



'''simple docstring'''

import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
	from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import MobileNetVaImageProcessor


class 	a__     (				__A	):


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.config_class(**self.inputs_dict      )
					self.parent.assertTrue(hasattr(UpperCamelCase__,					'''tf_padding'''      )      )
					self.parent.assertTrue(hasattr(UpperCamelCase__,					'''depth_multiplier'''      )      )



class 	a__     :


	def __init__(self : Optional[int],					__UpperCAmelCase : List[str],					__UpperCAmelCase : Optional[Any]=13,					__UpperCAmelCase : Tuple=3,					__UpperCAmelCase : Any=32,					__UpperCAmelCase : List[str]=0.25,					__UpperCAmelCase : Union[str, Any]=8,					__UpperCAmelCase : Any=8,					__UpperCAmelCase : Union[str, Any]=6,					__UpperCAmelCase : List[Any]=32,					__UpperCAmelCase : List[str]=True,					__UpperCAmelCase : Optional[int]=True,					__UpperCAmelCase : List[str]=True,					__UpperCAmelCase : Tuple="relu6",					__UpperCAmelCase : Any=1280,					__UpperCAmelCase : Optional[int]=0.1,					__UpperCAmelCase : int=0.02,					__UpperCAmelCase : Optional[Any]=True,					__UpperCAmelCase : Optional[int]=True,					__UpperCAmelCase : Union[str, Any]=10,					__UpperCAmelCase : List[Any]=None,					)     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= parent
					SCREAMING_SNAKE_CASE  :						Dict      					= batch_size
					SCREAMING_SNAKE_CASE  :						int      					= num_channels
					SCREAMING_SNAKE_CASE  :						List[Any]      					= image_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= depth_multiplier
					SCREAMING_SNAKE_CASE  :						Dict      					= depth_divisible_by
					SCREAMING_SNAKE_CASE  :						List[Any]      					= min_depth
					SCREAMING_SNAKE_CASE  :						List[Any]      					= expand_ratio
					SCREAMING_SNAKE_CASE  :						List[str]      					= tf_padding
					SCREAMING_SNAKE_CASE  :						List[Any]      					= output_stride
					SCREAMING_SNAKE_CASE  :						List[Any]      					= first_layer_is_expansion
					SCREAMING_SNAKE_CASE  :						Dict      					= finegrained_output
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= hidden_act
					SCREAMING_SNAKE_CASE  :						str      					= last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier      )
					SCREAMING_SNAKE_CASE  :						int      					= classifier_dropout_prob
					SCREAMING_SNAKE_CASE  :						int      					= use_labels
					SCREAMING_SNAKE_CASE  :						Any      					= is_training
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= num_labels
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= initializer_range
					SCREAMING_SNAKE_CASE  :						List[Any]      					= scope


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.get_config()

					return config, pixel_values, labels, pixel_labels


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

					"""simple docstring"""

					return MobileNetVaConfig(
					    num_channels=self.num_channels,					image_size=self.image_size,					depth_multiplier=self.depth_multiplier,					depth_divisible_by=self.depth_divisible_by,					min_depth=self.min_depth,					expand_ratio=self.expand_ratio,					output_stride=self.output_stride,					first_layer_is_expansion=self.first_layer_is_expansion,					finegrained_output=self.finegrained_output,					hidden_act=self.hidden_act,					tf_padding=self.tf_padding,					classifier_dropout_prob=self.classifier_dropout_prob,					initializer_range=self.initializer_range,					)


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= MobileNetVaModel(config=UpperCamelCase__      )
					model.to(UpperCamelCase__      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						str      					= model(UpperCamelCase__      )
					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,
					    ),					)
					self.parent.assertEqual(
					    result.pooler_output.shape,					(self.batch_size, self.last_hidden_size),					)


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.num_labels
					SCREAMING_SNAKE_CASE  :						int      					= MobileNetVaForImageClassification(UpperCamelCase__      )
					model.to(UpperCamelCase__      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						Tuple      					= model(UpperCamelCase__,					labels=UpperCamelCase__      )
					self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= self.num_labels
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= MobileNetVaForSemanticSegmentation(UpperCamelCase__      )
					model.to(UpperCamelCase__      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						Any      					= model(UpperCamelCase__      )
					self.parent.assertEqual(
					    result.logits.shape,					(
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    ),					)
					SCREAMING_SNAKE_CASE  :						Dict      					= model(UpperCamelCase__,					labels=UpperCamelCase__      )
					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 : List[Any]      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE  :						List[Any]      					= config_and_inputs
					SCREAMING_SNAKE_CASE  :						List[str]      					= {'pixel_values': pixel_values}
					return config, inputs_dict



@require_torch
class 	a__     (				__A,			__A,			unittest.TestCase	):
	__magic_name__		:    Any	    =     (
	    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)
	__magic_name__		:    Optional[int]	    =     (
	    {
	        """feature-extraction""": MobileNetVaModel,
	        """image-classification""": MobileNetVaForImageClassification,
	        """image-segmentation""": MobileNetVaForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	__magic_name__		:    Dict	    =     False
	__magic_name__		:    Optional[Any]	    =     False
	__magic_name__		:    Tuple	    =     False
	__magic_name__		:    Tuple	    =     False


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= MobileNetVaModelTester(self      )
					SCREAMING_SNAKE_CASE  :						int      					= MobileNetVaConfigTester(self,					config_class=UpperCamelCase__,					has_text_modality=UpperCamelCase__      )


	def 				lowercase__  (self : Dict      )     ->			Dict:

					"""simple docstring"""

					self.config_tester.run_common_tests()


	@unittest.skip(reason='''MobileNetV2 does not use inputs_embeds'''      )
	def 				lowercase__  (self : Optional[Any]      )     ->			int:

					"""simple docstring"""

					pass


	@unittest.skip(reason='''MobileNetV2 does not support input and output embeddings'''      )
	def 				lowercase__  (self : Optional[Any]      )     ->			Tuple:

					"""simple docstring"""

					pass


	@unittest.skip(reason='''MobileNetV2 does not output attentions'''      )
	def 				lowercase__  (self : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

					pass


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE  :						Optional[int]      					= ['pixel_values']
									self.assertListEqual(arg_names[:1],					UpperCamelCase__      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase__      )


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

					"""simple docstring"""

					def check_hidden_states_output(__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : int      ):
									SCREAMING_SNAKE_CASE  :						int      					= model_class(UpperCamelCase__      )
									model.to(UpperCamelCase__      )
									model.eval()

									with torch.no_grad():
													SCREAMING_SNAKE_CASE  :						Any      					= model(**self._prepare_for_class(UpperCamelCase__,					UpperCamelCase__      )      )

									SCREAMING_SNAKE_CASE  :						List[Any]      					= outputs.hidden_states

									SCREAMING_SNAKE_CASE  :						Dict      					= 16
									self.assertEqual(len(UpperCamelCase__      ),					UpperCamelCase__      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= True
									check_hidden_states_output(UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__      )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE  :						List[str]      					= True

									check_hidden_states_output(UpperCamelCase__,					UpperCamelCase__,					UpperCamelCase__      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__      )


	def 				lowercase__  (self : Dict      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*UpperCamelCase__      )






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

					"""simple docstring"""

					for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE  :						Tuple      					= MobileNetVaModel.from_pretrained(UpperCamelCase__      )
									self.assertIsNotNone(UpperCamelCase__      )






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						List[str]      					= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
				return image




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


	@cached_property
	def 				lowercase__  (self : Dict      )     ->			Tuple:

					"""simple docstring"""

					return (
					    MobileNetVaImageProcessor.from_pretrained('''google/mobilenet_v2_1.0_224'''      ) if is_vision_available() else None
					)


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= MobileNetVaForImageClassification.from_pretrained('''google/mobilenet_v2_1.0_224'''      ).to(UpperCamelCase__      )

					SCREAMING_SNAKE_CASE  :						Tuple      					= self.default_image_processor
					SCREAMING_SNAKE_CASE  :						List[Any]      					= prepare_img()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= image_processor(images=UpperCamelCase__,					return_tensors='''pt'''      ).to(UpperCamelCase__      )

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

					# verify the logits
					SCREAMING_SNAKE_CASE  :						List[str]      					= torch.Size((1, 1001)      )
					self.assertEqual(outputs.logits.shape,					UpperCamelCase__      )

					SCREAMING_SNAKE_CASE  :						Dict      					= torch.tensor([0.2445, -1.1993, 0.1905]      ).to(UpperCamelCase__      )

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






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= MobileNetVaForSemanticSegmentation.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model.to(UpperCamelCase__      )

					SCREAMING_SNAKE_CASE  :						int      					= MobileNetVaImageProcessor.from_pretrained('''google/deeplabv3_mobilenet_v2_1.0_513'''      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= prepare_img()
					SCREAMING_SNAKE_CASE  :						int      					= image_processor(images=UpperCamelCase__,					return_tensors='''pt'''      ).to(UpperCamelCase__      )

					# forward pass
					with torch.no_grad():
									SCREAMING_SNAKE_CASE  :						List[str]      					= model(**UpperCamelCase__      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= outputs.logits

					# verify the logits
					SCREAMING_SNAKE_CASE  :						str      					= torch.Size((1, 21, 65, 65)      )
					self.assertEqual(logits.shape,					UpperCamelCase__      )

					SCREAMING_SNAKE_CASE  :						Any      					= torch.tensor(
					    [
					        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
					        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
					        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
					    ],					device=UpperCamelCase__,					)

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









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :str       ):
				SCREAMING_SNAKE_CASE  :						int      					= 0
				if start < end:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= randint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						List[str]      					= a[end]
								SCREAMING_SNAKE_CASE  :						List[str]      					= a[pivot]
								SCREAMING_SNAKE_CASE  :						Dict      					= temp

								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= _in_place_partition(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								count += _in_place_quick_sort(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	p - 1       )
								count += _in_place_quick_sort(_SCREAMING_SNAKE_CASE    ,	p + 1    ,	_SCREAMING_SNAKE_CASE       )
				return count






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				SCREAMING_SNAKE_CASE  :						Any      					= 0
				SCREAMING_SNAKE_CASE  :						Tuple      					= randint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= a[end]
				SCREAMING_SNAKE_CASE  :						int      					= a[pivot]
				SCREAMING_SNAKE_CASE  :						Tuple      					= temp
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= start - 1
				for index in range(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ):
								count += 1
								if a[index] < a[end]:  # check if current val is less than pivot value
												SCREAMING_SNAKE_CASE  :						Tuple      					= new_pivot_index + 1
												SCREAMING_SNAKE_CASE  :						Dict      					= a[new_pivot_index]
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= a[index]
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= temp

				SCREAMING_SNAKE_CASE  :						List[Any]      					= a[new_pivot_index + 1]
				SCREAMING_SNAKE_CASE  :						Any      					= a[end]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= temp
				return new_pivot_index + 1, count


snake_case_			=     TemporaryFile()
snake_case_			=     100  # 1000 elements are to be sorted


snake_case_			,				snake_case_			=     0, 1  # mean and standard deviation
snake_case_			=     np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
snake_case_			=     np.load(outfile)
snake_case_			=     len(M) - 1
snake_case_			=     _in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)




print(z)




