




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


_lowerCamelCase			:	List[str]							 =     logging.get_logger(__name__)

_lowerCamelCase			:	str							 =     {"""vocab_file""": """sentencepiece.bpe.model"""}

_lowerCamelCase			:	Dict							 =     {
    """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"""
        ),
    },
}

_lowerCamelCase			:	Optional[int]							 =     {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

_lowerCamelCase			:	List[Any]							 =     """▁"""


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   VOCAB_FILES_NAMES
    UpperCAmelCase__													=   PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__													=   ['''input_ids''', '''attention_mask''']


    def __init__(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Tuple="<s>"      ,				UpperCAmelCase__   :			Any="</s>"      ,				UpperCAmelCase__   :			str="</s>"      ,				UpperCAmelCase__   :			int="<s>"      ,				UpperCAmelCase__   :			Dict="<unk>"      ,				UpperCAmelCase__   :			Dict="<pad>"      ,				UpperCAmelCase__   :			Dict="<mask>"      ,				UpperCAmelCase__   :			Optional[Dict[str, Any]] = None      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->None:





           '''simple docstring'''

           A__   				=					AddedToken(UpperCAmelCase__      ,				lstrip=UpperCAmelCase__      ,				rstrip=UpperCAmelCase__) if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__) else mask_token

           A__   				=					{} if sp_model_kwargs is None else sp_model_kwargs

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

           A__   				=					vocab_file
           A__   				=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(str(UpperCAmelCase__))

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

           A__   				=					len(self.sp_model) - 1
           A__   				=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

           if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           A__   				=					[self.cls_token_id]
           A__   				=					[self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None      ,				UpperCAmelCase__   :			bool = False)		->List[int]:





           '''simple docstring'''

           if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=UpperCAmelCase__      ,				token_ids_a=UpperCAmelCase__      ,				already_has_special_tokens=UpperCAmelCase__)

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			List[int]      ,				UpperCAmelCase__   :			Optional[List[int]] = None)		->List[int]:





           '''simple docstring'''

           A__   				=					[self.sep_token_id]
           A__   				=					[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  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Tuple:





           '''simple docstring'''

           return len(self.sp_model)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->str:





           '''simple docstring'''

           A__   				=					{self.convert_ids_to_tokens(UpperCAmelCase__): i for i in range(self.vocab_size)}
           vocab.update(self.added_tokens_encoder)
           return vocab


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			str)		->List[str]:





           '''simple docstring'''

           return self.sp_model.encode(UpperCAmelCase__      ,				out_type=UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any      ,				UpperCAmelCase__   :			Optional[int])		->List[Any]:





           '''simple docstring'''

           if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
           A__   				=					self.sp_model.PieceToId(UpperCAmelCase__)

           return spm_id if spm_id else self.unk_token_id


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			List[Any])		->List[Any]:





           '''simple docstring'''

           if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			int)		->List[Any]:





           '''simple docstring'''

           A__   				=					[]
           A__   				=					''''''
           A__   				=					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(UpperCAmelCase__) + token
                         A__   				=					True
                         A__   				=					[]
                  else:
                         current_sub_tokens.append(UpperCAmelCase__)
                         A__   				=					False
           out_string += self.sp_model.decode(UpperCAmelCase__)
           return out_string.strip()


    def __getstate__(    self   :			Optional[Any])		->Tuple:





           '''simple docstring'''

           A__   				=					self.__dict__.copy()
           A__   				=					None
           return state


    def __setstate__(    self   :			str      ,				UpperCAmelCase__   :			Union[str, Any])		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					d

           # for backward compatibility
           if not hasattr(self      ,				'''sp_model_kwargs'''):
                  A__   				=					{}

           A__   				=					spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(self.vocab_file)





    def  SCREAMING_SNAKE_CASE							(    self   :			List[str]      ,				UpperCAmelCase__   :			str      ,				UpperCAmelCase__   :			Optional[str] = None)		->Tuple[str]:





           '''simple docstring'''

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

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

           return (out_vocab_file,)








from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_lowerCamelCase			:	str							 =     logging.get_logger(__name__)


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   ['''pixel_values''']


    def __init__(    self   :			List[Any]      ,				UpperCAmelCase__   :			bool = True      ,				UpperCAmelCase__   :			int = 32      ,				UpperCAmelCase__   :			Dict=PILImageResampling.BILINEAR      ,				UpperCAmelCase__   :			bool = True      ,				**UpperCAmelCase__   :			Optional[int]      ,				)		->None:





           '''simple docstring'''

           A__   				=					do_resize
           A__   				=					do_rescale
           A__   				=					size_divisor
           A__   				=					resample
           super().__init__(**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			Dict)		->np.ndarray:





           '''simple docstring'''

           A__				,							A__   				=					get_image_size(UpperCAmelCase__)
           # Rounds the height and width down to the closest multiple of size_divisor
           A__   				=					height // size_divisor * size_divisor
           A__   				=					width // size_divisor * size_divisor
           A__   				=					resize(UpperCAmelCase__      ,				(new_h, new_w)      ,				resample=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)
           return image


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			np.ndarray      ,				UpperCAmelCase__   :			float      ,				UpperCAmelCase__   :			Optional[ChannelDimension] = None      ,				**UpperCAmelCase__   :			List[str])		->np.ndarray:





           '''simple docstring'''

           return rescale(image=UpperCAmelCase__      ,				scale=UpperCAmelCase__      ,				data_format=UpperCAmelCase__      ,				**UpperCAmelCase__)





    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				UpperCAmelCase__   :			Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[int] = None      ,				UpperCAmelCase__   :			Dict=None      ,				UpperCAmelCase__   :			Optional[bool] = None      ,				UpperCAmelCase__   :			Optional[Union[TensorType, str]] = None      ,				UpperCAmelCase__   :			ChannelDimension = ChannelDimension.FIRST      ,				**UpperCAmelCase__   :			List[str]      ,				)		->BatchFeature:





           '''simple docstring'''

           A__   				=					do_resize if do_resize is not None else self.do_resize
           A__   				=					do_rescale if do_rescale is not None else self.do_rescale
           A__   				=					size_divisor if size_divisor is not None else self.size_divisor
           A__   				=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                  raise ValueError('''size_divisor is required for resizing''')

           A__   				=					make_list_of_images(UpperCAmelCase__)

           if not valid_images(UpperCAmelCase__):
                  raise ValueError('''Invalid image(s)''')

           # All transformations expect numpy arrays.
           A__   				=					[to_numpy_array(UpperCAmelCase__) for img in images]

           if do_resize:
                  A__   				=					[self.resize(UpperCAmelCase__      ,				size_divisor=UpperCAmelCase__      ,				resample=UpperCAmelCase__) for image in images]

           if do_rescale:
                  A__   				=					[self.rescale(UpperCAmelCase__      ,				scale=1 / 255) for image in images]

           A__   				=					[to_channel_dimension_format(UpperCAmelCase__      ,				UpperCAmelCase__) for image in images]

           A__   				=					{'''pixel_values''': images}
           return BatchFeature(data=UpperCAmelCase__      ,				tensor_type=UpperCAmelCase__)


