





'''simple docstring'''




from typing import Dict, Iterable, 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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   ["pixel_values"]



     def __init__(   self							:			Optional[Any]   ,       __snake_case							:			bool = True   ,       __snake_case							:			Dict[str, int] = None   ,       __snake_case							:			PILImageResampling = PILImageResampling.BICUBIC   ,       __snake_case							:			bool = True   ,       __snake_case							:			Dict[str, int] = None   ,       __snake_case							:			bool = True   ,       __snake_case							:			Union[int, float] = 1 / 2_55   ,       __snake_case							:			bool = True   ,       __snake_case							:			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN   ,       __snake_case							:			Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD   ,       **__snake_case							:			Tuple   ,       )->	None:
        super().__init__(**__snake_case						)
        snake_case 						=   size if size is not None else {"""shortest_edge""": 2_24}
        snake_case 						=   get_size_dict(__snake_case   ,       default_to_square=__snake_case						)
        snake_case 						=   crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
        snake_case 						=   get_size_dict(__snake_case   ,       param_name="""crop_size"""						)

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



     def      lowerCAmelCase			(   self							:			str   ,       __snake_case							:			np.ndarray   ,       __snake_case							:			Dict[str, int]   ,       __snake_case							:			PILImageResampling = PILImageResampling.BICUBIC   ,       __snake_case							:			Optional[Union[str, ChannelDimension]] = None   ,       **__snake_case							:			str   ,       )->	np.ndarray:
        snake_case 						=   get_size_dict(__snake_case   ,       default_to_square=__snake_case						)
        # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
        if "shortest_edge" in size:
           snake_case 						=   int((2_56 / 2_24) * size["""shortest_edge"""]						)
           snake_case 						=   get_resize_output_image_size(__snake_case   ,       size=__snake_case   ,       default_to_square=__snake_case						)
           snake_case 						=   {"""height""": output_size[0], """width""": output_size[1]}
        if "height" not in size_dict or "width" not in size_dict:
           raise ValueError(
               f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''						)
        return resize(
            __snake_case   ,       size=(size_dict["""height"""], size_dict["""width"""])   ,       resample=__snake_case   ,       data_format=__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Union[str, Any]   ,       __snake_case							:			np.ndarray   ,       __snake_case							:			Dict[str, int]   ,       __snake_case							:			Optional[Union[str, ChannelDimension]] = None   ,       **__snake_case							:			Any   ,       )->	np.ndarray:
        snake_case 						=   get_size_dict(__snake_case						)
        if "height" not in size or "width" not in size:
           raise ValueError(f'''Size dict 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      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			np.ndarray   ,       __snake_case							:			Union[int, float]   ,       __snake_case							:			Optional[Union[str, ChannelDimension]] = None   ,       **__snake_case							:			List[str]   ,       )->	np.ndarray:
        return rescale(__snake_case   ,       scale=__snake_case   ,       data_format=__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			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							:			Dict   ,       )->	np.ndarray:
        return normalize(__snake_case   ,       mean=__snake_case   ,       std=__snake_case   ,       data_format=__snake_case   ,       **__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			ImageInput   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			Optional[Dict[str, int]] = None   ,       __snake_case							:			PILImageResampling = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			Optional[Dict[str, int]] = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			Optional[float] = None   ,       __snake_case							:			Optional[bool] = None   ,       __snake_case							:			Optional[Union[float, Iterable[float]]] = None   ,       __snake_case							:			Optional[Union[float, Iterable[float]]] = None   ,       __snake_case							:			Optional[TensorType] = None   ,       __snake_case							:			ChannelDimension = ChannelDimension.FIRST   ,       **__snake_case							:			int   ,       )->	BatchFeature:
        snake_case 						=   do_resize if do_resize is not None else self.do_resize
        snake_case 						=   resample if resample is not None else self.resample
        snake_case 						=   do_center_crop if do_center_crop is not None else self.do_center_crop
        snake_case 						=   do_rescale if do_rescale is not None else self.do_rescale
        snake_case 						=   rescale_factor if rescale_factor is not None else self.rescale_factor
        snake_case 						=   do_normalize if do_normalize is not None else self.do_normalize
        snake_case 						=   image_mean if image_mean is not None else self.image_mean
        snake_case 						=   image_std if image_std is not None else self.image_std

        snake_case 						=   size if size is not None else self.size
        snake_case 						=   get_size_dict(__snake_case   ,       default_to_square=__snake_case						)
        snake_case 						=   crop_size if crop_size is not None else self.crop_size
        snake_case 						=   get_size_dict(__snake_case   ,       param_name="""crop_size"""						)

        snake_case 						=   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:
           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."""						)

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

        if do_resize:
           snake_case 						=   [self.resize(__snake_case   ,       __snake_case   ,       __snake_case						) for image in images]

        if do_center_crop:
           snake_case 						=   [self.center_crop(__snake_case   ,       __snake_case						) for image in images]

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

        if do_normalize:
           snake_case 						=   [self.normalize(__snake_case   ,       __snake_case   ,       __snake_case						) for image in images]

        snake_case 						=   [to_channel_dimension_format(__snake_case   ,       __snake_case						) for image in images]

        snake_case 						=   {"""pixel_values""": images}
        return BatchFeature(data=__snake_case   ,       tensor_type=__snake_case						)








'''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_rembert import RemBertTokenizer
else:
 _SCREAMING_SNAKE_CASE							     =			None

_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE							     =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

_SCREAMING_SNAKE_CASE							     =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

_SCREAMING_SNAKE_CASE							     =			{
    "google/rembert": 256,
}

_SCREAMING_SNAKE_CASE							     =			"▁"
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   VOCAB_FILES_NAMES
     snake_case_						      =   PRETRAINED_VOCAB_FILES_MAP
     snake_case_						      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     snake_case_						      =   RemBertTokenizer



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

        super().__init__(
            __snake_case   ,       tokenizer_file=__snake_case   ,       do_lower_case=__snake_case   ,       remove_space=__snake_case   ,       keep_accents=__snake_case   ,       bos_token=__snake_case   ,       eos_token=__snake_case   ,       unk_token=__snake_case   ,       sep_token=__snake_case   ,       pad_token=__snake_case   ,       cls_token=__snake_case   ,       mask_token=__snake_case   ,       **__snake_case   ,       )

        snake_case 						=   do_lower_case
        snake_case 						=   remove_space
        snake_case 						=   keep_accents
        snake_case 						=   vocab_file
        snake_case 						=   False if not self.vocab_file else True



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.sep_token_id]
        snake_case 						=   [self.cls_token_id]
        if token_ids_a is None:
           return cls + token_ids_a + sep
        return cls + token_ids_a + sep + token_ids_a + sep



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None   ,       __snake_case							:			bool = False						)->	List[int]:

        if already_has_special_tokens:
           if token_ids_a is not None:
              raise ValueError(
                  """You should not supply a second sequence if the provided sequence of """
                  """ids is already formatted with special tokens for the model."""						)
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

        if token_ids_a is not None:
           return [1] + ([0] * len(__snake_case						)) + [1] + ([0] * len(__snake_case						)) + [1]
        return [1] + ([0] * len(__snake_case						)) + [1]



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			List[int]   ,       __snake_case							:			Optional[List[int]] = None						)->	List[int]:
        snake_case 						=   [self.sep_token_id]
        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						) * [0] + len(token_ids_a + sep						) * [1]



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			str   ,       __snake_case							:			Optional[str] = None						)->	Tuple[str]:
        if not os.path.isdir(__snake_case						):
           logger.error("""Vocabulary path ({}) should be a directory""".format(__snake_case						)						)
           return
        snake_case 						=   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						):
           copyfile(self.vocab_file   ,       __snake_case						)

        return (out_vocab_file,)

