






'''simple docstring'''

import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__											=     logging.getLogger()







def 					lowercase__				(   )->				List[Any]:
      UpperCamelCase       						=      argparse.ArgumentParser()
      parser.add_argument("""-f"""					)
      UpperCamelCase       						=      parser.parse_args()
      return args.f







def 					lowercase__				(   __UpperCamelCase					)->				Tuple:
      UpperCamelCase       						=      {}
      UpperCamelCase       						=      os.path.join(__UpperCamelCase	,		"""all_results.json"""					)
      if os.path.exists(__UpperCamelCase					):
            with open(__UpperCamelCase	,		"""r"""					) as f:
                  UpperCamelCase       						=      json.load(__UpperCamelCase					)
      else:
            raise ValueError(F"can't find {path}"					)
      return results







def 					lowercase__				(   )->				int:
      UpperCamelCase       						=      torch.cuda.is_available() and torch_device == """cuda"""
      return is_using_cuda and is_apex_available()


SCREAMING_SNAKE_CASE__											=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class        a_		(    lowerCamelCase       ):

  @classmethod
  def 						A__		(   cls   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      tempfile.mkdtemp()
        UpperCamelCase       						=      os.path.join(cls.tmpdir    ,	"""default_config.yml"""   )
        write_basic_config(save_location=cls.configPath   )
        UpperCamelCase       						=      ["""accelerate""", """launch""", """--config_file""", cls.configPath]

  @classmethod
  def 						A__		(   cls   )						->   Optional[Any]:



        """simple docstring"""






        shutil.rmtree(cls.tmpdir   )

  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        if is_cuda_and_apex_available():
              testargs.append("""--fp16"""   )

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,	0.7_5   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""glue_no_trainer"""   )   )   )

  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        if torch.cuda.device_count() > 1:
              # Skipping because there are not enough batches to train the model + would need a drop_last to work.
              return

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertLess(result["""perplexity"""]    ,	100   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""clm_no_trainer"""   )   )   )

  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertLess(result["""perplexity"""]    ,	42   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""mlm_no_trainer"""   )   )   )

  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      7 if get_gpu_count() > 1 else 2

        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,	0.7_5   )
        self.assertLess(result["""train_loss"""]    ,	0.5   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""ner_no_trainer"""   )   )   )

  @unittest.skip(reason="""Fix me @muellerzr"""   )
  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
        self.assertGreaterEqual(result["""eval_f1"""]    ,	28   )
        self.assertGreaterEqual(result["""eval_exact"""]    ,	28   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""qa_no_trainer"""   )   )   )

  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,	0.8   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""swag_no_trainer"""   )   )   )

  @slow
  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_rouge1"""]    ,	10   )
        self.assertGreaterEqual(result["""eval_rouge2"""]    ,	2   )
        self.assertGreaterEqual(result["""eval_rougeL"""]    ,	7   )
        self.assertGreaterEqual(result["""eval_rougeLsum"""]    ,	7   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""summarization_no_trainer"""   )   )   )

  @slow
  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_bleu"""]    ,	30   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""epoch_0"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""translation_no_trainer"""   )   )   )

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



        """simple docstring"""






        UpperCamelCase       						=      logging.StreamHandler(sys.stdout   )
        logger.addHandler(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        self.assertGreaterEqual(result["""eval_overall_accuracy"""]    ,	0.1_0   )


  @mock.patch.dict(os.environ    ,	{"""WANDB_MODE""": """offline"""}   )
  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.get_auto_remove_tmp_dir()
        UpperCamelCase       						=      F"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

        if is_cuda_and_apex_available():
              testargs.append("""--fp16"""   )

        run_command(self._launch_args + testargs   )
        UpperCamelCase       						=      get_results(_SCREAMING_SNAKE_CASE   )
        # The base model scores a 25%
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,	0.6   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""step_1"""   )   )   )
        self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE    ,	"""image_classification_no_trainer"""   )   )   )








'''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   )   )
