





"""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 (
    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


lowerCamelCase_          =	logging.get_logger(__name__)


if is_vision_available():
  import PIL






class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						['''pixel_values''']




       def __init__(		self	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = False	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = 1 / 2_55	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   None:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

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

             __SCREAMING_SNAKE_CASE     :int	       =						do_resize
             __SCREAMING_SNAKE_CASE     :Dict	       =						size
             __SCREAMING_SNAKE_CASE     :Tuple	       =						resample
             __SCREAMING_SNAKE_CASE     :str	       =						do_thumbnail
             __SCREAMING_SNAKE_CASE     :str	       =						do_align_long_axis
             __SCREAMING_SNAKE_CASE     :List[str]	       =						do_pad
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						do_rescale
             __SCREAMING_SNAKE_CASE     :List[str]	       =						rescale_factor
             __SCREAMING_SNAKE_CASE     :List[str]	       =						do_normalize
             __SCREAMING_SNAKE_CASE     :List[str]	       =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						image_std if image_std is not None else IMAGENET_STANDARD_STD




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[str]	       =						get_image_size(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Tuple	       =						size['''height'''], size['''width''']

             if (output_width < output_height and input_width > input_height) or (
                 output_width > output_height and input_width < input_height
             ):
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						np.rotaa(SCREAMING_SNAKE_CASE__	,3							)

             if data_format is not None:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						to_channel_dimension_format(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)

             return image




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = False	,SCREAMING_SNAKE_CASE__ = None	,)    ->   np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Tuple	       =						size['''height'''], size['''width''']
             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Optional[Any]	       =						get_image_size(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :List[str]	       =						output_width - input_width
             __SCREAMING_SNAKE_CASE     :List[str]	       =						output_height - input_height

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

             __SCREAMING_SNAKE_CASE     :Tuple	       =						delta_height - pad_top
             __SCREAMING_SNAKE_CASE     :Dict	       =						delta_width - pad_left

             __SCREAMING_SNAKE_CASE     :int	       =						((pad_top, pad_bottom), (pad_left, pad_right))
             return pad(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,data_format=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)    ->   Any:





             """simple docstring"""






             logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''							)
             return self.pad_image(*SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[str]	       =						get_image_size(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[str]	       =						size['''height'''], size['''width''']

             # We always resize to the smallest of either the input or output size.
             __SCREAMING_SNAKE_CASE     :Any	       =						min(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						min(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)

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

             if input_height > input_width:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						int(input_width * height / input_height							)
             elif input_width > input_height:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						int(input_height * width / input_width							)

             return resize(
                 SCREAMING_SNAKE_CASE__	,size=(height, width)	,resample=SCREAMING_SNAKE_CASE__	,reducing_gap=2.0	,data_format=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   np.ndarray:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :int	       =						get_size_dict(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						min(size['''height''']	,size['''width''']							)
             __SCREAMING_SNAKE_CASE     :int	       =						get_resize_output_image_size(SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,default_to_square=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						resize(SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,resample=SCREAMING_SNAKE_CASE__	,data_format=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__							)
             return resized_image




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   Union[str, Any]:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,**SCREAMING_SNAKE_CASE__	,)    ->   np.ndarray:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = False	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST	,**SCREAMING_SNAKE_CASE__	,)    ->   PIL.Image.Image:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						do_resize if do_resize is not None else self.do_resize
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						size if size is not None else self.size
             if isinstance(SCREAMING_SNAKE_CASE__	,(tuple, list)							):
                   # Previous feature extractor had size in (width, height) format
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						size[::-1]
             __SCREAMING_SNAKE_CASE     :List[str]	       =						get_size_dict(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						resample if resample is not None else self.resample
             __SCREAMING_SNAKE_CASE     :Any	       =						do_thumbnail if do_thumbnail is not None else self.do_thumbnail
             __SCREAMING_SNAKE_CASE     :Dict	       =						do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
             __SCREAMING_SNAKE_CASE     :str	       =						do_pad if do_pad is not None else self.do_pad
             __SCREAMING_SNAKE_CASE     :Any	       =						do_rescale if do_rescale is not None else self.do_rescale
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						rescale_factor if rescale_factor is not None else self.rescale_factor
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						do_normalize if do_normalize is not None else self.do_normalize
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						image_mean if image_mean is not None else self.image_mean
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						image_std if image_std is not None else self.image_std

             __SCREAMING_SNAKE_CASE     :str	       =						make_list_of_images(SCREAMING_SNAKE_CASE__							)

             if not valid_images(SCREAMING_SNAKE_CASE__							):
                   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.
             __SCREAMING_SNAKE_CASE     :int	       =						[to_numpy_array(SCREAMING_SNAKE_CASE__							) for image in images]

             if do_align_long_axis:
                   __SCREAMING_SNAKE_CASE     :int	       =						[self.align_long_axis(SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_resize:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[self.resize(image=SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,resample=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_thumbnail:
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						[self.thumbnail(image=SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_pad:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						[self.pad_image(image=SCREAMING_SNAKE_CASE__	,size=SCREAMING_SNAKE_CASE__	,random_padding=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_rescale:
                   __SCREAMING_SNAKE_CASE     :int	       =						[self.rescale(image=SCREAMING_SNAKE_CASE__	,scale=SCREAMING_SNAKE_CASE__							) for image in images]

             if do_normalize:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						[self.normalize(image=SCREAMING_SNAKE_CASE__	,mean=SCREAMING_SNAKE_CASE__	,std=SCREAMING_SNAKE_CASE__							) for image in images]

             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						[to_channel_dimension_format(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) for image in images]

             __SCREAMING_SNAKE_CASE     :Any	       =						{'''pixel_values''': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__	,tensor_type=SCREAMING_SNAKE_CASE__							)






"""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


lowerCamelCase_          =	logging.get_logger(__name__)


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

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

lowerCamelCase_          =	{
    "RUCAIBox/mvp": 1_0_2_4,
}



@lru_cache()
def   __lowerCamelCase     (							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						(
          list(range(ord('''!'''							)				,     ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)				,     ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)				,     ord('''ÿ'''							) + 1							)							)
      )
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						bs[:]
      __SCREAMING_SNAKE_CASE     :str	       =						0
      for b in range(2**8							):
            if b not in bs:
                  bs.append(a_							)
                  cs.append(2**8 + n							)
                  n += 1
      __SCREAMING_SNAKE_CASE     :int	       =						[chr(a_							) for n in cs]
      return dict(zip(a_				,     a_							)							)



def   __lowerCamelCase     (							a_  :	Dict							)    ->   List[str]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						set()
      __SCREAMING_SNAKE_CASE     :Dict	       =						word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            __SCREAMING_SNAKE_CASE     :List[str]	       =						char
      return pairs







class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   str					=						VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_     :   Tuple					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_     :   List[Any]					=						['''input_ids''', '''attention_mask''']




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__="replace"	,SCREAMING_SNAKE_CASE__="<s>"	,SCREAMING_SNAKE_CASE__="</s>"	,SCREAMING_SNAKE_CASE__="</s>"	,SCREAMING_SNAKE_CASE__="<s>"	,SCREAMING_SNAKE_CASE__="<unk>"	,SCREAMING_SNAKE_CASE__="<pad>"	,SCREAMING_SNAKE_CASE__="<mask>"	,SCREAMING_SNAKE_CASE__=False	,**SCREAMING_SNAKE_CASE__	,)    ->   Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else bos_token
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else eos_token
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else sep_token
             __SCREAMING_SNAKE_CASE     :Dict	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else cls_token
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else unk_token
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else pad_token

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

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

             # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
             __SCREAMING_SNAKE_CASE     :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 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             return len(self.encoder							)




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             if token in self.cache:
                   return self.cache[token]
             __SCREAMING_SNAKE_CASE     :Tuple	       =						tuple(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						get_pairs(SCREAMING_SNAKE_CASE__							)

             if not pairs:
                   return token

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

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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						[]
             for token in re.findall(self.pat	,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						''''''.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(SCREAMING_SNAKE_CASE__							).split(''' '''							)							)
             return bpe_tokens




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





             """simple docstring"""






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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             return self.decoder.get(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						''''''.join(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''	,errors=self.errors							)
             return text




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   Tuple[str]:





             """simple docstring"""






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

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

             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						0
             with open(SCREAMING_SNAKE_CASE__	,'''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 SCREAMING_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!'''							)
                               __SCREAMING_SNAKE_CASE     :int	       =						token_index
                         writer.write(''' '''.join(SCREAMING_SNAKE_CASE__							) + '''\n'''							)
                         index += 1

             return vocab_file, merge_file




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   List[int]:





             """simple docstring"""






             if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						[self.cls_token_id]
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = False							)    ->   List[int]:





             """simple docstring"""






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

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




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   List[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[Any]	       =						[self.sep_token_id]
             __SCREAMING_SNAKE_CASE     :str	       =						[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 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=False	,**SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






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