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


if is_vision_available():
  import PIL


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

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						["pixel_values"]




      def __init__(       self						:				Tuple						,					A						:				bool = True						,					A						:				Dict[str, int] = None						,					A						:				int = 0.9						,					A						:				PILImageResampling = PILImageResampling.BICUBIC						,					A						:				bool = True						,					A						:				Dict[str, int] = None						,					A						:				Union[int, float] = 1 / 255						,					A						:				bool = True						,					A						:				bool = True						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					**A						:				Dict						,					):
             super().__init__(**A       )
             _UpperCAmelCase     :  Tuple							      =    size if size is not None else {"shortest_edge": 224}
             _UpperCAmelCase     :  List[Any]							      =    get_size_dict(A						,					default_to_square=A       )
             _UpperCAmelCase     :  Any							      =    crop_size if crop_size is not None else {"height": 224, "width": 224}
             _UpperCAmelCase     :  Tuple							      =    get_size_dict(A						,					param_name="crop_size"       )

             _UpperCAmelCase     :  Dict							      =    do_resize
             _UpperCAmelCase     :  List[str]							      =    size
             _UpperCAmelCase     :  Optional[Any]							      =    crop_pct
             _UpperCAmelCase     :  Union[str, Any]							      =    resample
             _UpperCAmelCase     :  List[str]							      =    do_center_crop
             _UpperCAmelCase     :  Any							      =    crop_size
             _UpperCAmelCase     :  Tuple							      =    do_rescale
             _UpperCAmelCase     :  Union[str, Any]							      =    rescale_factor
             _UpperCAmelCase     :  Union[str, Any]							      =    do_normalize
             _UpperCAmelCase     :  Optional[Any]							      =    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
             _UpperCAmelCase     :  Optional[int]							      =    image_std if image_std is not None else IMAGENET_DEFAULT_STD




      def      _A	(       self						:				Optional[Any]						,					A						:				np.ndarray						,					A						:				Dict[str, int]						,					A						:				Optional[float] = None						,					A						:				PILImageResampling = PILImageResampling.BICUBIC						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Any						,					):
             _UpperCAmelCase     :  List[str]							      =    get_size_dict(A						,					default_to_square=A       )
             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:
                           _UpperCAmelCase     :  Optional[int]							      =    int(size["shortest_edge"] / crop_pct       )
                    elif "height" in size and "width" in size:
                           if size["height"] == size["width"]:
                                  _UpperCAmelCase     :  Any							      =    int(size["height"] / crop_pct       )
                           else:
                                  _UpperCAmelCase     :  List[Any]							      =    (int(size["height"] / crop_pct       ), int(size["width"] / crop_pct       ))
                    else:
                           raise ValueError("Invalid size for resize: {}".format(A       )       )

                    _UpperCAmelCase     :  Optional[int]							      =    get_resize_output_image_size(A						,					size=A						,					default_to_square=A       )
             else:
                    if "shortest_edge" in size:
                           _UpperCAmelCase     :  Dict							      =    get_resize_output_image_size(A						,					size=size["shortest_edge"]						,					default_to_square=A       )
                    elif "height" in size and "width" in size:
                           _UpperCAmelCase     :  List[str]							      =    (size["height"], size["width"])
                    else:
                           raise ValueError("Invalid size for resize: {}".format(A       )       )

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




      def      _A	(       self						:				Dict						,					A						:				np.ndarray						,					A						:				Dict[str, int]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				List[Any]						,					):
             _UpperCAmelCase     :  Tuple							      =    get_size_dict(A       )
             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(A						,					size=(size["height"], size["width"])						,					data_format=A						,					**A       )




      def      _A	(       self						:				List[str]						,					A						:				np.ndarray						,					A						:				Union[int, float]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Any						,					):
             return rescale(A						,					scale=A						,					data_format=A						,					**A       )




      def      _A	(       self						:				Optional[int]						,					A						:				np.ndarray						,					A						:				Union[float, List[float]]						,					A						:				Union[float, List[float]]						,					A						:				Optional[Union[str, ChannelDimension]] = None						,					**A						:				Optional[int]						,					):
             return normalize(A						,					mean=A						,					std=A						,					data_format=A						,					**A       )





      def      _A	(       self						:				int						,					A						:				ImageInput						,					A						:				bool = None						,					A						:				Dict[str, int] = None						,					A						:				int = None						,					A						:				PILImageResampling = None						,					A						:				bool = None						,					A						:				Dict[str, int] = None						,					A						:				bool = None						,					A						:				float = None						,					A						:				bool = None						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[float, List[float]]] = None						,					A						:				Optional[Union[str, TensorType]] = None						,					A						:				ChannelDimension = ChannelDimension.FIRST						,					**A						:				Optional[Any]						,					):
             _UpperCAmelCase     :  Dict							      =    do_resize if do_resize is not None else self.do_resize
             _UpperCAmelCase     :  List[Any]							      =    crop_pct if crop_pct is not None else self.crop_pct
             _UpperCAmelCase     :  Dict							      =    resample if resample is not None else self.resample
             _UpperCAmelCase     :  Dict							      =    do_center_crop if do_center_crop is not None else self.do_center_crop
             _UpperCAmelCase     :  Tuple							      =    do_rescale if do_rescale is not None else self.do_rescale
             _UpperCAmelCase     :  List[Any]							      =    rescale_factor if rescale_factor is not None else self.rescale_factor
             _UpperCAmelCase     :  List[Any]							      =    do_normalize if do_normalize is not None else self.do_normalize
             _UpperCAmelCase     :  Union[str, Any]							      =    image_mean if image_mean is not None else self.image_mean
             _UpperCAmelCase     :  Tuple							      =    image_std if image_std is not None else self.image_std

             _UpperCAmelCase     :  List[str]							      =    size if size is not None else self.size
             _UpperCAmelCase     :  str							      =    get_size_dict(A						,					default_to_square=A       )
             _UpperCAmelCase     :  Dict							      =    crop_size if crop_size is not None else self.crop_size
             _UpperCAmelCase     :  List[Any]							      =    get_size_dict(A						,					param_name="crop_size"       )

             _UpperCAmelCase     :  List[str]							      =    make_list_of_images(A       )

             if not valid_images(A       ):
                    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.
             _UpperCAmelCase     :  Optional[int]							      =    [to_numpy_array(A       ) for image in images]

             if do_resize:
                    _UpperCAmelCase     :  Optional[int]							      =    [self.resize(image=A						,					size=A						,					crop_pct=A						,					resample=A       ) for image in images]

             if do_center_crop:
                    _UpperCAmelCase     :  str							      =    [self.center_crop(image=A						,					size=A       ) for image in images]

             if do_rescale:
                    _UpperCAmelCase     :  List[str]							      =    [self.rescale(image=A						,					scale=A       ) for image in images]

             if do_normalize:
                    _UpperCAmelCase     :  Union[str, Any]							      =    [self.normalize(image=A						,					mean=A						,					std=A       ) for image in images]

             _UpperCAmelCase     :  Union[str, Any]							      =    [to_channel_dimension_format(A						,					A       ) for image in images]

             _UpperCAmelCase     :  str							      =    {"pixel_values": images}
             return BatchFeature(data=A						,					tensor_type=A       )


'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  None


__SCREAMING_SNAKE_CASE       :     Dict     =  logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  """▁"""

__SCREAMING_SNAKE_CASE       :     str     =  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE       :     int     =  {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
    """tokenizer_file""": {
        """google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
    },
}

__SCREAMING_SNAKE_CASE       :     str     =  {
    """google/pegasus-xsum""": 512,
}

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						VOCAB_FILES_NAMES
      __UpperCamelCase:  Dict						=						PRETRAINED_VOCAB_FILES_MAP
      __UpperCamelCase:  List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __UpperCamelCase:  Optional[int]						=						PegasusTokenizer
      __UpperCamelCase:  Optional[Any]						=						["input_ids", "attention_mask"]




      def __init__(       self						:				Dict						,					A						:				List[str]=None						,					A						:				Union[str, Any]=None						,					A						:				Optional[int]="<pad>"						,					A						:				Tuple="</s>"						,					A						:				Union[str, Any]="<unk>"						,					A						:				Union[str, Any]="<mask_2>"						,					A						:				Dict="<mask_1>"						,					A						:				Union[str, Any]=None						,					A						:				int=103						,					**A						:				Optional[Any]						,					):
             _UpperCAmelCase     :  Dict							      =    offset

             if additional_special_tokens is not None:
                    if not isinstance(A						,					A       ):
                           raise TypeError(
                               F"""additional_special_tokens should be of type {type(A       )}, but is"""
                               F""" {type(A       )}"""       )

                    _UpperCAmelCase     :  Optional[int]							      =    (
                        ([mask_token_sent] + additional_special_tokens)
                        if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                        else additional_special_tokens
                    )
                    # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                    additional_special_tokens_extended += [
                        F"""<unk_{i}>""" for i in range(len(A       )						,					self.offset - 1       )
                    ]

                    if len(set(A       )       ) != len(A       ):
                           raise ValueError(
                               "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                               F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."""       )
                    _UpperCAmelCase     :  Any							      =    additional_special_tokens_extended
             else:
                    _UpperCAmelCase     :  Dict							      =    [mask_token_sent] if mask_token_sent is not None else []
                    additional_special_tokens += [F"""<unk_{i}>""" for i in range(2						,					self.offset       )]

             super().__init__(
                 A						,					tokenizer_file=A						,					pad_token=A						,					eos_token=A						,					unk_token=A						,					mask_token=A						,					mask_token_sent=A						,					offset=A						,					additional_special_tokens=A						,					**A						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    vocab_file
             _UpperCAmelCase     :  Optional[Any]							      =    False if not self.vocab_file else True




      def      _A	(       self						:				List[str]						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Any							      =    set(self.all_special_ids       )  # call it once instead of inside list comp
             all_special_ids.remove(self.unk_token_id       )  # <unk> is only sometimes special

             if all_special_ids != set(range(len(self.additional_special_tokens       ) + 3       )       ):
                    raise ValueError(
                        "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                        F""" {len(self.additional_special_tokens       )} additional_special_tokens, but got {all_special_ids}"""       )

             return [1 if x in all_special_ids else 0 for x in seq]




      def      _A	(       self						:				str						,					A						:				List						,					A						:				Optional[List] = None						,					A						:				bool = False       ):
             if already_has_special_tokens:
                    return self._special_token_mask(A       )
             elif token_ids_a is None:
                    return self._special_token_mask(A       ) + [1]
             else:
                    return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]




      def      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]						,					A						:				int=None       ):
             if token_ids_a is None:
                    return token_ids_a + [self.eos_token_id]
             # We don't expect to process pairs, but leave the pair logic for API consistency
             return token_ids_a + token_ids_a + [self.eos_token_id]





      def      _A	(       self						:				Union[str, Any]						,					A						:				str						,					A						:				Optional[str] = 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(A       ):
                    logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
                    return
             _UpperCAmelCase     :  List[Any]							      =    os.path.join(
                 A						,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

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

             return (out_vocab_file,)

