
'''simple docstring'''
import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax
		import jax.numpy as jnp

		from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor






class    UpperCAmelCase_				(	unittest.TestCase					):





	def __init__(    self ,       lowercase_ ,       lowercase_=3 ,       lowercase_=32 ,       lowercase_=3 ,       lowercase_=10 ,       lowercase_=[10, 20, 30, 40] ,       lowercase_=[1, 1, 2, 1] ,       lowercase_=True ,       lowercase_=True ,       lowercase_="relu" ,       lowercase_=3 ,       lowercase_=None ,       ):
					snake_case_ :    List[Any]     			=						parent
					snake_case_ :    Optional[int]     			=						batch_size
					snake_case_ :    Tuple     			=						image_size
					snake_case_ :    Optional[Any]     			=						num_channels
					snake_case_ :    List[str]     			=						embeddings_size
					snake_case_ :    Dict     			=						hidden_sizes
					snake_case_ :    Union[str, Any]     			=						depths
					snake_case_ :    Any     			=						is_training
					snake_case_ :    Optional[Any]     			=						use_labels
					snake_case_ :    Optional[int]     			=						hidden_act
					snake_case_ :    List[Any]     			=						num_labels
					snake_case_ :    Optional[int]     			=						scope
					snake_case_ :    Union[str, Any]     			=						len(lowercase_)





	def        snake_case__		(    self):
					snake_case_ :    List[Any]     			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

					snake_case_ :    List[str]     			=						self.get_config()

					return config, pixel_values





	def        snake_case__		(    self):
					return RegNetConfig(
					    num_channels=self.num_channels ,       embeddings_size=self.embeddings_size ,       hidden_sizes=self.hidden_sizes ,       depths=self.depths ,       hidden_act=self.hidden_act ,       num_labels=self.num_labels ,       image_size=self.image_size ,       )





	def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
					snake_case_ :    List[str]     			=						FlaxRegNetModel(config=lowercase_)
					snake_case_ :    Dict     			=						model(lowercase_)

					# Output shape (b, c, h, w)
					self.parent.assertEqual(
					    result.last_hidden_state.shape ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,       )





	def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
					snake_case_ :    Union[str, Any]     			=						self.num_labels
					snake_case_ :    Dict     			=						FlaxRegNetForImageClassification(config=lowercase_)
					snake_case_ :    Tuple     			=						model(lowercase_)
					self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels))





	def        snake_case__		(    self):
					snake_case_ :    Any     			=						self.prepare_config_and_inputs()
					snake_case_ :    str     			=						config_and_inputs
					snake_case_ :    Union[str, Any]     			=						{"pixel_values": pixel_values}
					return config, inputs_dict






@require_flax
class    UpperCAmelCase_				(	snake_case__    ,						unittest.TestCase					):
	UpperCAmelCase_		    =						(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

	UpperCAmelCase_		    =						False
	UpperCAmelCase_		    =						False
	UpperCAmelCase_		    =						False





	def        snake_case__		(    self):
					snake_case_ :    Union[str, Any]     			=						FlaxRegNetModelTester(self)
					snake_case_ :    List[Any]     			=						ConfigTester(self ,       config_class=lowercase_ ,       has_text_modality=lowercase_)





	def        snake_case__		(    self):
					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        snake_case__		(    self):
					return





	def        snake_case__		(    self):
					snake_case_ :    str     			=						self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase_)





	def        snake_case__		(    self):
					snake_case_ :    Tuple     			=						self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowercase_)





	@unittest.skip(reason="RegNet does not use inputs_embeds")
	def        snake_case__		(    self):
					pass





	@unittest.skip(reason="RegNet does not support input and output embeddings")
	def        snake_case__		(    self):
					pass





	def        snake_case__		(    self):
					snake_case_ :    Optional[Any]     			=						self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									snake_case_ :    Dict     			=						model_class(lowercase_)
									snake_case_ :    str     			=						inspect.signature(model.__call__)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									snake_case_ :    str     			=						[*signature.parameters.keys()]

									snake_case_ :    int     			=						["pixel_values"]
									self.assertListEqual(arg_names[:1] ,       lowercase_)





	def        snake_case__		(    self):
					def check_hidden_states_output(lowercase_ ,       lowercase_ ,       lowercase_):
									snake_case_ :    Tuple     			=						model_class(lowercase_)

									snake_case_ :    Optional[int]     			=						model(**self._prepare_for_class(lowercase_ ,       lowercase_))

									snake_case_ :    Optional[Any]     			=						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									snake_case_ :    Union[str, Any]     			=						self.model_tester.num_stages
									self.assertEqual(len(lowercase_) ,       expected_num_stages + 1)

					snake_case_ :    Dict     			=						self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									snake_case_ :    int     			=						True
									check_hidden_states_output(lowercase_ ,       lowercase_ ,       lowercase_)

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

									check_hidden_states_output(lowercase_ ,       lowercase_ ,       lowercase_)





	def        snake_case__		(    self):
					snake_case_ :    Tuple     			=						self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__):
													snake_case_ :    Union[str, Any]     			=						self._prepare_for_class(lowercase_ ,       lowercase_)
													snake_case_ :    str     			=						model_class(lowercase_)

													@jax.jit
													def model_jitted(lowercase_ ,       **lowercase_):
																	return model(pixel_values=lowercase_ ,       **lowercase_)

													with self.subTest("JIT Enabled"):
																	snake_case_ :    Optional[Any]     			=						model_jitted(**lowercase_).to_tuple()

													with self.subTest("JIT Disabled"):
																	with jax.disable_jit():
																					snake_case_ :    Union[str, Any]     			=						model_jitted(**lowercase_).to_tuple()

													self.assertEqual(len(lowercase_) ,       len(lowercase_))
													for jitted_output, output in zip(lowercase_ ,       lowercase_):
																	self.assertEqual(jitted_output.shape ,       output.shape)


def 						UpperCamelCase_     (	):
				"""simple docstring"""






				snake_case_ :    List[Any]     			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
				return image






@require_flax
class    UpperCAmelCase_				(	unittest.TestCase					):





	@cached_property
	def        snake_case__		(    self):
					return AutoImageProcessor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None





	@slow
	def        snake_case__		(    self):
					snake_case_ :    Optional[int]     			=						FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040")

					snake_case_ :    Optional[Any]     			=						self.default_image_processor
					snake_case_ :    Optional[Any]     			=						prepare_img()
					snake_case_ :    str     			=						image_processor(images=lowercase_ ,       return_tensors="np")

					snake_case_ :    Tuple     			=						model(**lowercase_)

					# verify the logits
					snake_case_ :    List[str]     			=						(1, 10_00)
					self.assertEqual(outputs.logits.shape ,       lowercase_)

					snake_case_ :    int     			=						jnp.array([-0.4_180, -1.5_051, -3.4_836])

					self.assertTrue(jnp.allclose(outputs.logits[0, :3] ,       lowercase_ ,       atol=1E-4))






'''simple docstring'''
from __future__ import annotations

from statistics import mean

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Any     			=						[0] * no_of_processes
    snake_case_ :    Optional[int]     			=						[0] * no_of_processes

    # Initialize remaining_time to waiting_time.

    for i in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    str     			=						burst_time[i]
    snake_case_ :    list[int]     			=						[]

    snake_case_ :    Tuple     			=						0
    snake_case_ :    Optional[int]     			=						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:
        snake_case_ :    Any     			=						[]
        snake_case_ :    Dict     			=						-1

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

        if len(__SCREAMING_SNAKE_CASE       ) > 0:
            snake_case_ :    Dict     			=						ready_process[0]
            for i in ready_process:
                if remaining_time[i] < remaining_time[target_process]:
                    snake_case_ :    Optional[int]     			=						i
            total_time += burst_time[target_process]
            completed += 1
            snake_case_ :    Tuple     			=						0
            snake_case_ :    int     			=						(
                total_time - arrival_time[target_process] - burst_time[target_process]
            )
        else:
            total_time += 1

    return waiting_time

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Optional[int]     			=						[0] * no_of_processes
    for i in range(__SCREAMING_SNAKE_CASE       ):
        snake_case_ :    Dict     			=						burst_time[i] + waiting_time[i]
    return turn_around_time


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

  a_											=						4
  a_											=						[2, 5, 3, 7]
  a_											=						[0, 0, 0, 0]
  a_											=						calculate_waitingtime(arrival_time, burst_time, no_of_processes)
  a_											=						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}""")




