
'''simple docstring'''







import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
						from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image


class 			lowerCAmelCase :




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=13					,						UpperCamelCase=64					,						UpperCamelCase=2					,						UpperCamelCase=3					,						UpperCamelCase=True					,						UpperCamelCase=True					,						UpperCamelCase=32					,						UpperCamelCase=5					,						UpperCamelCase=4					,						UpperCamelCase=37					,						UpperCamelCase="gelu"					,						UpperCamelCase=0.1					,						UpperCamelCase=0.1					,						UpperCamelCase=10					,						UpperCamelCase=0.02					,						UpperCamelCase=[1, 16, 4, 4]					,						UpperCamelCase=None					,						):
								_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				= backbone_featmap_shape

								# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								# the number of patches is based on the feature map of the backbone, which by default uses an output stride
								# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
								_SCREAMING_SNAKE_CASE				= (self.image_size // 32) ** 2
								_SCREAMING_SNAKE_CASE				= num_patches + 1




		def     lowercase       (				self      ):
								_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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= {
								    "global_padding": "same",
								    "layer_type": "bottleneck",
								    "depths": [3, 4, 9],
								    "out_features": ["stage1", "stage2", "stage3"],
								    "embedding_dynamic_padding": True,
								    "hidden_sizes": [4, 8, 16, 32],
								    "num_groups": 2,
								}

								return ViTHybridConfig(
								    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					,						backbone_featmap_shape=self.backbone_featmap_shape					,						backbone_config=UpperCamelCase_					,						)




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= ViTHybridModel(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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= self.type_sequence_label_size
								_SCREAMING_SNAKE_CASE				= ViTHybridForImageClassification(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)      )




		def     lowercase       (				self      ):
								_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 (				__lowerCamelCase		,     __lowerCamelCase		,     unittest.TestCase			):
		a							:					Optional[Any]       =					(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
		a							:					List[str]       =					(
		    {'feature-extraction': ViTHybridModel, 'image-classification': ViTHybridForImageClassification}
		    if is_torch_available()
		    else {}
		)
		a							:					Union[str, Any]       =					False
		a							:					List[str]       =					False
		a							:					Tuple       =					False




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= ViTHybridModelTester(self      )
								_SCREAMING_SNAKE_CASE				= ConfigTester(self					,						config_class=UpperCamelCase_					,						has_text_modality=UpperCamelCase_					,						hidden_size=37      )




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




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




		def     lowercase       (				self      ):
								_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     lowercase       (				self      ):
								_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     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*UpperCamelCase_      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_      )




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.model_tester.prepare_config_and_inputs_for_common()

								_SCREAMING_SNAKE_CASE				= _config_zero_init(UpperCamelCase_      )
								for model_class in self.all_model_classes:
														_SCREAMING_SNAKE_CASE				= model_class(config=UpperCamelCase_      )
														# Skip the check for the backbone
														for name, module in model.named_modules():
																				if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																										_SCREAMING_SNAKE_CASE				= [F'{name}.{key}' for key in module.state_dict().keys()]
																										break

														for name, param in model.named_parameters():
																				if param.requires_grad:
																										if name in backbone_params:
																																continue
																										self.assertIn(
																										    ((param.data.mean() * 1e9).round() / 1e9).item()					,						[0.0, 1.0]					,						msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,						)




		@slow
		def     lowercase       (				self      ):
								for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_SCREAMING_SNAKE_CASE				= ViTHybridModel.from_pretrained(UpperCamelCase_      )
														self.assertIsNotNone(UpperCamelCase_      )



def 			_a		(	):
						_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     lowercase       (				self      ):
								return (
								    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
								    if is_vision_available()
								    else None
								)




		@slow
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]      ).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_000)      )
								self.assertEqual(outputs.logits.shape					,						UpperCamelCase_      )

								_SCREAMING_SNAKE_CASE				= torch.tensor([-1.90_90, -0.49_93, -0.23_89]      ).to(UpperCamelCase_      )

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




		@slow
		@require_accelerate
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"      )
								_SCREAMING_SNAKE_CASE				= ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"					,						device_map="auto"      )

								_SCREAMING_SNAKE_CASE				= prepare_img()

								_SCREAMING_SNAKE_CASE				= image_processor(images=UpperCamelCase_					,						return_tensors="pt"      )
								_SCREAMING_SNAKE_CASE				= model(**UpperCamelCase_      )
								_SCREAMING_SNAKE_CASE				= outputs.logits
								# model predicts one of the 1000 ImageNet classes
								_SCREAMING_SNAKE_CASE				= logits.argmax(-1      ).item()

								self.assertTrue(model.config.idalabel[predicted_class_idx]					,						"tabby, tabby cat"      )

'''simple docstring'''







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

						# A local function to see if a dot lands in the circle.
						def is_in_circle(_SCREAMING_SNAKE_CASE		:   float	,			_SCREAMING_SNAKE_CASE		:   float  ) -> bool:
												_SCREAMING_SNAKE_CASE				= sqrt((x**2) + (y**2)  )
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						_SCREAMING_SNAKE_CASE				= mean(
						    int(is_in_circle(uniform(-1.0	,			1.0  )	,			uniform(-1.0	,			1.0  )  )  )
						    for _ in range(_SCREAMING_SNAKE_CASE  )  )
						# The ratio of the area for circle to square is pi/4.
						_SCREAMING_SNAKE_CASE				= proportion * 4
						print(F'The estimated value of pi is {pi_estimate}'  )
						print(F'The numpy value of pi is {pi}'  )
						print(F'The total error is {abs(pi - pi_estimate  )}'  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   Callable[[float], float]	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0	,			):
						return mean(
						    function_to_integrate(uniform(_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )  ) for _ in range(_SCREAMING_SNAKE_CASE  )  ) * (max_value - min_value)



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int	,			_SCREAMING_SNAKE_CASE		:   float = 0.0	,			_SCREAMING_SNAKE_CASE		:   float = 1.0  ):

						def identity_function(_SCREAMING_SNAKE_CASE		:   float  ) -> float:
												return x

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE  )
						_SCREAMING_SNAKE_CASE				= (max_value * max_value - min_value * min_value) / 2

						print("******************"  )
						print(F'Estimating area under y=x where x varies from {min_value} to {max_value}'  )
						print(F'Estimated value is {estimated_value}'  )
						print(F'Expected value is {expected_value}'  )
						print(F'Total error is {abs(estimated_value - expected_value  )}'  )
						print("******************"  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   int  ):

						def function_to_integrate(_SCREAMING_SNAKE_CASE		:   float  ) -> float:
												return sqrt(4.0 - x * x  )

						_SCREAMING_SNAKE_CASE				= area_under_curve_estimator(
						    _SCREAMING_SNAKE_CASE	,			_SCREAMING_SNAKE_CASE	,			0.0	,			2.0  )

						print("******************"  )
						print("Estimating pi using area_under_curve_estimator"  )
						print(F'Estimated value is {estimated_value}'  )
						print(F'Expected value is {pi}'  )
						print(F'Total error is {abs(estimated_value - pi  )}'  )
						print("******************"  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()