



'''simple docstring'''





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


lowercase_		       =  logging.get_logger(__name__)

# General docstring
lowercase_		       =  "RegNetConfig"

# Base docstring
lowercase_		       =  "facebook/regnet-y-040"
lowercase_		       =  [1, 1_088, 7, 7]

# Image classification docstring
lowercase_		       =  "facebook/regnet-y-040"
lowercase_		       =  "tabby, tabby cat"

lowercase_		       =  [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]






class __A			(  nn.Module						):



 '''simple docstring'''







 def __init__(self ,   A ,   A ,   A = 3 ,   A = 1 ,   A = 1 ,   A = "relu" ,   )    ->     Any:


      """simple docstring"""



      super().__init__()
      _a      =  nn.Convad(
          A ,   A ,   kernel_size=A ,   stride=A ,   padding=kernel_size // 2 ,   groups=A ,   bias=A ,   )
      _a      =  nn.BatchNormad(A       )
      _a      =  ACTaFN[activation] if activation is not None else nn.Identity()



 def 					a__  (self ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  self.convolution(A       )
      _a      =  self.normalization(A       )
      _a      =  self.activation(A       )
      return hidden_state






class __A			(  nn.Module						):



 '''simple docstring'''







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


      """simple docstring"""



      super().__init__()
      _a      =  RegNetConvLayer(
          config.num_channels ,   config.embedding_size ,   kernel_size=3 ,   stride=2 ,   activation=config.hidden_act       )
      _a      =  config.num_channels



 def 					a__  (self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      _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(A       )
      return hidden_state






class __A			(  nn.Module						):



 '''simple docstring'''







 def __init__(self ,   A ,   A ,   A = 2       )    ->     Any:


      """simple docstring"""



      super().__init__()
      _a      =  nn.Convad(A ,   A ,   kernel_size=1 ,   stride=A ,   bias=A       )
      _a      =  nn.BatchNormad(A       )



 def 					a__  (self ,   A       )    ->     Tensor:


      """simple docstring"""



      _a      =  self.convolution(A       )
      _a      =  self.normalization(A       )
      return hidden_state






class __A			(  nn.Module						):



 '''simple docstring'''







 def __init__(self ,   A ,   A       )    ->     Any:


      """simple docstring"""



      super().__init__()

      _a      =  nn.AdaptiveAvgPoolad((1, 1)       )
      _a      =  nn.Sequential(
          nn.Convad(A ,   A ,   kernel_size=1       ) ,   nn.ReLU() ,   nn.Convad(A ,   A ,   kernel_size=1       ) ,   nn.Sigmoid() ,   )



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      _a      =  self.pooler(A       )
      _a      =  self.attention(A       )
      _a      =  hidden_state * attention
      return hidden_state






class __A			(  nn.Module						):



 '''simple docstring'''







 def __init__(self ,   A ,   A ,   A ,   A = 1       )    ->     List[Any]:


      """simple docstring"""



      super().__init__()
      _a      =  in_channels != out_channels or stride != 1
      _a      =  max(1 ,   out_channels // config.groups_width       )
      _a      =  (
          RegNetShortCut(A ,   A ,   stride=A       ) if should_apply_shortcut else nn.Identity()
      )
      _a      =  nn.Sequential(
          RegNetConvLayer(A ,   A ,   kernel_size=1 ,   activation=config.hidden_act       ) ,   RegNetConvLayer(A ,   A ,   stride=A ,   groups=A ,   activation=config.hidden_act       ) ,   RegNetConvLayer(A ,   A ,   kernel_size=1 ,   activation=A       ) ,   )
      _a      =  ACTaFN[config.hidden_act]



 def 					a__  (self ,   A       )    ->     Any:


      """simple docstring"""



      _a      =  hidden_state
      _a      =  self.layer(A       )
      _a      =  self.shortcut(A       )
      hidden_state += residual
      _a      =  self.activation(A       )
      return hidden_state






class __A			(  nn.Module						):



 '''simple docstring'''







 def __init__(self ,   A ,   A ,   A ,   A = 1       )    ->     Optional[int]:


      """simple docstring"""



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



 def 					a__  (self ,   A       )    ->     List[Any]:


      """simple docstring"""



      _a      =  hidden_state
      _a      =  self.layer(A       )
      _a      =  self.shortcut(A       )
      hidden_state += residual
      _a      =  self.activation(A       )
      return hidden_state






class __A			(  nn.Module						):



 '''simple docstring'''







 def __init__(self ,   A ,   A ,   A ,   A = 2 ,   A = 2 ,   )    ->     List[str]:


      """simple docstring"""



      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(
              A ,   A ,   A ,   stride=A ,   ) ,   *[layer(A ,   A ,   A       ) for _ in range(depth - 1       )] ,   )



 def 					a__  (self ,   A       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  self.layers(A       )
      return hidden_state






class __A			(  nn.Module						):



 '''simple docstring'''







 def __init__(self ,   A       )    ->     Optional[int]:


      """simple docstring"""



      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(
              A ,   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(A ,   config.depths[1:]       ):
           self.stages.append(RegNetStage(A ,   A ,   A ,   depth=A       )       )



 def 					a__  (self ,   A ,   A = False ,   A = True       )    ->     BaseModelOutputWithNoAttention:


      """simple docstring"""



      _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(A       )

      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=A ,   hidden_states=A       )






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       int          =       RegNetConfig
 __lowerCamelCase    :       int          =       'regnet'
 __lowerCamelCase    :       Optional[int]          =       'pixel_values'
 __lowerCamelCase    :       Any          =       True



 def 					a__  (self ,   A       )    ->     Optional[int]:


      """simple docstring"""



      if isinstance(A ,   nn.Convad       ):
           nn.init.kaiming_normal_(module.weight ,   mode='''fan_out''' ,   nonlinearity='''relu'''       )
      elif isinstance(A ,   (nn.BatchNormad, nn.GroupNorm)       ):
           nn.init.constant_(module.weight ,   1       )
           nn.init.constant_(module.bias ,   0       )



 def 					a__  (self ,   A ,   A=False       )    ->     Any:


      """simple docstring"""



      if isinstance(A ,   A       ):
           _a      =  value





lowercase_		       =  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"

lowercase_		       =  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.'   ,   A   ,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __A			(  A						):



 '''simple docstring'''







 def __init__(self ,   A       )    ->     Optional[int]:


      """simple docstring"""



      super().__init__(A       )
      _a      =  config
      _a      =  RegNetEmbeddings(A       )
      _a      =  RegNetEncoder(A       )
      _a      =  nn.AdaptiveAvgPoolad((1, 1)       )
      # Initialize weights and apply final processing
      self.post_init()



 @add_start_docstrings_to_model_forward(A       )
 @add_code_sample_docstrings(
     checkpoint=_CHECKPOINT_FOR_DOC ,   output_type=A ,   config_class=_CONFIG_FOR_DOC ,   modality='''vision''' ,   expected_output=_EXPECTED_OUTPUT_SHAPE ,   )
 def 					a__  (self ,   A ,   A = None ,   A = None       )    ->     BaseModelOutputWithPoolingAndNoAttention:


      """simple docstring"""



      _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(A       )

      _a      =  self.encoder(
          A ,   output_hidden_states=A ,   return_dict=A       )

      _a      =  encoder_outputs[0]

      _a      =  self.pooler(A       )

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

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






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



 '''simple docstring'''







 def __init__(self ,   A       )    ->     List[Any]:


      """simple docstring"""



      super().__init__(A       )
      _a      =  config.num_labels
      _a      =  RegNetModel(A       )
      # 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(A       )
 @add_code_sample_docstrings(
     checkpoint=_IMAGE_CLASS_CHECKPOINT ,   output_type=A ,   config_class=_CONFIG_FOR_DOC ,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,   )
 def 					a__  (self ,   A = None ,   A = None ,   A = None ,   A = None ,   )    ->     ImageClassifierOutputWithNoAttention:


      """simple docstring"""



      _a      =  return_dict if return_dict is not None else self.config.use_return_dict

      _a      =  self.regnet(A ,   output_hidden_states=A ,   return_dict=A       )

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

      _a      =  self.classifier(A       )

      _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(A ,   A       )
           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(A ,   A       )

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

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








'''simple docstring'''





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  int(__A)

     # Initialize Result
     _a      =  []

     # Traverse through all denomination
     for denomination in reversed(__A):
          # Find denominations
          while int(__A) >= int(__A):
               total_value -= int(__A)
               answer.append(__A)  # Append the "answers" array

     return answer


# Driver Code
if __name__ == "__main__":
  lowercase_		       =  []
  lowercase_		       =  "0"

  if (
      input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
      == "y"
  ):
    lowercase_		       =  int(input("Enter the number of denominations you want to add: ").strip())

    for i in range(0, n):
      denominations.append(int(input(F"""Denomination {i}: """).strip()))
    lowercase_		       =  input("Enter the change you want to make in Indian Currency: ").strip()
  else:
    # All denominations of Indian Currency if user does not enter
    lowercase_		       =  [1, 2, 5, 10, 20, 50, 100, 500, 2_000]
    lowercase_		       =  input("Enter the change you want to make: ").strip()

  if int(value) == 0 or int(value) < 0:
    print("The total value cannot be zero or negative.")

  else:
    print(F"""Following is minimal change for {value}: """)
    lowercase_		       =  find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=" ")



