

'''simple docstring'''



import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_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 transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
		from transformers.models.mobilevitva.modeling_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    make_divisible,
		)


if is_vision_available():
		from PIL import Image

		from transformers import MobileViTImageProcessor



class lowerCamelCase			(  __SCREAMING_SNAKE_CASE						):



			'''simple docstring'''






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





									'''simple docstring'''
									A__					:       List[str]     =self.config_class(**self.inputs_dict			)
									self.parent.assertTrue(hasattr(_a		,      """width_multiplier"""			)			)



class lowerCamelCase			:



			'''simple docstring'''






			def __init__(				self	:						Optional[int]		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Optional[Any]=13		,      lowerCAmelCase_	:						List[Any]=64		,      lowerCAmelCase_	:						List[str]=2		,      lowerCAmelCase_	:						Optional[Any]=3		,      lowerCAmelCase_	:						Optional[int]="swish"		,      lowerCAmelCase_	:						Union[str, Any]=3		,      lowerCAmelCase_	:						Tuple=32		,      lowerCAmelCase_	:						Any=0.1		,      lowerCAmelCase_	:						Tuple=0.02		,      lowerCAmelCase_	:						Optional[Any]=True		,      lowerCAmelCase_	:						List[str]=True		,      lowerCAmelCase_	:						int=10		,      lowerCAmelCase_	:						List[str]=None		,      lowerCAmelCase_	:						List[Any]=0.25		,      lowerCAmelCase_	:						int=0.0		,      lowerCAmelCase_	:						Union[str, Any]=0.0		,      )      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Optional[Any]     =parent
									A__					:       Optional[Any]     =batch_size
									A__					:       Dict     =image_size
									A__					:       Optional[Any]     =patch_size
									A__					:       Tuple     =num_channels
									A__					:       Dict     =make_divisible(5_12 * width_multiplier		,      divisor=8			)
									A__					:       List[Any]     =hidden_act
									A__					:       Dict     =conv_kernel_size
									A__					:       Optional[int]     =output_stride
									A__					:       Tuple     =classifier_dropout_prob
									A__					:       Optional[int]     =use_labels
									A__					:       Optional[Any]     =is_training
									A__					:       str     =num_labels
									A__					:       Optional[int]     =initializer_range
									A__					:       Any     =scope
									A__					:       Any     =width_multiplier
									A__					:       str     =ffn_dropout
									A__					:       List[Any]     =attn_dropout


			def lowercase__						(				self	:						int			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       str     =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

									A__					:       List[Any]     =None
									A__					:       Optional[Any]     =None
									if self.use_labels:
															A__					:       Union[str, Any]     =ids_tensor([self.batch_size]		,      self.num_labels			)
															A__					:       Union[str, Any]     =ids_tensor([self.batch_size, self.image_size, self.image_size]		,      self.num_labels			)

									A__					:       int     =self.get_config()

									return config, pixel_values, labels, pixel_labels


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





									'''simple docstring'''
									return MobileViTVaConfig(
									    image_size=self.image_size		,      patch_size=self.patch_size		,      num_channels=self.num_channels		,      hidden_act=self.hidden_act		,      conv_kernel_size=self.conv_kernel_size		,      output_stride=self.output_stride		,      classifier_dropout_prob=self.classifier_dropout_prob		,      initializer_range=self.initializer_range		,      width_multiplier=self.width_multiplier		,      ffn_dropout=self.ffn_dropout_prob		,      attn_dropout=self.attn_dropout_prob		,      )


			def lowercase__						(				self	:						str		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						str		,      lowerCAmelCase_	:						List[Any]		,      lowerCAmelCase_	:						Optional[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Optional[int]     =MobileViTVaModel(config=_a			)
									model.to(_a			)
									model.eval()
									A__					:       Any     =model(_a			)
									self.parent.assertEqual(
									    result.last_hidden_state.shape		,      (
									        self.batch_size,
									        self.last_hidden_size,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )		,      )


			def lowercase__						(				self	:						int		,      lowerCAmelCase_	:						int		,      lowerCAmelCase_	:						Any		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Optional[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       List[str]     =self.num_labels
									A__					:       int     =MobileViTVaForImageClassification(_a			)
									model.to(_a			)
									model.eval()
									A__					:       Any     =model(_a		,      labels=_a			)
									self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)			)


			def lowercase__						(				self	:						Optional[int]		,      lowerCAmelCase_	:						Any		,      lowerCAmelCase_	:						List[str]		,      lowerCAmelCase_	:						Optional[Any]		,      lowerCAmelCase_	:						Tuple			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Dict     =self.num_labels
									A__					:       List[Any]     =MobileViTVaForSemanticSegmentation(_a			)
									model.to(_a			)
									model.eval()
									A__					:       Tuple     =model(_a			)
									self.parent.assertEqual(
									    result.logits.shape		,      (
									        self.batch_size,
									        self.num_labels,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )		,      )
									A__					:       Dict     =model(_a		,      labels=_a			)
									self.parent.assertEqual(
									    result.logits.shape		,      (
									        self.batch_size,
									        self.num_labels,
									        self.image_size // self.output_stride,
									        self.image_size // self.output_stride,
									    )		,      )



			def lowercase__						(				self	:						Tuple			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       int     =self.prepare_config_and_inputs()
									A__   ,						A__   ,						A__   ,						A__					:       Dict     =config_and_inputs
									A__					:       Any     ={"""pixel_values""": pixel_values}
									return config, inputs_dict



@require_torch
class lowerCamelCase			(  __SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	(
			    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)

			__snake_case        =	(
			    {
			        'feature-extraction': MobileViTVaModel,
			        'image-classification': MobileViTVaForImageClassification,
			        'image-segmentation': MobileViTVaForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			__snake_case        =	False
			__snake_case        =	False
			__snake_case        =	False
			__snake_case        =	False


			def lowercase__						(				self	:						List[str]			)      ->		Tuple:





									'''simple docstring'''
									A__					:       Optional[int]     =MobileViTVaModelTester(self			)
									A__					:       Any     =MobileViTVaConfigTester(self		,      config_class=_a		,      has_text_modality=_a			)


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





									'''simple docstring'''
									self.config_tester.run_common_tests()


			@unittest.skip(reason="""MobileViTV2 does not use inputs_embeds"""			)
			def lowercase__						(				self	:						List[Any]			)      ->		Dict:





									'''simple docstring'''
									pass


			@unittest.skip(reason="""MobileViTV2 does not support input and output embeddings"""			)
			def lowercase__						(				self	:						List[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									pass


			@unittest.skip(reason="""MobileViTV2 does not output attentions"""			)
			def lowercase__						(				self	:						Optional[Any]			)      ->		List[Any]:





									'''simple docstring'''
									pass


			@require_torch_multi_gpu
			@unittest.skip(reason="""Got `CUDA error: misaligned address` for tests after this one being run."""			)
			def lowercase__						(				self	:						Any			)      ->		Dict:





									'''simple docstring'''
									pass


			@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""			)
			def lowercase__						(				self	:						List[Any]			)      ->		Union[str, Any]:





									'''simple docstring'''
									pass


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





									'''simple docstring'''
									A__   ,						A__					:       Dict     =self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															A__					:       Union[str, Any]     =model_class(_a			)
															A__					:       int     =inspect.signature(model.forward			)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															A__					:       Optional[int]     =[*signature.parameters.keys()]

															A__					:       Optional[int]     =["""pixel_values"""]
															self.assertListEqual(arg_names[:1]		,      _a			)


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





									'''simple docstring'''
									A__					:       Any     =self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_a			)


			def lowercase__						(				self	:						Tuple			)      ->		Optional[Any]:





									'''simple docstring'''
									def check_hidden_states_output(lowerCAmelCase_	:						List[str]		,      lowerCAmelCase_	:						Tuple		,      lowerCAmelCase_	:						Optional[int]			):
															A__					:       str     =model_class(_a			)
															model.to(_a			)
															model.eval()

															with torch.no_grad():
																					A__					:       Optional[int]     =model(**self._prepare_for_class(_a		,      _a			)			)

															A__					:       Union[str, Any]     =outputs.hidden_states

															A__					:       Optional[int]     =5
															self.assertEqual(len(_a			)		,      _a			)

															# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
															# with the width and height being successively divided by 2.
															A__					:       Dict     =2
															for i in range(len(_a			)			):
																					self.assertListEqual(
																					    list(hidden_states[i].shape[-2:]			)		,      [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]		,      )
																					divisor *= 2

															self.assertEqual(self.model_tester.output_stride		,      divisor // 2			)

									A__   ,						A__					:       int     =self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															A__					:       Dict     =True
															check_hidden_states_output(_a		,      _a		,      _a			)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															A__					:       Union[str, Any]     =True

															check_hidden_states_output(_a		,      _a		,      _a			)


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





									'''simple docstring'''
									A__					:       str     =self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_a			)


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





									'''simple docstring'''
									A__					:       str     =self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_a			)



			@slow
			def lowercase__						(				self	:						Tuple			)      ->		str:





									'''simple docstring'''
									for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															A__					:       List[Any]     =MobileViTVaModel.from_pretrained(_a			)
															self.assertIsNotNone(_a			)


def 						__lowerCamelCase	(       )	->							str:






						"""simple docstring"""





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



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



			'''simple docstring'''






			@cached_property
			def lowercase__						(				self	:						Optional[Any]			)      ->		List[str]:





									'''simple docstring'''
									return (
									    MobileViTImageProcessor.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""			)
									    if is_vision_available()
									    else None
									)


			@slow
			def lowercase__						(				self	:						List[str]			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Tuple     =MobileViTVaForImageClassification.from_pretrained("""apple/mobilevitv2-1.0-imagenet1k-256"""			).to(
									    _a			)

									A__					:       Dict     =self.default_image_processor
									A__					:       Optional[Any]     =prepare_img()
									A__					:       Any     =image_processor(images=_a		,      return_tensors="""pt"""			).to(_a			)

									# forward pass
									with torch.no_grad():
															A__					:       Any     =model(**_a			)

									# verify the logits
									A__					:       Union[str, Any]     =torch.Size((1, 10_00)			)
									self.assertEqual(outputs.logits.shape		,      _a			)

									A__					:       List[Any]     =torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]			).to(_a			)

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


			@slow
			def lowercase__						(				self	:						Tuple			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""			)
									A__					:       Any     =model.to(_a			)

									A__					:       Optional[Any]     =MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""			)

									A__					:       Tuple     =prepare_img()
									A__					:       Union[str, Any]     =image_processor(images=_a		,      return_tensors="""pt"""			).to(_a			)

									# forward pass
									with torch.no_grad():
															A__					:       List[Any]     =model(**_a			)
									A__					:       List[str]     =outputs.logits

									# verify the logits
									A__					:       int     =torch.Size((1, 21, 32, 32)			)
									self.assertEqual(logits.shape		,      _a			)

									A__					:       Any     =torch.tensor(
									    [
									        [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]],
									        [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]],
									        [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]],
									    ]		,      device=_a		,      )

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



			@slow
			def lowercase__						(				self	:						str			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       int     =MobileViTVaForSemanticSegmentation.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""			)
									A__					:       Any     =model.to(_a			)

									A__					:       Any     =MobileViTImageProcessor.from_pretrained("""shehan97/mobilevitv2-1.0-voc-deeplabv3"""			)

									A__					:       Union[str, Any]     =prepare_img()
									A__					:       int     =image_processor(images=_a		,      return_tensors="""pt"""			).to(_a			)

									# forward pass
									with torch.no_grad():
															A__					:       Any     =model(**_a			)

									A__					:       str     =outputs.logits.detach().cpu()

									A__					:       Tuple     =image_processor.post_process_semantic_segmentation(outputs=_a		,      target_sizes=[(50, 60)]			)
									A__					:       int     =torch.Size((50, 60)			)
									self.assertEqual(segmentation[0].shape		,      _a			)

									A__					:       Union[str, Any]     =image_processor.post_process_semantic_segmentation(outputs=_a			)
									A__					:       int     =torch.Size((32, 32)			)
									self.assertEqual(segmentation[0].shape		,      _a			)




'''simple docstring'''



from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						float			)      ->		float:





									'''simple docstring'''
									return 0.0


def 						__lowerCamelCase	(       __snake_case					:		np.ndarray,		__snake_case					:		int	)	->							tuple[int | float, int | float]:






						"""simple docstring"""





						A__					:       Tuple     =min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
						A__					:       str     =max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
						return lowest, highest
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       Any     =512
						A__					:       int     =[1] + [0] * (size - 1)
						A__					:       int     =[filter_type.process(__snake_case	) for item in inputs]

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       List[Any]     =np.abs(np.fft.fft(__snake_case	)	)
						A__					:       int     =20 * np.logaa(__snake_case	)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24,		samplerate / 2 - 1	)
						plt.xlabel("""Frequency (Hz)"""	)
						plt.xscale("""log"""	)

						# Display within reasonable bounds
						A__					:       Union[str, Any]     =get_bounds(__snake_case,		__snake_case	)
						plt.ylim(max([-80, bounds[0]]	),		min([80, bounds[1]]	)	)
						plt.ylabel("""Gain (dB)"""	)

						plt.plot(__snake_case	)
						plt.show()
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       List[Any]     =512
						A__					:       List[Any]     =[1] + [0] * (size - 1)
						A__					:       Dict     =[filter_type.process(__snake_case	) for item in inputs]

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       str     =np.angle(np.fft.fft(__snake_case	)	)

						# Frequencies on log scale from 24 to nyquist frequency
						plt.xlim(24,		samplerate / 2 - 1	)
						plt.xlabel("""Frequency (Hz)"""	)
						plt.xscale("""log"""	)

						plt.ylim(-2 * pi,		2 * pi	)
						plt.ylabel("""Phase shift (Radians)"""	)
						plt.plot(np.unwrap(__snake_case,		-2 * pi	)	)
						plt.show()

