    rstrip=A__				) if isinstance(A__      ,    A__				) else sep_token
      __lowerCAmelCase								=					AddedToken(A__      ,    lstrip=A__      ,    rstrip=A__				) if isinstance(A__      ,    A__				) else cls_token
      __lowerCAmelCase								=					AddedToken(A__      ,    lstrip=A__      ,    rstrip=A__				) if isinstance(A__      ,    A__				) else unk_token
      __lowerCAmelCase								=					AddedToken(A__      ,    lstrip=A__      ,    rstrip=A__				) if isinstance(A__      ,    A__				) else pad_token

      # Mask token behave like a normal word, i.e. include the space before it
      __lowerCAmelCase								=					AddedToken(A__      ,    lstrip=A__      ,    rstrip=A__				) if isinstance(A__      ,    A__				) else mask_token

      super().__init__(
          errors=A__      ,    bos_token=A__      ,    eos_token=A__      ,    unk_token=A__      ,    sep_token=A__      ,    cls_token=A__      ,    pad_token=A__      ,    mask_token=A__      ,    add_prefix_space=A__      ,    **A__      ,    )
      with open(A__      ,    encoding='''utf-8'''				) as vocab_handle:
        __lowerCAmelCase								=					json.load(A__				)
      __lowerCAmelCase								=					{v: k for k, v in self.encoder.items()}
      __lowerCAmelCase								=					errors  # how to handle errors in decoding
      __lowerCAmelCase								=					bytes_to_unicode()
      __lowerCAmelCase								=					{v: k for k, v in self.byte_encoder.items()}
      with open(A__      ,    encoding='''utf-8'''				) as merges_handle:
        __lowerCAmelCase								=					merges_handle.read().split('''\n'''				)[1:-1]
      __lowerCAmelCase								=					[tuple(merge.split()				) for merge in bpe_merges]
      __lowerCAmelCase								=					dict(zip(A__      ,    range(len(A__				)				)				)				)
      __lowerCAmelCase								=					{}
      __lowerCAmelCase								=					add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      __lowerCAmelCase								=					re.compile(r'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''				)





    @property
    def 						A		( self    :		Union[str, Any]				):

      """simple docstring"""





      return len(self.encoder				)





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      return dict(self.encoder      ,    **self.added_tokens_encoder				)





    def 						A		( self    :		str      ,    snake_case    :		Union[str, Any]				):

      """simple docstring"""





      if token in self.cache:
        return self.cache[token]
      __lowerCAmelCase								=					tuple(A__				)
      __lowerCAmelCase								=					get_pairs(A__				)

      if not pairs:
        return token

      while True:
        __lowerCAmelCase								=					min(A__      ,    key=lambda snake_case				: self.bpe_ranks.get(A__      ,    float('''inf'''				)				)				)
        if bigram not in self.bpe_ranks:
          break
        __lowerCAmelCase						,    __lowerCAmelCase								=					bigram
        __lowerCAmelCase								=					[]
        __lowerCAmelCase								=					0
        while i < len(A__				):
          try:
            __lowerCAmelCase								=					word.index(A__      ,    A__				)
          except ValueError:
            new_word.extend(word[i:]				)
            break
          else:
            new_word.extend(word[i:j]				)
            __lowerCAmelCase								=					j

          if word[i] == first and i < len(A__				) - 1 and word[i + 1] == second:
            new_word.append(first + second				)
            i += 2
          else:
            new_word.append(word[i]				)
            i += 1
        __lowerCAmelCase								=					tuple(A__				)
        __lowerCAmelCase								=					new_word
        if len(A__				) == 1:
          break
        else:
          __lowerCAmelCase								=					get_pairs(A__				)
      __lowerCAmelCase								=					''' '''.join(A__				)
      __lowerCAmelCase								=					word
      return word





    def 						A		( self    :		Any      ,    snake_case    :		int				):

      """simple docstring"""





      __lowerCAmelCase								=					[]
      for token in re.findall(self.pat      ,    A__				):
        __lowerCAmelCase								=					''''''.join(
            self.byte_encoder[b] for b in token.encode('''utf-8'''				)				)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
        bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A__				).split(''' '''				)				)
      return bpe_tokens





    def 						A		( self    :		Any      ,    snake_case    :		Dict				):

      """simple docstring"""





      return self.encoder.get(A__      ,    self.encoder.get(self.unk_token				)				)





    def 						A		( self    :		Any      ,    snake_case    :		Dict				):

      """simple docstring"""





      return self.decoder.get(A__				)





    def 						A		( self    :		List[str]      ,    snake_case    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					''''''.join(A__				)
      __lowerCAmelCase								=					bytearray([self.byte_decoder[c] for c in text]				).decode('''utf-8'''      ,    errors=self.errors				)
      return text





    def 						A		( self    :		Dict      ,    snake_case    :		str      ,    snake_case    :		Optional[str] = None				):

      """simple docstring"""





      if not os.path.isdir(A__				):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
        return
      __lowerCAmelCase								=					os.path.join(
          A__      ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
      __lowerCAmelCase								=					os.path.join(
          A__      ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)

      with open(A__      ,    '''w'''      ,    encoding='''utf-8'''				) as f:
        f.write(json.dumps(self.encoder      ,    indent=2      ,    sort_keys=A__      ,    ensure_ascii=A__				) + '''\n'''				)

      __lowerCAmelCase								=					0
      with open(A__      ,    '''w'''      ,    encoding='''utf-8'''				) as writer:
        writer.write('''#version: 0.2\n'''				)
        for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      ,    key=lambda snake_case				: kv[1]				):
          if index != token_index:
            logger.warning(
                f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                ''' Please check that the tokenizer is not corrupted!'''				)
            __lowerCAmelCase								=					token_index
          writer.write(''' '''.join(A__				) + '''\n'''				)
          index += 1

      return vocab_file, merge_file





    def 						A		( self    :		int      ,    snake_case    :		List[int]      ,    snake_case    :		Optional[List[int]] = None				):

      """simple docstring"""





      if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      __lowerCAmelCase								=					[self.cls_token_id]
      __lowerCAmelCase								=					[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep





    def 						A		( self    :		Union[str, Any]      ,    snake_case    :		List[int]      ,    snake_case    :		Optional[List[int]] = None      ,    snake_case    :		bool = False				):

      """simple docstring"""





      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=A__      ,    token_ids_a=A__      ,    already_has_special_tokens=A__				)

      if token_ids_a is None:
        return [1] + ([0] * len(A__				)) + [1]
      return [1] + ([0] * len(A__				)) + [1, 1] + ([0] * len(A__				)) + [1]





    def 						A		( self    :		Union[str, Any]      ,    snake_case    :		List[int]      ,    snake_case    :		Optional[List[int]] = None				):

      """simple docstring"""





      __lowerCAmelCase								=					[self.sep_token_id]
      __lowerCAmelCase								=					[self.cls_token_id]

      if token_ids_a is None:
        return len(cls + token_ids_a + sep				) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]





    def 						A		( self    :		Any      ,    snake_case    :		str      ,    snake_case    :		Optional[Any]=False      ,    **snake_case    :		Any				):

      """simple docstring"""





      __lowerCAmelCase								=					kwargs.pop('''add_prefix_space'''      ,    self.add_prefix_space				)
      if (is_split_into_words or add_prefix_space) and (len(A__				) > 0 and not text[0].isspace()):
        __lowerCAmelCase								=					''' ''' + text
      return (text, kwargs)






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


__SCREAMING_SNAKE_CASE     : Dict      =logging.get_logger(__name__)


if is_vision_available():
      import PIL






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:						Any  =["pixel_values"]




     def __init__(     self	: Union[str, Any]						,	A__	: bool = True						,	A__	: Dict[str, int] = None						,	A__	: PILImageResampling = PILImageResampling.BICUBIC						,	A__	: bool = True						,	A__	: Dict[str, int] = None						,	A__	: bool = True						,	A__	: Union[int, float] = 1 / 2_5_5						,	A__	: bool = True						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: bool = True						,	**A__	: Optional[int]						,	)					->							None:
           super().__init__(**A__      )
           lowerCamelCase__   	=					size if size is not None else {'shortest_edge': 2_2_4}
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
           lowerCamelCase__   	=					get_size_dict(A__      )

           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 			__snake_case (     self	: Union[str, Any]						,	A__	: np.ndarray						,	A__	: Dict[str, int]						,	A__	: PILImageResampling = PILImageResampling.BICUBIC						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: Optional[int]						,	)					->							np.ndarray:
           lowerCamelCase__   	=					get_size_dict(A__						,	default_to_square=A__      )
           lowerCamelCase__   	=					get_resize_output_image_size(
               A__						,	size=(size['height'], size['width'])						,	default_to_square=A__      )
           return resize(A__						,	size=A__						,	resample=A__						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: int						,	A__	: np.ndarray						,	A__	: Dict[str, int]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: List[Any]						,	)					->							np.ndarray:
           lowerCamelCase__   	=					get_size_dict(A__      )
           return center_crop(A__						,	size=(size['height'], size['width'])						,	data_format=A__						,	**A__      )




     def 			__snake_case (     self	: Dict						,	A__	: np.ndarray						,	A__	: Union[int, float]						,	A__	: Optional[Union[str, ChannelDimension]] = None						,	**A__	: List[Any]						,	)					->							Tuple:
           return rescale(A__						,	scale=A__						,	data_format=A__						,	**A__      )




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




     def 			__snake_case (     self	: Tuple						,	A__	: ImageInput						,	A__	: bool = None						,	A__	: Dict[str, int] = None						,	A__	: PILImageResampling = None						,	A__	: bool = None						,	A__	: int = None						,	A__	: bool = None						,	A__	: float = None						,	A__	: bool = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: Optional[Union[float, List[float]]] = None						,	A__	: bool = None						,	A__	: Optional[Union[str, TensorType]] = None						,	A__	: Optional[ChannelDimension] = ChannelDimension.FIRST						,	**A__	: List[str]						,	)					->							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(A__						,	default_to_square=A__      )
           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(A__      )
           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(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.'      )

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

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

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

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

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

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

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

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