'''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:
      lowercase__  :      List[Any]        =					None


lowercase__  :      Any        =					logging.get_logger(__name__)


lowercase__  :      int        =					"▁"

lowercase__  :      Any        =					{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowercase__  :      List[Any]        =					{
    "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"
    },
}

lowercase__  :      Optional[int]        =					{
    "google/pegasus-xsum": 512,
}







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	VOCAB_FILES_NAMES
  lowerCAmelCase__										=	PRETRAINED_VOCAB_FILES_MAP
  lowerCAmelCase__										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  lowerCAmelCase__										=	PegasusTokenizer
  lowerCAmelCase__										=	['''input_ids''', '''attention_mask''']






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

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

              UpperCAmelCase_            =  (
                  ([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(UpperCAmelCase__  )  ,							self.offset - 1  )
              ]

              if len(set(UpperCAmelCase__  )  ) != len(UpperCAmelCase__  ):
                    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_            =  additional_special_tokens_extended
        else:
              UpperCAmelCase_            =  [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__(
            UpperCAmelCase__  ,							tokenizer_file=UpperCAmelCase__  ,							pad_token=UpperCAmelCase__  ,							eos_token=UpperCAmelCase__  ,							unk_token=UpperCAmelCase__  ,							mask_token=UpperCAmelCase__  ,							mask_token_sent=UpperCAmelCase__  ,							offset=UpperCAmelCase__  ,							additional_special_tokens=UpperCAmelCase__  ,							**UpperCAmelCase__  ,							)
        UpperCAmelCase_            =  vocab_file
        UpperCAmelCase_            =  False if not self.vocab_file else True






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       Tuple  )				->Optional[Any]:
        UpperCAmelCase_            =  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  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       List  ,							UpperCAmelCase__			:       Optional[List] = None  ,							UpperCAmelCase__			:       bool = False  )				->List[int]:
        if already_has_special_tokens:
              return self._special_token_mask(UpperCAmelCase__  )
        elif token_ids_a is None:
              return self._special_token_mask(UpperCAmelCase__  ) + [1]
        else:
              return self._special_token_mask(token_ids_a + token_ids_a  ) + [1]






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       Tuple=None  )				->List[int]:
        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  lowerCAmelCase__ (      self			:       Dict  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Optional[str] = None  )				->Tuple[str]:
        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(UpperCAmelCase__  ):
              logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
              return
        UpperCAmelCase_            =  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__  ):
              copyfile(self.vocab_file  ,							UpperCAmelCase__  )

        return (out_vocab_file,)






'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
      import torch

if is_vision_available():
      import PIL


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



def  __lowerCamelCase       (      _UpperCamelCase  :  np.ndarray							,    _UpperCamelCase  :  Union[int, Iterable[int]]							,    _UpperCamelCase  :  bool							,    _UpperCamelCase  :  int					):


      '''simple docstring'''


      def constraint_to_multiple_of(_UpperCamelCase  :  int							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Optional[int]=0							,    _UpperCamelCase  :  str=None					):
            UpperCAmelCase_            =  round(val / multiple					) * multiple

            if max_val is not None and x > max_val:
                  UpperCAmelCase_            =  math.floor(val / multiple					) * multiple

            if x < min_val:
                  UpperCAmelCase_            =  math.ceil(val / multiple					) * multiple

            return x

      UpperCAmelCase_            =  (output_size, output_size) if isinstance(_UpperCamelCase							,    _UpperCamelCase					) else output_size

      UpperCAmelCase_      ,		UpperCAmelCase_            =  get_image_size(_UpperCamelCase					)
      UpperCAmelCase_      ,		UpperCAmelCase_            =  output_size

      # determine new height and width
      UpperCAmelCase_            =  output_height / input_height
      UpperCAmelCase_            =  output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width					) < abs(1 - scale_height					):
                  # fit width
                  UpperCAmelCase_            =  scale_width
            else:
                  # fit height
                  UpperCAmelCase_            =  scale_height

      UpperCAmelCase_            =  constraint_to_multiple_of(scale_height * input_height							,    multiple=_UpperCamelCase					)
      UpperCAmelCase_            =  constraint_to_multiple_of(scale_width * input_width							,    multiple=_UpperCamelCase					)

      return (new_height, new_width)







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	['''pixel_values''']






  def __init__(      self			:       Any  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       PILImageResampling = PILImageResampling.BILINEAR  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[int, float] = 1 / 255  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							**UpperCAmelCase__			:       str  ,							)				->None:
        super().__init__(**UpperCAmelCase__  )
        UpperCAmelCase_            =  size if size is not None else {'''height''': 384, '''width''': 384}
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  )
        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size
        UpperCAmelCase_            =  keep_aspect_ratio
        UpperCAmelCase_            =  ensure_multiple_of
        UpperCAmelCase_            =  resample
        UpperCAmelCase_            =  do_rescale
        UpperCAmelCase_            =  rescale_factor
        UpperCAmelCase_            =  do_normalize
        UpperCAmelCase_            =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        UpperCAmelCase_            =  image_std if image_std is not None else IMAGENET_STANDARD_STD






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       np.ndarray  ,							UpperCAmelCase__			:       Dict[str, int]  ,							UpperCAmelCase__			:       bool = False  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       PILImageResampling = PILImageResampling.BICUBIC  ,							UpperCAmelCase__			:       Optional[Union[str, ChannelDimension]] = None  ,							**UpperCAmelCase__			:       List[str]  ,							)				->np.ndarray:
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  )
        if "height" not in size or "width" not in size:
              raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""  )
        UpperCAmelCase_            =  get_resize_output_image_size(
            UpperCAmelCase__  ,							output_size=(size['''height'''], size['''width'''])  ,							keep_aspect_ratio=UpperCAmelCase__  ,							multiple=UpperCAmelCase__  ,							)
        return resize(UpperCAmelCase__  ,							size=UpperCAmelCase__  ,							resample=UpperCAmelCase__  ,							data_format=UpperCAmelCase__  ,							**UpperCAmelCase__  )






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






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






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       ImageInput  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       int = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       int = None  ,							UpperCAmelCase__			:       PILImageResampling = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       float = None  ,							UpperCAmelCase__			:       bool = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = None  ,							UpperCAmelCase__			:       Optional[Union[str, TensorType]] = None  ,							UpperCAmelCase__			:       ChannelDimension = ChannelDimension.FIRST  ,							**UpperCAmelCase__			:       Any  ,							)				->PIL.Image.Image:
        UpperCAmelCase_            =  do_resize if do_resize is not None else self.do_resize
        UpperCAmelCase_            =  size if size is not None else self.size
        UpperCAmelCase_            =  get_size_dict(UpperCAmelCase__  )
        UpperCAmelCase_            =  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
        UpperCAmelCase_            =  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
        UpperCAmelCase_            =  resample if resample is not None else self.resample
        UpperCAmelCase_            =  do_rescale if do_rescale is not None else self.do_rescale
        UpperCAmelCase_            =  rescale_factor if rescale_factor is not None else self.rescale_factor
        UpperCAmelCase_            =  do_normalize if do_normalize is not None else self.do_normalize
        UpperCAmelCase_            =  image_mean if image_mean is not None else self.image_mean
        UpperCAmelCase_            =  image_std if image_std is not None else self.image_std

        UpperCAmelCase_            =  make_list_of_images(UpperCAmelCase__  )

        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 or resample is None:
              raise ValueError('''Size and resample must be specified if do_resize 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_            =  [to_numpy_array(UpperCAmelCase__  ) for image in images]

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

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

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

        UpperCAmelCase_            =  [to_channel_dimension_format(UpperCAmelCase__  ,							UpperCAmelCase__  ) for image in images]

        UpperCAmelCase_            =  {'''pixel_values''': images}
        return BatchFeature(data=UpperCAmelCase__  ,							tensor_type=UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       List[Tuple] = None  )				->Optional[Any]:
        UpperCAmelCase_            =  outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
              if len(UpperCAmelCase__  ) != len(UpperCAmelCase__  ):
                    raise ValueError(
                        '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''  )

              if is_torch_tensor(UpperCAmelCase__  ):
                    UpperCAmelCase_            =  target_sizes.numpy()

              UpperCAmelCase_            =  []

              for idx in range(len(UpperCAmelCase__  )  ):
                    UpperCAmelCase_            =  torch.nn.functional.interpolate(
                        logits[idx].unsqueeze(dim=0  )  ,							size=target_sizes[idx]  ,							mode='''bilinear'''  ,							align_corners=UpperCAmelCase__  )
                    UpperCAmelCase_            =  resized_logits[0].argmax(dim=0  )
                    semantic_segmentation.append(UpperCAmelCase__  )
        else:
              UpperCAmelCase_            =  logits.argmax(dim=1  )
              UpperCAmelCase_            =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

        return semantic_segmentation





