






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__snake_case    :    Optional[Any]         =     {
    """configuration_clip""": [
        """CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """CLIPConfig""",
        """CLIPOnnxConfig""",
        """CLIPTextConfig""",
        """CLIPVisionConfig""",
    ],
    """processing_clip""": ["""CLIPProcessor"""],
    """tokenization_clip""": ["""CLIPTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    List[Any]         =     ["""CLIPTokenizerFast"""]

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Tuple         =     ["""CLIPFeatureExtractor"""]
					__snake_case    :    Optional[int]         =     ["""CLIPImageProcessor"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Optional[Any]         =     [
					    """CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CLIPModel""",
					    """CLIPPreTrainedModel""",
					    """CLIPTextModel""",
					    """CLIPTextModelWithProjection""",
					    """CLIPVisionModel""",
					    """CLIPVisionModelWithProjection""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Union[str, Any]         =     [
					    """TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFCLIPModel""",
					    """TFCLIPPreTrainedModel""",
					    """TFCLIPTextModel""",
					    """TFCLIPVisionModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case    :    Optional[Any]         =     [
					    """FlaxCLIPModel""",
					    """FlaxCLIPPreTrainedModel""",
					    """FlaxCLIPTextModel""",
					    """FlaxCLIPTextPreTrainedModel""",
					    """FlaxCLIPVisionModel""",
					    """FlaxCLIPVisionPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_clip import (
					    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    CLIPConfig,
					    CLIPOnnxConfig,
					    CLIPTextConfig,
					    CLIPVisionConfig,
					)
					from .processing_clip import CLIPProcessor
					from .tokenization_clip import CLIPTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_clip_fast import CLIPTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_clip import CLIPFeatureExtractor
										from .image_processing_clip import CLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_clip import (
										    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CLIPModel,
										    CLIPPreTrainedModel,
										    CLIPTextModel,
										    CLIPTextModelWithProjection,
										    CLIPVisionModel,
										    CLIPVisionModelWithProjection,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_clip import (
										    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCLIPModel,
										    TFCLIPPreTrainedModel,
										    TFCLIPTextModel,
										    TFCLIPVisionModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_clip import (
										    FlaxCLIPModel,
										    FlaxCLIPPreTrainedModel,
										    FlaxCLIPTextModel,
										    FlaxCLIPTextPreTrainedModel,
										    FlaxCLIPVisionModel,
										    FlaxCLIPVisionPreTrainedModel,
										)


else:
					import sys

					__snake_case    :    Optional[int]         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













from __future__ import annotations

import pandas as pd







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   list[int]:

						"""simple docstring"""



						lowerCAmelCase__    						=						[0] * no_of_processes
						lowerCAmelCase__    						=						[0] * no_of_processes
						# Copy the burst time into remaining_time[]
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						burst_time[i]

						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						9_9999_9999
						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						False

						# Process until all processes are completed
						while complete != no_of_processes:
												for j in range(UpperCamelCase_		):
																		if arrival_time[j] <= increment_time and remaining_time[j] > 0:
																								if remaining_time[j] < minm:
																														lowerCAmelCase__    						=						remaining_time[j]
																														lowerCAmelCase__    						=						j
																														lowerCAmelCase__    						=						True

												if not check:
																		increment_time += 1
																		continue
												remaining_time[short] -= 1

												lowerCAmelCase__    						=						remaining_time[short]
												if minm == 0:
																		lowerCAmelCase__    						=						9_9999_9999

												if remaining_time[short] == 0:
																		complete += 1
																		lowerCAmelCase__    						=						False

																		# Find finish time of current process
																		lowerCAmelCase__    						=						increment_time + 1

																		# Calculate waiting time
																		lowerCAmelCase__    						=						finish_time - arrival_time[short]
																		lowerCAmelCase__    						=						finar - burst_time[short]

																		if waiting_time[short] < 0:
																								lowerCAmelCase__    						=						0

        # Increment time
												increment_time += 1
						return waiting_time







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					list[int]		)  ->   list[int]:

						"""simple docstring"""



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







def 	_UpperCamelCase   (    UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					list[int]    ,       UpperCamelCase_       :					int		)  ->   None:

						"""simple docstring"""



						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						0
						for i in range(UpperCamelCase_		):
												lowerCAmelCase__    						=						total_waiting_time + waiting_time[i]
												lowerCAmelCase__    						=						total_turn_around_time + turn_around_time[i]
						print(F"Average waiting time = {total_waiting_time / no_of_processes:.5f}"		)
						print('Average turn around time ='    ,       total_turn_around_time / no_of_processes		)


if __name__ == "__main__":
					print("""Enter how many process you want to analyze""")
					__snake_case    :    Dict         =     int(input())
					__snake_case    :    List[Any]         =     [0] * no_of_processes
					__snake_case    :    str         =     [0] * no_of_processes
					__snake_case    :    Optional[int]         =     list(range(1, no_of_processes + 1))

					for i in range(no_of_processes):
										print("""Enter the arrival time and burst time for process:--""" + str(i + 1))
										__snake_case     , __snake_case    :    Any         =     map(int, input().split())

					__snake_case    :    str         =     calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					__snake_case    :    Dict         =     burst_time
					__snake_case    :    Optional[int]         =     no_of_processes
					__snake_case    :    str         =     waiting_time
					__snake_case    :    int         =     calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					__snake_case    :    Dict         =     pd.DataFrame(
					    list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
					    columns=[
					        """Process""",
					        """BurstTime""",
					        """ArrivalTime""",
					        """WaitingTime""",
					        """TurnAroundTime""",
					    ],
					)

					# Printing the dataFrame
					pd.set_option("""display.max_rows""", fcfs.shape[0] + 1)
					print(fcfs)





