




'''simple docstring'''


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

import regex as re

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


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

UpperCAmelCase_ 		=      logging.get_logger(__name__)


UpperCAmelCase_ 		=      {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_ 		=      {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

UpperCAmelCase_ 		=      {"facebook/blenderbot-3B": 1_28}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					(
      list(range(ord('!'			)				,					ord('~'			) + 1			)			) + list(range(ord('¡'			)				,					ord('¬'			) + 1			)			) + list(range(ord('®'			)				,					ord('ÿ'			) + 1			)			)
  )
  __a			=					bs[:]
  __a			=					0
  for b in range(2**8			):
    if b not in bs:
      bs.append(__UpperCamelCase			)
      cs.append(2**8 + n			)
      n += 1
  __a			=					[chr(__UpperCamelCase			) for n in cs]
  return dict(zip(__UpperCamelCase				,					__UpperCamelCase			)			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Tuple			):
  __a			=					set()
  __a			=					word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)			)
    __a			=					char
  return pairs





class    __lowercase						(  UpperCAmelCase_ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      ["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      ,							)				->    Tuple:
     __a			=					AddedToken(_snake_case      ,							lstrip=_snake_case      ,							rstrip=_snake_case      ) if isinstance(_snake_case      ,							_snake_case      ) else bos_token
     __a			=					AddedToken(_snake_case      ,							lstrip=_snake_case      ,							rstrip=_snake_case      ) if isinstance(_snake_case      ,							_snake_case      ) else eos_token
     __a			=					AddedToken(_snake_case      ,							lstrip=_snake_case      ,							rstrip=_snake_case      ) if isinstance(_snake_case      ,							_snake_case      ) else sep_token
     __a			=					AddedToken(_snake_case      ,							lstrip=_snake_case      ,							rstrip=_snake_case      ) if isinstance(_snake_case      ,							_snake_case      ) else cls_token
     __a			=					AddedToken(_snake_case      ,							lstrip=_snake_case      ,							rstrip=_snake_case      ) if isinstance(_snake_case      ,							_snake_case      ) else unk_token
     __a			=					AddedToken(_snake_case      ,							lstrip=_snake_case      ,							rstrip=_snake_case      ) if isinstance(_snake_case      ,							_snake_case      ) else pad_token

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

     super().__init__(
         errors=_snake_case      ,							bos_token=_snake_case      ,							eos_token=_snake_case      ,							unk_token=_snake_case      ,							sep_token=_snake_case      ,							cls_token=_snake_case      ,							pad_token=_snake_case      ,							mask_token=_snake_case      ,							add_prefix_space=_snake_case      ,							**_snake_case      ,							)

     with open(_snake_case      ,							encoding='utf-8'      ) as vocab_handle:
       __a			=					json.load(_snake_case      )
     __a			=					{v: k for k, v in self.encoder.items()}
     __a			=					errors  # how to handle errors in decoding
     __a			=					bytes_to_unicode()
     __a			=					{v: k for k, v in self.byte_encoder.items()}
     with open(_snake_case      ,							encoding='utf-8'      ) as merges_handle:
       __a			=					merges_handle.read().split('\n'      )[1:-1]
     __a			=					[tuple(merge.split()      ) for merge in bpe_merges]
     __a			=					dict(zip(_snake_case      ,							range(len(_snake_case      )      )      )      )
     __a			=					{}
     __a			=					add_prefix_space

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







   @property
   # Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     return len(self.encoder      )







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     return dict(self.encoder      ,							**self.added_tokens_encoder      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Tuple:


     if token in self.cache:
       return self.cache[token]
     __a			=					tuple(_snake_case      )
     __a			=					get_pairs(_snake_case      )

     if not pairs:
       return token

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

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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Union[str, Any]:
     __a			=					[]
     for token in re.findall(self.pat      ,							_snake_case      ):
       __a			=					''.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(_snake_case      ).split(' '      )      )
     return bpe_tokens







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[str]:
     return self.encoder.get(_snake_case      ,							self.encoder.get(self.unk_token      )      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Union[str, Any]:
     return self.decoder.get(_snake_case      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Union[str, Any]:
     __a			=					''.join(_snake_case      )
     __a			=					bytearray([self.byte_decoder[c] for c in text]      ).decode('utf-8'      ,							errors=self.errors      )
     return text







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Optional[int]:


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

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

     __a			=					0
     with open(_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 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			=					token_index
         writer.write(' '.join(_snake_case      ) + '\n'      )
         index += 1

     return vocab_file, merge_file







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = False      )				->    int:


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

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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[Any]:
     __a			=					[self.sep_token_id]
     __a			=					[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      ,							UpperCamelCase      ,							UpperCamelCase=False      ,							**UpperCamelCase      )				->    Any:
     __a			=					kwargs.pop('add_prefix_space'      ,							self.add_prefix_space      )
     if (is_split_into_words or add_prefix_space) and (len(_snake_case      ) > 0 and not text[0].isspace()):
       __a			=					' ' + text
     return (text, kwargs)







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Optional[int]:
     return token_ids_a + [self.eos_token_id]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[str]:
     __a			=					[]
     for is_user, text in conversation.iter_texts():
       if is_user:
         # We need to space prefix as it's being done within blenderbot
         inputs.append(' ' + text      )
       else:
         # Generated responses should contain them already.
         inputs.append(_snake_case      )

     __a			=					'  '.join(_snake_case      )
     __a			=					self.encode(_snake_case      )
     if len(_snake_case      ) > self.model_max_length:
       __a			=					input_ids[-self.model_max_length :]
       logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."      )
     return input_ids









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


if is_vision_available():
  import PIL


UpperCAmelCase_ 		=      logging.get_logger(__name__)





class    __lowercase						(  __magic_name__ ):
   _a							=      ["""pixel_values"""]







   def __init__(	self      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = PILImageResampling.BILINEAR      ,							UpperCamelCase = True      ,							UpperCamelCase = 1 / 255      ,							UpperCamelCase = True      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    None:
     super().__init__(**UpperCamelCase      )
     __a			=					size if size is not None else {'shortest_edge': 384}
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					do_resize
     __a			=					size
     # Default value set here for backwards compatibility where the value in config is None
     __a			=					crop_pct if crop_pct is not None else 224 / 256
     __a			=					resample
     __a			=					do_rescale
     __a			=					rescale_factor
     __a			=					do_normalize
     __a			=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     __a			=					image_std if image_std is not None else IMAGENET_STANDARD_STD







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = PILImageResampling.BICUBIC      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    np.ndarray:
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )
     if "shortest_edge" not in size:
       raise ValueError(f"Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"      )
     __a			=					size['shortest_edge']

     if shortest_edge < 384:
       # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
       __a			=					int(shortest_edge / crop_pct      )
       __a			=					get_resize_output_image_size(UpperCamelCase      ,							size=UpperCamelCase      ,							default_to_square=UpperCamelCase      )
       __a			=					resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
       # then crop to (shortest_edge, shortest_edge)
       return center_crop(image=UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )
     else:
       # warping (no cropping) when evaluated at 384 or larger
       return resize(
           UpperCamelCase      ,							size=(shortest_edge, shortest_edge)      ,							resample=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    Any:
     return rescale(UpperCamelCase      ,							scale=UpperCamelCase      ,							data_format=UpperCamelCase      ,							**UpperCamelCase      )







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







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = None      ,							UpperCamelCase = ChannelDimension.FIRST      ,							**UpperCamelCase      ,							)				->    PIL.Image.Image:
     __a			=					do_resize if do_resize is not None else self.do_resize
     __a			=					crop_pct if crop_pct is not None else self.crop_pct
     __a			=					resample if resample is not None else self.resample
     __a			=					do_rescale if do_rescale is not None else self.do_rescale
     __a			=					rescale_factor if rescale_factor is not None else self.rescale_factor
     __a			=					do_normalize if do_normalize is not None else self.do_normalize
     __a			=					image_mean if image_mean is not None else self.image_mean
     __a			=					image_std if image_std is not None else self.image_std

     __a			=					size if size is not None else self.size
     __a			=					get_size_dict(UpperCamelCase      ,							default_to_square=UpperCamelCase      )

     __a			=					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 or resample is None:
       raise ValueError('Size and resample must be specified if do_resize is True.'      )

     if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
       raise ValueError('crop_pct must be specified if size < 384.'      )

     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.'      )

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

     if do_resize:
       __a			=					[self.resize(image=UpperCamelCase      ,							size=UpperCamelCase      ,							crop_pct=UpperCamelCase      ,							resample=UpperCamelCase      ) for image in images]

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

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

     __a			=					[to_channel_dimension_format(UpperCamelCase      ,							UpperCamelCase      ) for image in images]

     __a			=					{'pixel_values': images}
     return BatchFeature(data=UpperCamelCase      ,							tensor_type=UpperCamelCase      )



