import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
      from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING







def     _a				(      lowercase__  :			str ):







     '''simple docstring'''


     return 1.0 / (1.0 + np.exp(-_outputs ))







def     _a				(      lowercase__  :			Tuple ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Any			=		np.max(_outputs     ,  axis=-1     ,  keepdims=lowercase__ )
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		np.exp(_outputs - maxes )
     return shifted_exp / shifted_exp.sum(axis=-1     ,  keepdims=lowercase__ )

class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     'sigmoid'
  lowercase_			=     'softmax'
  lowercase_			=     'none'




@add_end_docstrings(
    UpperCamelCase_   ,						r'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    '   ,						)
class       snake_case					(      UpperCamelCase_						):
  lowercase_			=     False
  lowercase_			=     ClassificationFunction.NONE




  def __init__(      self     :	Tuple     , **a_     :	Dict				)->       List[str]:




       """simple docstring"""
       super().__init__(**a_				)

       self.check_model_type(
           TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
           if self.framework == 'tf'
           else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING				)




  def 	__lowercase(      self     :	Union[str, Any]     , a_     :	str=None     , a_     :	Any=None     , a_     :	Optional[Any]=""     , **a_     :	str				)->       Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokenizer_kwargs

       SCREAMING_SNAKE_CASE__				:				Tuple			=		{}
       if hasattr(self.model.config     , 'return_all_scores'				) and return_all_scores is None:
            SCREAMING_SNAKE_CASE__				:				Dict			=		self.model.config.return_all_scores

       if isinstance(a_     , a_				) or top_k is None:
            SCREAMING_SNAKE_CASE__				:				int			=		top_k
            SCREAMING_SNAKE_CASE__				:				List[str]			=		False
       elif return_all_scores is not None:
            warnings.warn(
                '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
                ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'     , a_     , )
            if return_all_scores:
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		None
            else:
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		1

       if isinstance(a_     , a_				):
            SCREAMING_SNAKE_CASE__				:				int			=		ClassificationFunction[function_to_apply.upper()]

       if function_to_apply is not None:
            SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		function_to_apply
       return preprocess_params, {}, postprocess_params




  def __call__(      self     :	List[str]     , *a_     :	List[Any]     , **a_     :	List[str]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		super().__call__(*a_     , **a_				)
       # TODO try and retrieve it in a nicer way from _sanitize_parameters.
       SCREAMING_SNAKE_CASE__				:				int			=		'top_k' not in kwargs
       if isinstance(args[0]     , a_				) and _legacy:
            # This pipeline is odd, and return a list when single item is run
            return [result]
       else:
            return result




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				str			=		self.framework
       if isinstance(a_     , a_				):
            return self.tokenizer(**a_     , return_tensors=a_     , **a_				)
       elif isinstance(a_     , a_				) and len(a_				) == 1 and isinstance(inputs[0]     , a_				) and len(inputs[0]				) == 2:
            # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
            return self.tokenizer(
                text=inputs[0][0]     , text_pair=inputs[0][1]     , return_tensors=a_     , **a_				)
       elif isinstance(a_     , a_				):
            # This is likely an invalid usage of the pipeline attempting to pass text pairs.
            raise ValueError(
                'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
                ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'				)
       return self.tokenizer(a_     , return_tensors=a_     , **a_				)




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




       """simple docstring"""
       return self.model(**a_				)




  def 	__lowercase(      self     :	str     , a_     :	Tuple     , a_     :	Union[str, Any]=None     , a_     :	str=1     , a_     :	Union[str, Any]=True				)->       Any:




       """simple docstring"""
       # `_legacy` is used to determine if we're running the naked pipeline and in backward
       # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
       # the more natural result containing the list.
       # Default value before `set_parameters`
       if function_to_apply is None:
            if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                 SCREAMING_SNAKE_CASE__				:				Tuple			=		ClassificationFunction.SIGMOID
            elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                 SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		ClassificationFunction.SOFTMAX
            elif hasattr(self.model.config     , 'function_to_apply'				) and function_to_apply is None:
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		self.model.config.function_to_apply
            else:
                 SCREAMING_SNAKE_CASE__				:				List[str]			=		ClassificationFunction.NONE

       SCREAMING_SNAKE_CASE__				:				int			=		model_outputs['logits'][0]
       SCREAMING_SNAKE_CASE__				:				Tuple			=		outputs.numpy()

       if function_to_apply == ClassificationFunction.SIGMOID:
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		sigmoid(a_				)
       elif function_to_apply == ClassificationFunction.SOFTMAX:
            SCREAMING_SNAKE_CASE__				:				str			=		softmax(a_				)
       elif function_to_apply == ClassificationFunction.NONE:
            SCREAMING_SNAKE_CASE__				:				Dict			=		outputs
       else:
            raise ValueError(F'''Unrecognized `function_to_apply` argument: {function_to_apply}'''				)

       if top_k == 1 and _legacy:
            return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

       SCREAMING_SNAKE_CASE__				:				Any			=		[
           {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(a_				)
       ]
       if not _legacy:
            dict_scores.sort(key=lambda a_				: x["score"]     , reverse=a_				)
            if top_k is not None:
                 SCREAMING_SNAKE_CASE__				:				Optional[int]			=		dict_scores[:top_k]
       return dict_scores




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_				)



