"""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


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)


if is_vision_available():
			import PIL






class 				snake_case__     (   snake_case_   ):
	_snake_case   :			Optional[Any]           =		["""pixel_values"""]


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

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


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = PILImageResampling.BICUBIC  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
								__a     				=  get_size_dict(lowerCamelCase  ,						default_to_square=lowerCamelCase		)
								if "shortest_edge" not in size:
															raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"		)
								__a     				=  get_resize_output_image_size(lowerCamelCase  ,						size=size["shortest_edge"]  ,						default_to_square=lowerCamelCase		)
								return resize(lowerCamelCase  ,						size=lowerCamelCase  ,						resample=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
								__a     				=  get_size_dict(lowerCamelCase		)
								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(lowerCamelCase  ,						size=(size["height"], size["width"])  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
								return rescale(lowerCamelCase  ,						scale=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)


	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						**lowerCamelCase  ,						):
								return normalize(lowerCamelCase  ,						mean=lowerCamelCase  ,						std=lowerCamelCase  ,						data_format=lowerCamelCase  ,						**lowerCamelCase		)






	def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = None  ,						lowerCamelCase = ChannelDimension.FIRST  ,						**lowerCamelCase  ,						):
								__a     				=  do_resize if do_resize is not None else self.do_resize
								__a     				=  size if size is not None else self.size
								__a     				=  get_size_dict(lowerCamelCase  ,						param_name="size"  ,						default_to_square=lowerCamelCase		)
								__a     				=  resample if resample is not None else self.resample
								__a     				=  do_center_crop if do_center_crop is not None else self.do_center_crop
								__a     				=  crop_size if crop_size is not None else self.crop_size
								__a     				=  get_size_dict(lowerCamelCase  ,						param_name="crop_size"  ,						default_to_square=lowerCamelCase		)
								__a     				=  do_rescale if do_rescale is not None else self.do_rescale
								__a     				=  rescale_factor if rescale_factor is not None else self.rescale_factor
								__a     				=  do_normalize if do_normalize is not None else self.do_normalize
								__a     				=  image_mean if image_mean is not None else self.image_mean
								__a     				=  image_std if image_std is not None else self.image_std
								__a     				=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__a     				=  make_list_of_images(lowerCamelCase		)

								if not valid_images(lowerCamelCase		):
															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     				=  [convert_to_rgb(lowerCamelCase		) for image in images]

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

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

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

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

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

								__a     				=  [to_channel_dimension_format(lowerCamelCase  ,						lowerCamelCase		) for image in images]

								__a     				=  {"pixel_values": images}
								return BatchFeature(data=lowerCamelCase  ,						tensor_type=lowerCamelCase		)

"""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:
   SCREAMING_SNAKE_CASE__:List[Any]			=	None


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Tuple			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """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""",
    },
}

SCREAMING_SNAKE_CASE__:List[str]			=	{
    """camembert-base""": 512,
}

SCREAMING_SNAKE_CASE__:str			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[Any]           =		VOCAB_FILES_NAMES
 _snake_case   :			List[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[int]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Any           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		CamembertTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						lowerCamelCase=["<s>NOTUSED", "</s>NOTUSED"]  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

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

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        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 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __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]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        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(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

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

        return (out_vocab_file,)
