
"""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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__									=					logging.get_logger(__name__)


if is_vision_available():
			import PIL


class  _lowerCAmelCase    (    lowerCamelCase    ):
							lowercase_  :  Dict      =				['pixel_values']



							def __init__(   self			,    a_ = True			,    a_ = None			,    a_ = PILImageResampling.BICUBIC			,    a_ = True			,    a_ = None			,    a_ = True			,    a_ = 1 / 255			,    a_ = True			,    a_ = None			,    a_ = None			,    a_ = True			,    **a_			,    )				->				None:
								super().__init__(**SCREAMING_SNAKE_CASE_ )
								_UpperCAmelCase			=    size if size is not None else {"shortest_edge": 224}
								_UpperCAmelCase			=    get_size_dict(SCREAMING_SNAKE_CASE_			,    default_to_square=SCREAMING_SNAKE_CASE_ )
								_UpperCAmelCase			=    crop_size if crop_size is not None else {"height": 224, "width": 224}
								_UpperCAmelCase			=    get_size_dict(SCREAMING_SNAKE_CASE_			,    default_to_square=SCREAMING_SNAKE_CASE_			,    param_name="crop_size" )

								_UpperCAmelCase			=    do_resize
								_UpperCAmelCase			=    size
								_UpperCAmelCase			=    resample
								_UpperCAmelCase			=    do_center_crop
								_UpperCAmelCase			=    crop_size
								_UpperCAmelCase			=    do_rescale
								_UpperCAmelCase			=    rescale_factor
								_UpperCAmelCase			=    do_normalize
								_UpperCAmelCase			=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								_UpperCAmelCase			=    image_std if image_std is not None else OPENAI_CLIP_STD
								_UpperCAmelCase			=    do_convert_rgb



							def    _a (   self			,    a_			,    a_			,    a_ = PILImageResampling.BICUBIC			,    a_ = None			,    **a_			,    )				->				np.ndarray:
								_UpperCAmelCase			=    get_size_dict(SCREAMING_SNAKE_CASE_			,    default_to_square=SCREAMING_SNAKE_CASE_ )
								if "shortest_edge" not in size:
									raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
								_UpperCAmelCase			=    get_resize_output_image_size(SCREAMING_SNAKE_CASE_			,    size=size["shortest_edge"]			,    default_to_square=SCREAMING_SNAKE_CASE_ )
								return resize(SCREAMING_SNAKE_CASE_			,    size=SCREAMING_SNAKE_CASE_			,    resample=SCREAMING_SNAKE_CASE_			,    data_format=SCREAMING_SNAKE_CASE_			,    **SCREAMING_SNAKE_CASE_ )



							def    _a (   self			,    a_			,    a_			,    a_ = None			,    **a_			,    )				->				np.ndarray:
								_UpperCAmelCase			=    get_size_dict(SCREAMING_SNAKE_CASE_ )
								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(SCREAMING_SNAKE_CASE_			,    size=(size["height"], size["width"])			,    data_format=SCREAMING_SNAKE_CASE_			,    **SCREAMING_SNAKE_CASE_ )



							def    _a (   self			,    a_			,    a_			,    a_ = None			,    **a_			,    )				->				Dict:
								return rescale(SCREAMING_SNAKE_CASE_			,    scale=SCREAMING_SNAKE_CASE_			,    data_format=SCREAMING_SNAKE_CASE_			,    **SCREAMING_SNAKE_CASE_ )



							def    _a (   self			,    a_			,    a_			,    a_			,    a_ = None			,    **a_			,    )				->				np.ndarray:
								return normalize(SCREAMING_SNAKE_CASE_			,    mean=SCREAMING_SNAKE_CASE_			,    std=SCREAMING_SNAKE_CASE_			,    data_format=SCREAMING_SNAKE_CASE_			,    **SCREAMING_SNAKE_CASE_ )



							def    _a (   self			,    a_			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = ChannelDimension.FIRST			,    **a_			,    )				->				PIL.Image.Image:
								_UpperCAmelCase			=    do_resize if do_resize is not None else self.do_resize
								_UpperCAmelCase			=    size if size is not None else self.size
								_UpperCAmelCase			=    get_size_dict(SCREAMING_SNAKE_CASE_			,    param_name="size"			,    default_to_square=SCREAMING_SNAKE_CASE_ )
								_UpperCAmelCase			=    resample if resample is not None else self.resample
								_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(SCREAMING_SNAKE_CASE_			,    param_name="crop_size"			,    default_to_square=SCREAMING_SNAKE_CASE_ )
								_UpperCAmelCase			=    do_rescale if do_rescale is not None else self.do_rescale
								_UpperCAmelCase			=    rescale_factor if rescale_factor is not None else self.rescale_factor
								_UpperCAmelCase			=    do_normalize if do_normalize is not None else self.do_normalize
								_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			=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								_UpperCAmelCase			=    make_list_of_images(SCREAMING_SNAKE_CASE_ )

								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." )

								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." )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
									_UpperCAmelCase			=    [convert_to_rgb(SCREAMING_SNAKE_CASE_ ) for image in images]

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

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

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

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

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

								_UpperCAmelCase			=    [to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,    SCREAMING_SNAKE_CASE_ ) for image in images]

								_UpperCAmelCase			=    {"pixel_values": images}
								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,)




