


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     UpperCamelCase:


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1_3     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=6_4     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=2     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=3     ,    SCREAMING_SNAKE_CASE	:		List[str]=True     ,    SCREAMING_SNAKE_CASE	:		List[Any]=True     ,    SCREAMING_SNAKE_CASE	:		Any=3_2     ,    SCREAMING_SNAKE_CASE	:		Any=5     ,    SCREAMING_SNAKE_CASE	:		List[Any]=4     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=3_7     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Dict=0.1     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_0     ,    SCREAMING_SNAKE_CASE	:		int=0.02     ,    SCREAMING_SNAKE_CASE	:		Dict=[1, 1_6, 4, 4]     ,    SCREAMING_SNAKE_CASE	:		List[str]=None     ,    )			->	int:




											'''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				=		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
											__snake_case				=		(self.image_size // 3_2) ** 2
											__snake_case				=		num_patches + 1


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[Any]:




											'''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 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	int:




											'''simple docstring'''

											__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, 1_6, 3_2],
											    "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=SCREAMING_SNAKE_CASE     ,    initializer_range=self.initializer_range     ,    backbone_featmap_shape=self.backbone_featmap_shape     ,    backbone_config=SCREAMING_SNAKE_CASE     ,    )


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	int:




											'''simple docstring'''

											__snake_case				=		ViTHybridModel(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.last_hidden_state.shape     ,    (self.batch_size, self.seq_length, self.hidden_size)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		self.type_sequence_label_size
											__snake_case				=		ViTHybridForImageClassification(SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()
											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    labels=SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape     ,    (self.batch_size, self.type_sequence_label_size)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	str:




											'''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     UpperCamelCase(  _a     ,							_a     ,							unittest.TestCase   ):
					snake_case_		:				Union[str, Any]										=	(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
					snake_case_		:				str										=	(
					    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
					    if is_torch_available()
					    else {}
					)
					snake_case_		:				Tuple										=	False
					snake_case_		:				Optional[Any]										=	False
					snake_case_		:				Dict										=	False


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	int:




											'''simple docstring'''

											__snake_case				=		ViTHybridModelTester(self						)
											__snake_case				=		ConfigTester(self     ,    config_class=SCREAMING_SNAKE_CASE     ,    has_text_modality=SCREAMING_SNAKE_CASE     ,    hidden_size=3_7						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple						)			->	List[Any]:




											'''simple docstring'''

											self.config_tester.run_common_tests()


					@unittest.skip(reason="ViT does not use inputs_embeds"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[Any]:




											'''simple docstring'''

											pass


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	int:




											'''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(SCREAMING_SNAKE_CASE						)
																	self.assertIsInstance(model.get_input_embeddings()     ,    (nn.Module)						)
																	__snake_case				=		model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE     ,    nn.Linear						)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Optional[Any]:




											'''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(SCREAMING_SNAKE_CASE						)
																	__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]     ,    SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	List[str]:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()

											__snake_case				=		_config_zero_init(SCREAMING_SNAKE_CASE						)
											for model_class in self.all_model_classes:
																	__snake_case				=		model_class(config=SCREAMING_SNAKE_CASE						)
																	# Skip the check for the backbone
																	for name, module in model.named_modules():
																							if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																													__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 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Tuple:




											'''simple docstring'''

											for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__snake_case				=		ViTHybridModel.from_pretrained(SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE						)





def 	_lowerCAmelCase       (    )						->	List[str]:


						'''simple docstring'''






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






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


					@cached_property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	int:




											'''simple docstring'''

											return (
											    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
											    if is_vision_available()
											    else None
											)


					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	str:




											'''simple docstring'''

											__snake_case				=		ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
											    SCREAMING_SNAKE_CASE						)

											__snake_case				=		self.default_image_processor
											__snake_case				=		prepare_img()
											__snake_case				=		image_processor(images=SCREAMING_SNAKE_CASE     ,    return_tensors="pt"						).to(SCREAMING_SNAKE_CASE						)

											# forward pass
											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE						)

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

											__snake_case				=		torch.tensor([-1.9090, -0.4993, -0.2389]						).to(SCREAMING_SNAKE_CASE						)

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






					@slow
					@require_accelerate
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384"						)
											__snake_case				=		ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384"     ,    device_map="auto"						)

											__snake_case				=		prepare_img()

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

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









from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase = 10**-10 )						->	float:


						'''simple docstring'''






						__snake_case				=		a
						while True:
												__snake_case				=		Decimal(_lowerCAmelCase ) - (
												    Decimal(eval(_lowerCAmelCase ) ) / Decimal(eval(str(diff(_lowerCAmelCase ) ) ) )  # noqa: S307
												)
												# This number dictates the accuracy of the answer
												if abs(eval(_lowerCAmelCase ) ) < precision:  # noqa: S307
																		return float(_lowerCAmelCase )


# Let's Execute
if __name__ == "__main__":
	# Find root of trigonometric function
	# Find value of pi
	print(f'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
	# Find root of polynomial
	print(f'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
	# Find Square Root of 5
	print(f'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
	# Exponential Roots
	print(f'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')





