






"""simple docstring"""







import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       return 1.0 / (1.0 + np.exp(-_outputs		))






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			np.max(_outputs	,      axis=-1	,      keepdims=lowerCAmelCase_		)
       UpperCAmelCase	    =			np.exp(_outputs - maxes		)
       return shifted_exp / shifted_exp.sum(axis=-1	,      keepdims=lowerCAmelCase_		)





class   UpperCamelCase_ (       __lowerCamelCase  ):
  _A       :					Tuple				=	'''sigmoid'''
  _A       :					List[str]				=	'''softmax'''
  _A       :					Tuple				=	'''none'''





@add_end_docstrings(
    __lowerCamelCase   ,					r'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    '   ,					)
class   UpperCamelCase_ (       __lowerCamelCase  ):
  _A       :					List[str]				=	False
  _A       :					Any				=	ClassificationFunction.NONE



  def __init__(      self ,		**snake_case__				)			->			Optional[Any]:



         """simple docstring"""





         super().__init__(**a_				)

         self.check_model_type(
             TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
             if self.framework == """tf"""
             else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING				)



  def 		UpperCamelCase_				(      self ,		snake_case__=None ,		snake_case__=None ,		snake_case__="" ,		**snake_case__				)			->			List[str]:



         """simple docstring"""





         UpperCAmelCase	    =			tokenizer_kwargs

         UpperCAmelCase	    =			{}
         if hasattr(self.model.config ,		"""return_all_scores"""				) and return_all_scores is None:
                UpperCAmelCase	    =			self.model.config.return_all_scores

         if isinstance(a_ ,		a_				) or top_k is None:
                UpperCAmelCase	    =			top_k
                UpperCAmelCase	    =			False
         elif return_all_scores is not None:
                warnings.warn(
                    """`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"""
                    """ `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.""" ,		a_ ,		)
                if return_all_scores:
                       UpperCAmelCase	    =			None
                else:
                       UpperCAmelCase	    =			1

         if isinstance(a_ ,		a_				):
                UpperCAmelCase	    =			ClassificationFunction[function_to_apply.upper()]

         if function_to_apply is not None:
                UpperCAmelCase	    =			function_to_apply
         return preprocess_params, {}, postprocess_params



  def __call__(      self ,		*snake_case__ ,		**snake_case__				)			->			Optional[int]:



         """simple docstring"""





         UpperCAmelCase	    =			super().__call__(*a_ ,		**a_				)
         # TODO try and retrieve it in a nicer way from _sanitize_parameters.
         UpperCAmelCase	    =			"top_k" not in kwargs
         if isinstance(args[0] ,		a_				) and _legacy:
                # This pipeline is odd, and return a list when single item is run
                return [result]
         else:
                return result



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		**snake_case__				)			->			List[Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self.framework
         if isinstance(a_ ,		a_				):
                return self.tokenizer(**a_ ,		return_tensors=a_ ,		**a_				)
         elif isinstance(a_ ,		a_				) and len(a_				) == 1 and isinstance(inputs[0] ,		a_				) and len(inputs[0]				) == 2:
                # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
                return self.tokenizer(
                    text=inputs[0][0] ,		text_pair=inputs[0][1] ,		return_tensors=a_ ,		**a_				)
         elif isinstance(a_ ,		a_				):
                # This is likely an invalid usage of the pipeline attempting to pass text pairs.
                raise ValueError(
                    """The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"""
                    """ dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair."""				)
         return self.tokenizer(a_ ,		return_tensors=a_ ,		**a_				)



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			int:



         """simple docstring"""





         return self.model(**a_				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__=None ,		snake_case__=1 ,		snake_case__=True				)			->			Optional[int]:



         """simple docstring"""





         if function_to_apply is None:
                if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                       UpperCAmelCase	    =			ClassificationFunction.SIGMOID
                elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                       UpperCAmelCase	    =			ClassificationFunction.SOFTMAX
                elif hasattr(self.model.config ,		"""function_to_apply"""				) and function_to_apply is None:
                       UpperCAmelCase	    =			self.model.config.function_to_apply
                else:
                       UpperCAmelCase	    =			ClassificationFunction.NONE

         UpperCAmelCase	    =			model_outputs["logits"][0]
         UpperCAmelCase	    =			outputs.numpy()

         if function_to_apply == ClassificationFunction.SIGMOID:
                UpperCAmelCase	    =			sigmoid(a_				)
         elif function_to_apply == ClassificationFunction.SOFTMAX:
                UpperCAmelCase	    =			softmax(a_				)
         elif function_to_apply == ClassificationFunction.NONE:
                UpperCAmelCase	    =			outputs
         else:
                raise ValueError(f'''Unrecognized `function_to_apply` argument: {function_to_apply}'''				)

         if top_k == 1 and _legacy:
                return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

         UpperCAmelCase	    =			[
             {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(a_				)
         ]
         if not _legacy:
                dict_scores.sort(key=lambda snake_case__				: x["score"] ,		reverse=a_				)
                if top_k is not None:
                       UpperCAmelCase	    =			dict_scores[:top_k]
         return dict_scores








"""simple docstring"""







from __future__ import annotations

import pandas as pd






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       UpperCAmelCase	    =			[0] * no_of_processes
       # Copy the burst time into remaining_time[]
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i]

       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			999999999
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			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	    =			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	    =			999999999

              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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[0] * no_of_processes
       for i in range(lowerCAmelCase		):
              UpperCAmelCase	    =			burst_time[i] + waiting_time[i]
       return turn_around_time






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			0
       UpperCAmelCase	    =			0
       for i in range(lowerCAmelCase		):
              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''')
 lowerCAmelCase_	:    Optional[Any]            =      int(input())
 lowerCAmelCase_	:    List[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    Optional[Any]            =      [0] * no_of_processes
 lowerCAmelCase_	:    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))
  lowerCAmelCase_     ,					lowerCAmelCase_	:    Optional[Any]            =      map(int, input().split())

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

 lowerCAmelCase_	:    str            =      burst_time
 lowerCAmelCase_	:    List[Any]            =      no_of_processes
 lowerCAmelCase_	:    int            =      waiting_time
 lowerCAmelCase_	:    int            =      calculate_turnaroundtime(bt, n, wt)

 calculate_average_times(waiting_time, turn_around_time, no_of_processes)

 lowerCAmelCase_	:    List[Any]            =      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)
