

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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


_lowerCamelCase      :					List[Any]					    =      logging.get_logger(__name__)





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    ["""pixel_values"""]

       def __init__(							self ,   lowercase__ = True ,   lowercase__ = None ,   lowercase__ = 0.9 ,   lowercase__ = PILImageResampling.BICUBIC ,   lowercase__ = True ,   lowercase__ = None ,   lowercase__ = 1 / 2_5_5 ,   lowercase__ = True ,   lowercase__ = True ,   lowercase__ = None ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             super().__init__(**lowercase__     )
             __A    =size if size is not None else {'''shortest_edge''': 2_2_4}
             __A    =get_size_dict(lowercase__ ,   default_to_square=lowercase__     )
             __A    =crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
             __A    =get_size_dict(lowercase__ ,   param_name='''crop_size'''     )

             __A    =do_resize
             __A    =size
             __A    =crop_pct
             __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 IMAGENET_DEFAULT_MEAN
             __A    =image_std if image_std is not None else IMAGENET_DEFAULT_STD

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = PILImageResampling.BICUBIC ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =get_size_dict(lowercase__ ,   default_to_square=lowercase__     )
             if "shortest_edge" not in size and ("height" not in size or "width" not in size):
                   raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''     )
             if crop_pct is not None:
                   if "shortest_edge" in size:
                         __A    =int(size['''shortest_edge'''] / crop_pct     )
                   elif "height" in size and "width" in size:
                         if size["height"] == size["width"]:
                               __A    =int(size['''height'''] / crop_pct     )
                         else:
                               __A    =(int(size['''height'''] / crop_pct     ), int(size['''width'''] / crop_pct     ))
                   else:
                         raise ValueError('''Invalid size for resize: {}'''.format(lowercase__     )     )

                   __A    =get_resize_output_image_size(lowercase__ ,   size=lowercase__ ,   default_to_square=lowercase__     )
             else:
                   if "shortest_edge" in size:
                         __A    =get_resize_output_image_size(lowercase__ ,   size=size['''shortest_edge'''] ,   default_to_square=lowercase__     )
                   elif "height" in size and "width" in size:
                         __A    =(size['''height'''], size['''width'''])
                   else:
                         raise ValueError('''Invalid size for resize: {}'''.format(lowercase__     )     )

             return resize(lowercase__ ,   size=lowercase__ ,   resample=lowercase__ ,   data_format=lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =get_size_dict(lowercase__     )
             if "height" not in size or "width" not in size:
                   raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''     )
             return center_crop(lowercase__ ,   size=(size['''height'''], size['''width''']) ,   data_format=lowercase__ ,   **lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__ ,   lowercase__ = None ,   **lowercase__ ,   ):







             '''simple docstring'''



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


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = None ,   lowercase__ = ChannelDimension.FIRST ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =do_resize if do_resize is not None else self.do_resize
             __A    =crop_pct if crop_pct is not None else self.crop_pct
             __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    =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    =size if size is not None else self.size
             __A    =get_size_dict(lowercase__ ,   default_to_square=lowercase__     )
             __A    =crop_size if crop_size is not None else self.crop_size
             __A    =get_size_dict(lowercase__ ,   param_name='''crop_size'''     )

             __A    =make_list_of_images(lowercase__     )

             if not valid_images(lowercase__     ):
                   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 or resample is None:
                   raise ValueError('''Size and resample must be specified if do_resize is True.'''     )

             if do_center_crop and crop_pct is None:
                   raise ValueError('''Crop_pct 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.
             __A    =[to_numpy_array(lowercase__     ) for image in images]

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

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

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

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

             __A    =[to_channel_dimension_format(lowercase__ ,   lowercase__     ) for image in images]

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





from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase      :					Any					    =      logging.get_logger(__name__)


_lowerCamelCase      :					Tuple					    =      {
    '''nielsr/canine-s''': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_lowerCamelCase      :					List[str]					    =      111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_lowerCamelCase      :					List[str]					    =      0

_lowerCamelCase      :					Any					    =      0xe_0_0_0
_lowerCamelCase      :					Union[str, Any]					    =      0xe_0_0_1
_lowerCamelCase      :					Any					    =      0xe_0_0_2
_lowerCamelCase      :					List[str]					    =      0xe_0_0_3
_lowerCamelCase      :					Any					    =      0xe_0_0_4

# Maps special codepoints to human-readable names.
_lowerCamelCase      :					Dict[int, str]					    =      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_lowerCamelCase      :					Dict[str, int]					    =      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

       def __init__(							self ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=False ,   lowercase__=2_0_4_8 ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else bos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else eos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else sep_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else cls_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else mask_token

             super().__init__(
                 bos_token=lowercase__ ,   eos_token=lowercase__ ,   sep_token=lowercase__ ,   cls_token=lowercase__ ,   pad_token=lowercase__ ,   mask_token=lowercase__ ,   add_prefix_space=lowercase__ ,   model_max_length=lowercase__ ,   **lowercase__ ,   )

             # Creates a mapping for looking up the IDs of special symbols.
             __A    ={}
             for codepoint, name in SPECIAL_CODEPOINTS.items():
                   __A    =codepoint

             # Creates a mapping for looking up the string forms of special symbol IDs.
             __A    ={
                 codepoint: name for name, codepoint in self._special_codepoints.items()
             }

             __A    =UNICODE_VOCAB_SIZE
             __A    =len(self._special_codepoints     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self._unicode_vocab_size

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return list(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   return ord(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid token: \'{token}\''''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   if index in SPECIAL_CODEPOINTS:
                         return SPECIAL_CODEPOINTS[index]
                   return chr(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid id: {index}'''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return "".join(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =cls + token_ids_a + sep
             if token_ids_a is not None:
                   result += token_ids_a + sep
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False     ):







             '''simple docstring'''



             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=lowercase__ ,   token_ids_a=lowercase__ ,   already_has_special_tokens=lowercase__     )

             __A    =[1] + ([0] * len(lowercase__     )) + [1]
             if token_ids_a is not None:
                   result += ([0] * len(lowercase__     )) + [1]
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =len(cls + token_ids_a + sep     ) * [0]
             if token_ids_a is not None:
                   result += len(token_ids_a + sep     ) * [1]
             return result


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             return ()


