'''simple docstring'''





import json
from typing import 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_bart import BartTokenizer


SCREAMING_SNAKE_CASE_:					Optional[int]  =logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:					Any  ={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE_:					str  ={
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

SCREAMING_SNAKE_CASE_:					List[str]  ={
    'facebook/bart-base': 10_24,
    'facebook/bart-large': 10_24,
    'facebook/bart-large-mnli': 10_24,
    'facebook/bart-large-cnn': 10_24,
    'facebook/bart-large-xsum': 10_24,
    'yjernite/bart_eli5': 10_24,
}




class 	__A				(			UpperCamelCase__   ):
    a__	:						Optional[Any]	       =  VOCAB_FILES_NAMES
    a__	:						Any	       =  PRETRAINED_VOCAB_FILES_MAP
    a__	:						List[Any]	       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__	:						List[str]	       =  ["""input_ids""", """attention_mask"""]
    a__	:						int	       =  BartTokenizer



    def __init__(self   :   List[Any]       ,      __a   :   Union[str, Any]=None       ,      __a   :   Any=None       ,      __a   :   Union[str, Any]=None       ,      __a   :   Union[str, Any]="replace"       ,      __a   :   int="<s>"       ,      __a   :   Tuple="</s>"       ,      __a   :   List[str]="</s>"       ,      __a   :   Optional[int]="<s>"       ,      __a   :   Tuple="<unk>"       ,      __a   :   List[Any]="<pad>"       ,      __a   :   List[Any]="<mask>"       ,      __a   :   Union[str, Any]=False       ,      __a   :   Optional[Any]=True       ,      **__a   :   Optional[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       ,      )

         UpperCAmelCase_	   =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
         if pre_tok_state.get("add_prefix_space"       ,      __a			) != add_prefix_space:
              UpperCAmelCase_	   =       getattr(__a       ,      pre_tok_state.pop("type"			)			)
              UpperCAmelCase_	   =       add_prefix_space
              UpperCAmelCase_	   =       pre_tok_class(**__a			)

         UpperCAmelCase_	   =       add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         UpperCAmelCase_	   =       "post_processor"
         UpperCAmelCase_	   =       getattr(self.backend_tokenizer       ,      __a       ,      __a			)
         if tokenizer_component_instance:
              UpperCAmelCase_	   =       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_	   =       tuple(state["sep"]			)
              if "cls" in state:
                   UpperCAmelCase_	   =       tuple(state["cls"]			)

              UpperCAmelCase_	   =       False

              if state.get("add_prefix_space"       ,      __a			) != add_prefix_space:
                   UpperCAmelCase_	   =       add_prefix_space
                   UpperCAmelCase_	   =       True

              if state.get("trim_offsets"       ,      __a			) != trim_offsets:
                   UpperCAmelCase_	   =       trim_offsets
                   UpperCAmelCase_	   =       True

              if changes_to_apply:
                   UpperCAmelCase_	   =       getattr(__a       ,      state.pop("type"			)			)
                   UpperCAmelCase_	   =       component_class(**__a			)
                   setattr(self.backend_tokenizer       ,      __a       ,      __a			)



    @property
    def  _lowercase						(self   :   List[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   :   List[Any]			):
         UpperCAmelCase_	   =       AddedToken(__a       ,      lstrip=__a       ,      rstrip=__a			) if isinstance(__a       ,      __a			) else value
         UpperCAmelCase_	   =       value



    def  _lowercase						(self   :   List[Any]       ,      *__a   :   Dict       ,      **__a   :   Dict			):
         UpperCAmelCase_	   =       kwargs.get("is_split_into_words"       ,      __a			)

         if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  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   :   Dict       ,      *__a   :   Tuple       ,      **__a   :   Dict			):
         UpperCAmelCase_	   =       kwargs.get("is_split_into_words"       ,      __a			)

         if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  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   :   Dict       ,      __a   :   str       ,      __a   :   Optional[str] = None			):
         UpperCAmelCase_	   =       self._tokenizer.model.save(__a       ,      name=__a			)
         return tuple(__a			)



    def  _lowercase						(self   :   Optional[Any]       ,      __a   :   Optional[Any]       ,      __a   :   List[str]=None			):
         UpperCAmelCase_	   =       [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






    def  _lowercase						(self   :   List[Any]       ,      __a   :   List[int]       ,      __a   :   Optional[List[int]] = None			):
         UpperCAmelCase_	   =       [self.sep_token_id]
         UpperCAmelCase_	   =       [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]





'''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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_:					Dict  =logging.get_logger(__name__)




class 	__A				(			UpperCamelCase__     ):
    a__	:						Any	       =  ["""pixel_values"""]



    def __init__(self   :   Any       ,      __a   :   bool = True       ,      __a   :   Optional[Dict[str, int]] = None       ,      __a   :   PILImageResampling = PILImageResampling.BICUBIC       ,      __a   :   bool = True       ,      __a   :   bool = True       ,      __a   :   Union[int, float] = 1 / 255       ,      __a   :   Dict[str, int] = None       ,      __a   :   bool = True       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      __a   :   Optional[Union[float, List[float]]] = None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         UpperCAmelCase_	   =       size if size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a			)
         UpperCAmelCase_	   =       crop_size if crop_size is not None else {"height": 224, "width": 224}
         UpperCAmelCase_	   =       get_size_dict(__a       ,      default_to_square=__a       ,      param_name="crop_size"			)

         UpperCAmelCase_	   =       do_resize
         UpperCAmelCase_	   =       do_rescale
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       do_center_crop
         UpperCAmelCase_	   =       crop_size
         UpperCAmelCase_	   =       size
         UpperCAmelCase_	   =       resample
         UpperCAmelCase_	   =       rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase_	   =       image_std if image_std is not None else IMAGENET_DEFAULT_STD



    def  _lowercase						(self   :   Tuple       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   PILImageResampling = PILImageResampling.BILINEAR       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Optional[Any]       ,      ):
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if "shortest_edge" in size:
              UpperCAmelCase_	   =       get_resize_output_image_size(__a       ,      size=size["shortest_edge"]       ,      default_to_square=__a			)
              # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
         elif "height" in size and "width" in size:
              UpperCAmelCase_	   =       (size["height"], size["width"])
         else:
              raise ValueError(f"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""			)
         return resize(__a       ,      size=__a       ,      resample=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   List[Any]       ,      __a   :   np.ndarray       ,      __a   :   Dict[str, int]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   int       ,      ):
         UpperCAmelCase_	   =       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, width). Got {size.keys()}"""			)
         return center_crop(__a       ,      size=(size["height"], size["width"])       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   str       ,      __a   :   np.ndarray       ,      __a   :   float       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   List[str]			):
         return rescale(__a       ,      scale=__a       ,      data_format=__a       ,      **__a			)



    def  _lowercase						(self   :   Any       ,      __a   :   np.ndarray       ,      __a   :   Union[float, List[float]]       ,      __a   :   Union[float, List[float]]       ,      __a   :   Optional[Union[str, ChannelDimension]] = None       ,      **__a   :   Dict       ,      ):
         return normalize(__a       ,      mean=__a       ,      std=__a       ,      data_format=__a       ,      **__a			)






    def  _lowercase						(self   :   Dict       ,      __a   :   ImageInput       ,      __a   :   Optional[bool] = None       ,      __a   :   Dict[str, int] = None       ,      __a   :   PILImageResampling = None       ,      __a   :   bool = None       ,      __a   :   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       ,      ):
         UpperCAmelCase_	   =       do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase_	   =       do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase_	   =       do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase_	   =       do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase_	   =       crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase_	   =       get_size_dict(__a       ,      param_name="crop_size"       ,      default_to_square=__a			)
         UpperCAmelCase_	   =       resample if resample is not None else self.resample
         UpperCAmelCase_	   =       rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase_	   =       image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase_	   =       image_std if image_std is not None else self.image_std

         UpperCAmelCase_	   =       size if size is not None else self.size
         UpperCAmelCase_	   =       get_size_dict(__a			)

         if not is_batched(__a			):
              UpperCAmelCase_	   =       [images]

         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."			)

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

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

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

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

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

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

         UpperCAmelCase_	   =       {"pixel_values": images}
         return BatchFeature(data=__a       ,      tensor_type=__a			)




