

'''simple docstring'''





import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


UpperCamelCase__  :      int			=    logging.get_logger(__name__)


UpperCamelCase__  :      str			=    {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCamelCase__  :      List[Any]			=    {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

UpperCamelCase__  :      Dict			=    {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}


@lru_cache()
def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						(
        list(range(ord('!'       ) ,   ord('~'       ) + 1       )       ) + list(range(ord('¡'       ) ,   ord('¬'       ) + 1       )       ) + list(range(ord('®'       ) ,   ord('ÿ'       ) + 1       )       )
    )
    SCREAMING_SNAKE_CASE_									=						bs[:]
    SCREAMING_SNAKE_CASE_									=						0
    for b in range(2**8       ):
        if b not in bs:
            bs.append(_SCREAMING_SNAKE_CASE       )
            cs.append(2**8 + n       )
            n += 1
    SCREAMING_SNAKE_CASE_									=						[chr(_SCREAMING_SNAKE_CASE       ) for n in cs]
    return dict(zip(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						set()
    SCREAMING_SNAKE_CASE_									=						word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        SCREAMING_SNAKE_CASE_									=						char
    return pairs





class       __snake_case  (    a__       ):
  __lowerCAmelCase     :	List[str]						 =  VOCAB_FILES_NAMES
  __lowerCAmelCase     :	List[str]						 =  PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase     :	int						 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase     :	Optional[int]						 =  ["input_ids", "attention_mask"]

  def __init__(					self						,		_A						,		_A						,		_A="replace"						,		_A="<s>"						,		_A="</s>"						,		_A="</s>"						,		_A="<s>"						,		_A="<unk>"						,		_A="<pad>"						,		_A="<mask>"						,		_A=False						,		**_A						,		):
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowerCamelCase_						,		lstrip=lowerCamelCase_						,		rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_						,		lowerCamelCase_) else bos_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowerCamelCase_						,		lstrip=lowerCamelCase_						,		rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_						,		lowerCamelCase_) else eos_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowerCamelCase_						,		lstrip=lowerCamelCase_						,		rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_						,		lowerCamelCase_) else sep_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowerCamelCase_						,		lstrip=lowerCamelCase_						,		rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_						,		lowerCamelCase_) else cls_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowerCamelCase_						,		lstrip=lowerCamelCase_						,		rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_						,		lowerCamelCase_) else unk_token
      SCREAMING_SNAKE_CASE_									=						AddedToken(lowerCamelCase_						,		lstrip=lowerCamelCase_						,		rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_						,		lowerCamelCase_) else pad_token

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

      super().__init__(
          errors=lowerCamelCase_						,		bos_token=lowerCamelCase_						,		eos_token=lowerCamelCase_						,		unk_token=lowerCamelCase_						,		sep_token=lowerCamelCase_						,		cls_token=lowerCamelCase_						,		pad_token=lowerCamelCase_						,		mask_token=lowerCamelCase_						,		add_prefix_space=lowerCamelCase_						,		**lowerCamelCase_						,		)

      with open(lowerCamelCase_						,		encoding='utf-8') as vocab_handle:
          SCREAMING_SNAKE_CASE_									=						json.load(lowerCamelCase_)
      SCREAMING_SNAKE_CASE_									=						{v: k for k, v in self.encoder.items()}
      SCREAMING_SNAKE_CASE_									=						errors  # how to handle errors in decoding
      SCREAMING_SNAKE_CASE_									=						bytes_to_unicode()
      SCREAMING_SNAKE_CASE_									=						{v: k for k, v in self.byte_encoder.items()}
      with open(lowerCamelCase_						,		encoding='utf-8') as merges_handle:
          SCREAMING_SNAKE_CASE_									=						merges_handle.read().split('\n')[1:-1]
      SCREAMING_SNAKE_CASE_									=						[tuple(merge.split()) for merge in bpe_merges]
      SCREAMING_SNAKE_CASE_									=						dict(zip(lowerCamelCase_						,		range(len(lowerCamelCase_))))
      SCREAMING_SNAKE_CASE_									=						{}
      SCREAMING_SNAKE_CASE_									=						add_prefix_space

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      SCREAMING_SNAKE_CASE_									=						re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+')

  @property
  def 						lowerCAmelCase__     (					self):
      return len(self.encoder)

  def 						lowerCAmelCase__     (					self):
      return dict(self.encoder						,		**self.added_tokens_encoder)

  def 						lowerCAmelCase__     (					self						,		_A):
      if token in self.cache:
          return self.cache[token]
      SCREAMING_SNAKE_CASE_									=						tuple(lowerCamelCase_)
      SCREAMING_SNAKE_CASE_									=						get_pairs(lowerCamelCase_)

      if not pairs:
          return token

      while True:
          SCREAMING_SNAKE_CASE_									=						min(lowerCamelCase_						,		key=lambda _A: self.bpe_ranks.get(lowerCamelCase_						,		float('inf')))
          if bigram not in self.bpe_ranks:
              break
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						bigram
          SCREAMING_SNAKE_CASE_									=						[]
          SCREAMING_SNAKE_CASE_									=						0
          while i < len(lowerCamelCase_):
              try:
                  SCREAMING_SNAKE_CASE_									=						word.index(lowerCamelCase_						,		lowerCamelCase_)
              except ValueError:
                  new_word.extend(word[i:])
                  break
              else:
                  new_word.extend(word[i:j])
                  SCREAMING_SNAKE_CASE_									=						j

              if word[i] == first and i < len(lowerCamelCase_) - 1 and word[i + 1] == second:
                  new_word.append(first + second)
                  i += 2
              else:
                  new_word.append(word[i])
                  i += 1
          SCREAMING_SNAKE_CASE_									=						tuple(lowerCamelCase_)
          SCREAMING_SNAKE_CASE_									=						new_word
          if len(lowerCamelCase_) == 1:
              break
          else:
              SCREAMING_SNAKE_CASE_									=						get_pairs(lowerCamelCase_)
      SCREAMING_SNAKE_CASE_									=						' '.join(lowerCamelCase_)
      SCREAMING_SNAKE_CASE_									=						word
      return word

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						[]
      for token in re.findall(self.pat						,		lowerCamelCase_):
          SCREAMING_SNAKE_CASE_									=						''.join(
              self.byte_encoder[b] for b in token.encode('utf-8'))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase_).split(' '))
      return bpe_tokens

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.encoder.get(lowerCamelCase_						,		self.encoder.get(self.unk_token))

  def 						lowerCAmelCase__     (					self						,		_A):
      return self.decoder.get(lowerCamelCase_)

  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						''.join(lowerCamelCase_)
      SCREAMING_SNAKE_CASE_									=						bytearray([self.byte_decoder[c] for c in text]).decode('utf-8'						,		errors=self.errors)
      return text

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      if not os.path.isdir(lowerCamelCase_):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_									=						os.path.join(
          lowerCamelCase_						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
      SCREAMING_SNAKE_CASE_									=						os.path.join(
          lowerCamelCase_						,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'])

      with open(lowerCamelCase_						,		'w'						,		encoding='utf-8') as f:
          f.write(json.dumps(self.encoder						,		indent=2						,		sort_keys=lowerCamelCase_						,		ensure_ascii=lowerCamelCase_) + '\n')

      SCREAMING_SNAKE_CASE_									=						0
      with open(lowerCamelCase_						,		'w'						,		encoding='utf-8') as writer:
          writer.write('#version: 0.2\n')
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,		key=lambda _A: kv[1]):
              if index != token_index:
                  logger.warning(
                      f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                      ' Please check that the tokenizer is not corrupted!')
                  SCREAMING_SNAKE_CASE_									=						token_index
              writer.write(' '.join(lowerCamelCase_) + '\n')
              index += 1

      return vocab_file, merge_file

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      SCREAMING_SNAKE_CASE_									=						[self.cls_token_id]
      SCREAMING_SNAKE_CASE_									=						[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = False):
      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=lowerCamelCase_						,		token_ids_a=lowerCamelCase_						,		already_has_special_tokens=lowerCamelCase_)

      if token_ids_a is None:
          return [1] + ([0] * len(lowerCamelCase_)) + [1]
      return [1] + ([0] * len(lowerCamelCase_)) + [1, 1] + ([0] * len(lowerCamelCase_)) + [1]

  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None):
      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 + sep + token_ids_a + sep) * [0]


  def 						lowerCAmelCase__     (					self						,		_A						,		_A=False						,		**_A):
      SCREAMING_SNAKE_CASE_									=						kwargs.pop('add_prefix_space'						,		self.add_prefix_space)
      if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase_) > 0 and not text[0].isspace()):
          SCREAMING_SNAKE_CASE_									=						' ' + text
      return (text, kwargs)





import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor



class       __snake_case  (    unittest.TestCase       ):

  def __init__(					self						,		_A						,		_A=7						,		_A=3						,		_A=18						,		_A=30						,		_A=400						,		_A=True						,		_A=None						,		_A=True						,		_A=None						,		_A=True						,		_A=[0.5, 0.5, 0.5]						,		_A=[0.5, 0.5, 0.5]						,		_A=False						,		):
      SCREAMING_SNAKE_CASE_									=						size if size is not None else {'height': 20, 'width': 20}
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else {'height': 18, 'width': 18}
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						batch_size
      SCREAMING_SNAKE_CASE_									=						num_channels
      SCREAMING_SNAKE_CASE_									=						image_size
      SCREAMING_SNAKE_CASE_									=						min_resolution
      SCREAMING_SNAKE_CASE_									=						max_resolution
      SCREAMING_SNAKE_CASE_									=						do_resize
      SCREAMING_SNAKE_CASE_									=						size
      SCREAMING_SNAKE_CASE_									=						do_center_crop
      SCREAMING_SNAKE_CASE_									=						crop_size
      SCREAMING_SNAKE_CASE_									=						do_normalize
      SCREAMING_SNAKE_CASE_									=						image_mean
      SCREAMING_SNAKE_CASE_									=						image_std
      SCREAMING_SNAKE_CASE_									=						do_reduce_labels


  def 						lowerCAmelCase__     (					self):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }






def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

    SCREAMING_SNAKE_CASE_									=						Image.open(dataset[0]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(dataset[1]['file']       )

    return image, map


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

    SCREAMING_SNAKE_CASE_									=						Image.open(ds[0]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[1]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[2]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[3]['file']       )

    return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	Union[str, Any]						 =  BeitImageProcessor if is_vision_available() else None

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						BeitImageProcessingTester(self)

  @property
  def 						lowerCAmelCase__     (					self):
      return self.image_processor_tester.prepare_image_processor_dict()

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(_A						,		'do_resize'))
      self.assertTrue(hasattr(_A						,		'size'))
      self.assertTrue(hasattr(_A						,		'do_center_crop'))
      self.assertTrue(hasattr(_A						,		'center_crop'))
      self.assertTrue(hasattr(_A						,		'do_normalize'))
      self.assertTrue(hasattr(_A						,		'image_mean'))
      self.assertTrue(hasattr(_A						,		'image_std'))

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size						,		{'height': 20, 'width': 20})
      self.assertEqual(image_processor.crop_size						,		{'height': 18, 'width': 18})
      self.assertEqual(image_processor.do_reduce_labels						,		_A)

      SCREAMING_SNAKE_CASE_									=						self.image_processing_class.from_dict(
          self.image_processor_dict						,		size=42						,		crop_size=84						,		reduce_labels=_A)
      self.assertEqual(image_processor.size						,		{'height': 42, 'width': 42})
      self.assertEqual(image_processor.crop_size						,		{'height': 84, 'width': 84})
      self.assertEqual(image_processor.do_reduce_labels						,		_A)

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		numpify=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		np.ndarray)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		torchify=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		torch.Tensor)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		torchify=_A)
      SCREAMING_SNAKE_CASE_									=						[]
      for image in image_inputs:
          self.assertIsInstance(_A						,		torch.Tensor)
          maps.append(torch.zeros(image.shape[-2:]).long())

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		maps[0]						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test not batched input (PIL images)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_single_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test batched input (PIL images)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_batch_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              2,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)


  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_single_inputs()
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 150)

      SCREAMING_SNAKE_CASE_									=						True
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)


