





import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
				from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments

@require_torch
class 	A_       (unittest.TestCase			):
	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				for model_result in results.values():
							for batch_size, sequence_length in zip(model_result['''bs''']							,					model_result['''ss''']		):
										UpperCAmelCase   		=				model_result['''result'''][batch_size][sequence_length]
										self.assertIsNotNone(_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tiny-gpt2'''
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result		)
				self.check_results_dict_not_empty(results.memory_inference_result		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sgugger/tiny-distilbert-classification'''
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=_A							,					only_pretrain_model=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result		)
				self.check_results_dict_not_empty(results.memory_inference_result		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tiny-gpt2'''
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					torchscript=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result		)
				self.check_results_dict_not_empty(results.memory_inference_result		)
	@unittest.skipIf(torch_device == '''cpu'''							,					'''Cant do half precision'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tiny-gpt2'''
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					fpaa=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result		)
				self.check_results_dict_not_empty(results.memory_inference_result		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tiny-gpt2'''
				UpperCAmelCase   		=				AutoConfig.from_pretrained(_A		)
				# set architectures equal to `None`
				UpperCAmelCase   		=				None
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A							,					configs=[config]		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result		)
				self.check_results_dict_not_empty(results.memory_inference_result		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tiny-gpt2'''
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result		)
				self.check_results_dict_not_empty(results.memory_train_result		)
	@unittest.skipIf(torch_device == '''cpu'''							,					'''Can\'t do half precision'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tiny-gpt2'''
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					fpaa=_A							,					multi_process=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result		)
				self.check_results_dict_not_empty(results.memory_train_result		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tiny-gpt2'''
				UpperCAmelCase   		=				AutoConfig.from_pretrained(_A		)
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A							,					configs=[config]		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result		)
				self.check_results_dict_not_empty(results.memory_inference_result		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tinier_bart'''
				UpperCAmelCase   		=				AutoConfig.from_pretrained(_A		)
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A							,					configs=[config]		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_inference_result		)
				self.check_results_dict_not_empty(results.memory_inference_result		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tiny-gpt2'''
				UpperCAmelCase   		=				AutoConfig.from_pretrained(_A		)
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A							,					configs=[config]		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result		)
				self.check_results_dict_not_empty(results.memory_train_result		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tinier_bart'''
				UpperCAmelCase   		=				AutoConfig.from_pretrained(_A		)
				UpperCAmelCase   		=				PyTorchBenchmarkArguments(
				    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=_A							,					)
				UpperCAmelCase   		=				PyTorchBenchmark(_A							,					configs=[config]		)
				UpperCAmelCase   		=				benchmark.run()
				self.check_results_dict_not_empty(results.time_train_result		)
				self.check_results_dict_not_empty(results.memory_train_result		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tiny-gpt2'''
				with tempfile.TemporaryDirectory() as tmp_dir:
							UpperCAmelCase   		=				PyTorchBenchmarkArguments(
							    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					save_to_csv=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					inference_time_csv_file=os.path.join(_A							,					'''inf_time.csv'''		)							,					train_memory_csv_file=os.path.join(_A							,					'''train_mem.csv'''		)							,					inference_memory_csv_file=os.path.join(_A							,					'''inf_mem.csv'''		)							,					train_time_csv_file=os.path.join(_A							,					'''train_time.csv'''		)							,					env_info_csv_file=os.path.join(_A							,					'''env.csv'''		)							,					multi_process=_A							,					)
							UpperCAmelCase   		=				PyTorchBenchmark(_A		)
							benchmark.run()
							self.assertTrue(Path(os.path.join(_A							,					'''inf_time.csv'''		)		).exists()		)
							self.assertTrue(Path(os.path.join(_A							,					'''train_time.csv'''		)		).exists()		)
							self.assertTrue(Path(os.path.join(_A							,					'''inf_mem.csv'''		)		).exists()		)
							self.assertTrue(Path(os.path.join(_A							,					'''train_mem.csv'''		)		).exists()		)
							self.assertTrue(Path(os.path.join(_A							,					'''env.csv'''		)		).exists()		)







	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				'''sshleifer/tiny-gpt2'''

				def _check_summary_is_not_empty(_A		):
							self.assertTrue(hasattr(_A							,					'''sequential'''		)		)
							self.assertTrue(hasattr(_A							,					'''cumulative'''		)		)
							self.assertTrue(hasattr(_A							,					'''current'''		)		)
							self.assertTrue(hasattr(_A							,					'''total'''		)		)

				with tempfile.TemporaryDirectory() as tmp_dir:
							UpperCAmelCase   		=				PyTorchBenchmarkArguments(
							    models=[MODEL_ID]							,					training=_A							,					inference=_A							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					log_filename=os.path.join(_A							,					'''log.txt'''		)							,					log_print=_A							,					trace_memory_line_by_line=_A							,					multi_process=_A							,					)
							UpperCAmelCase   		=				PyTorchBenchmark(_A		)
							UpperCAmelCase   		=				benchmark.run()
							_check_summary_is_not_empty(result.inference_summary		)
							_check_summary_is_not_empty(result.train_summary		)
							self.assertTrue(Path(os.path.join(_A							,					'''log.txt'''		)		).exists()		)










import cva
import numpy as np

class 	A_       :
	def __init__(    self							,					_A							,					_A		):






				'''simple docstring'''







				if k in (0.04, 0.06):
							UpperCAmelCase   		=				k
							UpperCAmelCase   		=				window_size
				else:
							raise ValueError('''invalid k value'''		)
	def __str__(    self		):






				'''simple docstring'''







				return str(self.k		)







	def      _lowercase				(    self							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				cva.imread(_A							,					0		)
				UpperCAmelCase		,			UpperCAmelCase   		=				img.shape
				UpperCAmelCase   		=				[]
				UpperCAmelCase   		=				img.copy()
				UpperCAmelCase   		=				cva.cvtColor(_A							,					cva.COLOR_GRAY2RGB		)
				UpperCAmelCase		,			UpperCAmelCase   		=				np.gradient(_A		)
				UpperCAmelCase   		=				dx**2
				UpperCAmelCase   		=				dy**2
				UpperCAmelCase   		=				dx * dy
				UpperCAmelCase   		=				0.04
				UpperCAmelCase   		=				self.window_size // 2
				for y in range(_A							,					h - offset		):
							for x in range(_A							,					w - offset		):
										UpperCAmelCase   		=				ixx[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										UpperCAmelCase   		=				iyy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()
										UpperCAmelCase   		=				ixy[
										    y - offset : y + offset + 1, x - offset : x + offset + 1
										].sum()

										UpperCAmelCase   		=				(wxx * wyy) - (wxy**2)
										UpperCAmelCase   		=				wxx + wyy
										UpperCAmelCase   		=				det - k * (trace**2)
										# Can change the value
										if r > 0.5:
													corner_list.append([x, y, r]		)
													color_img.itemset((y, x, 0)							,					0		)
													color_img.itemset((y, x, 1)							,					0		)
													color_img.itemset((y, x, 2)							,					2_5_5		)
				return color_img, corner_list


if __name__ == "__main__":
				__A				:    Tuple       =   HarrisCorner(0.04, 3)
				__A    , __A				:    List[Any]       =   edge_detect.detect("path_to_image")
				cva.imwrite("detect.png", color_img)



