'''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, logging


a=  logging.get_logger(__name__)






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						['''pixel_values''']







     def __init__(		self     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = PILImageResampling.BILINEAR     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = True     ,			_lowerCamelCase = 1 / 2_5_5     ,			_lowerCamelCase = True     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         super().__init__(**_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  size if size is not None else {'shortest_edge': 2_5_6}
         __UpperCamelCase	:	str		    =  get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase    )
         __UpperCamelCase	:	Dict		    =  crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         __UpperCamelCase	:	Tuple		    =  get_size_dict(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  do_resize
         __UpperCamelCase	:	Optional[Any]		    =  size
         __UpperCamelCase	:	Optional[int]		    =  resample
         __UpperCamelCase	:	Tuple		    =  do_center_crop
         __UpperCamelCase	:	List[str]		    =  crop_size
         __UpperCamelCase	:	List[str]		    =  do_rescale
         __UpperCamelCase	:	Dict		    =  rescale_factor
         __UpperCamelCase	:	str		    =  do_normalize
         __UpperCamelCase	:	Tuple		    =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __UpperCamelCase	:	str		    =  image_std if image_std is not None else IMAGENET_STANDARD_STD







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = PILImageResampling.BICUBIC     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	Optional[int]		    =  get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase    )
         if "shortest_edge" not in size:
             raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""    )
         __UpperCamelCase	:	Union[str, Any]		    =  get_resize_output_image_size(_lowerCamelCase     ,			size=size['shortest_edge']     ,			default_to_square=_lowerCamelCase    )
         return resize(_lowerCamelCase     ,			size=_lowerCamelCase     ,			resample=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	Dict		    =  get_size_dict(_lowerCamelCase    )
         return center_crop(_lowerCamelCase     ,			size=(size['height'], size['width'])     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase    ):
         return rescale(_lowerCamelCase     ,			scale=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         return normalize(_lowerCamelCase     ,			mean=_lowerCamelCase     ,			std=_lowerCamelCase     ,			data_format=_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = None     ,			_lowerCamelCase = ChannelDimension.FIRST     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	Dict		    =  do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase	:	Optional[int]		    =  size if size is not None else self.size
         __UpperCamelCase	:	Optional[int]		    =  get_size_dict(_lowerCamelCase     ,			default_to_square=_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  resample if resample is not None else self.resample
         __UpperCamelCase	:	List[Any]		    =  do_center_crop if do_center_crop is not None else self.do_center_crop
         __UpperCamelCase	:	Dict		    =  crop_size if crop_size is not None else self.crop_size
         __UpperCamelCase	:	Tuple		    =  get_size_dict(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase	:	Any		    =  rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase	:	str		    =  do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase	:	int		    =  image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase	:	Tuple		    =  image_std if image_std is not None else self.image_std

         __UpperCamelCase	:	Any		    =  make_list_of_images(_lowerCamelCase    )

         if not valid_images(_lowerCamelCase    ):
             raise ValueError(
                 'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
                 'torch.Tensor, tf.Tensor or jax.ndarray.'    )

         if do_resize and size is None:
             raise ValueError('Size must be specified if do_resize is True.'    )

         if do_center_crop and crop_size is None:
             raise ValueError('Crop size must be specified if do_center_crop is True.'    )

         if do_rescale and rescale_factor is None:
             raise ValueError('Rescale factor must be specified if do_rescale is True.'    )

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('Image mean and std must be specified if do_normalize is True.'    )

         # All transformations expect numpy arrays.
         __UpperCamelCase	:	Union[str, Any]		    =  [to_numpy_array(_lowerCamelCase    ) for image in images]

         if do_resize:
             __UpperCamelCase	:	List[Any]		    =  [self.resize(image=_lowerCamelCase     ,			size=_lowerCamelCase     ,			resample=_lowerCamelCase    ) for image in images]

         if do_center_crop:
             __UpperCamelCase	:	List[str]		    =  [self.center_crop(image=_lowerCamelCase     ,			size=_lowerCamelCase    ) for image in images]

         if do_rescale:
             __UpperCamelCase	:	Any		    =  [self.rescale(image=_lowerCamelCase     ,			scale=_lowerCamelCase    ) for image in images]

         if do_normalize:
             __UpperCamelCase	:	int		    =  [self.normalize(image=_lowerCamelCase     ,			mean=_lowerCamelCase     ,			std=_lowerCamelCase    ) for image in images]

         __UpperCamelCase	:	List[str]		    =  [to_channel_dimension_format(_lowerCamelCase     ,			_lowerCamelCase    ) for image in images]

         __UpperCamelCase	:	str		    =  {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase     ,			tensor_type=_lowerCamelCase    )




'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

a=  logging.get_logger(__name__)


a=  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

a=  {
    '''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'''
    },
}

a=  {'''facebook/blenderbot-3B''': 1_2_8}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']
     SCREAMING_SNAKE_CASE__		   =						BlenderbotTokenizer







     def __init__(		self     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase="replace"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<mask>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			**_lowerCamelCase     ,			):
         super().__init__(
             _lowerCamelCase     ,			_lowerCamelCase     ,			tokenizer_file=_lowerCamelCase     ,			errors=_lowerCamelCase     ,			bos_token=_lowerCamelCase     ,			eos_token=_lowerCamelCase     ,			sep_token=_lowerCamelCase     ,			cls_token=_lowerCamelCase     ,			unk_token=_lowerCamelCase     ,			pad_token=_lowerCamelCase     ,			mask_token=_lowerCamelCase     ,			add_prefix_space=_lowerCamelCase     ,			trim_offsets=_lowerCamelCase     ,			**_lowerCamelCase     ,			)

         __UpperCamelCase	:	str		    =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
             __UpperCamelCase	:	Any		    =  getattr(_lowerCamelCase     ,			pre_tok_state.pop('type'    )    )
             __UpperCamelCase	:	Dict		    =  add_prefix_space
             __UpperCamelCase	:	Optional[Any]		    =  pre_tok_class(**_lowerCamelCase    )

         __UpperCamelCase	:	str		    =  add_prefix_space

         __UpperCamelCase	:	Optional[int]		    =  'post_processor'
         __UpperCamelCase	:	Tuple		    =  getattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )
         if tokenizer_component_instance:
             __UpperCamelCase	:	str		    =  json.loads(tokenizer_component_instance.__getstate__()    )

             # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
             if "sep" in state:
                 __UpperCamelCase	:	List[Any]		    =  tuple(state['sep']    )
             if "cls" in state:
                 __UpperCamelCase	:	str		    =  tuple(state['cls']    )

             __UpperCamelCase	:	Tuple		    =  False

             if state.get('add_prefix_space'     ,			_lowerCamelCase    ) != add_prefix_space:
                 __UpperCamelCase	:	Dict		    =  add_prefix_space
                 __UpperCamelCase	:	str		    =  True

             if state.get('trim_offsets'     ,			_lowerCamelCase    ) != trim_offsets:
                 __UpperCamelCase	:	int		    =  trim_offsets
                 __UpperCamelCase	:	Any		    =  True

             if changes_to_apply:
                 __UpperCamelCase	:	Dict		    =  getattr(_lowerCamelCase     ,			state.pop('type'    )    )
                 __UpperCamelCase	:	Any		    =  component_class(**_lowerCamelCase    )
                 setattr(self.backend_tokenizer     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     @property
     # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
     def lowerCAmelCase     (		self    ):
         if self._mask_token is None:
             if self.verbose:
                 logger.error('Using mask_token, but it is not set yet.'    )
             return None
         return str(self._mask_token    )







     @mask_token.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  AddedToken(_lowerCamelCase     ,			lstrip=_lowerCamelCase     ,			rstrip=_lowerCamelCase    ) if isinstance(_lowerCamelCase     ,			_lowerCamelCase    ) else value
         __UpperCamelCase	:	Optional[Any]		    =  value







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )
         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			*_lowerCamelCase     ,			**_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  kwargs.get('is_split_into_words'     ,			_lowerCamelCase    )

         assert self.add_prefix_space or not is_split_into_words, (
             f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*_lowerCamelCase     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  self._tokenizer.model.save(_lowerCamelCase     ,			name=_lowerCamelCase    )
         return tuple(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	Union[str, Any]		    =  [self.sep_token_id]
         __UpperCamelCase	:	List[Any]		    =  [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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         return token_ids_a + [self.eos_token_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  []
         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(_lowerCamelCase    )

         __UpperCamelCase	:	Union[str, Any]		    =  '  '.join(_lowerCamelCase    )
         __UpperCamelCase	:	Union[str, Any]		    =  self.encode(_lowerCamelCase    )
         if len(_lowerCamelCase    ) > self.model_max_length:
             __UpperCamelCase	:	Tuple		    =  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



