





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase      :       List[str]									=  get_tests_dir("""fixtures/test_sentencepiece.model""")

if is_torch_available():
    from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowercase      :       Any									=  2_5_0_0_0_4
lowercase      :       Optional[int]									=  2_5_0_0_2_0





@require_sentencepiece
@require_tokenizers
class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Dict         =  MBartaaTokenizer
   __A   :				Any         =  MBartaaTokenizerFast
   __A   :				Any         =  True
   __A   :				Tuple         =  True

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          super().setUp()

          # We have a SentencePiece fixture for testing
          a__						:	str =				MBartaaTokenizer(lowercase					,  src_lang='en_XX'					,  tgt_lang='ro_RO'					,  keep_accents=lowercase)
          tokenizer.save_pretrained(self.tmpdirname)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Dict =				'<s>'
          a__						:	Union[str, Any] =				0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase)					,  lowercase)
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase)					,  lowercase)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Tuple =				list(self.get_tokenizer().get_vocab().keys())

          self.assertEqual(vocab_keys[0]					,  '<s>')
          self.assertEqual(vocab_keys[1]					,  '<pad>')
          self.assertEqual(vocab_keys[-1]					,  '<mask>')
          self.assertEqual(len(lowercase)					,  1054)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          self.assertEqual(self.get_tokenizer().vocab_size					,  1054)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Any =				MBartaaTokenizer(lowercase					,  src_lang='en_XX'					,  tgt_lang='ro_RO'					,  keep_accents=lowercase)

          a__						:	Dict =				tokenizer.tokenize('This is a test')
          self.assertListEqual(lowercase					,  ['▁This', '▁is', '▁a', '▁t', 'est'])

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(lowercase)					,  [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]					,  )

          a__						:	List[Any] =				tokenizer.tokenize('I was born in 92000, and this is falsé.')
          self.assertListEqual(
              lowercase					,  [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.']					,  )
          a__						:	Optional[Any] =				tokenizer.convert_tokens_to_ids(lowercase)
          self.assertListEqual(
              lowercase					,  [
                  value + tokenizer.fairseq_offset
                  for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
              ]					,  )

          a__						:	Optional[Any] =				tokenizer.convert_ids_to_tokens(lowercase)
          self.assertListEqual(
              lowercase					,  [SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.']					,  )

   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	List[str] =				{'input_ids': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=lowercase					,  model_name='facebook/mbart-large-50'					,  revision='d3913889c59cd5c9e456b269c376325eabad57e2'					,  )

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          if not self.test_slow_tokenizer:
                 # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                 return

          a__						:	Dict =				(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
                        a__						:	Tuple =				self.rust_tokenizer_class.from_pretrained(lowercase					,  **lowercase)
                        a__						:	List[str] =				self.tokenizer_class.from_pretrained(lowercase					,  **lowercase)

                        a__						:	int =				tempfile.mkdtemp()

                        a__						:	Dict =				tokenizer_r.save_pretrained(lowercase)
                        a__						:	Union[str, Any] =				tokenizer_p.save_pretrained(lowercase)

                        # Checks it save with the same files + the tokenizer.json file for the fast one
                        self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))
                        a__						:	Any =				tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f)
                        self.assertSequenceEqual(lowercase					,  lowercase)

                        # Checks everything loads correctly in the same way
                        a__						:	Any =				tokenizer_r.from_pretrained(lowercase)
                        a__						:	Any =				tokenizer_p.from_pretrained(lowercase)

                        # Check special tokens are set accordingly on Rust and Python
                        for key in tokenizer_pp.special_tokens_map:
                               self.assertTrue(hasattr(lowercase					,  lowercase))
                               # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                               # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                        shutil.rmtree(lowercase)

                        # Save tokenizer rust, legacy_format=True
                        a__						:	Any =				tempfile.mkdtemp()

                        a__						:	str =				tokenizer_r.save_pretrained(lowercase					,  legacy_format=lowercase)
                        a__						:	Dict =				tokenizer_p.save_pretrained(lowercase)

                        # Checks it save with the same files
                        self.assertSequenceEqual(lowercase					,  lowercase)

                        # Checks everything loads correctly in the same way
                        a__						:	Union[str, Any] =				tokenizer_r.from_pretrained(lowercase)
                        a__						:	Optional[Any] =				tokenizer_p.from_pretrained(lowercase)

                        # Check special tokens are set accordingly on Rust and Python
                        for key in tokenizer_pp.special_tokens_map:
                               self.assertTrue(hasattr(lowercase					,  lowercase))

                        shutil.rmtree(lowercase)

                        # Save tokenizer rust, legacy_format=False
                        a__						:	Any =				tempfile.mkdtemp()

                        a__						:	Optional[int] =				tokenizer_r.save_pretrained(lowercase					,  legacy_format=lowercase)
                        a__						:	str =				tokenizer_p.save_pretrained(lowercase)

                        # Checks it saved the tokenizer.json file
                        self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files))

                        # Checks everything loads correctly in the same way
                        a__						:	List[str] =				tokenizer_r.from_pretrained(lowercase)
                        a__						:	Dict =				tokenizer_p.from_pretrained(lowercase)

                        # Check special tokens are set accordingly on Rust and Python
                        for key in tokenizer_pp.special_tokens_map:
                               self.assertTrue(hasattr(lowercase					,  lowercase))

                        shutil.rmtree(lowercase)





@require_torch
@require_sentencepiece
@require_tokenizers
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   __A   :				str         =  '''facebook/mbart-large-50-one-to-many-mmt'''
   __A   :				Union[str, Any]         =  [
       ''' UN Chief Says There Is No Military Solution in Syria''',
       ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
   ]
   __A   :				Dict         =  [
       '''Şeful ONU declară că nu există o soluţie militară în Siria''',
       '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
       ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
       ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
   ]
   __A   :				List[Any]         =  [EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]

   @classmethod
   def 							__lowercase		(					cls)			->						Any:







          '''simple docstring'''

          a__						:	MBartaaTokenizer =				MBartaaTokenizer.from_pretrained(
              cls.checkpoint_name					,  src_lang='en_XX'					,  tgt_lang='ro_RO')
          a__						:	Dict =				1
          return cls

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']					,  25_0001)
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']					,  25_0004)
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']					,  25_0020)
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']					,  25_0038)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
          self.assertListEqual(self.expected_src_tokens					,  lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          self.assertIn(lowercase					,  self.tokenizer.all_special_ids)
          a__						:	Any =				[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
          a__						:	Any =				self.tokenizer.decode(lowercase					,  skip_special_tokens=lowercase)
          a__						:	List[str] =				self.tokenizer.decode(generated_ids[1:]					,  skip_special_tokens=lowercase)
          self.assertEqual(lowercase					,  lowercase)
          self.assertNotIn(self.tokenizer.eos_token					,  lowercase)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Tuple =				['this is gunna be a long sentence ' * 20]
          assert isinstance(src_text[0]					,  lowercase)
          a__						:	str =				10
          a__						:	Optional[Any] =				self.tokenizer(lowercase					,  max_length=lowercase					,  truncation=lowercase).input_ids[0]
          self.assertEqual(ids[0]					,  lowercase)
          self.assertEqual(ids[-1]					,  2)
          self.assertEqual(len(lowercase)					,  lowercase)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'])					,  [25_0053, 25_0001])

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	List[Any] =				tempfile.mkdtemp()
          a__						:	Dict =				self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(lowercase)
          a__						:	Optional[int] =				MBartaaTokenizer.from_pretrained(lowercase)
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,  lowercase)

   @require_torch
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.tokenizer(self.src_text					,  text_target=self.tgt_text					,  padding=lowercase					,  return_tensors='pt')
          a__						:	Dict =				shift_tokens_right(batch['labels']					,  self.tokenizer.pad_token_id)

          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          assert batch.input_ids[1][0] == EN_CODE
          assert batch.input_ids[1][-1] == 2
          assert batch.labels[1][0] == RO_CODE
          assert batch.labels[1][-1] == 2
          assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]

   @require_torch
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	List[Any] =				self.tokenizer(
              self.src_text					,  text_target=self.tgt_text					,  padding=lowercase					,  truncation=lowercase					,  max_length=len(self.expected_src_tokens)					,  return_tensors='pt'					,  )
          a__						:	Optional[int] =				shift_tokens_right(batch['labels']					,  self.tokenizer.pad_token_id)

          self.assertIsInstance(lowercase					,  lowercase)

          self.assertEqual((2, 14)					,  batch.input_ids.shape)
          self.assertEqual((2, 14)					,  batch.attention_mask.shape)
          a__						:	Union[str, Any] =				batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens					,  lowercase)
          self.assertEqual(2					,  batch.decoder_input_ids[0, 0])  # decoder_start_token_id
          # Test that special tokens are reset
          self.assertEqual(self.tokenizer.prefix_tokens					,  [EN_CODE])
          self.assertEqual(self.tokenizer.suffix_tokens					,  [self.tokenizer.eos_token_id])

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Any =				self.tokenizer(self.src_text					,  padding=lowercase					,  truncation=lowercase					,  max_length=3					,  return_tensors='pt')
          a__						:	int =				self.tokenizer(
              text_target=self.tgt_text					,  padding=lowercase					,  truncation=lowercase					,  max_length=10					,  return_tensors='pt')
          a__						:	Optional[int] =				targets['input_ids']
          a__						:	Optional[Any] =				shift_tokens_right(lowercase					,  self.tokenizer.pad_token_id)

          self.assertEqual(batch.input_ids.shape[1]					,  3)
          self.assertEqual(batch.decoder_input_ids.shape[1]					,  10)

   @require_torch
   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Tuple =				self.tokenizer._build_translation_inputs(
              'A test'					,  return_tensors='pt'					,  src_lang='en_XX'					,  tgt_lang='ar_AR')

          self.assertEqual(
              nested_simplify(lowercase)					,  {
                  # en_XX, A, test, EOS
                  'input_ids': [[25_0004, 62, 3034, 2]],
                  'attention_mask': [[1, 1, 1, 1]],
                  # ar_AR
                  'forced_bos_token_id': 25_0001,
              }					,  )









import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MODEL_MAPPING,
        SegformerForImageClassification,
        SegformerForSemanticSegmentation,
        SegformerModel,
    )
    from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import SegformerImageProcessor





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.config_class(**self.inputs_dict)
          self.parent.assertTrue(hasattr(lowercase					,  'hidden_sizes'))
          self.parent.assertTrue(hasattr(lowercase					,  'num_attention_heads'))
          self.parent.assertTrue(hasattr(lowercase					,  'num_encoder_blocks'))





class  A__    :




   """simple docstring"""



   def __init__(					self					,  lowercase					,  lowercase=13					,  lowercase=64					,  lowercase=3					,  lowercase=4					,  lowercase=[2, 2, 2, 2]					,  lowercase=[8, 4, 2, 1]					,  lowercase=[16, 32, 64, 128]					,  lowercase=[1, 4, 8, 16]					,  lowercase=[1, 2, 4, 8]					,  lowercase=True					,  lowercase=True					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=0.02					,  lowercase=3					,  lowercase=None					,  )			->						Tuple:







          '''simple docstring'''

          a__						:	Optional[Any] =				parent
          a__						:	int =				batch_size
          a__						:	Tuple =				image_size
          a__						:	Union[str, Any] =				num_channels
          a__						:	str =				num_encoder_blocks
          a__						:	Dict =				sr_ratios
          a__						:	Dict =				depths
          a__						:	Union[str, Any] =				hidden_sizes
          a__						:	str =				downsampling_rates
          a__						:	Tuple =				num_attention_heads
          a__						:	Optional[Any] =				is_training
          a__						:	Union[str, Any] =				use_labels
          a__						:	Any =				hidden_act
          a__						:	Optional[int] =				hidden_dropout_prob
          a__						:	int =				attention_probs_dropout_prob
          a__						:	Optional[Any] =				initializer_range
          a__						:	Tuple =				num_labels
          a__						:	Union[str, Any] =				scope

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Optional[int] =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	str =				None
          if self.use_labels:
                 a__						:	Dict =				ids_tensor([self.batch_size, self.image_size, self.image_size]					,  self.num_labels)

          a__						:	Any =				self.get_config()
          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          return SegformerConfig(
              image_size=self.image_size					,  num_channels=self.num_channels					,  num_encoder_blocks=self.num_encoder_blocks					,  depths=self.depths					,  hidden_sizes=self.hidden_sizes					,  num_attention_heads=self.num_attention_heads					,  hidden_act=self.hidden_act					,  hidden_dropout_prob=self.hidden_dropout_prob					,  attention_probs_dropout_prob=self.attention_probs_dropout_prob					,  initializer_range=self.initializer_range					,  )

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Dict:







          '''simple docstring'''

          a__						:	Dict =				SegformerModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Optional[Any] =				model(lowercase)
          a__						:	Optional[Any] =				self.image_size // (self.downsampling_rates[-1] * 2)
          self.parent.assertEqual(
              result.last_hidden_state.shape					,  (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						str:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.num_labels
          a__						:	List[str] =				SegformerForSemanticSegmentation(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[str] =				model(lowercase)
          self.parent.assertEqual(
              result.logits.shape					,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
          a__						:	int =				model(lowercase					,  labels=lowercase)
          self.parent.assertEqual(
              result.logits.shape					,  (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4))
          self.parent.assertGreater(result.loss					,  0.0)

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Optional[int]:







          '''simple docstring'''

          a__						:	Union[str, Any] =				1
          a__						:	Optional[int] =				SegformerForSemanticSegmentation(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Union[str, Any] =				torch.randint(0					,  1					,  (self.batch_size, self.image_size, self.image_size)).to(lowercase)
          a__						:	Optional[Any] =				model(lowercase					,  labels=lowercase)
          self.parent.assertGreater(result.loss					,  0.0)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Any =				self.prepare_config_and_inputs()
          a__ ,							a__ ,							a__						:	str =				config_and_inputs
          a__						:	Optional[int] =				{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Any         =  (
       (
           SegformerModel,
           SegformerForSemanticSegmentation,
           SegformerForImageClassification,
       )
       if is_torch_available()
       else ()
   )
   __A   :				List[str]         =  (
       {
           '''feature-extraction''': SegformerModel,
           '''image-classification''': SegformerForImageClassification,
           '''image-segmentation''': SegformerForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   __A   :				List[str]         =  True
   __A   :				Any         =  False
   __A   :				Any         =  False
   __A   :				str         =  False

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Union[str, Any] =				SegformerModelTester(self)
          a__						:	Optional[Any] =				SegformerConfigTester(self					,  config_class=lowercase)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_binary_image_segmentation(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	int =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_segmentation(*lowercase)

   @unittest.skip('SegFormer does not use inputs_embeds')
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          pass

   @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods')
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__ ,							a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	List[str] =				model_class(lowercase)
                 a__						:	Dict =				inspect.signature(model.forward)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 a__						:	Optional[int] =				[*signature.parameters.keys()]

                 a__						:	Union[str, Any] =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__ ,							a__						:	Dict =				self.model_tester.prepare_config_and_inputs_for_common()
          a__						:	Tuple =				True

          for model_class in self.all_model_classes:
                 a__						:	str =				True
                 a__						:	List[str] =				False
                 a__						:	int =				True
                 a__						:	List[Any] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	Optional[Any] =				model(**self._prepare_for_class(lowercase					,  lowercase))
                 a__						:	Optional[Any] =				outputs.attentions

                 a__						:	Dict =				sum(self.model_tester.depths)
                 self.assertEqual(len(lowercase)					,  lowercase)

                 # check that output_attentions also work using config
                 del inputs_dict["output_attentions"]
                 a__						:	Dict =				True
                 a__						:	int =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	Optional[int] =				model(**self._prepare_for_class(lowercase					,  lowercase))
                 a__						:	Optional[Any] =				outputs.attentions

                 self.assertEqual(len(lowercase)					,  lowercase)

                 # verify the first attentions (first block, first layer)
                 a__						:	Tuple =				(self.model_tester.image_size // 4) ** 2
                 a__						:	List[str] =				(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                 self.assertListEqual(
                     list(attentions[0].shape[-3:])					,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,  )

                 # verify the last attentions (last block, last layer)
                 a__						:	str =				(self.model_tester.image_size // 32) ** 2
                 a__						:	Optional[int] =				(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
                 self.assertListEqual(
                     list(attentions[-1].shape[-3:])					,  [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]					,  )
                 a__						:	Dict =				len(lowercase)

                 # Check attention is always last and order is fine
                 a__						:	List[Any] =				True
                 a__						:	Any =				True
                 a__						:	Dict =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()
                 with torch.no_grad():
                        a__						:	int =				model(**self._prepare_for_class(lowercase					,  lowercase))

                 self.assertEqual(out_len + 1					,  len(lowercase))

                 a__						:	int =				outputs.attentions

                 self.assertEqual(len(lowercase)					,  lowercase)
                 # verify the first attentions (first block, first layer)
                 a__						:	List[Any] =				(self.model_tester.image_size // 4) ** 2
                 a__						:	Union[str, Any] =				(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                 self.assertListEqual(
                     list(self_attentions[0].shape[-3:])					,  [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,  )

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          def check_hidden_states_output(lowercase					,  lowercase					,  lowercase):
                 a__						:	Optional[Any] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()

                 with torch.no_grad():
                        a__						:	int =				model(**self._prepare_for_class(lowercase					,  lowercase))

                 a__						:	Union[str, Any] =				outputs.hidden_states

                 a__						:	Any =				self.model_tester.num_encoder_blocks
                 self.assertEqual(len(lowercase)					,  lowercase)

                 # verify the first hidden states (first block)
                 self.assertListEqual(
                     list(hidden_states[0].shape[-3:])					,  [
                         self.model_tester.hidden_sizes[0],
                         self.model_tester.image_size // 4,
                         self.model_tester.image_size // 4,
                     ]					,  )

          a__ ,							a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	List[str] =				True
                 check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 a__						:	int =				True

                 check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          if not self.model_tester.is_training:
                 return

          a__ ,							a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs_for_common()
          a__						:	Tuple =				True

          for model_class in self.all_model_classes:
                 if model_class in get_values(lowercase):
                        continue

                 a__						:	Dict =				model_class(lowercase)
                 model.to(lowercase)
                 model.train()
                 a__						:	str =				self._prepare_for_class(lowercase					,  lowercase					,  return_labels=lowercase)
                 a__						:	Optional[int] =				model(**lowercase).loss
                 loss.backward()

   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          pass

   @slow
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[Any] =				SegformerModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		int:
       a__						:	str =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





@require_torch
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   @slow
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Dict =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	int =				SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
              lowercase)

          a__						:	Optional[int] =				prepare_img()
          a__						:	Optional[int] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[str] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Optional[int] =				model(lowercase)

          a__						:	Union[str, Any] =				torch.Size((1, model.config.num_labels, 128, 128))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Dict =				torch.tensor(
              [
                  [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
                  [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
                  [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
              ]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,  lowercase					,  atol=1e-4))

   @slow
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Dict =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	List[str] =				SegformerForSemanticSegmentation.from_pretrained(
              'nvidia/segformer-b1-finetuned-cityscapes-1024-1024').to(lowercase)

          a__						:	Dict =				prepare_img()
          a__						:	Optional[int] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	List[str] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Optional[Any] =				model(lowercase)

          a__						:	List[Any] =				torch.Size((1, model.config.num_labels, 128, 128))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Optional[Any] =				torch.tensor(
              [
                  [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
                  [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
                  [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
              ]).to(lowercase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]					,  lowercase					,  atol=1e-1))

   @slow
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	List[str] =				SegformerImageProcessor(
              image_scale=(512, 512)					,  keep_ratio=lowercase					,  align=lowercase					,  do_random_crop=lowercase)
          a__						:	List[str] =				SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512').to(
              lowercase)

          a__						:	Any =				prepare_img()
          a__						:	Optional[Any] =				image_processor(images=lowercase					,  return_tensors='pt')
          a__						:	Optional[int] =				encoded_inputs.pixel_values.to(lowercase)

          with torch.no_grad():
                 a__						:	Union[str, Any] =				model(lowercase)

          a__						:	int =				outputs.logits.detach().cpu()

          a__						:	List[str] =				image_processor.post_process_semantic_segmentation(outputs=lowercase					,  target_sizes=[(500, 300)])
          a__						:	Optional[Any] =				torch.Size((500, 300))
          self.assertEqual(segmentation[0].shape					,  lowercase)

          a__						:	Any =				image_processor.post_process_semantic_segmentation(outputs=lowercase)
          a__						:	Union[str, Any] =				torch.Size((128, 128))
          self.assertEqual(segmentation[0].shape					,  lowercase)


