

'''simple docstring'''
from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel


@require_tf
class      a__						:
   _SCREAMING_SNAKE_CASE					:  Tuple			 =							MarianConfig
   _SCREAMING_SNAKE_CASE					:  Optional[int]			 =							{}
   _SCREAMING_SNAKE_CASE					:  Dict			 =							'gelu'


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=13    ,       _UpperCamelCase=7    ,       _UpperCamelCase=True    ,       _UpperCamelCase=False    ,       _UpperCamelCase=99    ,       _UpperCamelCase=32    ,       _UpperCamelCase=2    ,       _UpperCamelCase=4    ,       _UpperCamelCase=37    ,       _UpperCamelCase=0.1    ,       _UpperCamelCase=0.1    ,       _UpperCamelCase=20    ,       _UpperCamelCase=2    ,       _UpperCamelCase=1    ,       _UpperCamelCase=0    ,       ):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     parent
      _lowercase    :   Any      =     batch_size
      _lowercase    :   Dict      =     seq_length
      _lowercase    :   Any      =     is_training
      _lowercase    :   str      =     use_labels
      _lowercase    :   Union[str, Any]      =     vocab_size
      _lowercase    :   Optional[int]      =     hidden_size
      _lowercase    :   int      =     num_hidden_layers
      _lowercase    :   Tuple      =     num_attention_heads
      _lowercase    :   Optional[int]      =     intermediate_size

      _lowercase    :   Optional[Any]      =     hidden_dropout_prob
      _lowercase    :   Any      =     attention_probs_dropout_prob
      _lowercase    :   List[Any]      =     max_position_embeddings
      _lowercase    :   Tuple      =     eos_token_id
      _lowercase    :   Union[str, Any]      =     pad_token_id
      _lowercase    :   Optional[Any]      =     bos_token_id


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Any      =     ids_tensor([self.batch_size, self.seq_length - 1]    ,       self.vocab_size						)
      _lowercase    :   Union[str, Any]      =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)    ,       1						)
      _lowercase    :   Tuple      =     tf.concat([input_ids, eos_tensor]    ,       axis=1						)

      _lowercase    :   Optional[int]      =     ids_tensor([self.batch_size, self.seq_length]    ,       self.vocab_size						)

      _lowercase    :   int      =     self.config_cls(
          vocab_size=self.vocab_size    ,       d_model=self.hidden_size    ,       encoder_layers=self.num_hidden_layers    ,       decoder_layers=self.num_hidden_layers    ,       encoder_attention_heads=self.num_attention_heads    ,       decoder_attention_heads=self.num_attention_heads    ,       encoder_ffn_dim=self.intermediate_size    ,       decoder_ffn_dim=self.intermediate_size    ,       dropout=self.hidden_dropout_prob    ,       attention_dropout=self.attention_probs_dropout_prob    ,       max_position_embeddings=self.max_position_embeddings    ,       eos_token_ids=[2]    ,       bos_token_id=self.bos_token_id    ,       pad_token_id=self.pad_token_id    ,       decoder_start_token_id=self.pad_token_id    ,       **self.config_updates    ,       )
      _lowercase    :   int      =     prepare_marian_inputs_dict(_UpperCamelCase    ,       _UpperCamelCase    ,       _UpperCamelCase						)
      return config, inputs_dict






   def 		_lowerCamelCase     (				self    ,       _UpperCamelCase    ,       _UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   int      =     TFMarianModel(config=_UpperCamelCase						).get_decoder()
      _lowercase    :   Any      =     inputs_dict["input_ids"]

      _lowercase    :   Union[str, Any]      =     input_ids[:1, :]
      _lowercase    :   Dict      =     inputs_dict["attention_mask"][:1, :]
      _lowercase    :   Tuple      =     inputs_dict["head_mask"]
      _lowercase    :   str      =     1

      # first forward pass
      _lowercase    :   Optional[Any]      =     model(_UpperCamelCase    ,       attention_mask=_UpperCamelCase    ,       head_mask=_UpperCamelCase    ,       use_cache=_UpperCamelCase						)

      _lowercase  ,   _lowercase    :   Optional[int]      =     outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      _lowercase    :   List[str]      =     ids_tensor((self.batch_size, 3)    ,       config.vocab_size						)
      _lowercase    :   int      =     tf.cast(ids_tensor((self.batch_size, 3)    ,       2						)    ,       tf.inta						)

      # append to next input_ids and
      _lowercase    :   int      =     tf.concat([input_ids, next_tokens]    ,       axis=-1						)
      _lowercase    :   List[Any]      =     tf.concat([attention_mask, next_attn_mask]    ,       axis=-1						)

      _lowercase    :   Union[str, Any]      =     model(_UpperCamelCase    ,       attention_mask=_UpperCamelCase						)[0]
      _lowercase    :   Tuple      =     model(_UpperCamelCase    ,       attention_mask=_UpperCamelCase    ,       past_key_values=_UpperCamelCase						)[0]

      self.parent.assertEqual(next_tokens.shape[1]    ,       output_from_past.shape[1]						)

      # select random slice
      _lowercase    :   Tuple      =     int(ids_tensor((1,)    ,       output_from_past.shape[-1]						)						)
      _lowercase    :   List[str]      =     output_from_no_past[:, -3:, random_slice_idx]
      _lowercase    :   Union[str, Any]      =     output_from_past[:, :, random_slice_idx]

      # test that outputs are equal for slice
      tf.debugging.assert_near(_UpperCamelCase    ,       _UpperCamelCase    ,       rtol=1E-3						)







def       _A	(			snake_case	,							snake_case	,							snake_case	,							snake_case=None	,							snake_case=None	,							snake_case=None	,							snake_case=None	,							snake_case=None	,							)    ->			List[str]:
   if attention_mask is None:
      _lowercase    :   List[Any]      =     tf.cast(tf.math.not_equal(snake_case	,							config.pad_token_id  )	,							tf.inta  )
   if decoder_attention_mask is None:
      _lowercase    :   Union[str, Any]      =     tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape	,							dtype=tf.inta  ),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,							config.pad_token_id  )	,							tf.inta  ),
          ]	,							axis=-1	,							)
   if head_mask is None:
      _lowercase    :   int      =     tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
   if decoder_head_mask is None:
      _lowercase    :   Optional[int]      =     tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
   if cross_attn_head_mask is None:
      _lowercase    :   Tuple      =     tf.ones((config.decoder_layers, config.decoder_attention_heads)  )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }


@require_tf
class      a__						(							lowerCamelCase_						,   lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  Dict			 =							(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
   _SCREAMING_SNAKE_CASE					:  Dict			 =							(TFMarianMTModel,) if is_tf_available() else ()
   _SCREAMING_SNAKE_CASE					:  int			 =							(
       {
           'conversational': TFMarianMTModel,
           'feature-extraction': TFMarianModel,
           'summarization': TFMarianMTModel,
           'text2text-generation': TFMarianMTModel,
           'translation': TFMarianMTModel,
       }
       if is_tf_available()
       else {}
   )
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							True
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							False
   _SCREAMING_SNAKE_CASE					:  Optional[Any]			 =							False


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   int      =     TFMarianModelTester(self						)
      _lowercase    :   List[str]      =     ConfigTester(self    ,       config_class=_UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      self.config_tester.run_common_tests()






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase						)




@require_tf
class      a__						(							unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  List[Any]			 =							1000  # show more chars for failing integration tests


   @classmethod
   def 		_lowerCamelCase     (				cls						):





      """simple docstring"""





      _lowercase    :   Dict      =     f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
      return cls


   @cached_property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return AutoTokenizer.from_pretrained(self.model_name						)


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.tokenizer.eos_token_id


   @cached_property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      warnings.simplefilter("error"						)
      _lowercase    :   TFMarianMTModel      =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
      assert isinstance(_UpperCamelCase    ,       _UpperCamelCase						)
      _lowercase    :   Optional[int]      =     model.config
      self.assertListEqual(c.bad_words_ids    ,       [[c.pad_token_id]]						)
      self.assertEqual(c.max_length    ,       512						)
      self.assertEqual(c.decoder_start_token_id    ,       c.pad_token_id						)
      return model


   def 		_lowerCamelCase     (				self    ,       **_UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     self.translate_src_text(**_UpperCamelCase						)
      self.assertListEqual(self.expected_text    ,       _UpperCamelCase						)






   def 		_lowerCamelCase     (				self    ,       **_UpperCamelCase						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     self.tokenizer(self.src_text    ,       **_UpperCamelCase    ,       padding=_UpperCamelCase    ,       return_tensors="tf"						)
      _lowercase    :   Any      =     self.model.generate(
          model_inputs.input_ids    ,       attention_mask=model_inputs.attention_mask    ,       num_beams=2    ,       max_length=128						)
      _lowercase    :   Optional[Any]      =     self.tokenizer.batch_decode(generated_ids.numpy()    ,       skip_special_tokens=_UpperCamelCase						)
      return generated_words




@require_sentencepiece
@require_tokenizers
@require_tf
class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  Optional[int]			 =							'mt'
   _SCREAMING_SNAKE_CASE					:  List[Any]			 =							'en'
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
   _SCREAMING_SNAKE_CASE					:  List[Any]			 =							['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']


   @unittest.skip("Skipping until #12647 is resolved."						)
   @slow
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  Dict			 =							'en'
   _SCREAMING_SNAKE_CASE					:  int			 =							'zh'
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							['My name is Wolfgang and I live in Berlin']
   _SCREAMING_SNAKE_CASE					:  Dict			 =							['我叫沃尔夫冈 我住在柏林']


   @unittest.skip("Skipping until #12647 is resolved."						)
   @slow
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class      a__						(							lowerCamelCase_						):
   _SCREAMING_SNAKE_CASE					:  Dict			 =							'en'
   _SCREAMING_SNAKE_CASE					:  int			 =							'ROMANCE'
   _SCREAMING_SNAKE_CASE					:  List[str]			 =							[
       '>>fr<< Don\'t spend so much time watching TV.',
       '>>pt<< Your message has been sent.',
       '>>es<< He\'s two years older than me.',
   ]
   _SCREAMING_SNAKE_CASE					:  Any			 =							[
       'Ne passez pas autant de temps à regarder la télé.',
       'A sua mensagem foi enviada.',
       'Es dos años más viejo que yo.',
   ]


   @unittest.skip("Skipping until #12647 is resolved."						)
   @slow
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      self._assert_generated_batch_equal_expected()






   @unittest.skip("Skipping until #12647 is resolved."						)
   @slow
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Optional[int]      =     TranslationPipeline(self.model    ,       self.tokenizer    ,       framework="tf"						)
      _lowercase    :   Optional[int]      =     pipeline(self.src_text						)
      self.assertEqual(self.expected_text    ,       [x["translation_text"] for x in output]						)









'''simple docstring'''
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 SegformerImageProcessor


class      a__						(							unittest.TestCase						):


   def __init__(				self    ,       _UpperCamelCase    ,       _UpperCamelCase=7    ,       _UpperCamelCase=3    ,       _UpperCamelCase=30    ,       _UpperCamelCase=400    ,       _UpperCamelCase=True    ,       _UpperCamelCase=None    ,       _UpperCamelCase=True    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=[0.5, 0.5, 0.5]    ,       _UpperCamelCase=False    ,       ):





      """simple docstring"""





      _lowercase    :   List[str]      =     size if size is not None else {"height": 30, "width": 30}
      _lowercase    :   str      =     parent
      _lowercase    :   Optional[int]      =     batch_size
      _lowercase    :   Optional[int]      =     num_channels
      _lowercase    :   Any      =     min_resolution
      _lowercase    :   Any      =     max_resolution
      _lowercase    :   Tuple      =     do_resize
      _lowercase    :   List[Any]      =     size
      _lowercase    :   Tuple      =     do_normalize
      _lowercase    :   Optional[Any]      =     image_mean
      _lowercase    :   Any      =     image_std
      _lowercase    :   Dict      =     do_reduce_labels






   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }







def       _A	(			)    ->			List[Any]:
   _lowercase    :   List[str]      =     load_dataset("hf-internal-testing/fixtures_ade20k"	,							split="test"  )

   _lowercase    :   Union[str, Any]      =     Image.open(dataset[0]["file"]  )
   _lowercase    :   Optional[int]      =     Image.open(dataset[1]["file"]  )

   return image, map







def       _A	(			)    ->			Optional[int]:
   _lowercase    :   List[Any]      =     load_dataset("hf-internal-testing/fixtures_ade20k"	,							split="test"  )

   _lowercase    :   int      =     Image.open(dataset[0]["file"]  )
   _lowercase    :   str      =     Image.open(dataset[1]["file"]  )
   _lowercase    :   Union[str, Any]      =     Image.open(dataset[2]["file"]  )
   _lowercase    :   Union[str, Any]      =     Image.open(dataset[3]["file"]  )

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


@require_torch
@require_vision
class      a__						(							lowerCamelCase_						,   unittest.TestCase						):
   _SCREAMING_SNAKE_CASE					:  Union[str, Any]			 =							SegformerImageProcessor if is_vision_available() else None


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     SegformerImageProcessingTester(self						)


   @property
   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      return self.image_processor_tester.prepare_image_processor_dict()


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_resize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "size"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_normalize"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_mean"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "image_std"						)						)
      self.assertTrue(hasattr(_UpperCamelCase    ,       "do_reduce_labels"						)						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Dict      =     self.image_processing_class.from_dict(self.image_processor_dict						)
      self.assertEqual(image_processor.size    ,       {"height": 30, "width": 30}						)
      self.assertEqual(image_processor.do_reduce_labels    ,       _UpperCamelCase						)

      _lowercase    :   Tuple      =     self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42    ,       reduce_labels=_UpperCamelCase						)
      self.assertEqual(image_processor.size    ,       {"height": 42, "width": 42}						)
      self.assertEqual(image_processor.do_reduce_labels    ,       _UpperCamelCase						)


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      pass


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      _lowercase    :   str      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase						)
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       Image.Image						)

      # Test not batched input
      _lowercase    :   Optional[int]      =     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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )

      # Test batched
      _lowercase    :   int      =     image_processing(_UpperCamelCase    ,       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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      _lowercase    :   List[str]      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       numpify=_UpperCamelCase						)
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       np.ndarray						)

      # Test not batched input
      _lowercase    :   Dict      =     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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )

      # Test batched
      _lowercase    :   List[Any]      =     image_processing(_UpperCamelCase    ,       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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   List[str]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _lowercase    :   List[Any]      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       torchify=_UpperCamelCase						)
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       torch.Tensor						)

      # Test not batched input
      _lowercase    :   Optional[int]      =     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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )

      # Test batched
      _lowercase    :   Dict      =     image_processing(_UpperCamelCase    ,       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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )


   def 		_lowerCamelCase     (				self						):





      """simple docstring"""





      _lowercase    :   Union[str, Any]      =     self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      _lowercase    :   Tuple      =     prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=_UpperCamelCase    ,       torchify=_UpperCamelCase						)
      _lowercase    :   Tuple      =     []
      for image in image_inputs:
         self.assertIsInstance(_UpperCamelCase    ,       torch.Tensor						)
         maps.append(torch.zeros(image.shape[-2:]						).long()						)

      # Test not batched input
      _lowercase    :   List[str]      =     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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              1,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.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
      _lowercase    :   Optional[int]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       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.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.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)
      _lowercase  ,   _lowercase    :   Optional[int]      =     prepare_semantic_single_inputs()

      _lowercase    :   int      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertEqual(
          encoding["pixel_values"].shape    ,       (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              1,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.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)
      _lowercase  ,   _lowercase    :   Dict      =     prepare_semantic_batch_inputs()

      _lowercase    :   List[str]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertEqual(
          encoding["pixel_values"].shape    ,       (
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.size["width"],
          )    ,       )
      self.assertEqual(
          encoding["labels"].shape    ,       (
              2,
              self.image_processor_tester.size["height"],
              self.image_processor_tester.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						):





      """simple docstring"""





      _lowercase    :   str      =     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
      _lowercase  ,   _lowercase    :   str      =     prepare_semantic_single_inputs()
      _lowercase    :   Optional[int]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 150						)

      _lowercase    :   str      =     True
      _lowercase    :   List[str]      =     image_processing(_UpperCamelCase    ,       _UpperCamelCase    ,       return_tensors="pt"						)
      self.assertTrue(encoding["labels"].min().item() >= 0						)
      self.assertTrue(encoding["labels"].max().item() <= 255						)






