
"""simple docstring"""


import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
		from ...processing_utils import ProcessorMixin
		from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
    '''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
    '''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Any   =     '''owlvit_text_model'''


						def __init__(			self  :   Optional[Any]	,A_  :   List[str]=4_9408	,A_  :   Optional[int]=512	,A_  :   Dict=2048	,A_  :   List[str]=12	,A_  :   Optional[Any]=8	,A_  :   List[Any]=16	,A_  :   List[str]="quick_gelu"	,A_  :   int=1e-5	,A_  :   int=0.0	,A_  :   List[str]=0.02	,A_  :   Tuple=1.0	,A_  :   Union[str, Any]=0	,A_  :   Tuple=4_9406	,A_  :   Optional[int]=4_9407	,**A_  :   int	,)  -> Union[str, Any]:
							super().__init__(pad_token_id=A_	,bos_token_id=A_	,eos_token_id=A_	,**A_   )

							A     = vocab_size
							A     = hidden_size
							A     = intermediate_size
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = max_position_embeddings
							A     = hidden_act
							A     = layer_norm_eps
							A     = attention_dropout
							A     = initializer_range
							A     = initializer_factor




						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   Dict	,A_  :   Union[str, os.PathLike]	,**A_  :   List[Any]   )  -> "PretrainedConfig":
							cls._set_token_in_kwargs(A_   )

							A ,   A     = cls.get_config_dict(A_	,**A_   )

							# get the text config dict if we are loading from OwlViTConfig
							if config_dict.get('model_type'   ) == "owlvit":
								A     = config_dict['text_config']

							if "model_type" in config_dict and hasattr(cls	,'model_type'   ) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
								    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'   )

							return cls.from_dict(A_	,**A_   )







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     List[Any]   =     '''owlvit_vision_model'''


						def __init__(			self  :   int	,A_  :   List[str]=768	,A_  :   Dict=3072	,A_  :   List[str]=12	,A_  :   int=12	,A_  :   Optional[Any]=3	,A_  :   Optional[int]=768	,A_  :   List[str]=32	,A_  :   Union[str, Any]="quick_gelu"	,A_  :   Optional[int]=1e-5	,A_  :   Optional[Any]=0.0	,A_  :   Tuple=0.02	,A_  :   Optional[int]=1.0	,**A_  :   List[Any]	,)  -> Optional[int]:
							super().__init__(**A_   )

							A     = hidden_size
							A     = intermediate_size
							A     = num_hidden_layers
							A     = num_attention_heads
							A     = num_channels
							A     = image_size
							A     = patch_size
							A     = hidden_act
							A     = layer_norm_eps
							A     = attention_dropout
							A     = initializer_range
							A     = initializer_factor




						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   str	,A_  :   Union[str, os.PathLike]	,**A_  :   Any   )  -> "PretrainedConfig":
							cls._set_token_in_kwargs(A_   )

							A ,   A     = cls.get_config_dict(A_	,**A_   )

							# get the vision config dict if we are loading from OwlViTConfig
							if config_dict.get('model_type'   ) == "owlvit":
								A     = config_dict['vision_config']

							if "model_type" in config_dict and hasattr(cls	,'model_type'   ) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
								    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'   )

							return cls.from_dict(A_	,**A_   )







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     '''owlvit'''
						_lowerCamelCase:     List[str]   =     True


						def __init__(			self  :   List[str]	,A_  :   int=None	,A_  :   str=None	,A_  :   Tuple=512	,A_  :   int=2.65_92	,A_  :   Optional[Any]=True	,**A_  :   Any	,)  -> Any:
							super().__init__(**A_   )

							if text_config is None:
								A     = {}
								logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'   )

							if vision_config is None:
								A     = {}
								logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.'   )

							A     = OwlViTTextConfig(**A_   )
							A     = OwlViTVisionConfig(**A_   )

							A     = projection_dim
							A     = logit_scale_init_value
							A     = return_dict
							A     = 1.0


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   Union[str, Any]	,A_  :   Union[str, os.PathLike]	,**A_  :   str   )  -> "PretrainedConfig":
							cls._set_token_in_kwargs(A_   )

							A ,   A     = cls.get_config_dict(A_	,**A_   )

							if "model_type" in config_dict and hasattr(cls	,'model_type'   ) and config_dict["model_type"] != cls.model_type:
								logger.warning(
								    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
								    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'   )

							return cls.from_dict(A_	,**A_   )


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   List[Any]	,A_  :   Dict	,A_  :   Dict	,**A_  :   List[Any]   )  -> Optional[int]:
							A     = {}
							A     = text_config
							A     = vision_config

							return cls.from_dict(A_	,**A_   )




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Tuple:
							A     = copy.deepcopy(self.__dict__   )
							A     = self.text_config.to_dict()
							A     = self.vision_config.to_dict()
							A     = self.__class__.model_type
							return output







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('input_ids', {0: 'batch', 1: 'sequence'}),
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							        ('attention_mask', {0: 'batch', 1: 'sequence'}),
							    ]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('logits_per_image', {0: 'batch'}),
							        ('logits_per_text', {0: 'batch'}),
							        ('text_embeds', {0: 'batch'}),
							        ('image_embeds', {0: 'batch'}),
							    ]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> float:
							return 1e-4


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   "ProcessorMixin"	,A_  :   int = -1	,A_  :   int = -1	,A_  :   Optional["TensorType"] = None	,)  -> Mapping[str, Any]:
							A     = super().generate_dummy_inputs(
							    processor.tokenizer	,batch_size=A_	,seq_length=A_	,framework=A_   )
							A     = super().generate_dummy_inputs(
							    processor.image_processor	,batch_size=A_	,framework=A_   )
							return {**text_input_dict, **image_input_dict}




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> int:
							return 14
"""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)
