'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

_A     : Any    =							logging.get_logger(__name__)







def 	UpperCamelCase_		(   snake_case_			:			List[str] )   ->	List[List[ImageInput]]:






 '''simple docstring'''

 if isinstance(_UpperCamelCase		,						(list, tuple) ) and isinstance(videos[0]		,						(list, tuple) ) and is_valid_image(videos[0][0] ):
  return videos

 elif isinstance(_UpperCamelCase		,						(list, tuple) ) and is_valid_image(videos[0] ):
  return [videos]

 elif is_valid_image(_UpperCamelCase ):
  return [[videos]]

 raise ValueError(f"""Could not make batched video from {videos}""" )







class 							_lowercase	(							UpperCAmelCase__     ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE	:			Optional[int]       =   ["""pixel_values"""]
 def __init__( self  :      List[str] ,			SCREAMING_SNAKE_CASE__  :      bool = True ,			SCREAMING_SNAKE_CASE__  :      Dict[str, int] = None ,			SCREAMING_SNAKE_CASE__  :      PILImageResampling = PILImageResampling.BILINEAR ,			SCREAMING_SNAKE_CASE__  :      bool = True ,			SCREAMING_SNAKE_CASE__  :      Dict[str, int] = None ,			SCREAMING_SNAKE_CASE__  :      bool = True ,			SCREAMING_SNAKE_CASE__  :      Union[int, float] = 1 / 2_55 ,			SCREAMING_SNAKE_CASE__  :      bool = True ,			SCREAMING_SNAKE_CASE__  :      bool = True ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[float, List[float]]] = None ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[float, List[float]]] = None ,			**SCREAMING_SNAKE_CASE__  :      Optional[int] ,			)    ->       List[Any]:
  super().__init__(**snake_case__				)
  __lowerCAmelCase											=		size if size is not None else {"""shortest_edge""": 2_56}
  __lowerCAmelCase											=		get_size_dict(snake_case__ ,			default_to_square=snake_case__				)
  __lowerCAmelCase											=		crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
  __lowerCAmelCase											=		get_size_dict(snake_case__ ,			param_name="""crop_size"""				)

  __lowerCAmelCase											=		do_resize
  __lowerCAmelCase											=		size
  __lowerCAmelCase											=		do_center_crop
  __lowerCAmelCase											=		crop_size
  __lowerCAmelCase											=		resample
  __lowerCAmelCase											=		do_rescale
  __lowerCAmelCase											=		rescale_factor
  __lowerCAmelCase											=		offset
  __lowerCAmelCase											=		do_normalize
  __lowerCAmelCase											=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  __lowerCAmelCase											=		image_std if image_std is not None else IMAGENET_STANDARD_STD
 def a   ( self  :      Any ,			SCREAMING_SNAKE_CASE__  :      np.ndarray ,			SCREAMING_SNAKE_CASE__  :      Dict[str, int] ,			SCREAMING_SNAKE_CASE__  :      PILImageResampling = PILImageResampling.BILINEAR ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[str, ChannelDimension]] = None ,			**SCREAMING_SNAKE_CASE__  :      Union[str, Any] ,			)    ->       List[str]:
  __lowerCAmelCase											=		get_size_dict(snake_case__ ,			default_to_square=snake_case__				)
  if "shortest_edge" in size:
   __lowerCAmelCase											=		get_resize_output_image_size(snake_case__ ,			size["""shortest_edge"""] ,			default_to_square=snake_case__				)
  elif "height" in size and "width" in size:
   __lowerCAmelCase											=		(size["""height"""], size["""width"""])
  else:
   raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
  return resize(snake_case__ ,			size=snake_case__ ,			resample=snake_case__ ,			data_format=snake_case__ ,			**snake_case__				)
 def a   ( self  :      Tuple ,			SCREAMING_SNAKE_CASE__  :      np.ndarray ,			SCREAMING_SNAKE_CASE__  :      Dict[str, int] ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[str, ChannelDimension]] = None ,			**SCREAMING_SNAKE_CASE__  :      Optional[Any] ,			)    ->       Dict:
  __lowerCAmelCase											=		get_size_dict(snake_case__				)
  if "height" not in size or "width" not in size:
   raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""				)
  return center_crop(snake_case__ ,			size=(size["""height"""], size["""width"""]) ,			data_format=snake_case__ ,			**snake_case__				)
 def a   ( self  :      int ,			SCREAMING_SNAKE_CASE__  :      np.ndarray ,			SCREAMING_SNAKE_CASE__  :      Union[int, float] ,			SCREAMING_SNAKE_CASE__  :      bool = True ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[str, ChannelDimension]] = None ,			**SCREAMING_SNAKE_CASE__  :      int ,			)    ->       str:
  __lowerCAmelCase											=		image.astype(np.floataa				)
  if offset:
   __lowerCAmelCase											=		image - (scale / 2)
  return rescale(snake_case__ ,			scale=snake_case__ ,			data_format=snake_case__ ,			**snake_case__				)
 def a   ( self  :      Optional[int] ,			SCREAMING_SNAKE_CASE__  :      np.ndarray ,			SCREAMING_SNAKE_CASE__  :      Union[float, List[float]] ,			SCREAMING_SNAKE_CASE__  :      Union[float, List[float]] ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[str, ChannelDimension]] = None ,			**SCREAMING_SNAKE_CASE__  :      str ,			)    ->       Optional[Any]:
  return normalize(snake_case__ ,			mean=snake_case__ ,			std=snake_case__ ,			data_format=snake_case__ ,			**snake_case__				)
 def a   ( self  :      Tuple ,			SCREAMING_SNAKE_CASE__  :      ImageInput ,			SCREAMING_SNAKE_CASE__  :      bool = None ,			SCREAMING_SNAKE_CASE__  :      Dict[str, int] = None ,			SCREAMING_SNAKE_CASE__  :      PILImageResampling = None ,			SCREAMING_SNAKE_CASE__  :      bool = None ,			SCREAMING_SNAKE_CASE__  :      Dict[str, int] = None ,			SCREAMING_SNAKE_CASE__  :      bool = None ,			SCREAMING_SNAKE_CASE__  :      float = None ,			SCREAMING_SNAKE_CASE__  :      bool = None ,			SCREAMING_SNAKE_CASE__  :      bool = None ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[float, List[float]]] = None ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[float, List[float]]] = None ,			SCREAMING_SNAKE_CASE__  :      Optional[ChannelDimension] = ChannelDimension.FIRST ,			)    ->       List[str]:






  if do_resize and size is None or resample is None:
   raise ValueError("""Size and resample 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."""				)

  if offset and not do_rescale:
   raise ValueError("""For offset, do_rescale must also be set to True."""				)

  # All transformations expect numpy arrays.
  __lowerCAmelCase											=		to_numpy_array(snake_case__				)

  if do_resize:
   __lowerCAmelCase											=		self.resize(image=snake_case__ ,			size=snake_case__ ,			resample=snake_case__				)

  if do_center_crop:
   __lowerCAmelCase											=		self.center_crop(snake_case__ ,			size=snake_case__				)

  if do_rescale:
   __lowerCAmelCase											=		self.rescale(image=snake_case__ ,			scale=snake_case__ ,			offset=snake_case__				)

  if do_normalize:
   __lowerCAmelCase											=		self.normalize(image=snake_case__ ,			mean=snake_case__ ,			std=snake_case__				)

  __lowerCAmelCase											=		to_channel_dimension_format(snake_case__ ,			snake_case__				)
  return image





 def a   ( self  :      int ,			SCREAMING_SNAKE_CASE__  :      ImageInput ,			SCREAMING_SNAKE_CASE__  :      bool = None ,			SCREAMING_SNAKE_CASE__  :      Dict[str, int] = None ,			SCREAMING_SNAKE_CASE__  :      PILImageResampling = None ,			SCREAMING_SNAKE_CASE__  :      bool = None ,			SCREAMING_SNAKE_CASE__  :      Dict[str, int] = None ,			SCREAMING_SNAKE_CASE__  :      bool = None ,			SCREAMING_SNAKE_CASE__  :      float = None ,			SCREAMING_SNAKE_CASE__  :      bool = None ,			SCREAMING_SNAKE_CASE__  :      bool = None ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[float, List[float]]] = None ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[float, List[float]]] = None ,			SCREAMING_SNAKE_CASE__  :      Optional[Union[str, TensorType]] = None ,			SCREAMING_SNAKE_CASE__  :      ChannelDimension = ChannelDimension.FIRST ,			**SCREAMING_SNAKE_CASE__  :      Dict ,			)    ->       Optional[int]:
  __lowerCAmelCase											=		do_resize if do_resize is not None else self.do_resize
  __lowerCAmelCase											=		resample if resample is not None else self.resample
  __lowerCAmelCase											=		do_center_crop if do_center_crop is not None else self.do_center_crop
  __lowerCAmelCase											=		do_rescale if do_rescale is not None else self.do_rescale
  __lowerCAmelCase											=		rescale_factor if rescale_factor is not None else self.rescale_factor
  __lowerCAmelCase											=		offset if offset is not None else self.offset
  __lowerCAmelCase											=		do_normalize if do_normalize is not None else self.do_normalize
  __lowerCAmelCase											=		image_mean if image_mean is not None else self.image_mean
  __lowerCAmelCase											=		image_std if image_std is not None else self.image_std

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

  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."""				)

  __lowerCAmelCase											=		make_batched(snake_case__				)

  __lowerCAmelCase											=		[
      [
          self._preprocess_image(
              image=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__ ,			offset=snake_case__ ,			do_normalize=snake_case__ ,			image_mean=snake_case__ ,			image_std=snake_case__ ,			data_format=snake_case__ ,			)
          for img in video
      ]
      for video in videos
  ]

  __lowerCAmelCase											=		{"""pixel_values""": videos}
  return BatchFeature(data=snake_case__ ,			tensor_type=snake_case__				)









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:
      a_     :    int     		=   None

a_     :    List[Any]     		=   logging.get_logger(__name__)
a_     :    List[Any]     		=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

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

a_     :    Union[str, Any]     		=   {
    "google/rembert": 256,
}

a_     :    List[Any]     		=   "▁"




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =RemBertTokenizer

    def __init__(      self			:   int				,     snake_case__			:   Any=None				,     snake_case__			:   Optional[Any]=None				,     snake_case__			:   Optional[int]=True				,     snake_case__			:   Any=True				,     snake_case__			:   Any=False				,     snake_case__			:   Dict="[CLS]"				,     snake_case__			:   List[str]="[SEP]"				,     snake_case__			:   List[str]="<unk>"				,     snake_case__			:   Union[str, Any]="[SEP]"				,     snake_case__			:   List[Any]="<pad>"				,     snake_case__			:   Tuple="[CLS]"				,     snake_case__			:   str="[MASK]"				,     **snake_case__			:   Union[str, Any]				,     ):

     """simple docstring"""






     SCREAMING_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__				,     )

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

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_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 UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None				,     snake_case__			:   bool = False     ):

     """simple docstring"""






     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 UpperCamelCase      (      self			:   int				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     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     ) * [0] + len(token_ids_a + sep     ) * [1]




    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   str				,     snake_case__			:   Optional[str] = None     ):

     """simple docstring"""






     if not os.path.isdir(snake_case__     ):
      logger.error('Vocabulary path ({}) should be a directory'.format(snake_case__     )     )
      return
     SCREAMING_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,)

