



'''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, 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_vision_available, logging


if is_vision_available():
						import PIL


UpperCamelCase__:   int								=			logging.get_logger(__name__)

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       ["""pixel_values"""]







	def __init__(						self			:     int		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = PIL.Image.BICUBIC		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     Union[int, float] = 1 / 255		,			__snake_case			:     bool = True		,			__snake_case			:     bool = True		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			__snake_case			:     Optional[Union[float, List[float]]] = None		,			**__snake_case			:     Any		,			)			->		None:
								super().__init__(**__snake_case		)
								UpperCAmelCase :       Any 							=		size if size is not None else {'''height''': 256, '''width''': 256}
								UpperCAmelCase :       int 							=		get_size_dict(__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase :       str 							=		get_size_dict(__snake_case		,			param_name='''crop_size'''		)

								UpperCAmelCase :       Dict 							=		do_resize
								UpperCAmelCase :       Any 							=		size
								UpperCAmelCase :       Optional[int] 							=		resample
								UpperCAmelCase :       List[str] 							=		do_center_crop
								UpperCAmelCase :       Optional[int] 							=		crop_size
								UpperCAmelCase :       Any 							=		do_rescale
								UpperCAmelCase :       int 							=		rescale_factor
								UpperCAmelCase :       Dict 							=		do_normalize
								UpperCAmelCase :       Any 							=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase :       List[str] 							=		image_std if image_std is not None else IMAGENET_STANDARD_STD







	def      A	(						self			:     Any		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     PILImageResampling = PIL.Image.BICUBIC		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Optional[int]		,			)			->		np.ndarray:
								UpperCAmelCase :       Dict 							=		get_size_dict(__snake_case		)
								if "height" not in size or "width" not in size:
															raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""		)
								return resize(
								    __snake_case		,			size=(size['''height'''], size['''width'''])		,			resample=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Any		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     int		,			)			->		np.ndarray:
								UpperCAmelCase :       Optional[int] 							=		get_size_dict(__snake_case		)
								if "height" not in size or "width" not in size:
															raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""		)
								return center_crop(__snake_case		,			size=(size['''height'''], size['''width'''])		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Union[int, float]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Optional[int]		,			)			->		List[Any]:
								return rescale(__snake_case		,			scale=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Union[float, List[float]]		,			__snake_case			:     Union[float, List[float]]		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     Any		,			)			->		np.ndarray:
								return normalize(__snake_case		,			mean=__snake_case		,			std=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







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

								UpperCAmelCase :       str 							=		size if size is not None else self.size
								UpperCAmelCase :       Tuple 							=		get_size_dict(__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		crop_size if crop_size is not None else self.crop_size
								UpperCAmelCase :       Optional[Any] 							=		get_size_dict(__snake_case		,			param_name='''crop_size'''		)

								UpperCAmelCase :       Any 							=		make_list_of_images(__snake_case		)

								if not valid_images(__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 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.
								UpperCAmelCase :       Any 							=		[to_numpy_array(__snake_case		) for image in images]

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

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

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

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

								UpperCAmelCase :       Optional[int] 							=		[to_channel_dimension_format(__snake_case		,			__snake_case		) for image in images]

								UpperCAmelCase :       List[Any] 							=		{'''pixel_values''': images}
								return BatchFeature(data=__snake_case		,			tensor_type=__snake_case		)










'''simple docstring'''






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__:   Tuple								=			logging.get_logger(__name__)

UpperCamelCase__:   Optional[int]								=			{"vocab_file": "sentencepiece.bpe.model"}

UpperCamelCase__:   Optional[int]								=			{
    "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"
        ),
    },
}

UpperCamelCase__:   Dict								=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

UpperCamelCase__:   Tuple								=			"▁"

class 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       VOCAB_FILES_NAMES
	lowerCamelCase__			 =       PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__			 =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__			 =       ["""input_ids""", """attention_mask"""]







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

								UpperCAmelCase :       Union[str, Any] 							=		{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__snake_case		,			eos_token=__snake_case		,			unk_token=__snake_case		,			sep_token=__snake_case		,			cls_token=__snake_case		,			pad_token=__snake_case		,			mask_token=__snake_case		,			sp_model_kwargs=self.sp_model_kwargs		,			**__snake_case		,			)

								UpperCAmelCase :       Optional[int] 							=		vocab_file
								UpperCAmelCase :       int 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(str(__snake_case		)		)

								UpperCAmelCase :       int 							=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

								UpperCAmelCase :       Optional[Any] 							=		len(self.sp_model		) - 1
								UpperCAmelCase :       Optional[int] 							=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def      A	(						self			:     Tuple		,			__snake_case			:     List[int]		,			__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]
								UpperCAmelCase :       Union[str, Any] 							=		[self.cls_token_id]
								UpperCAmelCase :       Optional[Any] 							=		[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







	def      A	(						self			:     int		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		,			__snake_case			:     bool = False		)			->		List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__snake_case		,			token_ids_a=__snake_case		,			already_has_special_tokens=__snake_case		)

								if token_ids_a is None:
															return [1] + ([0] * len(__snake_case		)) + [1]
								return [1] + ([0] * len(__snake_case		)) + [1, 1] + ([0] * len(__snake_case		)) + [1]







	def      A	(						self			:     Optional[int]		,			__snake_case			:     List[int]		,			__snake_case			:     Optional[List[int]] = None		)			->		List[int]:
								UpperCAmelCase :       Tuple 							=		[self.sep_token_id]
								UpperCAmelCase :       Tuple 							=		[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]







	@property
	def      A	(						self			:     Dict		)			->		Optional[int]:
								return len(self.sp_model		)







	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase :       Optional[Any] 							=		{self.convert_ids_to_tokens(__snake_case		): i for i in range(self.vocab_size		)}
								vocab.update(self.added_tokens_encoder		)
								return vocab







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     str		)			->		List[str]:
								return self.sp_model.encode(__snake_case		,			out_type=__snake_case		)







	def      A	(						self			:     int		,			__snake_case			:     int		)			->		int:
								if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
								UpperCAmelCase :       Optional[Any] 							=		self.sp_model.PieceToId(__snake_case		)

								return spm_id if spm_id else self.unk_token_id







	def      A	(						self			:     int		,			__snake_case			:     Any		)			->		List[Any]:
								if index in self.fairseq_ids_to_tokens:
															return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(__snake_case		)







	def      A	(						self			:     List[Any]		,			__snake_case			:     Union[str, Any]		)			->		List[str]:
								UpperCAmelCase :       Optional[Any] 							=		[]
								UpperCAmelCase :       int 							=		''''''
								UpperCAmelCase :       Union[str, Any] 							=		False
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						if not prev_is_special:
																													out_string += " "
																						out_string += self.sp_model.decode(__snake_case		) + token
																						UpperCAmelCase :       str 							=		True
																						UpperCAmelCase :       List[str] 							=		[]
															else:
																						current_sub_tokens.append(__snake_case		)
																						UpperCAmelCase :       Optional[int] 							=		False
								out_string += self.sp_model.decode(__snake_case		)
								return out_string.strip()







	def __getstate__(						self			:     Union[str, Any]		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[Any] 							=		self.__dict__.copy()
								UpperCAmelCase :       Any 							=		None
								return state







	def __setstate__(						self			:     Optional[int]		,			__snake_case			:     Union[str, Any]		)			->		List[Any]:
								UpperCAmelCase :       Dict 							=		d

								# for backward compatibility
								if not hasattr(self		,			'''sp_model_kwargs'''		):
															UpperCAmelCase :       Optional[Any] 							=		{}

								UpperCAmelCase :       Any 							=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
								self.sp_model.Load(self.vocab_file		)







	def      A	(						self			:     Optional[int]		,			__snake_case			:     str		,			__snake_case			:     Optional[str] = None		)			->		Tuple[str]:
								if not os.path.isdir(__snake_case		):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
															return
								UpperCAmelCase :       Union[str, Any] 							=		os.path.join(
								    __snake_case		,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

								if os.path.abspath(self.vocab_file		) != os.path.abspath(__snake_case		) and os.path.isfile(self.vocab_file		):
															copyfile(self.vocab_file		,			__snake_case		)
								elif not os.path.isfile(self.vocab_file		):
															with open(__snake_case		,			'''wb'''		) as fi:
																						UpperCAmelCase :       Any 							=		self.sp_model.serialized_model_proto()
																						fi.write(__snake_case		)

								return (out_vocab_file,)





