



from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


a_      :      Any    =     logging.get_logger(__name__)

# General docstring
a_      :      List[Any]    =     'RegNetConfig'

# Base docstring
a_      :      List[Any]    =     'facebook/regnet-y-040'
a_      :      int    =     [1, 10_88, 7, 7]

# Image classification docstring
a_      :      Union[str, Any]    =     'facebook/regnet-y-040'
a_      :      Any    =     'tabby, tabby cat'

a_      :      Dict    =     [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 3		,				SCREAMING_SNAKE_CASE = 1		,				SCREAMING_SNAKE_CASE = 1		,				SCREAMING_SNAKE_CASE = "relu"		,				) ->			Optional[Any]:
  super().__init__()
  a__												= nn.Convad(
      SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				kernel_size=SCREAMING_SNAKE_CASE		,				stride=SCREAMING_SNAKE_CASE		,				padding=kernel_size // 2		,				groups=SCREAMING_SNAKE_CASE		,				bias=SCREAMING_SNAKE_CASE		,				)
  a__												= nn.BatchNormad(SCREAMING_SNAKE_CASE      )
  a__												= ACTaFN[activation] if activation is not None else nn.Identity()



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  a__												= self.convolution(SCREAMING_SNAKE_CASE      )
  a__												= self.normalization(SCREAMING_SNAKE_CASE      )
  a__												= self.activation(SCREAMING_SNAKE_CASE      )
  return hidden_state





class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Dict:
  super().__init__()
  a__												= RegNetConvLayer(
      config.num_channels		,				config.embedding_size		,				kernel_size=3		,				stride=2		,				activation=config.hidden_act      )
  a__												= config.num_channels



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[Any]:
  a__												= pixel_values.shape[1]
  if num_channels != self.num_channels:
   raise ValueError(
       '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''      )
  a__												= self.embedder(SCREAMING_SNAKE_CASE      )
  return hidden_state





class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 2      ) ->			List[Any]:
  super().__init__()
  a__												= nn.Convad(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				kernel_size=1		,				stride=SCREAMING_SNAKE_CASE		,				bias=SCREAMING_SNAKE_CASE      )
  a__												= nn.BatchNormad(SCREAMING_SNAKE_CASE      )



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Tensor:
  a__												= self.convolution(SCREAMING_SNAKE_CASE      )
  a__												= self.normalization(SCREAMING_SNAKE_CASE      )
  return hidden_state





class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  super().__init__()

  a__												= nn.AdaptiveAvgPoolad((1, 1)      )
  a__												= nn.Sequential(
      nn.Convad(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				kernel_size=1      )		,				nn.ReLU()		,				nn.Convad(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				kernel_size=1      )		,				nn.Sigmoid()		,				)



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  # b c h w -> b c 1 1
  a__												= self.pooler(SCREAMING_SNAKE_CASE      )
  a__												= self.attention(SCREAMING_SNAKE_CASE      )
  a__												= hidden_state * attention
  return hidden_state





class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 1      ) ->			Any:
  super().__init__()
  a__												= in_channels != out_channels or stride != 1
  a__												= max(1		,				out_channels // config.groups_width      )
  a__												= (
      RegNetShortCut(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				stride=SCREAMING_SNAKE_CASE      ) if should_apply_shortcut else nn.Identity()
  )
  a__												= nn.Sequential(
      RegNetConvLayer(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				kernel_size=1		,				activation=config.hidden_act      )		,				RegNetConvLayer(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				stride=SCREAMING_SNAKE_CASE		,				groups=SCREAMING_SNAKE_CASE		,				activation=config.hidden_act      )		,				RegNetConvLayer(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				kernel_size=1		,				activation=SCREAMING_SNAKE_CASE      )		,				)
  a__												= ACTaFN[config.hidden_act]



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= hidden_state
  a__												= self.layer(SCREAMING_SNAKE_CASE      )
  a__												= self.shortcut(SCREAMING_SNAKE_CASE      )
  hidden_state += residual
  a__												= self.activation(SCREAMING_SNAKE_CASE      )
  return hidden_state





class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 1      ) ->			int:
  super().__init__()
  a__												= in_channels != out_channels or stride != 1
  a__												= max(1		,				out_channels // config.groups_width      )
  a__												= (
      RegNetShortCut(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				stride=SCREAMING_SNAKE_CASE      ) if should_apply_shortcut else nn.Identity()
  )
  a__												= nn.Sequential(
      RegNetConvLayer(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				kernel_size=1		,				activation=config.hidden_act      )		,				RegNetConvLayer(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				stride=SCREAMING_SNAKE_CASE		,				groups=SCREAMING_SNAKE_CASE		,				activation=config.hidden_act      )		,				RegNetSELayer(SCREAMING_SNAKE_CASE		,				reduced_channels=int(round(in_channels / 4      )      )      )		,				RegNetConvLayer(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				kernel_size=1		,				activation=SCREAMING_SNAKE_CASE      )		,				)
  a__												= ACTaFN[config.hidden_act]



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Any:
  a__												= hidden_state
  a__												= self.layer(SCREAMING_SNAKE_CASE      )
  a__												= self.shortcut(SCREAMING_SNAKE_CASE      )
  hidden_state += residual
  a__												= self.activation(SCREAMING_SNAKE_CASE      )
  return hidden_state





class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 2		,				SCREAMING_SNAKE_CASE = 2		,				) ->			List[Any]:
  super().__init__()

  a__												= RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

  a__												= nn.Sequential(
      # downsampling is done in the first layer with stride of 2
      layer(
          SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				stride=SCREAMING_SNAKE_CASE		,				)		,				*[layer(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) for _ in range(depth - 1      )]		,				)



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[Any]:
  a__												= self.layers(SCREAMING_SNAKE_CASE      )
  return hidden_state





class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  super().__init__()
  a__												= nn.ModuleList([]      )
  # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
  self.stages.append(
      RegNetStage(
          SCREAMING_SNAKE_CASE		,				config.embedding_size		,				config.hidden_sizes[0]		,				stride=2 if config.downsample_in_first_stage else 1		,				depth=config.depths[0]		,				)      )
  a__												= zip(config.hidden_sizes		,				config.hidden_sizes[1:]      )
  for (in_channels, out_channels), depth in zip(SCREAMING_SNAKE_CASE		,				config.depths[1:]      ):
   self.stages.append(RegNetStage(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				depth=SCREAMING_SNAKE_CASE      )      )



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = False		,				SCREAMING_SNAKE_CASE = True      ) ->			BaseModelOutputWithNoAttention:
  a__												= () if output_hidden_states else None

  for stage_module in self.stages:
   if output_hidden_states:
    a__												= hidden_states + (hidden_state,)

   a__												= stage_module(SCREAMING_SNAKE_CASE      )

  if output_hidden_states:
   a__												= hidden_states + (hidden_state,)

  if not return_dict:
   return tuple(v for v in [hidden_state, hidden_states] if v is not None      )

  return BaseModelOutputWithNoAttention(last_hidden_state=SCREAMING_SNAKE_CASE		,				hidden_states=SCREAMING_SNAKE_CASE      )





class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    str      						=					RegNetConfig
 _lowercase   :    List[Any]      						=					'''regnet'''
 _lowercase   :    List[str]      						=					'''pixel_values'''
 _lowercase   :    Dict      						=					True

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  if isinstance(SCREAMING_SNAKE_CASE		,				nn.Convad      ):
   nn.init.kaiming_normal_(module.weight		,				mode='''fan_out'''		,				nonlinearity='''relu'''      )
  elif isinstance(SCREAMING_SNAKE_CASE		,				(nn.BatchNormad, nn.GroupNorm)      ):
   nn.init.constant_(module.weight		,				1      )
   nn.init.constant_(module.bias		,				0      )



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False      ) ->			Any:
  if isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
   a__												= value





a_      :      Tuple    =     R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

a_      :      int    =     R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.\n'
@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.'''	,	_lowercase	,	)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE      ) ->			List[Any]:
  super().__init__(SCREAMING_SNAKE_CASE      )
  a__												= config
  a__												= RegNetEmbeddings(SCREAMING_SNAKE_CASE      )
  a__												= RegNetEncoder(SCREAMING_SNAKE_CASE      )
  a__												= nn.AdaptiveAvgPoolad((1, 1)      )
  # Initialize weights and apply final processing
  self.post_init()



 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE      )
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC		,				output_type=SCREAMING_SNAKE_CASE		,				config_class=_CONFIG_FOR_DOC		,				modality='''vision'''		,				expected_output=_EXPECTED_OUTPUT_SHAPE		,				)
 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None      ) ->			BaseModelOutputWithPoolingAndNoAttention:
  a__												= (
      output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
  )
  a__												= return_dict if return_dict is not None else self.config.use_return_dict

  a__												= self.embedder(SCREAMING_SNAKE_CASE      )

  a__												= self.encoder(
      SCREAMING_SNAKE_CASE		,				output_hidden_states=SCREAMING_SNAKE_CASE		,				return_dict=SCREAMING_SNAKE_CASE      )

  a__												= encoder_outputs[0]

  a__												= self.pooler(SCREAMING_SNAKE_CASE      )

  if not return_dict:
   return (last_hidden_state, pooled_output) + encoder_outputs[1:]

  return BaseModelOutputWithPoolingAndNoAttention(
      last_hidden_state=SCREAMING_SNAKE_CASE		,				pooler_output=SCREAMING_SNAKE_CASE		,				hidden_states=encoder_outputs.hidden_states		,				)





@add_start_docstrings(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''	,	_lowercase	,	)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE      ) ->			Union[str, Any]:
  super().__init__(SCREAMING_SNAKE_CASE      )
  a__												= config.num_labels
  a__												= RegNetModel(SCREAMING_SNAKE_CASE      )
  # classification head
  a__												= nn.Sequential(
      nn.Flatten()		,				nn.Linear(config.hidden_sizes[-1]		,				config.num_labels      ) if config.num_labels > 0 else nn.Identity()		,				)
  # initialize weights and apply final processing
  self.post_init()



 @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE      )
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT		,				output_type=SCREAMING_SNAKE_CASE		,				config_class=_CONFIG_FOR_DOC		,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,				)
 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				) ->			ImageClassifierOutputWithNoAttention:
  a__												= return_dict if return_dict is not None else self.config.use_return_dict

  a__												= self.regnet(SCREAMING_SNAKE_CASE		,				output_hidden_states=SCREAMING_SNAKE_CASE		,				return_dict=SCREAMING_SNAKE_CASE      )

  a__												= outputs.pooler_output if return_dict else outputs[1]

  a__												= self.classifier(SCREAMING_SNAKE_CASE      )

  a__												= None

  if labels is not None:
   if self.config.problem_type is None:
    if self.num_labels == 1:
     a__												= '''regression'''
    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
     a__												= '''single_label_classification'''
    else:
     a__												= '''multi_label_classification'''
   if self.config.problem_type == "regression":
    a__												= MSELoss()
    if self.num_labels == 1:
     a__												= loss_fct(logits.squeeze()		,				labels.squeeze()      )
    else:
     a__												= loss_fct(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
   elif self.config.problem_type == "single_label_classification":
    a__												= CrossEntropyLoss()
    a__												= loss_fct(logits.view(-1		,				self.num_labels      )		,				labels.view(-1      )      )
   elif self.config.problem_type == "multi_label_classification":
    a__												= BCEWithLogitsLoss()
    a__												= loss_fct(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

  if not return_dict:
   a__												= (logits,) + outputs[2:]
   return (loss,) + output if loss is not None else output

  return ImageClassifierOutputWithNoAttention(loss=SCREAMING_SNAKE_CASE		,				logits=SCREAMING_SNAKE_CASE		,				hidden_states=outputs.hidden_states      )










from statistics import mean

import numpy as np






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= 0
 # Number of processes finished
 a__												= 0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 a__												= [0] * no_of_process
 # List to include calculation results
 a__												= [0] * no_of_process

 # Sort by arrival time.
 a__												= [burst_time[i] for i in np.argsort(__UpperCAmelCase       )]
 a__												= [process_name[i] for i in np.argsort(__UpperCAmelCase       )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  a__												= 0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   a__												= arrival_time[i]

  a__												= 0
  # Index showing the location of the process being performed
  a__												= 0
  # Saves the current response ratio.
  a__												= 0
  for i in range(0							,  __UpperCAmelCase       ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    a__												= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    a__												= temp
    a__												= i

        # Calculate the turn around time
  a__												= current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  a__												= 1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= [0] * no_of_process
 for i in range(0							,  __UpperCAmelCase       ):
  a__												= turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
     a_      :      List[Any]    =     5
     a_      :      Optional[int]    =     ['A', 'B', 'C', 'D', 'E']
     a_      :      Union[str, Any]    =     [1, 2, 3, 4, 5]
     a_      :      Dict    =     [1, 2, 3, 4, 5]

     a_      :      List[Any]    =     calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     a_      :      int    =     calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
     for i in range(0, no_of_process):
          print(
              f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
              f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
          )

     print(f'average waiting time : {mean(waiting_time):.5f}')
     print(f'average turn around time : {mean(turn_around_time):.5f}')





