



"""simple docstring"""







import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock

import torch

from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config


# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)

lowercase__		:				Tuple          =	[
    """cross_validation.py""",
    """gradient_accumulation.py""",
    """local_sgd.py""",
    """multi_process_metrics.py""",
    """memory.py""",
    """automatic_gradient_accumulation.py""",
    """fsdp_with_peak_mem_tracking.py""",
    """deepspeed_with_config_support.py""",
    """megatron_lm_gpt_pretraining.py""",
]







class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      def      SCREAMING_SNAKE_CASE__ (      self				:      str    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      bool    ,			SCREAMING_SNAKE_CASE_				:      str = None    ,			SCREAMING_SNAKE_CASE_				:      list = None       ):
        lowerCAmelCase_   :				List[Any]								= None
        lowerCAmelCase_   :				int								= os.path.abspath(os.path.join('examples'    ,			'by_feature'       )       )
        lowerCAmelCase_   :				int								= os.path.abspath('examples'       )
        for item in os.listdir(SCREAMING_SNAKE_CASE_       ):
          if item not in EXCLUDE_EXAMPLES:
            lowerCAmelCase_   :				Optional[int]								= os.path.join(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
            if os.path.isfile(SCREAMING_SNAKE_CASE_       ) and ".py" in item_path:
              with self.subTest(
                  tested_script=SCREAMING_SNAKE_CASE_    ,			feature_script=SCREAMING_SNAKE_CASE_    ,			tested_section='main()' if parser_only else 'training_function()'    ,			):
                lowerCAmelCase_   :				int								= compare_against_test(
                    os.path.join(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
                lowerCAmelCase_   :				Tuple								= '\n'.join(SCREAMING_SNAKE_CASE_       )
                if special_strings is not None:
                  for string in special_strings:
                    lowerCAmelCase_   :				List[str]								= diff.replace(SCREAMING_SNAKE_CASE_    ,			''       )
                self.assertEqual(SCREAMING_SNAKE_CASE_    ,			''       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        self.one_complete_example('complete_nlp_example.py'    ,			SCREAMING_SNAKE_CASE_       )
        self.one_complete_example('complete_nlp_example.py'    ,			SCREAMING_SNAKE_CASE_       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Union[str, Any]								= os.path.abspath(os.path.join('examples'    ,			'cv_example.py'       )       )
        lowerCAmelCase_   :				List[Any]								= [
            ' ' * 1_6 + '{\n\n',
            ' ' * 2_0 + '"accuracy": eval_metric["accuracy"],\n\n',
            ' ' * 2_0 + '"f1": eval_metric["f1"],\n\n',
            ' ' * 2_0 + '"train_loss": total_loss.item() / len(train_dataloader),\n\n',
            ' ' * 2_0 + '"epoch": epoch,\n\n',
            ' ' * 1_6 + '},\n\n',
            ' ' * 1_6 + 'step=epoch,\n',
            ' ' * 1_2,
            ' ' * 8 + 'for step, batch in enumerate(active_dataloader):\n',
        ]
        self.one_complete_example('complete_cv_example.py'    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
        self.one_complete_example('complete_cv_example.py'    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )







@mock.patch.dict(os.environ,					{"""TESTING_MOCKED_DATALOADERS""": """1"""}  )
class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     False



      @classmethod
      def      SCREAMING_SNAKE_CASE__ (      cls				:      Tuple       ):
        super().setUpClass()
        lowerCAmelCase_   :				int								= tempfile.mkdtemp()
        lowerCAmelCase_   :				Dict								= os.path.join(cls._tmpdir    ,			'default_config.yml'       )

        write_basic_config(save_location=cls.configPath       )
        lowerCAmelCase_   :				List[Any]								= ['accelerate', 'launch', '--config_file', cls.configPath]



      @classmethod
      def      SCREAMING_SNAKE_CASE__ (      cls				:      Optional[int]       ):
        super().tearDownClass()
        shutil.rmtree(cls._tmpdir       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        lowerCAmelCase_   :				str								= F"\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps epoch\n        --output_dir {self.tmpdir}\n        ".split()
        run_command(self._launch_args + testargs       )
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir    ,			'epoch_0'       )       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				Optional[Any]								= F"\n        examples/by_feature/checkpointing.py\n        --checkpointing_steps 1\n        --output_dir {self.tmpdir}\n        ".split()
        lowerCAmelCase_   :				List[str]								= run_command(self._launch_args + testargs       )
        self.assertTrue(os.path.exists(os.path.join(self.tmpdir    ,			'step_2'       )       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				List[Any]								= F"\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir    ,			'epoch_0'       )}\n        ".split()
        lowerCAmelCase_   :				Tuple								= run_command(self._launch_args + testargs    ,			return_stdout=SCREAMING_SNAKE_CASE_       )
        self.assertNotIn('epoch 0:'    ,			SCREAMING_SNAKE_CASE_       )
        self.assertIn('epoch 1:'    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Union[str, Any]								= F"\n        examples/by_feature/checkpointing.py\n        --resume_from_checkpoint {os.path.join(self.tmpdir    ,			'step_2'       )}\n        ".split()
        lowerCAmelCase_   :				Dict								= run_command(self._launch_args + testargs    ,			return_stdout=SCREAMING_SNAKE_CASE_       )
        if torch.cuda.is_available():
          lowerCAmelCase_   :				Any								= torch.cuda.device_count()
        else:
          lowerCAmelCase_   :				Tuple								= 1
        if num_processes > 1:
          self.assertNotIn('epoch 0:'    ,			SCREAMING_SNAKE_CASE_       )
          self.assertIn('epoch 1:'    ,			SCREAMING_SNAKE_CASE_       )
        else:
          self.assertIn('epoch 0:'    ,			SCREAMING_SNAKE_CASE_       )
          self.assertIn('epoch 1:'    ,			SCREAMING_SNAKE_CASE_       )



      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				Tuple								= '\n        examples/by_feature/cross_validation.py\n        --num_folds 2\n        '.split()
        with mock.patch.dict(os.environ    ,			{'TESTING_MOCKED_DATALOADERS': '0'}       ):
          lowerCAmelCase_   :				Optional[Any]								= run_command(self._launch_args + testargs    ,			return_stdout=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				int								= re.findall('({.+})'    ,			SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Dict								= [r for r in results if 'accuracy' in r][-1]
          lowerCAmelCase_   :				Optional[int]								= ast.literal_eval(SCREAMING_SNAKE_CASE_       )
          self.assertGreaterEqual(results['accuracy']    ,			0.75       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Union[str, Any]								= ['examples/by_feature/multi_process_metrics.py']
        run_command(self._launch_args + testargs       )



      @require_trackers
      @mock.patch.dict(os.environ    ,			{'WANDB_MODE': 'offline'}       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        with tempfile.TemporaryDirectory() as tmpdir:
          lowerCAmelCase_   :				Dict								= F"\n            examples/by_feature/tracking.py\n            --with_tracking\n            --project_dir {tmpdir}\n            ".split()
          run_command(self._launch_args + testargs       )
          self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE_    ,			'tracking'       )       )       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				Optional[Any]								= ['examples/by_feature/gradient_accumulation.py']
        run_command(self._launch_args + testargs       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				List[Any]								= ['examples/by_feature/local_sgd.py']
        run_command(self._launch_args + testargs       )










"""simple docstring"""







from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
     import tensorflow as tf

     from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor







class       UpperCamelCase__   :




      """simple docstring"""




      def __init__(      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=3    ,			SCREAMING_SNAKE_CASE_				:      Any=3_2    ,			SCREAMING_SNAKE_CASE_				:      int=3    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=1_0    ,			SCREAMING_SNAKE_CASE_				:      Dict=[1_0, 2_0, 3_0, 4_0]    ,			SCREAMING_SNAKE_CASE_				:      int=[1, 1, 2, 1]    ,			SCREAMING_SNAKE_CASE_				:      int=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      int="relu"    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=3    ,			SCREAMING_SNAKE_CASE_				:      List[str]=None    ,			):
        lowerCAmelCase_   :				Optional[int]								= parent
        lowerCAmelCase_   :				Union[str, Any]								= batch_size
        lowerCAmelCase_   :				Optional[int]								= image_size
        lowerCAmelCase_   :				Optional[int]								= num_channels
        lowerCAmelCase_   :				Any								= embeddings_size
        lowerCAmelCase_   :				Dict								= hidden_sizes
        lowerCAmelCase_   :				Any								= depths
        lowerCAmelCase_   :				Optional[int]								= is_training
        lowerCAmelCase_   :				int								= use_labels
        lowerCAmelCase_   :				List[Any]								= hidden_act
        lowerCAmelCase_   :				Dict								= num_labels
        lowerCAmelCase_   :				Optional[int]								= scope
        lowerCAmelCase_   :				Tuple								= len(SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				List[str]								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

        lowerCAmelCase_   :				Union[str, Any]								= None
        if self.use_labels:
          lowerCAmelCase_   :				int								= ids_tensor([self.batch_size]    ,			self.num_labels       )

        lowerCAmelCase_   :				List[str]								= self.get_config()
        return config, pixel_values, labels



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        return RegNetConfig(
            num_channels=self.num_channels    ,			embeddings_size=self.embeddings_size    ,			hidden_sizes=self.hidden_sizes    ,			depths=self.depths    ,			hidden_act=self.hidden_act    ,			num_labels=self.num_labels    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				List[Any]								= TFRegNetModel(config=SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[Any]								= model(SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				int								= self.num_labels
        lowerCAmelCase_   :				int								= TFRegNetForImageClassification(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Dict								= model(SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				str								= self.prepare_config_and_inputs()
        lowerCAmelCase_						,lowerCAmelCase_						,lowerCAmelCase_   :				Tuple								= config_and_inputs
        lowerCAmelCase_   :				Optional[Any]								= {'pixel_values': pixel_values}
        return config, inputs_dict







@require_tf
class       UpperCamelCase__   (	lowercase_,					lowercase_,					unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
      _SCREAMING_SNAKE_CASE  				=     (
          {"""feature-extraction""": TFRegNetModel, """image-classification""": TFRegNetForImageClassification}
          if is_tf_available()
          else {}
      )

      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False
      _SCREAMING_SNAKE_CASE  				=     False



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				List[Any]								= TFRegNetModelTester(self       )
        lowerCAmelCase_   :				Tuple								= ConfigTester(self    ,			config_class=SCREAMING_SNAKE_CASE_    ,			has_text_modality=SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        return



      @unittest.skip(reason='RegNet does not use inputs_embeds'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        pass



      @unittest.skipIf(
          not is_tf_available() or len(tf.config.list_physical_devices('GPU'       )       ) == 0    ,			reason='TF does not support backprop for grouped convolutions on CPU.'    ,			)
      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Tuple       ):
        super().test_keras_fit()



      @unittest.skip(reason='RegNet does not support input and output embeddings'       )
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        pass



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				Optional[int]								= self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          lowerCAmelCase_   :				int								= model_class(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Dict								= inspect.signature(model.call       )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          lowerCAmelCase_   :				List[Any]								= [*signature.parameters.keys()]

          lowerCAmelCase_   :				Any								= ['pixel_values']
          self.assertListEqual(arg_names[:1]    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        lowerCAmelCase_   :				Dict								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
          lowerCAmelCase_   :				List[Any]								= model_class(SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Union[str, Any]								= model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )    ,			training=SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				List[str]								= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          lowerCAmelCase_   :				Union[str, Any]								= self.model_tester.num_stages
          self.assertEqual(len(SCREAMING_SNAKE_CASE_       )    ,			expected_num_stages + 1       )

          # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:]       )    ,			[self.model_tester.image_size // 2, self.model_tester.image_size // 2]    ,			)

        lowerCAmelCase_						,lowerCAmelCase_   :				List[Any]								= self.model_tester.prepare_config_and_inputs_for_common()
        lowerCAmelCase_   :				Optional[int]								= ['basic', 'bottleneck']
        for model_class in self.all_model_classes:
          for layer_type in layers_type:
            lowerCAmelCase_   :				str								= layer_type
            lowerCAmelCase_   :				List[str]								= 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"]
            lowerCAmelCase_   :				List[str]								= True

            check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_						,lowerCAmelCase_   :				int								= self.model_tester.prepare_config_and_inputs_for_common()

        def check_equivalence(SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      str={}       ):
          lowerCAmelCase_   :				Optional[Any]								= model(SCREAMING_SNAKE_CASE_    ,			return_dict=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				List[Any]								= model(SCREAMING_SNAKE_CASE_    ,			return_dict=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       ).to_tuple()

          def recursive_check(SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int       ):
            if isinstance(SCREAMING_SNAKE_CASE_    ,			(List, Tuple)       ):
              for tuple_iterable_value, dict_iterable_value in zip(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       ):
                recursive_check(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
            elif tuple_object is None:
              return
            else:
              self.assertTrue(
                  all(tf.equal(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )       )    ,			msg=(
                      'Tuple and dict output are not equal. Difference:'
                      F" {tf.math.reduce_max(tf.abs(tuple_object - dict_object       )       )}"
                  )    ,			)

            recursive_check(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

        for model_class in self.all_model_classes:
          lowerCAmelCase_   :				Union[str, Any]								= model_class(SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				List[Any]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Union[str, Any]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				Optional[int]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				str								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )

          lowerCAmelCase_   :				Tuple								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				int								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			{'output_hidden_states': True}       )

          lowerCAmelCase_   :				List[Any]								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          lowerCAmelCase_   :				Any								= self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )
          check_equivalence(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			{'output_hidden_states': True}       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        lowerCAmelCase_   :				List[str]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_       )





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase_   :				int								= TFRegNetModel.from_pretrained(SCREAMING_SNAKE_CASE_       )
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )




def 				UpperCamelCase_    (    )						->   List[str]:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[Any]								= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
  return image







@require_tf
@require_vision
class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      @cached_property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        return (
            AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
            if is_vision_available()
            else None
        )





      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				int								= TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

        lowerCAmelCase_   :				Tuple								= self.default_image_processor
        lowerCAmelCase_   :				Dict								= prepare_img()
        lowerCAmelCase_   :				Union[str, Any]								= image_processor(images=SCREAMING_SNAKE_CASE_    ,			return_tensors='tf'       )

        # forward pass
        lowerCAmelCase_   :				List[str]								= model(**SCREAMING_SNAKE_CASE_    ,			training=SCREAMING_SNAKE_CASE_       )

        # verify the logits
        lowerCAmelCase_   :				Optional[Any]								= tf.TensorShape((1, 1_0_0_0)       )
        self.assertEqual(outputs.logits.shape    ,			SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				List[Any]								= tf.constant([-0.41_80, -1.50_51, -3.48_36]       )

        tf.debugging.assert_near(outputs.logits[0, :3]    ,			SCREAMING_SNAKE_CASE_    ,			atol=1E-4       )





