




'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						from ..tf_utils import stable_softmax

a       :						Optional[Any]								=		logging.get_logger(__name__)



@add_end_docstrings(_UpperCamelCase	)
class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    Tuple      ,		**a_						:    str							):



						"""simple docstring"""




						super().__init__(**a_							)

						requires_backends(self      ,		"vision"							)
						self.check_model_type(
						    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						    if self.framework == "tf"
						    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING							)





			def __call__(      self						:    Optional[int]      ,		a_						:    Union[str, List[str], "Image", List["Image"]]      ,		**a_						:    Union[str, Any]							):



						"""simple docstring"""




						return super().__call__(a_      ,		**a_							)





			def 							A (      self						:    List[str]      ,		**a_						:    Any							):



						"""simple docstring"""




						__snake_case			=		{}
						if "candidate_labels" in kwargs:
									__snake_case			=		kwargs["candidate_labels"]
						if "hypothesis_template" in kwargs:
									__snake_case			=		kwargs["hypothesis_template"]

						return preprocess_params, {}, {}





			def 							A (      self						:    str      ,		a_						:    List[str]      ,		a_						:    Optional[Any]=None      ,		a_						:    Optional[Any]="This is a photo of {}."							):



						"""simple docstring"""




						__snake_case			=		load_image(a_							)
						__snake_case			=		self.image_processor(images=[image]      ,		return_tensors=self.framework							)
						__snake_case			=		candidate_labels
						__snake_case			=		[hypothesis_template.format(a_							) for x in candidate_labels]
						__snake_case			=		self.tokenizer(a_      ,		return_tensors=self.framework      ,		padding=a_							)
						__snake_case			=		[text_inputs]
						return inputs





			def 							A (      self						:    Tuple      ,		a_						:    Optional[int]							):



						"""simple docstring"""




						__snake_case			=		model_inputs.pop("candidate_labels"							)
						__snake_case			=		model_inputs.pop("text_inputs"							)
						if isinstance(text_inputs[0]      ,		a_							):
									__snake_case			=		text_inputs[0]
						else:
									# Batching case.
									__snake_case			=		text_inputs[0][0]

						__snake_case			=		self.model(**a_      ,		**a_							)

						__snake_case			=		{
						    "candidate_labels": candidate_labels,
						    "logits": outputs.logits_per_image,
						}
						return model_outputs






			def 							A (      self						:    Any      ,		a_						:    int							):



						"""simple docstring"""




						__snake_case			=		model_outputs.pop("candidate_labels"							)
						__snake_case			=		model_outputs["logits"][0]
						if self.framework == "pt":
									__snake_case			=		logits.softmax(dim=-1							).squeeze(-1							)
									__snake_case			=		probs.tolist()
									if not isinstance(a_      ,		a_							):
												__snake_case			=		[scores]
						elif self.framework == "tf":
									__snake_case			=		stable_softmax(a_      ,		axis=-1							)
									__snake_case			=		probs.numpy().tolist()
						else:
									raise ValueError(f'''Unsupported framework: {self.framework}'''							)

						__snake_case			=		[
						    {"score": score, "label": candidate_label}
						    for score, candidate_label in sorted(zip(a_      ,		a_							)      ,		key=lambda a_							: -x[0]							)
						]
						return result






"""simple docstring"""


from __future__ import annotations

import pandas as pd






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [0] * no_of_processes
  _UpperCAmelCase    			=   [0] * no_of_processes
  # Copy the burst time into remaining_time[]
  for i in range(lowercase		):
    _UpperCAmelCase    			=   burst_time[i]

  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   9_99_99_99_99
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   False

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

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

    _UpperCAmelCase    			=   remaining_time[short]
    if minm == 0:
      _UpperCAmelCase    			=   9_99_99_99_99

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

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

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

      if waiting_time[short] < 0:
        _UpperCAmelCase    			=   0

        # Increment time
    increment_time += 1
  return waiting_time






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   [0] * no_of_processes
  for i in range(lowercase		):
    _UpperCAmelCase    			=   burst_time[i] + waiting_time[i]
  return turn_around_time






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   0
  _UpperCAmelCase    			=   0
  for i in range(lowercase		):
    _UpperCAmelCase    			=   total_waiting_time + waiting_time[i]
    _UpperCAmelCase    			=   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""")
    UpperCAmelCase__			=    int(input())
    UpperCAmelCase__			=    [0] * no_of_processes
    UpperCAmelCase__			=    [0] * no_of_processes
    UpperCAmelCase__			=    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))
        UpperCAmelCase__		, UpperCAmelCase__			=    map(int, input().split())

    UpperCAmelCase__			=    calculate_waitingtime(arrival_time, burst_time, no_of_processes)

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

    calculate_average_times(waiting_time, turn_around_time, no_of_processes)

    UpperCAmelCase__			=    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)
