"""simple docstring"""
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   lowerCAmelCase_    :



    """simple docstring"""

    def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=13		,    lowerCAmelCase=30		,    lowerCAmelCase=2		,    lowerCAmelCase=3		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=5		,    lowerCAmelCase=4		,    lowerCAmelCase=37		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=10		,    lowerCAmelCase=0.02		,    lowerCAmelCase=3		,    lowerCAmelCase=None		,    lowerCAmelCase=2		,    ):






          """simple docstring"""

          snake_case								=			parent
          snake_case								=			batch_size
          snake_case								=			image_size
          snake_case								=			patch_size
          snake_case								=			num_channels
          snake_case								=			is_training
          snake_case								=			use_labels
          snake_case								=			hidden_size
          snake_case								=			num_hidden_layers
          snake_case								=			num_attention_heads
          snake_case								=			intermediate_size
          snake_case								=			hidden_act
          snake_case								=			hidden_dropout_prob
          snake_case								=			attention_probs_dropout_prob
          snake_case								=			type_sequence_label_size
          snake_case								=			initializer_range
          snake_case								=			scope
          snake_case								=			encoder_stride

          # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
          snake_case								=			(image_size // patch_size) ** 2
          snake_case								=			num_patches + 2

    def 		snake_case (				self    ):






          """simple docstring"""

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

          snake_case								=			None
          if self.use_labels:
                snake_case								=			ids_tensor([self.batch_size]		,    self.type_sequence_label_size    )

          snake_case								=			self.get_config()

          return config, pixel_values, labels

    def 		snake_case (				self    ):






          """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=lowerCAmelCase		,    initializer_range=self.initializer_range		,    encoder_stride=self.encoder_stride		,    )

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			DeiTModel(config=lowerCAmelCase    )
          model.to(lowerCAmelCase    )
          model.eval()
          snake_case								=			model(lowerCAmelCase    )
          self.parent.assertEqual(result.last_hidden_state.shape		,    (self.batch_size, self.seq_length, self.hidden_size)    )

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			DeiTForMaskedImageModeling(config=lowerCAmelCase    )
          model.to(lowerCAmelCase    )
          model.eval()
          snake_case								=			model(lowerCAmelCase    )
          self.parent.assertEqual(
              result.reconstruction.shape		,    (self.batch_size, self.num_channels, self.image_size, self.image_size)    )

          # test greyscale images
          snake_case								=			1
          snake_case								=			DeiTForMaskedImageModeling(lowerCAmelCase    )
          model.to(lowerCAmelCase    )
          model.eval()

          snake_case								=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
          snake_case								=			model(lowerCAmelCase    )
          self.parent.assertEqual(result.reconstruction.shape		,    (self.batch_size, 1, self.image_size, self.image_size)    )

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    ):






          """simple docstring"""

          snake_case								=			self.type_sequence_label_size
          snake_case								=			DeiTForImageClassification(lowerCAmelCase    )
          model.to(lowerCAmelCase    )
          model.eval()
          snake_case								=			model(lowerCAmelCase		,    labels=lowerCAmelCase    )
          self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)    )

          # test greyscale images
          snake_case								=			1
          snake_case								=			DeiTForImageClassification(lowerCAmelCase    )
          model.to(lowerCAmelCase    )
          model.eval()

          snake_case								=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
          snake_case								=			model(lowerCAmelCase		,    labels=lowerCAmelCase    )
          self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.type_sequence_label_size)    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.prepare_config_and_inputs()
          (
              (
              snake_case						
          )					,(
              snake_case						
          )					,(
              snake_case						
          )					,						
          )								=			config_and_inputs
          snake_case								=			{'pixel_values': pixel_values}
          return config, inputs_dict





@require_torch
class   lowerCAmelCase_    (			lowercase__     ,    lowercase__     ,    unittest.TestCase					):



    """simple docstring"""
    _lowerCAmelCase     :    Optional[int]			  =						(
        (
            DeiTModel,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    _lowerCAmelCase     :    Optional[Any]			  =						(
        {
            'feature-extraction': DeiTModel,
            'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    _lowerCAmelCase     :    Union[str, Any]			  =						False
    _lowerCAmelCase     :    int			  =						False
    _lowerCAmelCase     :    Dict			  =						False

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			DeiTModelTester(self    )
          snake_case								=			ConfigTester(self		,    config_class=lowerCAmelCase		,    has_text_modality=lowerCAmelCase		,    hidden_size=37    )

    def 		snake_case (				self    ):






          """simple docstring"""

          self.config_tester.run_common_tests()

    @unittest.skip(reason='DeiT does not use inputs_embeds'    )
    def 		snake_case (				self    ):






          """simple docstring"""

          pass

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case					,snake_case								=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case								=			model_class(lowerCAmelCase    )
                self.assertIsInstance(model.get_input_embeddings()		,    (nn.Module)    )
                snake_case								=			model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(lowerCAmelCase		,    nn.Linear    )    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case					,snake_case								=			self.model_tester.prepare_config_and_inputs_for_common()

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

                snake_case								=			['pixel_values']
                self.assertListEqual(arg_names[:1]		,    lowerCAmelCase    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCAmelCase    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase    )

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase    )

    def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase=False    ):






          """simple docstring"""

          snake_case								=			super()._prepare_for_class(lowerCAmelCase		,    lowerCAmelCase		,    return_labels=lowerCAmelCase    )

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

          return inputs_dict

    def 		snake_case (				self    ):






          """simple docstring"""

          if not self.model_tester.is_training:
                return

          snake_case					,snake_case								=			self.model_tester.prepare_config_and_inputs_for_common()
          snake_case								=			True

          for model_class in self.all_model_classes:
                # DeiTForImageClassificationWithTeacher supports inference-only
                if (
                    model_class in get_values(lowerCAmelCase    )
                    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                ):
                      continue
                snake_case								=			model_class(lowerCAmelCase    )
                model.to(lowerCAmelCase    )
                model.train()
                snake_case								=			self._prepare_for_class(lowerCAmelCase		,    lowerCAmelCase		,    return_labels=lowerCAmelCase    )
                snake_case								=			model(**lowerCAmelCase    ).loss
                loss.backward()

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case					,snake_case								=			self.model_tester.prepare_config_and_inputs_for_common()
          if not self.model_tester.is_training:
                return

          snake_case								=			False
          snake_case								=			True

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

    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case					,snake_case								=			self.model_tester.prepare_config_and_inputs_for_common()

          snake_case								=			[
              {'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(lowerCAmelCase    ),
                        *get_values(lowerCAmelCase    ),
                    ]
                    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
                ):
                      continue

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

                            snake_case								=			model_class(lowerCAmelCase    )
                            model.to(lowerCAmelCase    )
                            model.train()

                            snake_case								=			self._prepare_for_class(lowerCAmelCase		,    lowerCAmelCase		,    return_labels=lowerCAmelCase    )

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

                            snake_case								=			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=lowerCAmelCase    ) as warning_list:
                                  snake_case								=			model(**lowerCAmelCase    ).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 		snake_case (				self    ):






          """simple docstring"""

          for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case								=			DeiTModel.from_pretrained(lowerCAmelCase    )
                self.assertIsNotNone(lowerCAmelCase    )





def    lowerCAmelCase__    (			)					->				Any:





      """simple docstring"""






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



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



    """simple docstring"""

    @cached_property
    def 		snake_case (				self    ):






          """simple docstring"""

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

    @slow
    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'    ).to(
              lowerCAmelCase    )

          snake_case								=			self.default_image_processor
          snake_case								=			prepare_img()
          snake_case								=			image_processor(images=lowerCAmelCase		,    return_tensors='pt'    ).to(lowerCAmelCase    )

          # forward pass
          with torch.no_grad():
                snake_case								=			model(**lowerCAmelCase    )

          # verify the logits
          snake_case								=			torch.Size((1, 10_00)    )
          self.assertEqual(outputs.logits.shape		,    lowerCAmelCase    )

          snake_case								=			torch.tensor([-1.02_66, 0.19_12, -1.28_61]    ).to(lowerCAmelCase    )

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

    @slow
    @require_accelerate
    @require_torch_gpu
    def 		snake_case (				self    ):






          """simple docstring"""

          snake_case								=			DeiTModel.from_pretrained(
              'facebook/deit-base-distilled-patch16-224'		,    torch_dtype=torch.floataa		,    device_map='auto'    )
          snake_case								=			self.default_image_processor

          snake_case								=			prepare_img()
          snake_case								=			image_processor(images=lowerCAmelCase		,    return_tensors='pt'    )
          snake_case								=			inputs.pixel_values.to(lowerCAmelCase    )

          # forward pass to make sure inference works in fp16
          with torch.no_grad():
                snake_case								=			model(lowerCAmelCase    )



"""simple docstring"""
import math





def    lowerCAmelCase__    (			_UpperCamelCase       :				float ,			_UpperCamelCase       :				float					)					->				float:





						"""simple docstring"""






						if initial_intensity < 0:
												raise ValueError('The value of intensity cannot be negative'					)
												# handling of negative values of initial intensity
						if angle < 0 or angle > 3_6_0:
												raise ValueError('In Malus Law, the angle is in the range 0-360 degrees'					)
												# handling of values out of allowed range
						return initial_intensity * (math.cos(math.radians(_UpperCamelCase					)					) ** 2)


if __name__ == "__main__":
					import doctest

					doctest.testmod(name="malus_law")


