






'''simple docstring'''

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
     from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
     SCREAMING_SNAKE_CASE__											=     get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
     from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

SCREAMING_SNAKE_CASE__											=     1_2_8_0_2_2
SCREAMING_SNAKE_CASE__											=     1_2_8_0_2_8






@require_sentencepiece
class        a_		(    __a			, unittest.TestCase       ):
    lowercase  	=   MaMaaaTokenizer
    lowercase  	=   False
    lowercase  	=   False
    lowercase  	=   True

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



          """simple docstring"""






          super().setUp()

          UpperCamelCase       						=      ["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
          UpperCamelCase       						=      dict(zip(a_    ,	range(len(a_   )   )   )   )
          UpperCamelCase       						=      Path(self.tmpdirname   )
          save_json(a_    ,	save_dir / VOCAB_FILES_NAMES["""vocab_file"""]   )
          if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                copyfile(a_    ,	save_dir / VOCAB_FILES_NAMES["""spm_file"""]   )

          UpperCamelCase       						=      MaMaaaTokenizer.from_pretrained(self.tmpdirname   )
          tokenizer.save_pretrained(self.tmpdirname   )

    def 						A__		(   self    ,	**_SCREAMING_SNAKE_CASE   )						->   str:



          """simple docstring"""






          return MaMaaaTokenizer.from_pretrained(self.tmpdirname    ,	**a_   )

    def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   str:



          """simple docstring"""






          return (
              "This is a test",
              "This is a test",
          )

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



          """simple docstring"""






          UpperCamelCase       						=      """</s>"""
          UpperCamelCase       						=      0

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

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



          """simple docstring"""






          UpperCamelCase       						=      self.get_tokenizer()
          UpperCamelCase       						=      list(tokenizer.get_vocab().keys()   )

          self.assertEqual(vocab_keys[0]    ,	"""</s>"""   )
          self.assertEqual(vocab_keys[1]    ,	"""<unk>"""   )
          self.assertEqual(vocab_keys[-1]    ,	"""<s>"""   )
          self.assertEqual(len(a_   )    ,	tokenizer.vocab_size + len(tokenizer.get_added_vocab()   )   )

    @unittest.skip("""Skip this test while all models are still to be uploaded."""   )
    def 						A__		(   self   )						->   Dict:



          """simple docstring"""






          pass

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



          """simple docstring"""






          UpperCamelCase       						=      self.get_tokenizer()

          UpperCamelCase       						=      tokenizer.tokenize("""This is a test"""   )
          self.assertListEqual(a_    ,	["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(a_   )    ,	[2, 3, 4, 5, 6]    ,	)

          UpperCamelCase       						=      tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]   )
          self.assertListEqual(a_    ,	["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]   )

          UpperCamelCase       						=      tokenizer.convert_tokens_to_string(a_   )
          self.assertEqual(a_    ,	"""This is a test"""   )


    @slow
    def 						A__		(   self   )						->   int:



          """simple docstring"""






          UpperCamelCase       						=      {"""input_ids""": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 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], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 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, 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, 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], [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, 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=a_    ,	model_name="""facebook/m2m100_418M"""    ,	revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""    ,	)






@require_torch
@require_sentencepiece
@require_tokenizers
class        a_		(    unittest.TestCase       ):
    lowercase  	=   """facebook/m2m100_418M"""
    lowercase  	=   [
        """In my opinion, there are two levels of response from the French government.""",
        """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
    ]
    lowercase  	=   [
        """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
        """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
    ]

    # fmt: off
    lowercase  	=   [EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]

    @classmethod
    def 						A__		(   cls   )						->   Union[str, Any]:



          """simple docstring"""






          UpperCamelCase       						=      MaMaaaTokenizer.from_pretrained(
              cls.checkpoint_name    ,	src_lang="""en"""    ,	tgt_lang="""fr"""   )
          UpperCamelCase       						=      1
          return cls

    def 						A__		(   self   )						->   Dict:



          """simple docstring"""






          self.assertEqual(self.tokenizer.get_lang_id("""ar"""   )    ,	128006   )
          self.assertEqual(self.tokenizer.get_lang_id("""en"""   )    ,	128022   )
          self.assertEqual(self.tokenizer.get_lang_id("""ro"""   )    ,	128076   )
          self.assertEqual(self.tokenizer.get_lang_id("""mr"""   )    ,	128063   )

    def 						A__		(   self   )						->   int:



          """simple docstring"""






          UpperCamelCase       						=      self.tokenizer.get_vocab()
          self.assertEqual(len(a_   )    ,	self.tokenizer.vocab_size   )
          self.assertEqual(vocab["""<unk>"""]    ,	3   )
          self.assertIn(self.tokenizer.get_lang_token("""en"""   )    ,	a_   )

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



          """simple docstring"""






          UpperCamelCase       						=      """en"""
          UpperCamelCase       						=      self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens    ,	a_   )

    def 						A__		(   self   )						->   Any:



          """simple docstring"""






          self.assertIn(a_    ,	self.tokenizer.all_special_ids   )
          # fmt: off
          UpperCamelCase       						=      [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
          # fmt: on
          UpperCamelCase       						=      self.tokenizer.decode(a_    ,	skip_special_tokens=a_   )
          UpperCamelCase       						=      self.tokenizer.decode(generated_ids[1:]    ,	skip_special_tokens=a_   )
          self.assertEqual(a_    ,	a_   )
          self.assertNotIn(self.tokenizer.eos_token    ,	a_   )

    def 						A__		(   self   )						->   Tuple:



          """simple docstring"""






          UpperCamelCase       						=      tempfile.mkdtemp()
          UpperCamelCase       						=      self.tokenizer.lang_token_to_id
          self.tokenizer.save_pretrained(a_   )
          UpperCamelCase       						=      MaMaaaTokenizer.from_pretrained(a_   )
          self.assertDictEqual(new_tok.lang_token_to_id    ,	a_   )

    @require_torch
    def 						A__		(   self   )						->   Union[str, Any]:



          """simple docstring"""






          UpperCamelCase       						=      """en"""
          UpperCamelCase       						=      """fr"""

          UpperCamelCase       						=      self.tokenizer(self.src_text    ,	text_target=self.tgt_text    ,	padding=a_    ,	return_tensors="""pt"""   )

          UpperCamelCase       						=      shift_tokens_right(
              batch["""labels"""]    ,	self.tokenizer.pad_token_id    ,	self.tokenizer.eos_token_id   )

          for k in batch:
                UpperCamelCase       						=      batch[k].tolist()
          # batch = {k: v.tolist() for k,v in batch.items()}
          # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
          # batch.decoder_inputs_ids[0][0] ==
          assert batch.input_ids[1][0] == EN_CODE
          assert batch.input_ids[1][-1] == 2
          assert batch.labels[1][0] == FR_CODE
          assert batch.labels[1][-1] == 2
          assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]

    @require_torch
    def 						A__		(   self   )						->   Dict:



          """simple docstring"""






          UpperCamelCase       						=      """mr"""
          self.assertListEqual(self.tokenizer.prefix_tokens    ,	[self.tokenizer.get_lang_id("""mr"""   )]   )
          self.assertListEqual(self.tokenizer.suffix_tokens    ,	[self.tokenizer.eos_token_id]   )

          UpperCamelCase       						=      """zh"""
          self.assertListEqual(self.tokenizer.prefix_tokens    ,	[self.tokenizer.get_lang_id("""zh"""   )]   )
          self.assertListEqual(self.tokenizer.suffix_tokens    ,	[self.tokenizer.eos_token_id]   )

    @require_torch
    def 						A__		(   self   )						->   Optional[Any]:



          """simple docstring"""






          UpperCamelCase       						=      """mr"""
          self.tokenizer._switch_to_target_mode()
          self.assertListEqual(self.tokenizer.prefix_tokens    ,	[self.tokenizer.get_lang_id("""mr"""   )]   )
          self.assertListEqual(self.tokenizer.suffix_tokens    ,	[self.tokenizer.eos_token_id]   )
          self.tokenizer._switch_to_input_mode()
          self.assertListEqual(self.tokenizer.prefix_tokens    ,	[self.tokenizer.get_lang_id(self.tokenizer.src_lang   )]   )

          UpperCamelCase       						=      """zh"""
          self.tokenizer._switch_to_target_mode()
          self.assertListEqual(self.tokenizer.prefix_tokens    ,	[self.tokenizer.get_lang_id("""zh"""   )]   )
          self.assertListEqual(self.tokenizer.suffix_tokens    ,	[self.tokenizer.eos_token_id]   )
          self.tokenizer._switch_to_input_mode()
          self.assertListEqual(self.tokenizer.prefix_tokens    ,	[self.tokenizer.get_lang_id(self.tokenizer.src_lang   )]   )


    @require_torch
    def 						A__		(   self   )						->   int:



          """simple docstring"""






          UpperCamelCase       						=      self.tokenizer._build_translation_inputs("""A test"""    ,	return_tensors="""pt"""    ,	src_lang="""en"""    ,	tgt_lang="""ar"""   )

          self.assertEqual(
              nested_simplify(a_   )    ,	{
                  # en_XX, A, test, EOS
                  """input_ids""": [[128022, 58, 4183, 2]],
                  """attention_mask""": [[1, 1, 1, 1]],
                  # ar_AR
                  """forced_bos_token_id""": 128006,
              }    ,	)








'''simple docstring'''

import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor






class        a_		(    lowerCamelCase       ):

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.config_class(**self.inputs_dict   )
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""tf_padding"""   )   )
        self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""depth_multiplier"""   )   )






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=13    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE=0.2_5    ,	_SCREAMING_SNAKE_CASE=8    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=1024    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE="relu6"    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=10    ,	_SCREAMING_SNAKE_CASE=None    ,	)						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      image_size
        UpperCamelCase       						=      depth_multiplier
        UpperCamelCase       						=      min_depth
        UpperCamelCase       						=      tf_padding
        UpperCamelCase       						=      int(last_hidden_size * depth_multiplier   )
        UpperCamelCase       						=      output_stride
        UpperCamelCase       						=      hidden_act
        UpperCamelCase       						=      classifier_dropout_prob
        UpperCamelCase       						=      use_labels
        UpperCamelCase       						=      is_training
        UpperCamelCase       						=      num_labels
        UpperCamelCase       						=      initializer_range
        UpperCamelCase       						=      scope

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

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

        UpperCamelCase       						=      self.get_config()

        return config, pixel_values, labels, pixel_labels

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



        """simple docstring"""






        return MobileNetVaConfig(
            num_channels=self.num_channels    ,	image_size=self.image_size    ,	depth_multiplier=self.depth_multiplier    ,	min_depth=self.min_depth    ,	tf_padding=self.tf_padding    ,	hidden_act=self.hidden_act    ,	classifier_dropout_prob=self.classifier_dropout_prob    ,	initializer_range=self.initializer_range    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaModel(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,	(
                self.batch_size,
                self.last_hidden_size,
                self.image_size // self.output_stride,
                self.image_size // self.output_stride,
            )    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.num_labels
        UpperCamelCase       						=      MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)   )


  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      self.prepare_config_and_inputs()
        UpperCamelCase						,UpperCamelCase						,UpperCamelCase						,UpperCamelCase       						=      config_and_inputs
        UpperCamelCase       						=      {"""pixel_values""": pixel_values}
        return config, inputs_dict






@require_torch
class        a_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
  lowercase  	=   (
      {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
      if is_torch_available()
      else {}
  )

  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaModelTester(self   )
        UpperCamelCase       						=      MobileNetVaConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	has_text_modality=_SCREAMING_SNAKE_CASE   )

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



        """simple docstring"""






        self.config_tester.run_common_tests()

  @unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        pass

  @unittest.skip(reason="""MobileNetV1 does not output attentions"""   )
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        pass

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



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      inspect.signature(model.forward   )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCamelCase       						=      [*signature.parameters.keys()]

              UpperCamelCase       						=      ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        def check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
              model.to(_SCREAMING_SNAKE_CASE   )
              model.eval()

              with torch.no_grad():
                    UpperCamelCase       						=      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )   )

              UpperCamelCase       						=      outputs.hidden_states

              UpperCamelCase       						=      26
              self.assertEqual(len(_SCREAMING_SNAKE_CASE   )    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      True
              check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCamelCase       						=      True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE   )


  @slow
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase       						=      MobileNetVaModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def 					lowercase__				(   )->				Optional[Any]:
      UpperCamelCase       						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
      return image






@require_torch
@require_vision
class        a_		(    unittest.TestCase       ):

  @cached_property
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        return (
            MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""   ) if is_vision_available() else None
        )


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



        """simple docstring"""






        UpperCamelCase       						=      MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""   ).to(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.default_image_processor
        UpperCamelCase       						=      prepare_img()
        UpperCamelCase       						=      image_processor(images=_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).to(_SCREAMING_SNAKE_CASE   )

        # forward pass
        with torch.no_grad():
              UpperCamelCase       						=      model(**_SCREAMING_SNAKE_CASE   )

        # verify the logits
        UpperCamelCase       						=      torch.Size((1, 1001)   )
        self.assertEqual(outputs.logits.shape    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]   ).to(_SCREAMING_SNAKE_CASE   )

        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1e-4   )   )
