




import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 CvtForImageClassification, CvtModel
					from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor







class        __lowerCAmelCase					(			__UpperCamelCase					):

			"""simple docstring"""







			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       self.config_class(**self.inputs_dict				)
							self.parent.assertTrue(hasattr(snake_case__     ,		'embed_dim'				)				)
							self.parent.assertTrue(hasattr(snake_case__     ,		'num_heads'				)				)







class        __lowerCAmelCase					:

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__=13     ,		lowerCamelCase__=64     ,		lowerCamelCase__=3     ,		lowerCamelCase__=[16, 48, 96]     ,		lowerCamelCase__=[1, 3, 6]     ,		lowerCamelCase__=[1, 2, 10]     ,		lowerCamelCase__=[7, 3, 3]     ,		lowerCamelCase__=[4, 2, 2]     ,		lowerCamelCase__=[2, 1, 1]     ,		lowerCamelCase__=[2, 2, 2]     ,		lowerCamelCase__=[False, False, True]     ,		lowerCamelCase__=[0.0, 0.0, 0.0]     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-12     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=2     ,		)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       parent
							__lowerCamelCase									=       batch_size
							__lowerCamelCase									=       image_size
							__lowerCamelCase									=       patch_sizes
							__lowerCamelCase									=       patch_stride
							__lowerCamelCase									=       patch_padding
							__lowerCamelCase									=       is_training
							__lowerCamelCase									=       use_labels
							__lowerCamelCase									=       num_labels
							__lowerCamelCase									=       num_channels
							__lowerCamelCase									=       embed_dim
							__lowerCamelCase									=       num_heads
							__lowerCamelCase									=       stride_kv
							__lowerCamelCase									=       depth
							__lowerCamelCase									=       cls_token
							__lowerCamelCase									=       attention_drop_rate
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps


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



							'''simple docstring'''
							__lowerCamelCase									=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

							__lowerCamelCase									=       None
							if self.use_labels:
											__lowerCamelCase									=       ids_tensor([self.batch_size]     ,		self.num_labels				)

							__lowerCamelCase									=       self.get_config()
							return config, pixel_values, labels


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



							'''simple docstring'''
							return CvtConfig(
							    image_size=self.image_size     ,		num_labels=self.num_labels     ,		num_channels=self.num_channels     ,		embed_dim=self.embed_dim     ,		num_heads=self.num_heads     ,		patch_sizes=self.patch_sizes     ,		patch_padding=self.patch_padding     ,		patch_stride=self.patch_stride     ,		stride_kv=self.stride_kv     ,		depth=self.depth     ,		cls_token=self.cls_token     ,		attention_drop_rate=self.attention_drop_rate     ,		initializer_range=self.initializer_range     ,		)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       CvtModel(config=snake_case__				)
							model.to(snake_case__				)
							model.eval()
							__lowerCamelCase									=       model(snake_case__				)
							__lowerCamelCase									=       (self.image_size, self.image_size)
							__lowerCamelCase			,		__lowerCamelCase									=       image_size[0], image_size[1]
							for i in range(len(self.depth				)				):
											__lowerCamelCase									=       floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1				)
											__lowerCamelCase									=       floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1				)
							self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.embed_dim[-1], height, width)				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       self.num_labels
							__lowerCamelCase									=       CvtForImageClassification(snake_case__				)
							model.to(snake_case__				)
							model.eval()
							__lowerCamelCase									=       model(snake_case__     ,		labels=snake_case__				)
							self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)				)






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



							'''simple docstring'''
							__lowerCamelCase									=       self.prepare_config_and_inputs()
							__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase									=       config_and_inputs
							__lowerCamelCase									=       {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class        __lowerCAmelCase					(			__UpperCamelCase     ,							__UpperCamelCase     ,							unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			(CvtModel, CvtForImageClassification) if is_torch_available() else ()
			snake_case_  						=			(
			    {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
			    if is_torch_available()
			    else {}
			)

			snake_case_  						=			False
			snake_case_  						=			False
			snake_case_  						=			False
			snake_case_  						=			False
			snake_case_  						=			False


			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       CvtModelTester(self				)
							__lowerCamelCase									=       ConfigTester(self     ,		config_class=snake_case__     ,		has_text_modality=snake_case__     ,		hidden_size=37				)


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



							'''simple docstring'''
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()


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



							'''simple docstring'''
							return


			@unittest.skip(reason='Cvt does not output attentions'				)
			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							pass


			@unittest.skip(reason='Cvt does not use inputs_embeds'				)
			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							pass


			@unittest.skip(reason='Cvt does not support input and output embeddings'				)
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							pass


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



							'''simple docstring'''
							__lowerCamelCase			,		__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase									=       model_class(snake_case__				)
											__lowerCamelCase									=       inspect.signature(model.forward				)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											__lowerCamelCase									=       [*signature.parameters.keys()]

											__lowerCamelCase									=       ['pixel_values']
											self.assertListEqual(arg_names[:1]     ,		snake_case__				)


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



							'''simple docstring'''
							__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__				)


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



							'''simple docstring'''
							def check_hidden_states_output(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				):
											__lowerCamelCase									=       model_class(snake_case__				)
											model.to(snake_case__				)
											model.eval()

											with torch.no_grad():
															__lowerCamelCase									=       model(**self._prepare_for_class(snake_case__     ,		snake_case__				)				)

											__lowerCamelCase									=       outputs.hidden_states

											__lowerCamelCase									=       len(self.model_tester.depth				)
											self.assertEqual(len(snake_case__				)     ,		snake_case__				)

											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-3:]				)     ,		[
											        self.model_tester.embed_dim[0],
											        self.model_tester.image_size // 4,
											        self.model_tester.image_size // 4,
											    ]     ,		)

							__lowerCamelCase			,		__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											__lowerCamelCase									=       True
											check_hidden_states_output(snake_case__     ,		snake_case__     ,		snake_case__				)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											__lowerCamelCase									=       True

											check_hidden_states_output(snake_case__     ,		snake_case__     ,		snake_case__				)


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



							'''simple docstring'''
							__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case__				)


			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
			def  lowercase_       ( self				)       -> Optional[Any]:



							'''simple docstring'''
							pass






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



							'''simple docstring'''
							for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase									=       CvtModel.from_pretrained(snake_case__				)
											self.assertIsNotNone(snake_case__				)







def      lowerCamelCase_						(			)	->			Dict:





				"""simple docstring"""


				__lowerCamelCase									=       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[int]:



							'''simple docstring'''
							return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)






			@slow
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(snake_case__				)

							__lowerCamelCase									=       self.default_image_processor
							__lowerCamelCase									=       prepare_img()
							__lowerCamelCase									=       image_processor(images=snake_case__     ,		return_tensors='pt'				).to(snake_case__				)

							# forward pass
							with torch.no_grad():
											__lowerCamelCase									=       model(**snake_case__				)

							# verify the logits
							__lowerCamelCase									=       torch.Size((1, 1_000)				)
							self.assertEqual(outputs.logits.shape     ,		snake_case__				)

							__lowerCamelCase									=       torch.tensor([0.92_85, 0.90_15, -0.31_50]				).to(snake_case__				)

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



"""simple docstring"""




from __future__ import annotations

from statistics import mean







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [0] * no_of_processes
		lowerCAmelCase__   = [0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase__  ):
				lowerCAmelCase__   = burst_time[i]
		lowerCAmelCase__   = []

		lowerCAmelCase__   = 0
		lowerCAmelCase__   = 0

		# When processes are not completed,
		# A process whose arrival time has passed \
		# and has remaining execution time is put into the ready_process.
		# The shortest process in the ready_process, target_process is executed.

		while completed != no_of_processes:
				lowerCAmelCase__   = []
				lowerCAmelCase__   = -1

				for i in range(lowerCamelCase__  ):
						if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
								ready_process.append(lowerCamelCase__  )

				if len(lowerCamelCase__  ) > 0:
						lowerCAmelCase__   = ready_process[0]
						for i in ready_process:
								if remaining_time[i] < remaining_time[target_process]:
										lowerCAmelCase__   = i
						total_time += burst_time[target_process]
						completed += 1
						lowerCAmelCase__   = 0
						lowerCAmelCase__   = (
						    total_time - arrival_time[target_process] - burst_time[target_process]
						)
				else:
						total_time += 1

		return waiting_time







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = [0] * no_of_processes
		for i in range(lowerCamelCase__  ):
				lowerCAmelCase__   = burst_time[i] + waiting_time[i]
		return turn_around_time


if __name__ == "__main__":
			print("[TEST CASE 01]")

			__lowerCAmelCase     :				str    				=    4
			__lowerCAmelCase     :				Dict    				=    [2, 5, 3, 7]
			__lowerCAmelCase     :				List[str]    				=    [0, 0, 0, 0]
			__lowerCAmelCase     :				Union[str, Any]    				=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)
			__lowerCAmelCase     :				Union[str, Any]    				=    calculate_turnaroundtime(
			    burst_time, no_of_processes, waiting_time
			)

			# Printing the Result
			print("PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time")
			for i, process_id in enumerate(list(range(1, 5))):
						print(
						    F"{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"
						    F"{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"
						)
			print(F"\nAverage waiting time = {mean(waiting_time):.5f}")
			print(F"Average turnaround time = {mean(turn_around_time):.5f}")




