

'''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 					_snake_case		:






			def __init__(  self			,_snake_case			,_snake_case=13			,_snake_case=30			,_snake_case=2			,_snake_case=3			,_snake_case=True			,_snake_case=True			,_snake_case=32			,_snake_case=5			,_snake_case=4			,_snake_case=37			,_snake_case="gelu"			,_snake_case=0.1			,_snake_case=0.1			,_snake_case=10			,_snake_case=0.02			,_snake_case=3			,_snake_case=None			,_snake_case=2			,):
				UpperCAmelCase_     :      Any						=  parent
				UpperCAmelCase_     :      Optional[Any]						=  batch_size
				UpperCAmelCase_     :      List[Any]						=  image_size
				UpperCAmelCase_     :      Dict						=  patch_size
				UpperCAmelCase_     :      List[str]						=  num_channels
				UpperCAmelCase_     :      List[Any]						=  is_training
				UpperCAmelCase_     :      Tuple						=  use_labels
				UpperCAmelCase_     :      Dict						=  hidden_size
				UpperCAmelCase_     :      Dict						=  num_hidden_layers
				UpperCAmelCase_     :      Any						=  num_attention_heads
				UpperCAmelCase_     :      Union[str, Any]						=  intermediate_size
				UpperCAmelCase_     :      Tuple						=  hidden_act
				UpperCAmelCase_     :      Optional[int]						=  hidden_dropout_prob
				UpperCAmelCase_     :      Union[str, Any]						=  attention_probs_dropout_prob
				UpperCAmelCase_     :      str						=  type_sequence_label_size
				UpperCAmelCase_     :      str						=  initializer_range
				UpperCAmelCase_     :      Dict						=  scope
				UpperCAmelCase_     :      str						=  encoder_stride

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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

				UpperCAmelCase_     :      int						=  self.get_config()

				return config, pixel_values, labels






			def 		UpperCamelCase__							(  self     ):
				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=_snake_case			,initializer_range=self.initializer_range			,encoder_stride=self.encoder_stride			,)






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      List[str]						=  DeiTModel(config=_snake_case     )
				model.to(_snake_case     )
				model.eval()
				UpperCAmelCase_     :      Dict						=  model(_snake_case     )
				self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.seq_length, self.hidden_size)     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      int						=  DeiTForMaskedImageModeling(config=_snake_case     )
				model.to(_snake_case     )
				model.eval()
				UpperCAmelCase_     :      Dict						=  model(_snake_case     )
				self.parent.assertEqual(
				    result.reconstruction.shape			,(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

				# test greyscale images
				UpperCAmelCase_     :      Any						=  1
				UpperCAmelCase_     :      int						=  DeiTForMaskedImageModeling(_snake_case     )
				model.to(_snake_case     )
				model.eval()

				UpperCAmelCase_     :      int						=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
				UpperCAmelCase_     :      Tuple						=  model(_snake_case     )
				self.parent.assertEqual(result.reconstruction.shape			,(self.batch_size, 1, self.image_size, self.image_size)     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Optional[int]						=  self.type_sequence_label_size
				UpperCAmelCase_     :      List[Any]						=  DeiTForImageClassification(_snake_case     )
				model.to(_snake_case     )
				model.eval()
				UpperCAmelCase_     :      Dict						=  model(_snake_case			,labels=_snake_case     )
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.type_sequence_label_size)     )

				# test greyscale images
				UpperCAmelCase_     :      int						=  1
				UpperCAmelCase_     :      Tuple						=  DeiTForImageClassification(_snake_case     )
				model.to(_snake_case     )
				model.eval()

				UpperCAmelCase_     :      str						=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
				UpperCAmelCase_     :      int						=  model(_snake_case			,labels=_snake_case     )
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.type_sequence_label_size)     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      str						=  self.prepare_config_and_inputs()
				(
				    (
				    UpperCAmelCase_
				)					,					(
				    UpperCAmelCase_
				)					,					(
				    UpperCAmelCase_
				)					,					
				)     :      List[str]						=  config_and_inputs
				UpperCAmelCase_     :      Any						=  {"pixel_values": pixel_values}
				return config, inputs_dict




@require_torch
class 					_snake_case		(__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase):
			__A  :	Optional[Any]        =(
			    (
			        DeiTModel,
			        DeiTForImageClassification,
			        DeiTForImageClassificationWithTeacher,
			        DeiTForMaskedImageModeling,
			    )
			    if is_torch_available()
			    else ()
			)
			__A  :	str        =(
			    {
			        "feature-extraction": DeiTModel,
			        "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			__A  :	Optional[int]        =False
			__A  :	Optional[Any]        =False
			__A  :	List[str]        =False






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  DeiTModelTester(self     )
				UpperCAmelCase_     :      Union[str, Any]						=  ConfigTester(self			,config_class=_snake_case			,has_text_modality=_snake_case			,hidden_size=37     )






			def 		UpperCamelCase__							(  self     ):
				self.config_tester.run_common_tests()






			@unittest.skip(reason="DeiT does not use inputs_embeds"     )
			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      str						=  self.model_tester.prepare_config_and_inputs_for_common()

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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      Tuple						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					UpperCAmelCase_     :      Tuple						=  model_class(_snake_case     )
					UpperCAmelCase_     :      Dict						=  inspect.signature(model.forward     )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					UpperCAmelCase_     :      List[Any]						=  [*signature.parameters.keys()]

					UpperCAmelCase_     :      int						=  ["pixel_values"]
					self.assertListEqual(arg_names[:1]			,_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case=False     ):
				UpperCAmelCase_     :      str						=  super()._prepare_for_class(_snake_case			,_snake_case			,return_labels=_snake_case     )

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

				return inputs_dict






			def 		UpperCamelCase__							(  self     ):
				if not self.model_tester.is_training:
					return

				UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[Any]						=  self.model_tester.prepare_config_and_inputs_for_common()
				UpperCAmelCase_     :      int						=  True

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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      List[Any]						=  self.model_tester.prepare_config_and_inputs_for_common()
				if not self.model_tester.is_training:
					return

				UpperCAmelCase_     :      Union[str, Any]						=  False
				UpperCAmelCase_     :      Union[str, Any]						=  True

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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      List[str]						=  self.model_tester.prepare_config_and_inputs_for_common()

				UpperCAmelCase_     :      Any						=  [
				    {"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(_snake_case     ),
					        *get_values(_snake_case     ),
					    ]
					    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
					):
						continue

					for problem_type in problem_types:
						with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}'''     ):
							UpperCAmelCase_     :      Tuple						=  problem_type["title"]
							UpperCAmelCase_     :      Dict						=  problem_type["num_labels"]

							UpperCAmelCase_     :      int						=  model_class(_snake_case     )
							model.to(_snake_case     )
							model.train()

							UpperCAmelCase_     :      List[Any]						=  self._prepare_for_class(_snake_case			,_snake_case			,return_labels=_snake_case     )

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

							UpperCAmelCase_     :      Optional[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=_snake_case     ) as warning_list:
								UpperCAmelCase_     :      Dict						=  model(**_snake_case     ).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 		UpperCamelCase__							(  self     ):
				for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCAmelCase_     :      List[str]						=  DeiTModel.from_pretrained(_snake_case     )
					self.assertIsNotNone(_snake_case     )




def  a__       (    )     ->					str:


	"""simple docstring"""





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





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






			@cached_property
			def 		UpperCamelCase__							(  self     ):
				return (
				    DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"     )
				    if is_vision_available()
				    else None
				)






			@slow
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"     ).to(
				    _snake_case     )

				UpperCAmelCase_     :      Union[str, Any]						=  self.default_image_processor
				UpperCAmelCase_     :      Tuple						=  prepare_img()
				UpperCAmelCase_     :      Union[str, Any]						=  image_processor(images=_snake_case			,return_tensors="pt"     ).to(_snake_case     )

				# forward pass
				with torch.no_grad():
					UpperCAmelCase_     :      Any						=  model(**_snake_case     )

				# verify the logits
				UpperCAmelCase_     :      Optional[int]						=  torch.Size((1, 10_00)     )
				self.assertEqual(outputs.logits.shape			,_snake_case     )

				UpperCAmelCase_     :      Tuple						=  torch.tensor([-1.0266, 0.1912, -1.2861]     ).to(_snake_case     )

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






			@slow
			@require_accelerate
			@require_torch_gpu
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  DeiTModel.from_pretrained(
				    "facebook/deit-base-distilled-patch16-224"			,torch_dtype=torch.floataa			,device_map="auto"     )
				UpperCAmelCase_     :      Optional[Any]						=  self.default_image_processor

				UpperCAmelCase_     :      List[str]						=  prepare_img()
				UpperCAmelCase_     :      List[str]						=  image_processor(images=_snake_case			,return_tensors="pt"     )
				UpperCAmelCase_     :      Union[str, Any]						=  inputs.pixel_values.to(_snake_case     )

				# forward pass to make sure inference works in fp16
				with torch.no_grad():
					UpperCAmelCase_     :      Optional[int]						=  model(_snake_case     )












'''simple docstring'''

from __future__ import annotations

from collections import namedtuple



def 				snake_case       (  a_				:			float     , a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      UpperCamelCase_					: Tuple            =				namedtuple("""result"""     , """name value"""						)
      if (voltage, current, power).count(0						) != 1:
            raise ValueError("""Only one argument must be 0"""						)
      elif power < 0:
            raise ValueError(
                """Power cannot be negative in any electrical/electronics system"""						)
      elif voltage == 0:
            return result("""voltage"""     , power / current						)
      elif current == 0:
            return result("""current"""     , power / voltage						)
      elif power == 0:
            return result("""power"""     , float(round(abs(voltage * current						)     , 2						)						)						)
      else:
            raise ValueError("""Exactly one argument must be 0"""						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






