

"""simple docstring"""




from __future__ import annotations

from math import pi

# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
A_							:			Dict											= 1.0_5457_1817E-34  # unit of ℏ : J * s

A_							:			Tuple											= 3E8  # unit of c : m * s^-1

def 	A       (     snake_case__		,				snake_case__		,				snake_case__    ):




			'''simple docstring'''


			if (force, area, distance).count(0    ) != 1:
						raise ValueError("""One and only one argument must be 0"""    )
			if force < 0:
						raise ValueError("""Magnitude of force can not be negative"""    )
			if distance < 0:
						raise ValueError("""Distance can not be negative"""    )
			if area < 0:
						raise ValueError("""Area can not be negative"""    )
			if force == 0:
						SCREAMING_SNAKE_CASE__								=      (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
						    2_40 * (distance) ** 4
						)
						return {"force": force}
			elif area == 0:
						SCREAMING_SNAKE_CASE__								=      (2_40 * force * (distance) ** 4) / (
						    REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
						)
						return {"area": area}
			elif distance == 0:
						SCREAMING_SNAKE_CASE__								=      (
						    (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_40 * force)
						) ** (1 / 4)
						return {"distance": distance}
			raise ValueError("""One and only one argument must be 0"""    )


# Run doctest
if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""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						:

			def __init__(					self				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]=1_3    ,				__UpperCAmelCase				:						Optional[Any]=3_0    ,				__UpperCAmelCase				:						Tuple=2    ,				__UpperCAmelCase				:						Optional[int]=3    ,				__UpperCAmelCase				:						Tuple=True    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						Optional[int]=3_2    ,				__UpperCAmelCase				:						Optional[Any]=5    ,				__UpperCAmelCase				:						List[str]=4    ,				__UpperCAmelCase				:						int=3_7    ,				__UpperCAmelCase				:						str="gelu"    ,				__UpperCAmelCase				:						List[str]=0.1    ,				__UpperCAmelCase				:						Union[str, Any]=0.1    ,				__UpperCAmelCase				:						Optional[Any]=1_0    ,				__UpperCAmelCase				:						Dict=0.02    ,				__UpperCAmelCase				:						str=3    ,				__UpperCAmelCase				:						List[Any]=None    ,				__UpperCAmelCase				:						str=2    ,				)   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      image_size
						SCREAMING_SNAKE_CASE__								=      patch_size
						SCREAMING_SNAKE_CASE__								=      num_channels
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      scope
						SCREAMING_SNAKE_CASE__								=      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__								=      (image_size // patch_size) ** 2
						SCREAMING_SNAKE_CASE__								=      num_patches + 2

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

						SCREAMING_SNAKE_CASE__								=      self.get_config()

						return config, pixel_values, labels

			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[Any]:
						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=__UpperCAmelCase    ,				initializer_range=self.initializer_range    ,				encoder_stride=self.encoder_stride    ,				)

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      DeiTModel(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      DeiTForMaskedImageModeling(config=__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       )
						self.parent.assertEqual(
						    result.reconstruction.shape    ,				(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

						# test greyscale images
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      DeiTForMaskedImageModeling(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Optional[int]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassification(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()
						SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase    ,				labels=__UpperCAmelCase       )
						self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)       )

						# test greyscale images
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassification(__UpperCAmelCase       )
						model.to(__UpperCAmelCase       )
						model.eval()

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		(
						    SCREAMING_SNAKE_CASE__
						)							,		
						)								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {"""pixel_values""": pixel_values}
						return config, inputs_dict





@require_torch
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Tuple							=							(
			    (
			        DeiTModel,
			        DeiTForImageClassification,
			        DeiTForImageClassificationWithTeacher,
			        DeiTForMaskedImageModeling,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__   :		Any							=							(
			    {
			        'feature-extraction': DeiTModel,
			        'image-classification': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__   :		Any							=							False
			lowerCamelCase__   :		Optional[int]							=							False
			lowerCamelCase__   :		str							=							False

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      DeiTModelTester(self       )
						SCREAMING_SNAKE_CASE__								=      ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				has_text_modality=__UpperCAmelCase    ,				hidden_size=3_7       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Tuple:
						self.config_tester.run_common_tests()

			@unittest.skip(reason="""DeiT does not use inputs_embeds"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Any:
						pass

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Any:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE__								=      ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
						SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[int]    ,				__UpperCAmelCase				:						Optional[Any]    ,				__UpperCAmelCase				:						Any=False       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      super()._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

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

						return inputs_dict

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    List[str]:
						if not self.model_tester.is_training:
									return

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

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
									return

						SCREAMING_SNAKE_CASE__								=      False
						SCREAMING_SNAKE_CASE__								=      True

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

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      self.model_tester.prepare_config_and_inputs_for_common()

						SCREAMING_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(__UpperCAmelCase       ),
									        *get_values(__UpperCAmelCase       ),
									    ]
									    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__								=      problem_type["""title"""]
															SCREAMING_SNAKE_CASE__								=      problem_type["""num_labels"""]

															SCREAMING_SNAKE_CASE__								=      model_class(__UpperCAmelCase       )
															model.to(__UpperCAmelCase       )
															model.train()

															SCREAMING_SNAKE_CASE__								=      self._prepare_for_class(__UpperCAmelCase    ,				__UpperCAmelCase    ,				return_labels=__UpperCAmelCase       )

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

															SCREAMING_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=__UpperCAmelCase       ) as warning_list:
																		SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       ).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 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    Tuple:
						for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE__								=      DeiTModel.from_pretrained(__UpperCAmelCase       )
									self.assertIsNotNone(__UpperCAmelCase       )

def 	A       (     ):




			'''simple docstring'''


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





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

			@cached_property
			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    List[Any]:
						return (
						    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       )
						    if is_vision_available()
						    else None
						)

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""       ).to(
						    __UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.default_image_processor
						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       ).to(__UpperCAmelCase       )

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE__								=      model(**__UpperCAmelCase       )

						# verify the logits
						SCREAMING_SNAKE_CASE__								=      torch.Size((1, 1_0_0_0)       )
						self.assertEqual(outputs.logits.shape    ,				__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.tensor([-1.0_266, 0.1_912, -1.2_861]       ).to(__UpperCAmelCase       )

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





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

						SCREAMING_SNAKE_CASE__								=      prepare_img()
						SCREAMING_SNAKE_CASE__								=      image_processor(images=__UpperCAmelCase    ,				return_tensors="""pt"""       )
						SCREAMING_SNAKE_CASE__								=      inputs.pixel_values.to(__UpperCAmelCase       )

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


