



import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

UpperCAmelCase_		 =					1_6
UpperCAmelCase_		 =					3_2

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Accelerator	,   _snake_case	:int = 16				)  ->     Optional[Any]:
   _A      =       AutoTokenizer.from_pretrained('''bert-base-cased'''				)
   _A      =       load_dataset('''glue'''	,   '''mrpc'''				)

   def tokenize_function(_snake_case	:Optional[int]				):
      # max_length=None => use the model max length (it's actually the default)
      _A      =       tokenizer(examples['''sentence1''']	,   examples['''sentence2''']	,   truncation=_snake_case	,   max_length=_snake_case				)
      return outputs

   # Apply the method we just defined to all the examples in all the splits of the dataset
   # starting with the main process first:
   with accelerator.main_process_first():
      _A      =       datasets.map(
          _snake_case	,   batched=_snake_case	,   remove_columns=['''idx''', '''sentence1''', '''sentence2''']	,   )

   # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
   # transformers library
   _A      =       tokenized_datasets.rename_column('''label'''	,   '''labels'''				)

   def collate_fn(_snake_case	:Any				):
      # On TPU it's best to pad everything to the same length or training will be very slow.
      _A      =       128 if accelerator.distributed_type == DistributedType.TPU else None
      # When using mixed precision we want round multiples of 8/16
      if accelerator.mixed_precision == "fp8":
         _A      =       16
      elif accelerator.mixed_precision != "no":
         _A      =       8
      else:
         _A      =       None

      return tokenizer.pad(
          _snake_case	,   padding='''longest'''	,   max_length=_snake_case	,   pad_to_multiple_of=_snake_case	,   return_tensors='''pt'''	,   )

   # Instantiate dataloaders.
   _A      =       DataLoader(
       tokenized_datasets['''train''']	,   shuffle=_snake_case	,   collate_fn=_snake_case	,   batch_size=_snake_case				)
   _A      =       DataLoader(
       tokenized_datasets['''validation''']	,   shuffle=_snake_case	,   collate_fn=_snake_case	,   batch_size=_snake_case				)

   return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
   from accelerate.test_utils.training import mocked_dataloaders

   UpperCAmelCase_		 =					mocked_dataloaders  # noqa: F811

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]	,   _snake_case	:str				)  ->     Optional[int]:
   # For testing only
   if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,   _snake_case				) == "1":
      _A      =       2
   # Initialize Accelerator

   # New Code #
   # We pass in "all" to `log_with` to grab all available trackers in the environment
   # Note: If using a custom `Tracker` class, should be passed in here such as:
   # >>> log_with = ["all", MyCustomTrackerClassInstance()]
   if args.with_tracking:
      _A      =       Accelerator(
          cpu=args.cpu	,   mixed_precision=args.mixed_precision	,   log_with='''all'''	,   project_dir=args.project_dir				)
   else:
      _A      =       Accelerator(cpu=args.cpu	,   mixed_precision=args.mixed_precision				)
   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   _A      =       config['''lr''']
   _A      =       int(config['''num_epochs''']				)
   _A      =       int(config['''seed''']				)
   _A      =       int(config['''batch_size''']				)
   set_seed(_snake_case				)

   _A  ,    _A      =       get_dataloaders(_snake_case	,   _snake_case				)
   _A      =       evaluate.load('''glue'''	,   '''mrpc'''				)

   # If the batch size is too big we use gradient accumulation
   _A      =       1
   if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
      _A      =       batch_size // MAX_GPU_BATCH_SIZE
      _A      =       MAX_GPU_BATCH_SIZE

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   _A      =       AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''	,   return_dict=_snake_case				)

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   _A      =       model.to(accelerator.device				)

   # Instantiate optimizer
   _A      =       AdamW(params=model.parameters()	,   lr=_snake_case				)

   # Instantiate scheduler
   _A      =       get_linear_schedule_with_warmup(
       optimizer=_snake_case	,   num_warmup_steps=100	,   num_training_steps=(len(_snake_case				) * num_epochs) // gradient_accumulation_steps	,   )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   _A  ,    _A  ,    _A  ,    _A  ,    _A      =       accelerator.prepare(
       _snake_case	,   _snake_case	,   _snake_case	,   _snake_case	,   _snake_case				)

   # New Code #
   # We need to initialize the trackers we use. Overall configurations can also be stored
   if args.with_tracking:
      _A      =       os.path.split(_snake_case				)[-1].split('''.'''				)[0]
      accelerator.init_trackers(_snake_case	,   _snake_case				)

   # Now we train the model
   for epoch in range(_snake_case				):
      model.train()
      # New Code #
      # For our tracking example, we will log the total loss of each epoch
      if args.with_tracking:
         _A      =       0
      for step, batch in enumerate(_snake_case				):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device				)
         _A      =       model(**_snake_case				)
         _A      =       outputs.loss
         # New Code #
         if args.with_tracking:
            total_loss += loss.detach().float()
         _A      =       loss / gradient_accumulation_steps
         accelerator.backward(_snake_case				)
         if step % gradient_accumulation_steps == 0:
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(_snake_case				):
         # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
         batch.to(accelerator.device				)
         with torch.no_grad():
            _A      =       model(**_snake_case				)
         _A      =       outputs.logits.argmax(dim=-1				)
         _A  ,    _A      =       accelerator.gather_for_metrics((predictions, batch['''labels'''])				)
         metric.add_batch(
             predictions=_snake_case	,   references=_snake_case	,   )

      _A      =       metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}:'''	,   _snake_case				)

      # New Code #
      # To actually log, we call `Accelerator.log`
      # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
      if args.with_tracking:
         accelerator.log(
             {
                 '''accuracy''': eval_metric['''accuracy'''],
                 '''f1''': eval_metric['''f1'''],
                 '''train_loss''': total_loss.item() / len(_snake_case				),
                 '''epoch''': epoch,
             }	,   step=_snake_case	,   )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
   if args.with_tracking:
      accelerator.end_training()

def 	SCREAMING_SNAKE_CASE_							(	)  ->     List[Any]:
   _A      =       argparse.ArgumentParser(description='''Simple example of training script.'''				)
   parser.add_argument(
       '''--mixed_precision'''	,   type=_snake_case	,   default=_snake_case	,   choices=['''no''', '''fp16''', '''bf16''', '''fp8''']	,   help='''Whether to use mixed precision. Choose'''
       '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
       '''and an Nvidia Ampere GPU.'''	,   )
   parser.add_argument('''--cpu'''	,   action='''store_true'''	,   help='''If passed, will train on the CPU.'''				)
   parser.add_argument(
       '''--with_tracking'''	,   action='''store_true'''	,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''	,   )
   parser.add_argument(
       '''--project_dir'''	,   type=_snake_case	,   default='''logs'''	,   help='''Location on where to store experiment tracking logs` and relevent project information'''	,   )
   _A      =       parser.parse_args()
   _A      =       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
   training_function(_snake_case	,   _snake_case				)


if __name__ == "__main__":
   main()







"""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


UpperCAmelCase__					=logging.get_logger(__name__)

# General docstring
UpperCAmelCase__					="RegNetConfig"

# Base docstring
UpperCAmelCase__					="facebook/regnet-y-040"
UpperCAmelCase__					=[1, 1088, 7, 7]

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

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





class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						int   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 3   ,							A_						:						int = 1   ,							A_						:						int = 1   ,							A_						:						Optional[str] = "relu"   ,							):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						self.convolution(A_				)
       __lowercase						=						self.normalization(A_				)
       __lowercase						=						self.activation(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Dict   ,							A_						:						RegNetConfig				):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						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."""				)
       __lowercase						=						self.embedder(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Optional[int]   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 2				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						nn.Convad(A_   ,							A_   ,							kernel_size=1   ,							stride=A_   ,							bias=A_				)
       __lowercase						=						nn.BatchNormad(A_				)






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Tensor				):






       '''simple docstring'''


       __lowercase						=						self.convolution(A_				)
       __lowercase						=						self.normalization(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						int   ,							A_						:						int   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__()

       __lowercase						=						nn.AdaptiveAvgPoolad((1, 1)				)
       __lowercase						=						nn.Sequential(
           nn.Convad(A_   ,							A_   ,							kernel_size=1				)   ,							nn.ReLU()   ,							nn.Convad(A_   ,							A_   ,							kernel_size=1				)   ,							nn.Sigmoid()   ,							)






      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						self.pooler(A_				)
       __lowercase						=						self.attention(A_				)
       __lowercase						=						hidden_state * attention
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Any   ,							A_						:						RegNetConfig   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 1				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						in_channels != out_channels or stride != 1
       __lowercase						=						max(1   ,							out_channels // config.groups_width				)
       __lowercase						=						(
           RegNetShortCut(A_   ,							A_   ,							stride=A_				) if should_apply_shortcut else nn.Identity()
       )
       __lowercase						=						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_				)   ,							)
       __lowercase						=						ACTaFN[config.hidden_act]






      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						hidden_state
       __lowercase						=						self.layer(A_				)
       __lowercase						=						self.shortcut(A_				)
       hidden_state += residual
       __lowercase						=						self.activation(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						List[str]   ,							A_						:						RegNetConfig   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 1				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						in_channels != out_channels or stride != 1
       __lowercase						=						max(1   ,							out_channels // config.groups_width				)
       __lowercase						=						(
           RegNetShortCut(A_   ,							A_   ,							stride=A_				) if should_apply_shortcut else nn.Identity()
       )
       __lowercase						=						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_				)   ,							)
       __lowercase						=						ACTaFN[config.hidden_act]






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						hidden_state
       __lowercase						=						self.layer(A_				)
       __lowercase						=						self.shortcut(A_				)
       hidden_state += residual
       __lowercase						=						self.activation(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Optional[int]   ,							A_						:						RegNetConfig   ,							A_						:						int   ,							A_						:						int   ,							A_						:						int = 2   ,							A_						:						int = 2   ,							):






       '''simple docstring'''


       super().__init__()

       __lowercase						=						RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						self.layers(A_				)
       return hidden_state






class  lowerCamelCase__    (		nn.Module     ):
      def __init__(		self						:						Tuple   ,							A_						:						RegNetConfig				):






       '''simple docstring'''


       super().__init__()
       __lowercase						=						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]   ,							)				)
       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Tensor   ,							A_						:						bool = False   ,							A_						:						bool = True				):






       '''simple docstring'''


       __lowercase						=						() if output_hidden_states else None

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

        __lowercase						=						stage_module(A_				)

       if output_hidden_states:
        __lowercase						=						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  lowerCamelCase__    (		_a     ):
      a			:  List[str]           =		RegNetConfig
      a			:  int           =		"""regnet"""
      a			:  Tuple           =		"""pixel_values"""
      a			:  Dict           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						List[Any]				):






       '''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 SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						Dict   ,							A_						:						List[str]=False				):






       '''simple docstring'''


       if isinstance(A_   ,							A_				):
        __lowercase						=						value






UpperCAmelCase__					=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"

UpperCAmelCase__					=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  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						Any   ,							A_						:						Any				):






       '''simple docstring'''


       super().__init__(A_				)
       __lowercase						=						config
       __lowercase						=						RegNetEmbeddings(A_				)
       __lowercase						=						RegNetEncoder(A_				)
       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						Tensor   ,							A_						:						Optional[bool] = None   ,							A_						:						Optional[bool] = None				):






       '''simple docstring'''


       __lowercase						=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       __lowercase						=						return_dict if return_dict is not None else self.config.use_return_dict

       __lowercase						=						self.embedder(A_				)

       __lowercase						=						self.encoder(
           A_   ,							output_hidden_states=A_   ,							return_dict=A_				)

       __lowercase						=						encoder_outputs[0]

       __lowercase						=						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(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """      , _a      , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class  lowerCamelCase__    (		_a     ):
      def __init__(		self						:						int   ,							A_						:						int				):






       '''simple docstring'''


       super().__init__(A_				)
       __lowercase						=						config.num_labels
       __lowercase						=						RegNetModel(A_				)
       # classification head
       __lowercase						=						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 SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						Optional[torch.FloatTensor] = None   ,							A_						:						Optional[torch.LongTensor] = None   ,							A_						:						Optional[bool] = None   ,							A_						:						Optional[bool] = None   ,							):






       '''simple docstring'''


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

       __lowercase						=						self.regnet(A_   ,							output_hidden_states=A_   ,							return_dict=A_				)

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

       __lowercase						=						self.classifier(A_				)

       __lowercase						=						None

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

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

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