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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


UpperCAmelCase				 =   logging.get_logger(__name__)


if is_vision_available():
  import PIL






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   UpperCamelCase_					:    Union[str, Any]			      = ['pixel_values']
   def __init__(     self				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = PILImageResampling.BILINEAR				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = False				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = 1 / 2_55				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  **lowerCAmelCase_				,  )						->Dict:
        super().__init__(**_UpperCAmelCase     )

        a	:     Dict						      =    size if size is not None else {'''height''': 25_60, '''width''': 19_20}
        if isinstance(_UpperCAmelCase				,  (tuple, list)     ):
             # The previous feature extractor size parameter was in (width, height) format
             a	:     List[Any]						      =    size[::-1]
        a	:     Optional[Any]						      =    get_size_dict(_UpperCAmelCase     )

        a	:     Dict						      =    do_resize
        a	:     Any						      =    size
        a	:     int						      =    resample
        a	:     Optional[int]						      =    do_thumbnail
        a	:     Any						      =    do_align_long_axis
        a	:     List[str]						      =    do_pad
        a	:     Optional[Any]						      =    do_rescale
        a	:     Any						      =    rescale_factor
        a	:     Tuple						      =    do_normalize
        a	:     Dict						      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        a	:     Dict						      =    image_std if image_std is not None else IMAGENET_STANDARD_STD
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ = None     )						->Optional[Any]:
        a	:     int						      =    get_image_size(_UpperCAmelCase     )
        a	:     int						      =    size['''height'''], size['''width''']

        if (output_width < output_height and input_width > input_height) or (
            output_width > output_height and input_width < input_height
        ):
             a	:     Optional[int]						      =    np.rotaa(_UpperCAmelCase				,  3     )

        if data_format is not None:
             a	:     Union[str, Any]						      =    to_channel_dimension_format(_UpperCAmelCase				,  _UpperCAmelCase     )

        return image
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ = False				,  lowerCAmelCase_ = None				,  )						->Optional[int]:
        a	:     Optional[int]						      =    size['''height'''], size['''width''']
        a	:     Optional[Any]						      =    get_image_size(_UpperCAmelCase     )

        a	:     Optional[Any]						      =    output_width - input_width
        a	:     Dict						      =    output_height - input_height

        if random_padding:
             a	:     List[str]						      =    np.random.randint(low=0				,  high=delta_height + 1     )
             a	:     List[Any]						      =    np.random.randint(low=0				,  high=delta_width + 1     )
        else:
             a	:     Optional[int]						      =    delta_height // 2
             a	:     int						      =    delta_width // 2

        a	:     Dict						      =    delta_height - pad_top
        a	:     Optional[Any]						      =    delta_width - pad_left

        a	:     Optional[Any]						      =    ((pad_top, pad_bottom), (pad_left, pad_right))
        return pad(_UpperCAmelCase				,  _UpperCAmelCase				,  data_format=_UpperCAmelCase     )
   def _lowercase(     self				,  *lowerCAmelCase_				,  **lowerCAmelCase_     )						->Union[str, Any]:
        logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''     )
        return self.pad_image(*_UpperCAmelCase				,  **_UpperCAmelCase     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ = PILImageResampling.BICUBIC				,  lowerCAmelCase_ = None				,  **lowerCAmelCase_				,  )						->Optional[int]:
        a	:     List[str]						      =    get_image_size(_UpperCAmelCase     )
        a	:     List[str]						      =    size['''height'''], size['''width''']

        # We always resize to the smallest of either the input or output size.
        a	:     Tuple						      =    min(_UpperCAmelCase				,  _UpperCAmelCase     )
        a	:     Dict						      =    min(_UpperCAmelCase				,  _UpperCAmelCase     )

        if height == input_height and width == input_width:
             return image

        if input_height > input_width:
             a	:     Any						      =    int(input_width * height / input_height     )
        elif input_width > input_height:
             a	:     Union[str, Any]						      =    int(input_height * width / input_width     )

        return resize(
            _UpperCAmelCase				,  size=(height, width)				,  resample=_UpperCAmelCase				,  reducing_gap=2.0				,  data_format=_UpperCAmelCase				,  **_UpperCAmelCase     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ = PILImageResampling.BICUBIC				,  lowerCAmelCase_ = None				,  **lowerCAmelCase_				,  )						->Optional[int]:
        a	:     Optional[int]						      =    get_size_dict(_UpperCAmelCase     )
        a	:     int						      =    min(size['''height''']				,  size['''width''']     )
        a	:     Union[str, Any]						      =    get_resize_output_image_size(_UpperCAmelCase				,  size=_UpperCAmelCase				,  default_to_square=_UpperCAmelCase     )
        a	:     str						      =    resize(_UpperCAmelCase				,  size=_UpperCAmelCase				,  resample=_UpperCAmelCase				,  data_format=_UpperCAmelCase				,  **_UpperCAmelCase     )
        return resized_image
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ = None				,  **lowerCAmelCase_				,  )						->str:
        return rescale(_UpperCAmelCase				,  scale=_UpperCAmelCase				,  data_format=_UpperCAmelCase				,  **_UpperCAmelCase     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ = None				,  **lowerCAmelCase_				,  )						->Union[str, Any]:
        return normalize(_UpperCAmelCase				,  mean=_UpperCAmelCase				,  std=_UpperCAmelCase				,  data_format=_UpperCAmelCase				,  **_UpperCAmelCase     )


   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = False				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = ChannelDimension.FIRST				,  **lowerCAmelCase_				,  )						->List[str]:
        a	:     List[str]						      =    do_resize if do_resize is not None else self.do_resize
        a	:     Dict						      =    size if size is not None else self.size
        if isinstance(_UpperCAmelCase				,  (tuple, list)     ):
             # Previous feature extractor had size in (width, height) format
             a	:     Tuple						      =    size[::-1]
        a	:     int						      =    get_size_dict(_UpperCAmelCase     )
        a	:     Any						      =    resample if resample is not None else self.resample
        a	:     int						      =    do_thumbnail if do_thumbnail is not None else self.do_thumbnail
        a	:     Dict						      =    do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
        a	:     List[str]						      =    do_pad if do_pad is not None else self.do_pad
        a	:     Tuple						      =    do_rescale if do_rescale is not None else self.do_rescale
        a	:     List[Any]						      =    rescale_factor if rescale_factor is not None else self.rescale_factor
        a	:     List[Any]						      =    do_normalize if do_normalize is not None else self.do_normalize
        a	:     Optional[Any]						      =    image_mean if image_mean is not None else self.image_mean
        a	:     str						      =    image_std if image_std is not None else self.image_std

        a	:     str						      =    make_list_of_images(_UpperCAmelCase     )

        if not valid_images(_UpperCAmelCase     ):
             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_rescale and rescale_factor is None:
             raise ValueError('''Rescale factor must be specified if do_rescale is True.'''     )

        if do_pad and size is None:
             raise ValueError('''Size must be specified if do_pad 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	:     Optional[Any]						      =    [to_numpy_array(_UpperCAmelCase     ) for image in images]

        if do_align_long_axis:
             a	:     Dict						      =    [self.align_long_axis(_UpperCAmelCase				,  size=_UpperCAmelCase     ) for image in images]

        if do_resize:
             a	:     Any						      =    [self.resize(image=_UpperCAmelCase				,  size=_UpperCAmelCase				,  resample=_UpperCAmelCase     ) for image in images]

        if do_thumbnail:
             a	:     int						      =    [self.thumbnail(image=_UpperCAmelCase				,  size=_UpperCAmelCase     ) for image in images]

        if do_pad:
             a	:     int						      =    [self.pad_image(image=_UpperCAmelCase				,  size=_UpperCAmelCase				,  random_padding=_UpperCAmelCase     ) for image in images]

        if do_rescale:
             a	:     List[str]						      =    [self.rescale(image=_UpperCAmelCase				,  scale=_UpperCAmelCase     ) for image in images]

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

        a	:     Union[str, Any]						      =    [to_channel_dimension_format(_UpperCAmelCase				,  _UpperCAmelCase     ) for image in images]

        a	:     List[str]						      =    {'''pixel_values''': images}
        return BatchFeature(data=_UpperCAmelCase				,  tensor_type=_UpperCAmelCase     )






'''simple docstring'''






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


snake_case_     			=						logging.get_logger(__name__)


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

# See all MVP models at https://huggingface.co/models?filter=mvp
snake_case_     			=						{
    """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""",
    },
}

snake_case_     			=						{
    """RUCAIBox/mvp""": 10_24,
}
@lru_cache()
def 				_lowerCamelCase(	)		->	str:
    A							:							Dict				=    (
        list(range(ord('''!'''		)     ,							ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)     ,							ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)     ,							ord('''ÿ'''		) + 1		)		)
    )
    A							:							int				=    bs[:]
    A							:							Any				=    0
    for b in range(2**8		):
        if b not in bs:
            bs.append(UpperCamelCase__		)
            cs.append(2**8 + n		)
            n += 1
    A							:							Union[str, Any]				=    [chr(UpperCamelCase__		) for n in cs]
    return dict(zip(UpperCamelCase__     ,							UpperCamelCase__		)		)
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Any]		)		->	str:
    A							:							Tuple				=    set()
    A							:							Optional[Any]				=    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A							:							Tuple				=    char
    return pairs





class 	_lowercase    (				a					):
   _UpperCamelCase									= VOCAB_FILES_NAMES
   _UpperCamelCase									= PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase									= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase									= ["""input_ids""", """attention_mask"""]


   def __init__(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase="replace"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="</s>"     , _UpperCAmelCase="<s>"     , _UpperCAmelCase="<unk>"     , _UpperCAmelCase="<pad>"     , _UpperCAmelCase="<mask>"     , _UpperCAmelCase=False     , **_UpperCAmelCase     , ):
       A							:							int				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else bos_token
       A							:							List[str]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else eos_token
       A							:							List[str]				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else sep_token
       A							:							Dict				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else cls_token
       A							:							str				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else unk_token
       A							:							str				=    AddedToken(_UpperCAmelCase     , lstrip=_UpperCAmelCase     , rstrip=_UpperCAmelCase       ) if isinstance(_UpperCAmelCase     , _UpperCAmelCase       ) else pad_token

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

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

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


   @property
   def        snake_case					(			self       ):
       return len(self.encoder       )


   def        snake_case					(			self       ):
       return dict(self.encoder     , **self.added_tokens_encoder       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       if token in self.cache:
           return self.cache[token]
       A							:							Dict				=    tuple(_UpperCAmelCase       )
       A							:							Dict				=    get_pairs(_UpperCAmelCase       )

       if not pairs:
           return token

       while True:
           A							:							Optional[int]				=    min(_UpperCAmelCase     , key=lambda _UpperCAmelCase       : self.bpe_ranks.get(_UpperCAmelCase     , float('''inf'''       )       )       )
           if bigram not in self.bpe_ranks:
               break
           A,						A							:							Optional[int]				=    bigram
           A							:							str				=    []
           A							:							Any				=    0
           while i < len(_UpperCAmelCase       ):
               try:
                   A							:							List[Any]				=    word.index(_UpperCAmelCase     , _UpperCAmelCase       )
               except ValueError:
                   new_word.extend(word[i:]       )
                   break
               else:
                   new_word.extend(word[i:j]       )
                   A							:							str				=    j

               if word[i] == first and i < len(_UpperCAmelCase       ) - 1 and word[i + 1] == second:
                   new_word.append(first + second       )
                   i += 2
               else:
                   new_word.append(word[i]       )
                   i += 1
           A							:							Optional[Any]				=    tuple(_UpperCAmelCase       )
           A							:							int				=    new_word
           if len(_UpperCAmelCase       ) == 1:
               break
           else:
               A							:							Union[str, Any]				=    get_pairs(_UpperCAmelCase       )
       A							:							int				=    ''' '''.join(_UpperCAmelCase       )
       A							:							List[Any]				=    word
       return word


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							str				=    []
       for token in re.findall(self.pat     , _UpperCAmelCase       ):
           A							:							Optional[int]				=    ''''''.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(_UpperCAmelCase       ).split(''' '''       )       )
       return bpe_tokens


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.encoder.get(_UpperCAmelCase     , self.encoder.get(self.unk_token       )       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       return self.decoder.get(_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase       ):
       A							:							int				=    ''''''.join(_UpperCAmelCase       )
       A							:							Any				=    bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     , errors=self.errors       )
       return text


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if not os.path.isdir(_UpperCAmelCase       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
           return
       A							:							Optional[Any]				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
       A							:							Tuple				=    os.path.join(
           _UpperCAmelCase     , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

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

       A							:							str				=    0
       with open(_UpperCAmelCase     , '''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 _UpperCAmelCase       : 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!'''       )
                   A							:							int				=    token_index
               writer.write(''' '''.join(_UpperCAmelCase       ) + '''\n'''       )
               index += 1

       return vocab_file, merge_file


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A							:							Union[str, Any]				=    [self.cls_token_id]
       A							:							Any				=    [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = False       ):
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCAmelCase     , token_ids_a=_UpperCAmelCase     , already_has_special_tokens=_UpperCAmelCase       )

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


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       A							:							Union[str, Any]				=    [self.sep_token_id]
       A							:							Tuple				=    [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        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase=False     , **_UpperCAmelCase       ):
       A							:							Optional[int]				=    kwargs.pop('''add_prefix_space'''     , self.add_prefix_space       )
       if (is_split_into_words or add_prefix_space) and (len(_UpperCAmelCase       ) > 0 and not text[0].isspace()):
           A							:							Tuple				=    ''' ''' + text
       return (text, kwargs)
