
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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 logging


if is_vision_available():
						import PIL

__a         =   logging.get_logger(__name__)




def 							a		(				snake_case__:   int   ):


				'''simple docstring'''
				if isinstance(snake_case__		,  (list, tuple)   ) and isinstance(videos[0]		,  (list, tuple)   ) and is_valid_image(videos[0][0]   ):
								return videos

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

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

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


class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[Any]       	=      ['pixel_values']
	def __init__(			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BILINEAR			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Union[int, float] = 1 / 2_5_5			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				**SCREAMING_SNAKE_CASE_    :     List[str]			,				)			->	None:
					super().__init__(**SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   size if size is not None else {'''shortest_edge''': 2_5_6}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				param_name='''crop_size'''			)

					lowercase_	    =   do_resize
					lowercase_	    =   size
					lowercase_	    =   do_center_crop
					lowercase_	    =   crop_size
					lowercase_	    =   resample
					lowercase_	    =   do_rescale
					lowercase_	    =   rescale_factor
					lowercase_	    =   offset
					lowercase_	    =   do_normalize
					lowercase_	    =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					lowercase_	    =   image_std if image_std is not None else IMAGENET_STANDARD_STD
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = PILImageResampling.BILINEAR			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				)			->	np.ndarray:
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					if "shortest_edge" in size:
									lowercase_	    =   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:
									lowercase_	    =   (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    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     List[Any]			,				)			->	np.ndarray:
					lowercase_	    =   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    :     Dict			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Union[int, float]			,				SCREAMING_SNAKE_CASE_    :     bool = True			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				)			->	Tuple:
					lowercase_	    =   image.astype(np.floataa			)
					if offset:
									lowercase_	    =   image - (scale / 2)
					return rescale(SCREAMING_SNAKE_CASE_			,				scale=SCREAMING_SNAKE_CASE_			,				data_format=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     np.ndarray			,				SCREAMING_SNAKE_CASE_    :     Union[float, List[float]]			,				SCREAMING_SNAKE_CASE_    :     Union[float, List[float]]			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, ChannelDimension]] = None			,				**SCREAMING_SNAKE_CASE_    :     List[str]			,				)			->	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    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     ImageInput			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     float = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[ChannelDimension] = 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.'''			)

					if offset and not do_rescale:
									raise ValueError('''For offset, do_rescale must also be set to True.'''			)

					# All transformations expect numpy arrays.
					lowercase_	    =   to_numpy_array(SCREAMING_SNAKE_CASE_			)

					if do_resize:
									lowercase_	    =   self.resize(image=SCREAMING_SNAKE_CASE_			,				size=SCREAMING_SNAKE_CASE_			,				resample=SCREAMING_SNAKE_CASE_			)

					if do_center_crop:
									lowercase_	    =   self.center_crop(SCREAMING_SNAKE_CASE_			,				size=SCREAMING_SNAKE_CASE_			)

					if do_rescale:
									lowercase_	    =   self.rescale(image=SCREAMING_SNAKE_CASE_			,				scale=SCREAMING_SNAKE_CASE_			,				offset=SCREAMING_SNAKE_CASE_			)

					if do_normalize:
									lowercase_	    =   self.normalize(image=SCREAMING_SNAKE_CASE_			,				mean=SCREAMING_SNAKE_CASE_			,				std=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
					return image




	def 		_lowercase    (			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     ImageInput			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     PILImageResampling = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Dict[str, int] = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     float = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     bool = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[float, List[float]]] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Union[str, TensorType]] = None			,				SCREAMING_SNAKE_CASE_    :     ChannelDimension = ChannelDimension.FIRST			,				**SCREAMING_SNAKE_CASE_    :     Dict			,				)			->	PIL.Image.Image:
					lowercase_	    =   do_resize if do_resize is not None else self.do_resize
					lowercase_	    =   resample if resample is not None else self.resample
					lowercase_	    =   do_center_crop if do_center_crop is not None else self.do_center_crop
					lowercase_	    =   do_rescale if do_rescale is not None else self.do_rescale
					lowercase_	    =   rescale_factor if rescale_factor is not None else self.rescale_factor
					lowercase_	    =   offset if offset is not None else self.offset
					lowercase_	    =   do_normalize if do_normalize is not None else self.do_normalize
					lowercase_	    =   image_mean if image_mean is not None else self.image_mean
					lowercase_	    =   image_std if image_std is not None else self.image_std

					lowercase_	    =   size if size is not None else self.size
					lowercase_	    =   get_size_dict(SCREAMING_SNAKE_CASE_			,				default_to_square=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   crop_size if crop_size is not None else self.crop_size
					lowercase_	    =   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.'''			)

					lowercase_	    =   make_batched(SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   [
					    [
					        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_			,				offset=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
					]

					lowercase_	    =   {'''pixel_values''': videos}
					return BatchFeature(data=SCREAMING_SNAKE_CASE_			,				tensor_type=SCREAMING_SNAKE_CASE_			)






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						__a         =   None

__a         =   logging.get_logger(__name__)

__a         =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__a         =   {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__a         =   {
    'moussaKam/mbarthez': 1_0_2_4,
    'moussaKam/barthez': 1_0_2_4,
    'moussaKam/barthez-orangesum-title': 1_0_2_4,
}

__a         =   '▁'
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :int       	=      VOCAB_FILES_NAMES
	a     :Any       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :Optional[int]       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :Dict       	=      ['input_ids', 'attention_mask']
	a     :Optional[Any]       	=      BarthezTokenizer
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				SCREAMING_SNAKE_CASE_    :     Dict=None			,				SCREAMING_SNAKE_CASE_    :     Dict="<s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="</s>"			,				SCREAMING_SNAKE_CASE_    :     int="</s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="<s>"			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="<unk>"			,				SCREAMING_SNAKE_CASE_    :     List[str]="<pad>"			,				SCREAMING_SNAKE_CASE_    :     Any="<mask>"			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				)			->	Any:
					# Mask token behave like a normal word, i.e. include the space before it
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else mask_token

					super().__init__(
					    SCREAMING_SNAKE_CASE_			,				tokenizer_file=SCREAMING_SNAKE_CASE_			,				bos_token=SCREAMING_SNAKE_CASE_			,				eos_token=SCREAMING_SNAKE_CASE_			,				unk_token=SCREAMING_SNAKE_CASE_			,				sep_token=SCREAMING_SNAKE_CASE_			,				cls_token=SCREAMING_SNAKE_CASE_			,				pad_token=SCREAMING_SNAKE_CASE_			,				mask_token=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

					lowercase_	    =   vocab_file
					lowercase_	    =   False if not self.vocab_file else True
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:

					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]
					lowercase_	    =   [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					lowercase_	    =   [self.sep_token_id]
					lowercase_	    =   [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    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					if not self.can_save_slow_tokenizer:
									raise ValueError(
									    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
									    '''tokenizer.'''			)

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

					if os.path.abspath(self.vocab_file			) != os.path.abspath(SCREAMING_SNAKE_CASE_			):
									copyfile(self.vocab_file			,				SCREAMING_SNAKE_CASE_			)

					return (out_vocab_file,)




