




'''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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


UpperCamelCase_		:     List[Any]          =		logging.get_logger(__name__)





def       __a      (  _UpperCamelCase:       Tuple       )		->					List[List[ImageInput]]:

     """simple docstring"""




     if isinstance(_UpperCamelCase   ,		(list, tuple)       ) and isinstance(videos[0]   ,		(list, tuple)       ) and is_valid_image(videos[0][0]       ):
          return videos

     elif isinstance(_UpperCamelCase   ,		(list, tuple)       ) and is_valid_image(videos[0]       ):
          return [videos]

     elif is_valid_image(_UpperCamelCase       ):
          return [[videos]]

     raise ValueError(F"""Could not make batched video from {videos}"""       )





class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       List[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 = None   ,_SCREAMING_SNAKE_CASE = True   ,_SCREAMING_SNAKE_CASE = 1 / 255   ,_SCREAMING_SNAKE_CASE = True   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,**_SCREAMING_SNAKE_CASE   ,)				->     None:
          super().__init__(**_SCREAMING_SNAKE_CASE       )
          _snake_case				= size if size is not None else {"shortest_edge": 224}
          _snake_case				= get_size_dict(_SCREAMING_SNAKE_CASE   ,default_to_square=_SCREAMING_SNAKE_CASE       )
          _snake_case				= crop_size if crop_size is not None else {"height": 224, "width": 224}
          _snake_case				= get_size_dict(_SCREAMING_SNAKE_CASE   ,param_name="crop_size"       )

          _snake_case				= do_resize
          _snake_case				= size
          _snake_case				= do_center_crop
          _snake_case				= crop_size
          _snake_case				= resample
          _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   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR   ,_SCREAMING_SNAKE_CASE = None   ,**_SCREAMING_SNAKE_CASE   ,)				->     np.ndarray:
          _snake_case				= get_size_dict(_SCREAMING_SNAKE_CASE   ,default_to_square=_SCREAMING_SNAKE_CASE       )
          if "shortest_edge" in size:
               _snake_case				= get_resize_output_image_size(_SCREAMING_SNAKE_CASE   ,size["shortest_edge"]   ,default_to_square=_SCREAMING_SNAKE_CASE       )
          elif "height" in size and "width" in size:
               _snake_case				= (size["height"], size["width"])
          else:
               raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""       )
          return resize(_SCREAMING_SNAKE_CASE   ,size=_SCREAMING_SNAKE_CASE   ,resample=_SCREAMING_SNAKE_CASE   ,data_format=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None   ,**_SCREAMING_SNAKE_CASE   ,)				->     np.ndarray:
          _snake_case				= get_size_dict(_SCREAMING_SNAKE_CASE       )
          if "height" not in size or "width" not in size:
               raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""       )
          return center_crop(_SCREAMING_SNAKE_CASE   ,size=(size["height"], size["width"])   ,data_format=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None   ,**_SCREAMING_SNAKE_CASE   ,)				->     Optional[Any]:
          return rescale(_SCREAMING_SNAKE_CASE   ,scale=_SCREAMING_SNAKE_CASE   ,data_format=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






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






     def 							_lowercase      (					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 = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST   ,)				->     np.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_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(_SCREAMING_SNAKE_CASE       )

          if do_resize:
               _snake_case				= self.resize(image=_SCREAMING_SNAKE_CASE   ,size=_SCREAMING_SNAKE_CASE   ,resample=_SCREAMING_SNAKE_CASE       )

          if do_center_crop:
               _snake_case				= self.center_crop(_SCREAMING_SNAKE_CASE   ,size=_SCREAMING_SNAKE_CASE       )

          if do_rescale:
               _snake_case				= self.rescale(image=_SCREAMING_SNAKE_CASE   ,scale=_SCREAMING_SNAKE_CASE       )

          if do_normalize:
               _snake_case				= self.normalize(image=_SCREAMING_SNAKE_CASE   ,mean=_SCREAMING_SNAKE_CASE   ,std=_SCREAMING_SNAKE_CASE       )

          _snake_case				= to_channel_dimension_format(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          return image






     def 							_lowercase      (					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 = 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:
          _snake_case				= do_resize if do_resize is not None else self.do_resize
          _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				= 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				= size if size is not None else self.size
          _snake_case				= get_size_dict(_SCREAMING_SNAKE_CASE   ,default_to_square=_SCREAMING_SNAKE_CASE       )
          _snake_case				= crop_size if crop_size is not None else self.crop_size
          _snake_case				= get_size_dict(_SCREAMING_SNAKE_CASE   ,param_name="crop_size"       )

          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."       )

          _snake_case				= make_batched(_SCREAMING_SNAKE_CASE       )

          _snake_case				= [
              [
                  self._preprocess_image(
                      image=_SCREAMING_SNAKE_CASE   ,do_resize=_SCREAMING_SNAKE_CASE   ,size=_SCREAMING_SNAKE_CASE   ,resample=_SCREAMING_SNAKE_CASE   ,do_center_crop=_SCREAMING_SNAKE_CASE   ,crop_size=_SCREAMING_SNAKE_CASE   ,do_rescale=_SCREAMING_SNAKE_CASE   ,rescale_factor=_SCREAMING_SNAKE_CASE   ,do_normalize=_SCREAMING_SNAKE_CASE   ,image_mean=_SCREAMING_SNAKE_CASE   ,image_std=_SCREAMING_SNAKE_CASE   ,data_format=_SCREAMING_SNAKE_CASE   ,)
                  for img in video
              ]
              for video in videos
          ]

          _snake_case				= {"pixel_values": videos}
          return BatchFeature(data=_SCREAMING_SNAKE_CASE   ,tensor_type=_SCREAMING_SNAKE_CASE       )








'''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


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


UpperCamelCase_		:     Optional[Any]          =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase_		:     Dict          =		{
    '''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''',
    },
}

UpperCamelCase_		:     Tuple          =		{
    '''facebook/bart-base''': 1024,
    '''facebook/bart-large''': 1024,
    '''facebook/bart-large-mnli''': 1024,
    '''facebook/bart-large-cnn''': 1024,
    '''facebook/bart-large-xsum''': 1024,
    '''yjernite/bart_eli5''': 1024,
}

class   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       Union[str, Any]       							=		VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_				:       int       							=		PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_				:       Optional[Any]       							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_				:       Any       							=		["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE_				:       str       							=		BartTokenizer






     def __init__(					self   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_SCREAMING_SNAKE_CASE=None   ,_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=True   ,**_SCREAMING_SNAKE_CASE   ,)				->     List[Any]:
          super().__init__(
              _SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,tokenizer_file=_SCREAMING_SNAKE_CASE   ,errors=_SCREAMING_SNAKE_CASE   ,bos_token=_SCREAMING_SNAKE_CASE   ,eos_token=_SCREAMING_SNAKE_CASE   ,sep_token=_SCREAMING_SNAKE_CASE   ,cls_token=_SCREAMING_SNAKE_CASE   ,unk_token=_SCREAMING_SNAKE_CASE   ,pad_token=_SCREAMING_SNAKE_CASE   ,mask_token=_SCREAMING_SNAKE_CASE   ,add_prefix_space=_SCREAMING_SNAKE_CASE   ,trim_offsets=_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE   ,)

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

          _snake_case				= add_prefix_space

          # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
          _snake_case				= "post_processor"
          _snake_case				= getattr(self.backend_tokenizer   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
          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"   ,_SCREAMING_SNAKE_CASE       ) != add_prefix_space:
                    _snake_case				= add_prefix_space
                    _snake_case				= True

               if state.get("trim_offsets"   ,_SCREAMING_SNAKE_CASE       ) != trim_offsets:
                    _snake_case				= trim_offsets
                    _snake_case				= True

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






     @property
     def 							_lowercase      (					self       )				->     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   ,_SCREAMING_SNAKE_CASE       )				->     str:
          _snake_case				= AddedToken(_SCREAMING_SNAKE_CASE   ,lstrip=_SCREAMING_SNAKE_CASE   ,rstrip=_SCREAMING_SNAKE_CASE       ) if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ) else value
          _snake_case				= value






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     BatchEncoding:
          _snake_case				= kwargs.get("is_split_into_words"   ,_SCREAMING_SNAKE_CASE       )

          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(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )				->     BatchEncoding:
          _snake_case				= kwargs.get("is_split_into_words"   ,_SCREAMING_SNAKE_CASE       )

          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(*_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None       )				->     Tuple[str]:
          _snake_case				= self._tokenizer.model.save(_SCREAMING_SNAKE_CASE   ,name=_SCREAMING_SNAKE_CASE       )
          return tuple(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE=None       )				->     Tuple:
          _snake_case				= [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   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = 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]


