



'''simple docstring'''

import inspect
import unittest

from transformers import YolosConfig
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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import YolosForObjectDetection, YolosModel
	from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class 	a__     :


	def __init__(self : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any]=13,					__UpperCAmelCase : Optional[Any]=[30, 30],					__UpperCAmelCase : Tuple=2,					__UpperCAmelCase : Dict=3,					__UpperCAmelCase : Union[str, Any]=True,					__UpperCAmelCase : str=True,					__UpperCAmelCase : Optional[int]=32,					__UpperCAmelCase : Union[str, Any]=5,					__UpperCAmelCase : Optional[Any]=4,					__UpperCAmelCase : Dict=37,					__UpperCAmelCase : Tuple="gelu",					__UpperCAmelCase : Optional[int]=0.1,					__UpperCAmelCase : Tuple=0.1,					__UpperCAmelCase : Tuple=10,					__UpperCAmelCase : Tuple=0.02,					__UpperCAmelCase : Dict=3,					__UpperCAmelCase : Union[str, Any]=None,					__UpperCAmelCase : Union[str, Any]=8,					__UpperCAmelCase : int=10,					)     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= parent
					SCREAMING_SNAKE_CASE  :						List[Any]      					= batch_size
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= image_size
					SCREAMING_SNAKE_CASE  :						List[str]      					= patch_size
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= num_channels
					SCREAMING_SNAKE_CASE  :						int      					= is_training
					SCREAMING_SNAKE_CASE  :						str      					= use_labels
					SCREAMING_SNAKE_CASE  :						Tuple      					= hidden_size
					SCREAMING_SNAKE_CASE  :						str      					= num_hidden_layers
					SCREAMING_SNAKE_CASE  :						Any      					= num_attention_heads
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= intermediate_size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= hidden_act
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= hidden_dropout_prob
					SCREAMING_SNAKE_CASE  :						str      					= attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= type_sequence_label_size
					SCREAMING_SNAKE_CASE  :						Tuple      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Dict      					= num_labels
					SCREAMING_SNAKE_CASE  :						List[str]      					= scope
					SCREAMING_SNAKE_CASE  :						List[str]      					= n_targets
					SCREAMING_SNAKE_CASE  :						str      					= num_detection_tokens
					# we set the expected sequence length (which is used in several tests)
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= (image_size[1] // patch_size) * (image_size[0] // patch_size)
					SCREAMING_SNAKE_CASE  :						Dict      					= num_patches + 1 + self.num_detection_tokens


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= None
					if self.use_labels:
									# labels is a list of Dict (each Dict being the labels for a given example in the batch)
									SCREAMING_SNAKE_CASE  :						Dict      					= []
									for i in range(self.batch_size      ):
													SCREAMING_SNAKE_CASE  :						List[Any]      					= {}
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.randint(
													    high=self.num_labels,					size=(self.n_targets,),					device=_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE  :						Tuple      					= torch.rand(self.n_targets,					4,					device=_SCREAMING_SNAKE_CASE      )
													labels.append(_SCREAMING_SNAKE_CASE      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.get_config()

					return config, pixel_values, labels


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

					"""simple docstring"""

					return YolosConfig(
					    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,					num_detection_tokens=self.num_detection_tokens,					num_labels=self.num_labels,					)


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= YolosModel(config=_SCREAMING_SNAKE_CASE      )
					model.to(_SCREAMING_SNAKE_CASE      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= model(_SCREAMING_SNAKE_CASE      )
					self.parent.assertEqual(
					    result.last_hidden_state.shape,					(self.batch_size, self.expected_seq_len, self.hidden_size)      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= YolosForObjectDetection(_SCREAMING_SNAKE_CASE      )
					model.to(_SCREAMING_SNAKE_CASE      )
					model.eval()

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(pixel_values=_SCREAMING_SNAKE_CASE      )
					SCREAMING_SNAKE_CASE  :						Dict      					= model(_SCREAMING_SNAKE_CASE      )

					self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_detection_tokens, self.num_labels + 1)      )
					self.parent.assertEqual(result.pred_boxes.shape,					(self.batch_size, self.num_detection_tokens, 4)      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(pixel_values=_SCREAMING_SNAKE_CASE,					labels=_SCREAMING_SNAKE_CASE      )

					self.parent.assertEqual(result.loss.shape,					()      )
					self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_detection_tokens, self.num_labels + 1)      )
					self.parent.assertEqual(result.pred_boxes.shape,					(self.batch_size, self.num_detection_tokens, 4)      )






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

					"""simple docstring"""

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



@require_torch
class 	a__     (				lowerCAmelCase__,			lowerCAmelCase__,			unittest.TestCase	):
	__magic_name__		:    Dict	    =     (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
	__magic_name__		:    List[str]	    =     (
	    {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
	)

	__magic_name__		:    List[str]	    =     False
	__magic_name__		:    Optional[Any]	    =     False
	__magic_name__		:    int	    =     False
	__magic_name__		:    Optional[int]	    =     False


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Tuple,					__UpperCAmelCase : Union[str, Any]=False      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= super()._prepare_for_class(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					return_labels=_SCREAMING_SNAKE_CASE      )

					if return_labels:
									if model_class.__name__ == "YolosForObjectDetection":
													SCREAMING_SNAKE_CASE  :						int      					= []
													for i in range(self.model_tester.batch_size      ):
																	SCREAMING_SNAKE_CASE  :						List[Any]      					= {}
																	SCREAMING_SNAKE_CASE  :						Optional[int]      					= torch.ones(
																	    size=(self.model_tester.n_targets,),					device=_SCREAMING_SNAKE_CASE,					dtype=torch.long      )
																	SCREAMING_SNAKE_CASE  :						int      					= torch.ones(
																	    self.model_tester.n_targets,					4,					device=_SCREAMING_SNAKE_CASE,					dtype=torch.float      )
																	labels.append(_SCREAMING_SNAKE_CASE      )
													SCREAMING_SNAKE_CASE  :						int      					= labels

					return inputs_dict


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= YolosModelTester(self      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= ConfigTester(self,					config_class=_SCREAMING_SNAKE_CASE,					has_text_modality=_SCREAMING_SNAKE_CASE,					hidden_size=37      )


	def 				lowercase__  (self : int      )     ->			int:

					"""simple docstring"""

					self.config_tester.run_common_tests()


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

					"""simple docstring"""

					pass


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						Dict      					= model_class(_SCREAMING_SNAKE_CASE      )
									self.assertIsInstance(model.get_input_embeddings(),					(nn.Module)      )
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE,					nn.Linear      )      )


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

					"""simple docstring"""

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

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						Tuple      					= model_class(_SCREAMING_SNAKE_CASE      )
									SCREAMING_SNAKE_CASE  :						str      					= inspect.signature(model.forward      )
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ['''pixel_values''']
									self.assertListEqual(arg_names[:1],					_SCREAMING_SNAKE_CASE      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE  :						Any      					= True

					# in YOLOS, the seq_len is different
					SCREAMING_SNAKE_CASE  :						str      					= self.model_tester.expected_seq_len
					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= True
									SCREAMING_SNAKE_CASE  :						Tuple      					= False
									SCREAMING_SNAKE_CASE  :						Dict      					= True
									SCREAMING_SNAKE_CASE  :						List[Any]      					= model_class(_SCREAMING_SNAKE_CASE      )
									model.to(_SCREAMING_SNAKE_CASE      )
									model.eval()
									with torch.no_grad():
													SCREAMING_SNAKE_CASE  :						int      					= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE      )      )
									SCREAMING_SNAKE_CASE  :						List[str]      					= outputs.attentions
									self.assertEqual(len(_SCREAMING_SNAKE_CASE      ),					self.model_tester.num_hidden_layers      )

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									SCREAMING_SNAKE_CASE  :						str      					= True
									SCREAMING_SNAKE_CASE  :						Any      					= model_class(_SCREAMING_SNAKE_CASE      )
									model.to(_SCREAMING_SNAKE_CASE      )
									model.eval()
									with torch.no_grad():
													SCREAMING_SNAKE_CASE  :						int      					= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE      )      )
									SCREAMING_SNAKE_CASE  :						Dict      					= outputs.attentions
									self.assertEqual(len(_SCREAMING_SNAKE_CASE      ),					self.model_tester.num_hidden_layers      )

									self.assertListEqual(
									    list(attentions[0].shape[-3:]      ),					[self.model_tester.num_attention_heads, seq_len, seq_len],					)
									SCREAMING_SNAKE_CASE  :						Tuple      					= len(_SCREAMING_SNAKE_CASE      )

									# Check attention is always last and order is fine
									SCREAMING_SNAKE_CASE  :						str      					= True
									SCREAMING_SNAKE_CASE  :						Tuple      					= True
									SCREAMING_SNAKE_CASE  :						Tuple      					= model_class(_SCREAMING_SNAKE_CASE      )
									model.to(_SCREAMING_SNAKE_CASE      )
									model.eval()
									with torch.no_grad():
													SCREAMING_SNAKE_CASE  :						int      					= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE      )      )

									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 1
									self.assertEqual(out_len + added_hidden_states,					len(_SCREAMING_SNAKE_CASE      )      )

									SCREAMING_SNAKE_CASE  :						Optional[int]      					= outputs.attentions

									self.assertEqual(len(_SCREAMING_SNAKE_CASE      ),					self.model_tester.num_hidden_layers      )
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]      ),					[self.model_tester.num_attention_heads, seq_len, seq_len],					)


	def 				lowercase__  (self : Dict      )     ->			str:

					"""simple docstring"""

					def check_hidden_states_output(__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Any,					__UpperCAmelCase : Union[str, Any]      ):
									SCREAMING_SNAKE_CASE  :						List[Any]      					= model_class(_SCREAMING_SNAKE_CASE      )
									model.to(_SCREAMING_SNAKE_CASE      )
									model.eval()

									with torch.no_grad():
													SCREAMING_SNAKE_CASE  :						Dict      					= model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE      )      )

									SCREAMING_SNAKE_CASE  :						List[Any]      					= outputs.hidden_states

									SCREAMING_SNAKE_CASE  :						Dict      					= getattr(
									    self.model_tester,					'''expected_num_hidden_layers''',					self.model_tester.num_hidden_layers + 1      )
									self.assertEqual(len(_SCREAMING_SNAKE_CASE      ),					_SCREAMING_SNAKE_CASE      )

									# YOLOS has a different seq_length
									SCREAMING_SNAKE_CASE  :						List[Any]      					= self.model_tester.expected_seq_len

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]      ),					[seq_length, self.model_tester.hidden_size],					)

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

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						str      					= True
									check_hidden_states_output(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE      )

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

									check_hidden_states_output(_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE,					_SCREAMING_SNAKE_CASE      )


	def 				lowercase__  (self : Any      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_object_detection(*_SCREAMING_SNAKE_CASE      )






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

					"""simple docstring"""

					for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= YolosModel.from_pretrained(_SCREAMING_SNAKE_CASE      )
									self.assertIsNotNone(_SCREAMING_SNAKE_CASE      )






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




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


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

					"""simple docstring"""

					return AutoImageProcessor.from_pretrained('''hustvl/yolos-small'''      ) if is_vision_available() else None






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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= YolosForObjectDetection.from_pretrained('''hustvl/yolos-small'''      ).to(_SCREAMING_SNAKE_CASE      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.default_image_processor
					SCREAMING_SNAKE_CASE  :						str      					= prepare_img()
					SCREAMING_SNAKE_CASE  :						Dict      					= image_processor(images=_SCREAMING_SNAKE_CASE,					return_tensors='''pt'''      ).to(_SCREAMING_SNAKE_CASE      )

					# forward pass
					with torch.no_grad():
									SCREAMING_SNAKE_CASE  :						Tuple      					= model(inputs.pixel_values      )

					# verify outputs
					SCREAMING_SNAKE_CASE  :						str      					= torch.Size((1, 100, 92)      )
					self.assertEqual(outputs.logits.shape,					_SCREAMING_SNAKE_CASE      )

					SCREAMING_SNAKE_CASE  :						int      					= torch.tensor(
					    [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]],					device=_SCREAMING_SNAKE_CASE,					)
					SCREAMING_SNAKE_CASE  :						str      					= torch.tensor(
					    [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]],					device=_SCREAMING_SNAKE_CASE      )
					self.assertTrue(torch.allclose(outputs.logits[0, :3, :3],					_SCREAMING_SNAKE_CASE,					atol=1e-4      )      )
					self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3],					_SCREAMING_SNAKE_CASE,					atol=1e-4      )      )

					# verify postprocessing
					SCREAMING_SNAKE_CASE  :						int      					= image_processor.post_process_object_detection(
					    _SCREAMING_SNAKE_CASE,					threshold=0.3,					target_sizes=[image.size[::-1]]      )[0]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]      ).to(_SCREAMING_SNAKE_CASE      )
					SCREAMING_SNAKE_CASE  :						int      					= [75, 75, 17, 63, 17]
					SCREAMING_SNAKE_CASE  :						Any      					= torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]      ).to(_SCREAMING_SNAKE_CASE      )

					self.assertEqual(len(results['''scores''']      ),					5      )
					self.assertTrue(torch.allclose(results['''scores'''],					_SCREAMING_SNAKE_CASE,					atol=1e-4      )      )
					self.assertSequenceEqual(results['''labels'''].tolist(),					_SCREAMING_SNAKE_CASE      )
					self.assertTrue(torch.allclose(results['''boxes'''][0, :],					_SCREAMING_SNAKE_CASE      )      )









'''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)




