

from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					import torch

					from transformers.modeling_outputs import BaseModelOutput

					from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

@add_end_docstrings(_snake_case				)
class 				a  (	_snake_case				):


						def __init__(       self						,					**A_	):







										'''simple docstring'''


										super().__init__(**A_	)

										if self.framework == "tf":
														raise ValueError(f'The {self.__class__} is only available in PyTorch.'	)

										requires_backends(self						,					"vision"	)
										self.check_model_type(A_	)


						def __call__(       self						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										if "text_queries" in kwargs:
														_UpperCAmelCase       :		Optional[Any] 		=    kwargs.pop("text_queries"	)

										if isinstance(A_						,					(str, Image.Image)	):
														_UpperCAmelCase       :		Union[str, Any] 		=    {"image": image, "candidate_labels": candidate_labels}
										else:
														_UpperCAmelCase       :		List[str] 		=    image
										_UpperCAmelCase       :		Any 		=    super().__call__(A_						,					**A_	)
										return results


						def        _UpperCAmelCase				(       self						,					**A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    {}
										if "threshold" in kwargs:
														_UpperCAmelCase       :		str 		=    kwargs["threshold"]
										if "top_k" in kwargs:
														_UpperCAmelCase       :		List[Any] 		=    kwargs["top_k"]
										return {}, {}, postprocess_params


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    load_image(inputs["image"]	)
										_UpperCAmelCase       :		Union[str, Any] 		=    inputs["candidate_labels"]
										if isinstance(A_						,					A_	):
														_UpperCAmelCase       :		Dict 		=    candidate_labels.split(","	)

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.tensor([[image.height, image.width]]						,					dtype=torch.intaa	)
										for i, candidate_label in enumerate(A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    self.tokenizer(A_						,					return_tensors=self.framework	)
														_UpperCAmelCase       :		List[str] 		=    self.image_processor(A_						,					return_tensors=self.framework	)
														yield {
														    "is_last": i == len(A_	) - 1,
														    "target_size": target_size,
														    "candidate_label": candidate_label,
														    **text_inputs,
														    **image_features,
														}


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    model_inputs.pop("target_size"	)
										_UpperCAmelCase       :		List[Any] 		=    model_inputs.pop("candidate_label"	)
										_UpperCAmelCase       :		List[Any] 		=    model_inputs.pop("is_last"	)

										_UpperCAmelCase       :		Tuple 		=    self.model(**A_	)

										_UpperCAmelCase       :		List[str] 		=    {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
										return model_outputs


						def        _UpperCAmelCase				(       self						,					A_						,					A_=0.1						,					A_=None	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    []
										for model_output in model_outputs:
														_UpperCAmelCase       :		str 		=    model_output["candidate_label"]
														_UpperCAmelCase       :		Optional[Any] 		=    BaseModelOutput(A_	)
														_UpperCAmelCase       :		Optional[Any] 		=    self.image_processor.post_process_object_detection(
														    outputs=A_						,					threshold=A_						,					target_sizes=model_output["target_size"]	)[0]

														for index in outputs["scores"].nonzero():
																		_UpperCAmelCase       :		Union[str, Any] 		=    outputs["scores"][index].item()
																		_UpperCAmelCase       :		Optional[Any] 		=    self._get_bounding_box(outputs["boxes"][index][0]	)

																		_UpperCAmelCase       :		str 		=    {"score": score, "label": label, "box": box}
																		results.append(A_	)

										_UpperCAmelCase       :		Optional[int] 		=    sorted(A_						,					key=lambda A_	: x["score"]						,					reverse=A_	)
										if top_k:
														_UpperCAmelCase       :		int 		=    results[:top_k]

										return results





						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										if self.framework != "pt":
														raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."	)
										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		List[Any] 		=    box.int().tolist()
										_UpperCAmelCase       :		Dict 		=    {
										    "xmin": xmin,
										    "ymin": ymin,
										    "xmax": xmax,
										    "ymax": ymax,
										}
										return bbox





from __future__ import annotations

import pandas as pd

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       list[int]:
				_UpperCAmelCase       :		List[Any] 		=    [0] * no_of_processes
				_UpperCAmelCase       :		Dict 		=    [0] * no_of_processes
				# Copy the burst time into remaining_time[]
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		List[str] 		=    burst_time[i]

				_UpperCAmelCase       :		Any 		=    0
				_UpperCAmelCase       :		Union[str, Any] 		=    0
				_UpperCAmelCase       :		str 		=    9_9999_9999
				_UpperCAmelCase       :		List[Any] 		=    0
				_UpperCAmelCase       :		Any 		=    False

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

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

								_UpperCAmelCase       :		List[Any] 		=    remaining_time[short]
								if minm == 0:
												_UpperCAmelCase       :		List[Any] 		=    9_9999_9999

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

												# Find finish time of current process
												_UpperCAmelCase       :		Optional[int] 		=    increment_time + 1

												# Calculate waiting time
												_UpperCAmelCase       :		str 		=    finish_time - arrival_time[short]
												_UpperCAmelCase       :		Union[str, Any] 		=    finar - burst_time[short]

												if waiting_time[short] < 0:
																_UpperCAmelCase       :		Optional[int] 		=    0

        # Increment time
								increment_time += 1
				return waiting_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int  ,      lowerCAmelCase:  list[int] )   ->       list[int]:
				_UpperCAmelCase       :		str 		=    [0] * no_of_processes
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		str 		=    burst_time[i] + waiting_time[i]
				return turn_around_time

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  list[int]  ,      lowerCAmelCase:  int )   ->       None:
				_UpperCAmelCase       :		Dict 		=    0
				_UpperCAmelCase       :		List[str] 		=    0
				for i in range(lowerCAmelCase ):
								_UpperCAmelCase       :		Union[str, Any] 		=    total_waiting_time + waiting_time[i]
								_UpperCAmelCase       :		int 		=    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')
					SCREAMING_SNAKE_CASE_        =      int(input())
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      [0] * no_of_processes
					SCREAMING_SNAKE_CASE_        =      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))
										SCREAMING_SNAKE_CASE_    ,SCREAMING_SNAKE_CASE_        =      map(int, input().split())

					SCREAMING_SNAKE_CASE_        =      calculate_waitingtime(arrival_time, burst_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      burst_time
					SCREAMING_SNAKE_CASE_        =      no_of_processes
					SCREAMING_SNAKE_CASE_        =      waiting_time
					SCREAMING_SNAKE_CASE_        =      calculate_turnaroundtime(bt, n, wt)

					calculate_average_times(waiting_time, turn_around_time, no_of_processes)

					SCREAMING_SNAKE_CASE_        =      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)


