"""simple docstring"""



from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
       import torch


__lowercase		:		Union[str, Any]														=							logging.get_logger(__name__)



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[int]							=					['''pixel_values''']
    def __init__(		self							:     List[Any]	,      A_							:     bool = True	,      A_							:     Optional[Dict[str, int]] = None	,      A_							:     PILImageResampling = PILImageResampling.BILINEAR	,      A_							:     bool = True	,      A_							:     Dict[str, int] = None	,      A_							:     bool = True	,      A_							:     Union[int, float] = 1 / 255	,      A_							:     bool = True	,      A_							:     Optional[Union[float, List[float]]] = None	,      A_							:     Optional[Union[float, List[float]]] = None	,      **A_							:     Tuple	,      )    ->       None:
         super().__init__(**A_    )
         __snake_case				=							size if size is not None else {'''shortest_edge''': 256}
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )
         __snake_case				=							do_resize
         __snake_case				=							size
         __snake_case				=							resample
         __snake_case				=							do_center_crop
         __snake_case				=							crop_size
         __snake_case				=							do_rescale
         __snake_case				=							rescale_factor
         __snake_case				=							do_normalize
         __snake_case				=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         __snake_case				=							image_std if image_std is not None else IMAGENET_STANDARD_STD
    def  lowercase					(		self							:     Optional[int]	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     PILImageResampling = PILImageResampling.BICUBIC	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     List[str]	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         if "shortest_edge" not in size:
              raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"    )
         __snake_case				=							get_resize_output_image_size(A_	,      size=size['''shortest_edge''']	,      default_to_square=A_    )
         return resize(A_	,      size=A_	,      resample=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Tuple	,      A_							:     np.ndarray	,      A_							:     Dict[str, int]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         __snake_case				=							get_size_dict(A_    )
         if "height" not in size or "width" not in size:
              raise ValueError(f"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}"    )
         return center_crop(A_	,      size=(size['''height'''], size['''width'''])	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Dict	,      A_							:     np.ndarray	,      A_							:     float	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Dict    )    ->       np.ndarray:
         return rescale(A_	,      scale=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Optional[int]	,      A_							:     np.ndarray	,      A_							:     Union[float, List[float]]	,      A_							:     Union[float, List[float]]	,      A_							:     Optional[Union[str, ChannelDimension]] = None	,      **A_							:     Optional[int]	,      )    ->       np.ndarray:
         return normalize(A_	,      mean=A_	,      std=A_	,      data_format=A_	,      **A_    )
    def  lowercase					(		self							:     Any	,      A_							:     ImageInput	,      A_							:     Optional[bool] = None	,      A_							:     Dict[str, int] = None	,      A_							:     PILImageResampling = None	,      A_							:     bool = None	,      A_							:     Dict[str, int] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[float] = None	,      A_							:     Optional[bool] = None	,      A_							:     Optional[Union[float, List[float]]] = None	,      A_							:     Optional[Union[float, List[float]]] = None	,      A_							:     Optional[Union[str, TensorType]] = None	,      A_							:     Union[str, ChannelDimension] = ChannelDimension.FIRST	,      **A_							:     Dict	,      )    ->       Union[str, Any]:
         __snake_case				=							do_resize if do_resize is not None else self.do_resize
         __snake_case				=							size if size is not None else self.size
         __snake_case				=							get_size_dict(A_	,      default_to_square=A_    )
         __snake_case				=							resample if resample is not None else self.resample
         __snake_case				=							do_center_crop if do_center_crop is not None else self.do_center_crop
         __snake_case				=							crop_size if crop_size is not None else self.crop_size
         __snake_case				=							get_size_dict(A_	,      param_name='''crop_size'''    )
         __snake_case				=							do_rescale if do_rescale is not None else self.do_rescale
         __snake_case				=							rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case				=							do_normalize if do_normalize is not None else self.do_normalize
         __snake_case				=							image_mean if image_mean is not None else self.image_mean
         __snake_case				=							image_std if image_std is not None else self.image_std

         __snake_case				=							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.'''    )

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

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

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

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

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

         __snake_case				=							[to_channel_dimension_format(A_	,      A_    ) for image in images]

         __snake_case				=							{'''pixel_values''': images}
         return BatchFeature(data=A_	,      tensor_type=A_    )







    def  lowercase					(		self							:     int	,      A_							:     Dict	,      A_							:     List[Tuple] = None    )    ->       Any:
         __snake_case				=							outputs.logits

         # Resize logits and compute semantic segmentation maps
         if target_sizes is not None:
              if len(A_    ) != len(A_    ):
                   raise ValueError(
                       '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''    )

              if is_torch_tensor(A_    ):
                   __snake_case				=							target_sizes.numpy()

              __snake_case				=							[]

              for idx in range(len(A_    )    ):
                   __snake_case				=							torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0    )	,      size=target_sizes[idx]	,      mode='''bilinear'''	,      align_corners=A_    )
                   __snake_case				=							resized_logits[0].argmax(dim=0    )
                   semantic_segmentation.append(A_    )
         else:
              __snake_case				=							logits.argmax(dim=1    )
              __snake_case				=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]    )]

         return semantic_segmentation
"""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

__lowercase		:		int														=							logging.get_logger(__name__)


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

__lowercase		:		List[Any]														=							{
    "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"
    },
}

__lowercase		:		List[str]														=							{"facebook/blenderbot-3B": 128}



class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[Any]							=					VOCAB_FILES_NAMES
    UpperCamelCase_	:					int							=					PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_	:					Tuple							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase_	:					int							=					['''input_ids''', '''attention_mask''']
    UpperCamelCase_	:					str							=					BlenderbotTokenizer
    def __init__(		self							:     Union[str, Any]	,      A_							:     Any=None	,      A_							:     Optional[int]=None	,      A_							:     Optional[Any]=None	,      A_							:     Union[str, Any]="replace"	,      A_							:     Union[str, Any]="<s>"	,      A_							:     Union[str, Any]="</s>"	,      A_							:     Optional[int]="</s>"	,      A_							:     List[Any]="<s>"	,      A_							:     Union[str, Any]="<unk>"	,      A_							:     Any="<pad>"	,      A_							:     List[str]="<mask>"	,      A_							:     int=False	,      A_							:     Tuple=True	,      **A_							:     Optional[Any]	,      )    ->       int:
         super().__init__(
             A_	,      A_	,      tokenizer_file=A_	,      errors=A_	,      bos_token=A_	,      eos_token=A_	,      sep_token=A_	,      cls_token=A_	,      unk_token=A_	,      pad_token=A_	,      mask_token=A_	,      add_prefix_space=A_	,      trim_offsets=A_	,      **A_	,      )

         __snake_case				=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
         if pre_tok_state.get('''add_prefix_space'''	,      A_    ) != add_prefix_space:
              __snake_case				=							getattr(A_	,      pre_tok_state.pop('''type'''    )    )
              __snake_case				=							add_prefix_space
              __snake_case				=							pre_tok_class(**A_    )

         __snake_case				=							add_prefix_space

         __snake_case				=							'''post_processor'''
         __snake_case				=							getattr(self.backend_tokenizer	,      A_	,      A_    )
         if tokenizer_component_instance:
              __snake_case				=							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:
                   __snake_case				=							tuple(state['''sep''']    )
              if "cls" in state:
                   __snake_case				=							tuple(state['''cls''']    )

              __snake_case				=							False

              if state.get('''add_prefix_space'''	,      A_    ) != add_prefix_space:
                   __snake_case				=							add_prefix_space
                   __snake_case				=							True

              if state.get('''trim_offsets'''	,      A_    ) != trim_offsets:
                   __snake_case				=							trim_offsets
                   __snake_case				=							True

              if changes_to_apply:
                   __snake_case				=							getattr(A_	,      state.pop('''type'''    )    )
                   __snake_case				=							component_class(**A_    )
                   setattr(self.backend_tokenizer	,      A_	,      A_    )
    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         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  lowercase					(		self							:     int	,      A_							:     Union[str, Any]    )    ->       List[Any]:
         __snake_case				=							AddedToken(A_	,      lstrip=A_	,      rstrip=A_    ) if isinstance(A_	,      A_    ) else value
         __snake_case				=							value
    def  lowercase					(		self							:     Any	,      *A_							:     List[str]	,      **A_							:     Optional[int]    )    ->       BatchEncoding:
         __snake_case				=							kwargs.get('''is_split_into_words'''	,      A_    )
         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(*A_	,      **A_    )
    def  lowercase					(		self							:     str	,      *A_							:     Tuple	,      **A_							:     str    )    ->       BatchEncoding:
         __snake_case				=							kwargs.get('''is_split_into_words'''	,      A_    )

         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(*A_	,      **A_    )
    def  lowercase					(		self							:     str	,      A_							:     str	,      A_							:     Optional[str] = None    )    ->       Tuple[str]:
         __snake_case				=							self._tokenizer.model.save(A_	,      name=A_    )
         return tuple(A_    )
    def  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       List[int]:
         __snake_case				=							[self.sep_token_id]
         __snake_case				=							[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  lowercase					(		self							:     Tuple	,      A_							:     List[int]	,      A_							:     Optional[List[int]] = None    )    ->       Any:
         return token_ids_a + [self.eos_token_id]







    def  lowercase					(		self							:     Optional[Any]	,      A_							:     "Conversation"    )    ->       List[int]:
         __snake_case				=							[]
         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(A_    )

         __snake_case				=							'''  '''.join(A_    )
         __snake_case				=							self.encode(A_    )
         if len(A_    ) > self.model_max_length:
              __snake_case				=							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