

"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		List[Any]							=							['pixel_values']

			def __init__(					self				:						List[Any]    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						Optional[Dict[str, int]] = None    ,				__UpperCAmelCase				:						PILImageResampling = PILImageResampling.BICUBIC    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						Union[int, float] = 1 / 2_5_5    ,				__UpperCAmelCase				:						Dict[str, int] = None    ,				__UpperCAmelCase				:						bool = True    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				**__UpperCAmelCase				:						str    ,				)   ->    None:
						super().__init__(**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase    ,				param_name="""crop_size"""       )

						SCREAMING_SNAKE_CASE__								=      do_resize
						SCREAMING_SNAKE_CASE__								=      do_rescale
						SCREAMING_SNAKE_CASE__								=      do_normalize
						SCREAMING_SNAKE_CASE__								=      do_center_crop
						SCREAMING_SNAKE_CASE__								=      crop_size
						SCREAMING_SNAKE_CASE__								=      size
						SCREAMING_SNAKE_CASE__								=      resample
						SCREAMING_SNAKE_CASE__								=      rescale_factor
						SCREAMING_SNAKE_CASE__								=      image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						SCREAMING_SNAKE_CASE__								=      image_std if image_std is not None else IMAGENET_DEFAULT_STD

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						np.ndarray    ,				__UpperCAmelCase				:						Dict[str, int]    ,				__UpperCAmelCase				:						PILImageResampling = PILImageResampling.BILINEAR    ,				__UpperCAmelCase				:						Optional[Union[str, ChannelDimension]] = None    ,				**__UpperCAmelCase				:						List[Any]    ,				)   ->    np.ndarray:
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase       )

						if "shortest_edge" in size:
									SCREAMING_SNAKE_CASE__								=      get_resize_output_image_size(__UpperCAmelCase    ,				size=size["""shortest_edge"""]    ,				default_to_square=__UpperCAmelCase       )
									# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
						elif "height" in size and "width" in size:
									SCREAMING_SNAKE_CASE__								=      (size["""height"""], size["""width"""])
						else:
									raise ValueError(F"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""       )
						return resize(__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				resample=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						np.ndarray    ,				__UpperCAmelCase				:						Dict[str, int]    ,				__UpperCAmelCase				:						Optional[Union[str, ChannelDimension]] = None    ,				**__UpperCAmelCase				:						Optional[Any]    ,				)   ->    np.ndarray:
						SCREAMING_SNAKE_CASE__								=      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				:						Union[str, Any]    ,				__UpperCAmelCase				:						np.ndarray    ,				__UpperCAmelCase				:						float    ,				__UpperCAmelCase				:						Optional[Union[str, ChannelDimension]] = None    ,				**__UpperCAmelCase				:						Union[str, Any]       )   ->    np.ndarray:
						return rescale(__UpperCAmelCase    ,				scale=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase       )

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						ImageInput    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				__UpperCAmelCase				:						Dict[str, int] = None    ,				__UpperCAmelCase				:						PILImageResampling = None    ,				__UpperCAmelCase				:						bool = None    ,				__UpperCAmelCase				:						int = None    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				__UpperCAmelCase				:						Optional[float] = None    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				__UpperCAmelCase				:						Optional[Union[float, List[float]]] = None    ,				__UpperCAmelCase				:						Optional[Union[str, TensorType]] = None    ,				__UpperCAmelCase				:						Union[str, ChannelDimension] = ChannelDimension.FIRST    ,				**__UpperCAmelCase				:						Any    ,				)   ->    BatchFeature:
						SCREAMING_SNAKE_CASE__								=      do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__								=      do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__								=      do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE__								=      do_center_crop if do_center_crop is not None else self.do_center_crop
						SCREAMING_SNAKE_CASE__								=      crop_size if crop_size is not None else self.crop_size
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase    ,				param_name="""crop_size"""    ,				default_to_square=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      resample if resample is not None else self.resample
						SCREAMING_SNAKE_CASE__								=      rescale_factor if rescale_factor is not None else self.rescale_factor
						SCREAMING_SNAKE_CASE__								=      image_mean if image_mean is not None else self.image_mean
						SCREAMING_SNAKE_CASE__								=      image_std if image_std is not None else self.image_std

						SCREAMING_SNAKE_CASE__								=      size if size is not None else self.size
						SCREAMING_SNAKE_CASE__								=      get_size_dict(__UpperCAmelCase       )

						if not is_batched(__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      [images]

						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."""       )

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

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

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

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

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

						SCREAMING_SNAKE_CASE__								=      [to_channel_dimension_format(__UpperCAmelCase    ,				__UpperCAmelCase       ) for image in images]

						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": images}
						return BatchFeature(data=__UpperCAmelCase    ,				tensor_type=__UpperCAmelCase       )





"""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


A_							:			Optional[int]											= logging.get_logger(__name__)

A_							:			Union[str, Any]											= {"vocab_file": "sentencepiece.bpe.model"}

A_							:			str											= {
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    }
}

A_							:			Tuple											= {
    "camembert-base": 512,
}

A_							:			int											= "▁"





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		int							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		List[Any]							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		List[str]							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		Optional[int]							=							['input_ids', 'attention_mask']

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

						SCREAMING_SNAKE_CASE__								=      {} 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    ,				additional_special_tokens=__UpperCAmelCase    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**__UpperCAmelCase    ,				)
						SCREAMING_SNAKE_CASE__								=      spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(str(__UpperCAmelCase       )       )
						SCREAMING_SNAKE_CASE__								=      vocab_file
						# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
						# sentencepiece vocabulary (this is the case for <s> and </s>
						SCREAMING_SNAKE_CASE__								=      {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
						SCREAMING_SNAKE_CASE__								=      len(self.fairseq_tokens_to_ids       )
						SCREAMING_SNAKE_CASE__								=      len(self.sp_model       ) + len(self.fairseq_tokens_to_ids       )
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None    ,				__UpperCAmelCase				:						bool = False       )   ->    List[int]:
						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				:						Tuple    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None       )   ->    List[int]:
						SCREAMING_SNAKE_CASE__								=      [self.sep_token_id]
						SCREAMING_SNAKE_CASE__								=      [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				:						Any       )   ->    Any:
						return len(self.fairseq_tokens_to_ids       ) + len(self.sp_model       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      {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				:						Optional[int]    ,				__UpperCAmelCase				:						str       )   ->    List[str]:
						return self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						Tuple       )   ->    Optional[Any]:
						if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
						elif self.sp_model.PieceToId(__UpperCAmelCase       ) == 0:
									# Convert sentence piece unk token to fairseq unk token index
									return self.unk_token_id
						return self.fairseq_offset + self.sp_model.PieceToId(__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						int       )   ->    Dict:
						if index in self.fairseq_ids_to_tokens:
									return self.fairseq_ids_to_tokens[index]
						return self.sp_model.IdToPiece(index - self.fairseq_offset       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      """"""
						SCREAMING_SNAKE_CASE__								=      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
												SCREAMING_SNAKE_CASE__								=      True
												SCREAMING_SNAKE_CASE__								=      []
									else:
												current_sub_tokens.append(__UpperCAmelCase       )
												SCREAMING_SNAKE_CASE__								=      False
						out_string += self.sp_model.decode(__UpperCAmelCase       )
						return out_string.strip()

			def __getstate__(					self				:						Optional[Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__								=      self.__dict__.copy()
						SCREAMING_SNAKE_CASE__								=      None
						return state

			def __setstate__(					self				:						int    ,				__UpperCAmelCase				:						Optional[int]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      d

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

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
						if not os.path.isdir(__UpperCAmelCase       ):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
									return
						SCREAMING_SNAKE_CASE__								=      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:
												SCREAMING_SNAKE_CASE__								=      self.sp_model.serialized_model_proto()
												fi.write(__UpperCAmelCase       )

						return (out_vocab_file,)


