






import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, 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 EfficientNetForImageClassification, EfficientNetModel
					from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor


class      __SCREAMING_SNAKE_CASE       :






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase=13  ,    _UpperCamelCase=32  ,    _UpperCamelCase=3  ,    _UpperCamelCase=[3, 3, 5]  ,    _UpperCamelCase=[32, 16, 24]  ,    _UpperCamelCase=[16, 24, 40]  ,    _UpperCamelCase=[1, 1, 2]  ,    _UpperCamelCase=[1, 1, 2]  ,    _UpperCamelCase=[1, 6, 6]  ,    _UpperCamelCase=True  ,    _UpperCamelCase=True  ,    _UpperCamelCase=37  ,    _UpperCamelCase="gelu"  ,    _UpperCamelCase=10  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    parent
												lowerCAmelCase__         =    batch_size
												lowerCAmelCase__         =    image_size
												lowerCAmelCase__         =    num_channels
												lowerCAmelCase__         =    kernel_sizes
												lowerCAmelCase__         =    in_channels
												lowerCAmelCase__         =    out_channels
												lowerCAmelCase__         =    strides
												lowerCAmelCase__         =    num_block_repeats
												lowerCAmelCase__         =    expand_ratios
												lowerCAmelCase__         =    is_training
												lowerCAmelCase__         =    hidden_act
												lowerCAmelCase__         =    num_labels
												lowerCAmelCase__         =    use_labels






						def    UpperCamelCase__   (						self ):






												"""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    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return EfficientNetConfig(
												    num_channels=self.num_channels  ,    kernel_sizes=self.kernel_sizes  ,    in_channels=self.in_channels  ,    out_channels=self.out_channels  ,    strides=self.strides  ,    num_block_repeats=self.num_block_repeats  ,    expand_ratios=self.expand_ratios  ,    hidden_act=self.hidden_act  ,    num_labels=self.num_labels  ,    )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    EfficientNetModel(config=_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												lowerCAmelCase__         =    model(_UpperCamelCase )
												# expected last hidden states: B, C, H // 4, W // 4
												self.parent.assertEqual(
												    result.last_hidden_state.shape  ,    (self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)  ,    )






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    EfficientNetForImageClassification(_UpperCamelCase )
												model.to(_UpperCamelCase )
												model.eval()
												lowerCAmelCase__         =    model(_UpperCamelCase  ,    labels=_UpperCamelCase )
												self.parent.assertEqual(result.logits.shape  ,    (self.batch_size, self.num_labels) )







						def    UpperCamelCase__   (						self ):






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




@require_torch
class      __SCREAMING_SNAKE_CASE       ( a_		,       a_		,       unittest.TestCase):
						_SCREAMING_SNAKE_CASE						:							int				=       (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
						_SCREAMING_SNAKE_CASE						:							Dict				=       (
						    {'feature-extraction': EfficientNetModel, 'image-classification': EfficientNetForImageClassification}
						    if is_torch_available()
						    else {}
						)

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






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    EfficientNetModelTester(self )
												lowerCAmelCase__         =    ConfigTester(
												    self  ,    config_class=_UpperCamelCase  ,    has_text_modality=_UpperCamelCase  ,    hidden_size=37 )






						def    UpperCamelCase__   (						self ):






												"""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    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return






						@unittest.skip(reason='EfficientNet does not use inputs_embeds' )
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												pass






						@unittest.skip(reason='EfficientNet does not support input and output embeddings' )
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												pass






						@unittest.skip(reason='EfficientNet does not use feedforward chunking' )
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												pass






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		lowerCAmelCase__         =    model_class(_UpperCamelCase )
																		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]  ,    _UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												def check_hidden_states_output(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):
																		lowerCAmelCase__         =    model_class(_UpperCamelCase )
																		model.to(_UpperCamelCase )
																		model.eval()

																		with torch.no_grad():
																								lowerCAmelCase__         =    model(**self._prepare_for_class(_UpperCamelCase  ,    _UpperCamelCase ) )

																		lowerCAmelCase__         =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
																		lowerCAmelCase__         =    sum(config.num_block_repeats ) * 4
																		self.assertEqual(len(_UpperCamelCase )  ,    _UpperCamelCase )

																		# EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:] )  ,    [self.model_tester.image_size // 2, self.model_tester.image_size // 2]  ,    )

												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		lowerCAmelCase__         =    True
																		check_hidden_states_output(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )

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

																		check_hidden_states_output(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_UpperCamelCase )






						@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												pass






						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowerCAmelCase__         =    EfficientNetModel.from_pretrained(_UpperCamelCase )
																		self.assertIsNotNone(_UpperCamelCase )







						@is_pipeline_test
						@require_vision
						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												super().test_pipeline_image_classification()







def 						_UpperCamelCase (     )	-> int:

						"""simple docstring"""



						lowerCAmelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
						return image


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






						@cached_property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return AutoImageProcessor.from_pretrained('google/efficientnet-b7' ) if is_vision_available() else None







						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    EfficientNetForImageClassification.from_pretrained('google/efficientnet-b7' ).to(_UpperCamelCase )

												lowerCAmelCase__         =    self.default_image_processor
												lowerCAmelCase__         =    prepare_img()
												lowerCAmelCase__         =    image_processor(images=_UpperCamelCase  ,    return_tensors='pt' ).to(_UpperCamelCase )

												# forward pass
												with torch.no_grad():
																		lowerCAmelCase__         =    model(**_UpperCamelCase )

												# verify the logits
												lowerCAmelCase__         =    torch.Size((1, 10_00) )
												self.assertEqual(outputs.logits.shape  ,    _UpperCamelCase )

												lowerCAmelCase__         =    torch.tensor([0.00_01, 0.00_02, 0.00_02] ).to(_UpperCamelCase )
												self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,    _UpperCamelCase  ,    atol=1E-4 ) )






from __future__ import annotations

from statistics import mean


def SCREAMING_SNAKE_CASE					(  lowercase_:						list[int]					):
       _UpperCamelCase  :							str	    =	2
       _UpperCamelCase  :							Optional[int]	    =	list(lowercase_					)
       _UpperCamelCase  :							Union[str, Any]	    =	[0] * len(lowercase_					)
       _UpperCamelCase  :							List[str]	    =	0
       while True:
              _UpperCamelCase  :							List[str]	    =	True
              for i, burst_time in enumerate(lowercase_					):
                     if rem_burst_times[i] > 0:
                            _UpperCamelCase  :							List[str]	    =	False
                            if rem_burst_times[i] > quantum:
                                   t += quantum
                                   rem_burst_times[i] -= quantum
                            else:
                                   t += rem_burst_times[i]
                                   _UpperCamelCase  :							str	    =	t - burst_time
                                   _UpperCamelCase  :							Union[str, Any]	    =	0
              if done is True:
                     return waiting_times


def SCREAMING_SNAKE_CASE					(  lowercase_:						list[int]		,    lowercase_:						list[int]					):
       return [burst + waiting for burst, waiting in zip(lowercase_		,    lowercase_					)]


if __name__ == "__main__":
 UpperCAmelCase      		=					[3, 5, 7]
 UpperCAmelCase      		=					calculate_waiting_times(burst_times)
 UpperCAmelCase      		=					calculate_turn_around_times(burst_times, waiting_times)
 print('''Process ID \tBurst Time \tWaiting Time \tTurnaround Time''')
 for i, burst_time in enumerate(burst_times):
  print(
      f'''  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '''
      f'''{turn_around_times[i]}'''
  )
 print(f'''\nAverage waiting time = {mean(waiting_times):.5f}''')
 print(f'''Average turn around time = {mean(turn_around_times):.5f}''')



