import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
SCREAMING_SNAKE_CASE__		:					Optional[Any]						=					pytest.mark.integration


SCREAMING_SNAKE_CASE__		:					int						=					{"comet"}
SCREAMING_SNAKE_CASE__		:					Optional[int]						=					importlib.util.find_spec("fairseq") is not None

SCREAMING_SNAKE_CASE__		:					List[str]						=					{"code_eval"}
SCREAMING_SNAKE_CASE__		:					Union[str, Any]						=					os.name == "nt"

SCREAMING_SNAKE_CASE__		:					Optional[Any]						=					{"bertscore", "frugalscore", "perplexity"}
SCREAMING_SNAKE_CASE__		:					Tuple						=					importlib.util.find_spec("transformers") is not None







def     _a				(      lowercase__  :			Union[str, Any] ):







     '''simple docstring'''


     @wraps(lowercase__ )
     def wrapper(self  :			Any     ,  lowercase__  :			List[Any] ):
          if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
               self.skipTest('"test requires Fairseq"' )
          else:
               test_case(self     ,  lowercase__ )

     return wrapper







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     @wraps(lowercase__ )
     def wrapper(self  :			Optional[Any]     ,  lowercase__  :			int ):
          if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
               self.skipTest('"test requires transformers"' )
          else:
               test_case(self     ,  lowercase__ )

     return wrapper







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     @wraps(lowercase__ )
     def wrapper(self  :			Any     ,  lowercase__  :			Dict ):
          if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
               self.skipTest('"test not supported on Windows"' )
          else:
               test_case(self     ,  lowercase__ )

     return wrapper







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[metric_dir.split(os.sep )[-2] for metric_dir in glob.glob('./metrics/*/' )]
     return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished

@parameterized.named_parameters(get_local_metric_names()						)
@for_all_test_methods(
    UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_						)
@local
class       snake_case					(      parameterized.TestCase						):
  lowercase_			=     {}
  lowercase_			=     None




  @pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning'				)
  @pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning'				)
  def 	__lowercase(      self     :	List[Any]     , a_     :	Any				)->       Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		'[...]'
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		importlib.import_module(
           datasets.load.metric_module_factory(os.path.join('metrics'     , a_				)				).module_path				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		datasets.load.import_main_class(metric_module.__name__     , dataset=a_				)
       # check parameters
       SCREAMING_SNAKE_CASE__				:				List[str]			=		inspect.signature(metric._compute				).parameters
       self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()				)				)  # no **kwargs
       # run doctest
       with self.patch_intensive_calls(a_     , metric_module.__name__				):
            with self.use_local_metrics():
                 try:
                      SCREAMING_SNAKE_CASE__				:				Optional[int]			=		doctest.testmod(a_     , verbose=a_     , raise_on_error=a_				)
                 except doctest.UnexpectedException as e:
                      raise e.exc_info[1]  # raise the exception that doctest caught
       self.assertEqual(results.failed     , 0				)
       self.assertGreater(results.attempted     , 1				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		'[...]'
       SCREAMING_SNAKE_CASE__				:				str			=		importlib.import_module(
           datasets.load.metric_module_factory(os.path.join('metrics'     , a_				)				).module_path				)
       # run doctest
       with self.use_local_metrics():
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		doctest.testmod(a_     , verbose=a_     , raise_on_error=a_				)
       self.assertEqual(results.failed     , 0				)
       self.assertGreater(results.attempted     , 1				)




  @contextmanager
  def 	__lowercase(      self     :	List[Any]     , a_     :	Optional[Any]     , a_     :	Tuple				)->       Union[str, Any]:




       """simple docstring"""
       if metric_name in self.INTENSIVE_CALLS_PATCHER:
            with self.INTENSIVE_CALLS_PATCHER[metric_name](a_				):
                 yield
       else:
            yield




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




       """simple docstring"""
       def load_local_metric(a_     :	Any     , *a_     :	str     , **a_     :	Optional[int]				):
            return load_metric(os.path.join('metrics'     , a_				)     , *a_     , **a_				)

       with patch('datasets.load_metric'				) as mock_load_metric:
            SCREAMING_SNAKE_CASE__				:				Tuple			=		load_local_metric
            yield




  @classmethod
  def 	__lowercase(      cls     :	Optional[int]     , a_     :	List[str]				)->       List[str]:




       """simple docstring"""
       def wrapper(a_     :	str				):
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		contextmanager(a_				)
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		patcher
            return patcher

       return wrapper







@LocalMetricTest.register_intensive_calls_patcher('bleurt' )
def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     import tensorflow.compat.va as tf
     from bleurt.score import Predictor

     tf.flags.DEFINE_string('sv'     ,  ''     ,  '' )  # handle pytest cli flags

     class       snake_case					(      UpperCamelCase_						):




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




            """simple docstring"""
            assert len(input_dict['input_ids']				) == 2
            return np.array([1.03, 1.04]				)

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
     with patch('bleurt.score._create_predictor' ) as mock_create_predictor:
          SCREAMING_SNAKE_CASE__				:				int			=		MockedPredictor()
          yield







@LocalMetricTest.register_intensive_calls_patcher('bertscore' )
def     _a				(      lowercase__  :			Dict ):







     '''simple docstring'''


     import torch

     def bert_cos_score_idf(lowercase__  :			Optional[Any]     ,  lowercase__  :			Dict     ,  *lowercase__  :			Dict     ,  **lowercase__  :			List[str] ):
          return torch.tensor([[1.0, 1.0, 1.0]] * len(lowercase__ ) )

     # mock get_model which is supposed to do download a bert model
     # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
     with patch('bert_score.scorer.get_model' ), patch(
         'bert_score.scorer.bert_cos_score_idf' ) as mock_bert_cos_score_idf:
          SCREAMING_SNAKE_CASE__				:				Optional[int]			=		bert_cos_score_idf
          yield







@LocalMetricTest.register_intensive_calls_patcher('comet' )
def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     def load_from_checkpoint(lowercase__  :			Optional[int] ):

          class       snake_case					:




            def 	__lowercase(      self     :	Tuple     , a_     :	List[str]     , *a_     :	Optional[Any]     , **a_     :	str				)->       Optional[Any]:




                 """simple docstring"""
                 assert len(a_				) == 2
                 SCREAMING_SNAKE_CASE__				:				Any			=		[0.19, 0.92]
                 return scores, sum(a_				) / len(a_				)




          return Model()

     # mock load_from_checkpoint which is supposed to do download a bert model
     # mock load_from_checkpoint which is supposed to do download a bert model
     with patch('comet.download_model' ) as mock_download_model:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		None
          with patch('comet.load_from_checkpoint' ) as mock_load_from_checkpoint:
               SCREAMING_SNAKE_CASE__				:				List[str]			=		load_from_checkpoint
               yield







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[Any]			=		load_metric(os.path.join('metrics'     ,  'seqeval' ) )
     SCREAMING_SNAKE_CASE__				:				int			=		'ERROR'
     SCREAMING_SNAKE_CASE__				:				str			=		f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
     with pytest.raises(lowercase__     ,  match=re.escape(lowercase__ ) ):
          metric.compute(predictions=[]     ,  references=[]     ,  scheme=lowercase__ )




import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

      from transformers import (
          MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          MODEL_MAPPING,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
          DeiTModel,
      )
      from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor

class       snake_case					:




  def __init__(      self     :	str     , a_     :	List[str]     , a_     :	Tuple=13     , a_     :	Dict=30     , a_     :	Optional[int]=2     , a_     :	Tuple=3     , a_     :	Dict=True     , a_     :	int=True     , a_     :	Optional[Any]=32     , a_     :	List[str]=5     , a_     :	Any=4     , a_     :	Dict=37     , a_     :	Dict="gelu"     , a_     :	int=0.1     , a_     :	Optional[Any]=0.1     , a_     :	Any=10     , a_     :	List[str]=0.02     , a_     :	Any=3     , a_     :	List[str]=None     , a_     :	Optional[int]=2     , )->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		parent
       SCREAMING_SNAKE_CASE__				:				int			=		batch_size
       SCREAMING_SNAKE_CASE__				:				int			=		image_size
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		patch_size
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		num_channels
       SCREAMING_SNAKE_CASE__				:				int			=		is_training
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		use_labels
       SCREAMING_SNAKE_CASE__				:				str			=		hidden_size
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		num_hidden_layers
       SCREAMING_SNAKE_CASE__				:				Tuple			=		num_attention_heads
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		intermediate_size
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		hidden_act
       SCREAMING_SNAKE_CASE__				:				List[str]			=		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__				:				List[str]			=		type_sequence_label_size
       SCREAMING_SNAKE_CASE__				:				str			=		initializer_range
       SCREAMING_SNAKE_CASE__				:				List[str]			=		scope
       SCREAMING_SNAKE_CASE__				:				str			=		encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		(image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		num_patches + 2




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

       SCREAMING_SNAKE_CASE__				:				Dict			=		None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__				:				int			=		ids_tensor([self.batch_size]     , self.type_sequence_label_size				)

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.get_config()

       return config, pixel_values, labels




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




       """simple docstring"""
       return DeiTConfig(
           image_size=self.image_size     , patch_size=self.patch_size     , num_channels=self.num_channels     , hidden_size=self.hidden_size     , num_hidden_layers=self.num_hidden_layers     , num_attention_heads=self.num_attention_heads     , intermediate_size=self.intermediate_size     , hidden_act=self.hidden_act     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , is_decoder=a_     , initializer_range=self.initializer_range     , encoder_stride=self.encoder_stride     , )




  def 	__lowercase(      self     :	List[str]     , a_     :	List[str]     , a_     :	Optional[Any]     , a_     :	str				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		DeiTModel(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(a_				)
       self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.seq_length, self.hidden_size)				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		DeiTForMaskedImageModeling(config=a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_				)
       self.parent.assertEqual(
           result.reconstruction.shape     , (self.batch_size, self.num_channels, self.image_size, self.image_size)				)

       # test greyscale images
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		1
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		DeiTForMaskedImageModeling(a_				)
       model.to(a_				)
       model.eval()

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       SCREAMING_SNAKE_CASE__				:				int			=		model(a_				)
       self.parent.assertEqual(result.reconstruction.shape     , (self.batch_size, 1, self.image_size, self.image_size)				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		self.type_sequence_label_size
       SCREAMING_SNAKE_CASE__				:				Tuple			=		DeiTForImageClassification(a_				)
       model.to(a_				)
       model.eval()
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(a_     , labels=a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.type_sequence_label_size)				)

       # test greyscale images
       SCREAMING_SNAKE_CASE__				:				Any			=		1
       SCREAMING_SNAKE_CASE__				:				int			=		DeiTForImageClassification(a_				)
       model.to(a_				)
       model.eval()

       SCREAMING_SNAKE_CASE__				:				Dict			=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(a_     , labels=a_				)
       self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.type_sequence_label_size)				)




  def 	__lowercase(      self     :	int				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							(
           SCREAMING_SNAKE_CASE__
       )			,							
       )				:				List[Any]			=		config_and_inputs
       SCREAMING_SNAKE_CASE__				:				Dict			=		{'pixel_values': pixel_values}
       return config, inputs_dict




@require_torch
class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     (
      (
          DeiTModel,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
      )
      if is_torch_available()
      else ()
  )
  lowercase_			=     (
      {
          'feature-extraction': DeiTModel,
          'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
      }
      if is_torch_available()
      else {}
  )

  lowercase_			=     False
  lowercase_			=     False
  lowercase_			=     False




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		DeiTModelTester(self				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		ConfigTester(self     , config_class=a_     , has_text_modality=a_     , hidden_size=37				)




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




       """simple docstring"""
       self.config_tester.run_common_tests()




  @unittest.skip(reason='DeiT does not use inputs_embeds'				)
  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       pass




  def 	__lowercase(      self     :	str				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model_class(a_				)
            self.assertIsInstance(model.get_input_embeddings()     , (nn.Module)				)
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(a_     , nn.Linear				)				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.model_tester.prepare_config_and_inputs_for_common()

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

            SCREAMING_SNAKE_CASE__				:				List[Any]			=		['pixel_values']
            self.assertListEqual(arg_names[:1]     , a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*a_				)




  def 	__lowercase(      self     :	str     , a_     :	str     , a_     :	Tuple     , a_     :	Union[str, Any]=False				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		super()._prepare_for_class(a_     , a_     , return_labels=a_				)

       if return_labels:
            if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                 del inputs_dict["labels"]

       return inputs_dict




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




       """simple docstring"""
       if not self.model_tester.is_training:
            return

       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		True

       for model_class in self.all_model_classes:
            # DeiTForImageClassificationWithTeacher supports inference-only
            if (
                model_class in get_values(a_				)
                or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
            ):
                 continue
            SCREAMING_SNAKE_CASE__				:				Tuple			=		model_class(a_				)
            model.to(a_				)
            model.train()
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		self._prepare_for_class(a_     , a_     , return_labels=a_				)
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(**a_				).loss
            loss.backward()




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
            return

       SCREAMING_SNAKE_CASE__				:				List[str]			=		False
       SCREAMING_SNAKE_CASE__				:				Tuple			=		True

       for model_class in self.all_model_classes:
            if model_class in get_values(a_				) or not model_class.supports_gradient_checkpointing:
                 continue
            # DeiTForImageClassificationWithTeacher supports inference-only
            if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
                 continue
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model_class(a_				)
            model.gradient_checkpointing_enable()
            model.to(a_				)
            model.train()
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self._prepare_for_class(a_     , a_     , return_labels=a_				)
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(**a_				).loss
            loss.backward()




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE__				:				List[str]			=		[
           {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
           {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
           {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
       ]

       for model_class in self.all_model_classes:
            if (
                model_class
                not in [
                    *get_values(a_				),
                    *get_values(a_				),
                ]
                or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
            ):
                 continue

            for problem_type in problem_types:
                 with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''				):
                      SCREAMING_SNAKE_CASE__				:				int			=		problem_type['title']
                      SCREAMING_SNAKE_CASE__				:				Tuple			=		problem_type['num_labels']

                      SCREAMING_SNAKE_CASE__				:				str			=		model_class(a_				)
                      model.to(a_				)
                      model.train()

                      SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self._prepare_for_class(a_     , a_     , return_labels=a_				)

                      if problem_type["num_labels"] > 1:
                           SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		inputs['labels'].unsqueeze(1				).repeat(1     , problem_type['num_labels']				)

                      SCREAMING_SNAKE_CASE__				:				Any			=		inputs['labels'].to(problem_type['dtype']				)

                      # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                      # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                      # they have the same size." which is a symptom something in wrong for the regression problem.
                      # See https://github.com/huggingface/transformers/issues/11780
                      with warnings.catch_warnings(record=a_				) as warning_list:
                           SCREAMING_SNAKE_CASE__				:				str			=		model(**a_				).loss
                      for w in warning_list:
                           if "Using a target size that is different to the input size" in str(w.message				):
                                raise ValueError(
                                    F'''Something is going wrong in the regression problem: intercepted {w.message}'''				)

                      loss.backward()




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




       """simple docstring"""
       for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		DeiTModel.from_pretrained(a_				)
            self.assertIsNotNone(a_				)







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				int			=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
     return image







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




  @cached_property
  def 	__lowercase(      self     :	int				)->       Dict:




       """simple docstring"""
       return (
           DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'				)
           if is_vision_available()
           else None
       )




  @slow
  def 	__lowercase(      self     :	Any				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'				).to(
           a_				)

       SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.default_image_processor
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		prepare_img()
       SCREAMING_SNAKE_CASE__				:				List[str]			=		image_processor(images=a_     , return_tensors='pt'				).to(a_				)

       # forward pass
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(**a_				)

       # verify the logits
       SCREAMING_SNAKE_CASE__				:				int			=		torch.Size((1, 1000)				)
       self.assertEqual(outputs.logits.shape     , a_				)

       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		torch.tensor([-1.0266, 0.1912, -1.2861]				).to(a_				)

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




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		DeiTModel.from_pretrained(
           'facebook/deit-base-distilled-patch16-224'     , torch_dtype=torch.floataa     , device_map='auto'				)
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.default_image_processor

       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		prepare_img()
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		image_processor(images=a_     , return_tensors='pt'				)
       SCREAMING_SNAKE_CASE__				:				str			=		inputs.pixel_values.to(a_				)

       # forward pass to make sure inference works in fp16
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__				:				List[Any]			=		model(a_				)



