





'''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


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


if is_vision_available():
  import PIL


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			['''pixel_values''']


     def __init__(		self		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = PILImageResampling.BICUBIC		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = True		,      _UpperCAmelCase = 1 / 255		,      _UpperCAmelCase = True		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = True		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__(**_UpperCAmelCase)
          __A		:					int						=			size if size is not None else {'shortest_edge': 224}
          __A		:					Any						=			get_size_dict(_UpperCAmelCase		,      default_to_square=_UpperCAmelCase)
          __A		:					Union[str, Any]						=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
          __A		:					List[str]						=			get_size_dict(_UpperCAmelCase		,      default_to_square=_UpperCAmelCase		,      param_name='crop_size')

          __A		:					Optional[int]						=			do_resize
          __A		:					str						=			size
          __A		:					List[Any]						=			resample
          __A		:					Dict						=			do_center_crop
          __A		:					Any						=			crop_size
          __A		:					Tuple						=			do_rescale
          __A		:					List[str]						=			rescale_factor
          __A		:					List[Any]						=			do_normalize
          __A		:					Optional[Any]						=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          __A		:					Optional[int]						=			image_std if image_std is not None else OPENAI_CLIP_STD
          __A		:					Optional[Any]						=			do_convert_rgb


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = PILImageResampling.BICUBIC		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					List[str]						=			get_size_dict(_UpperCAmelCase		,      default_to_square=_UpperCAmelCase)
          if "shortest_edge" not in size:
               raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
          __A		:					List[str]						=			get_resize_output_image_size(_UpperCAmelCase		,      size=size['shortest_edge']		,      default_to_square=_UpperCAmelCase)
          return resize(_UpperCAmelCase		,      size=_UpperCAmelCase		,      resample=_UpperCAmelCase		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					List[str]						=			get_size_dict(_UpperCAmelCase)
          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(_UpperCAmelCase		,      size=(size['height'], size['width'])		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          return rescale(_UpperCAmelCase		,      scale=_UpperCAmelCase		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          return normalize(_UpperCAmelCase		,      mean=_UpperCAmelCase		,      std=_UpperCAmelCase		,      data_format=_UpperCAmelCase		,      **_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = None		,      _UpperCAmelCase = ChannelDimension.FIRST		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					Tuple						=			do_resize if do_resize is not None else self.do_resize
          __A		:					int						=			size if size is not None else self.size
          __A		:					Any						=			get_size_dict(_UpperCAmelCase		,      param_name='size'		,      default_to_square=_UpperCAmelCase)
          __A		:					Dict						=			resample if resample is not None else self.resample
          __A		:					str						=			do_center_crop if do_center_crop is not None else self.do_center_crop
          __A		:					str						=			crop_size if crop_size is not None else self.crop_size
          __A		:					str						=			get_size_dict(_UpperCAmelCase		,      param_name='crop_size'		,      default_to_square=_UpperCAmelCase)
          __A		:					Optional[Any]						=			do_rescale if do_rescale is not None else self.do_rescale
          __A		:					int						=			rescale_factor if rescale_factor is not None else self.rescale_factor
          __A		:					Union[str, Any]						=			do_normalize if do_normalize is not None else self.do_normalize
          __A		:					Union[str, Any]						=			image_mean if image_mean is not None else self.image_mean
          __A		:					Any						=			image_std if image_std is not None else self.image_std
          __A		:					Any						=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          __A		:					int						=			make_list_of_images(_UpperCAmelCase)

          if not valid_images(_UpperCAmelCase):
               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:
               __A		:					Any						=			[convert_to_rgb(_UpperCAmelCase) for image in images]

          # All transformations expect numpy arrays.
          __A		:					Union[str, Any]						=			[to_numpy_array(_UpperCAmelCase) for image in images]

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

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

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

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

          __A		:					Optional[Any]						=			[to_channel_dimension_format(_UpperCAmelCase		,      _UpperCAmelCase) for image in images]

          __A		:					int						=			{'pixel_values': images}
          return BatchFeature(data=_UpperCAmelCase		,      tensor_type=_UpperCAmelCase)






'''simple docstring'''




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_camembert import CamembertTokenizer
else:
  lowercase__       : Optional[int]         =       None


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

lowercase__       : Optional[Any]         =       {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase__       : List[str]         =       {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowercase__       : Dict         =       {
    '''camembert-base''': 5_12,
}

lowercase__       : str         =       '''▁'''


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			VOCAB_FILES_NAMES
     lowerCAmelCase						     =			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase						     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase						     =			['''input_ids''', '''attention_mask''']
     lowerCAmelCase						     =			CamembertTokenizer


     def __init__(		self		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="</s>"		,      _UpperCAmelCase="<s>"		,      _UpperCAmelCase="<unk>"		,      _UpperCAmelCase="<pad>"		,      _UpperCAmelCase="<mask>"		,      _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					int						=			AddedToken(_UpperCAmelCase		,      lstrip=_UpperCAmelCase		,      rstrip=_UpperCAmelCase) if isinstance(_UpperCAmelCase		,      _UpperCAmelCase) else mask_token

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

          __A		:					List[str]						=			vocab_file
          __A		:					Optional[int]						=			False if not self.vocab_file else True


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __A		:					Optional[Any]						=			[self.cls_token_id]
          __A		:					Optional[int]						=			[self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          __A		:					Optional[int]						=			[self.sep_token_id]
          __A		:					List[str]						=			[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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase = None):

          '''simple docstring'''






          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(_UpperCAmelCase):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory')
               return
          __A		:					List[Any]						=			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):
               copyfile(self.vocab_file		,      _UpperCAmelCase)

          return (out_vocab_file,)