



'''simple docstring'''






import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCAmelCase: str							=						getLogger(__name__)
def 				lowerCamelCase__	(						_A			,      _A			,      _A			,      _A = 8			,      _A = 1024			,      _A="val"			,      _A=None			,      _A=False			,      _A="summarization"			,      _A=None			,      _A=1			,      _A = None			,      _A=""			,      **_A			,      ):
  a						: str					       =					str(_A    )
  assert local_rank is not None
  torch.distributed.init_process_group(backend='nccl'			,      rank=_A    )

  a						: str					       =					Path(_A    )
  a						: Dict					       =					save_dir.joinpath(f"""rank_{local_rank}_output.json"""    )
  torch.cuda.set_device(_A    )
  a						: Union[str, Any]					       =					AutoModelForSeqaSeqLM.from_pretrained(_A    ).cuda()
  if fpaa:
    a						: str					       =					model.half()
  # determine if we need to increase num_beams
  use_task_specific_params(_A			,      _A    )  # update config with task specific params
  a						: List[str]					       =					generate_kwargs.pop('num_beams'			,      model.config.num_beams    )  # AttributeError risk?
  if num_return_sequences > num_beams:
    a						: List[str]					       =					num_return_sequences

  a						: List[Any]					       =					AutoTokenizer.from_pretrained(_A    )
  logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""    )  # if this is wrong, check config.model_type.

  if max_source_length is None:
    a						: List[str]					       =					tokenizer.model_max_length
  if prefix is None:
    a						: Optional[Any]					       =					prefix or getattr(model.config			,      'prefix'			,      ''    ) or ''
  a						: Dict					       =					SeqaSeqDataset(
      _A			,      _A			,      _A			,      max_target_length=1024			,      type_path=_A			,      n_obs=_A			,      prefix=_A			,      **_A			,      )
  # I set shuffle=True for a more accurate progress bar.
  # If all the longest samples are first, the prog bar estimate is too high at the beginning.
  a						: Optional[int]					       =					ds.make_sortish_sampler(_A			,      distributed=_A			,      add_extra_examples=_A			,      shuffle=_A    )
  a						: List[Any]					       =					DataLoader(_A			,      sampler=_A			,      batch_size=_A			,      collate_fn=ds.collate_fn    )
  a						: int					       =					[]
  for batch in tqdm(_A    ):
    a						: Optional[Any]					       =					model.generate(
        input_ids=batch['input_ids'].to(model.device    )			,      attention_mask=batch['attention_mask'].to(model.device    )			,      num_return_sequences=_A			,      num_beams=_A			,      **_A			,      )
    a						: List[Any]					       =					tokenizer.batch_decode(_A			,      skip_special_tokens=_A			,      clean_up_tokenization_spaces=_A    )
    a						: Optional[int]					       =					batch['ids']
    if num_return_sequences > 1:
      a						: List[Any]					       =					chunks(_A			,      _A    )  # batch size chunks, each of size num_return_seq
    for i, pred in enumerate(_A    ):
      results.append({'pred': pred, 'id': ids[i].item()}    )
  save_json(_A			,      _A    )
  return results, sampler.num_replicas
def 				lowerCamelCase__	(						):
  a						: Union[str, Any]					       =					argparse.ArgumentParser(
      epilog='Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'    )
  parser.add_argument('--data_dir'			,      type=_A			,      help='like cnn_dm/test.source'    )
  parser.add_argument(
      '--model_name'			,      type=_A			,      help='like facebook/bart-large-cnn,t5-base, etc.'			,      default='sshleifer/distilbart-xsum-12-3'			,      )
  parser.add_argument('--save_dir'			,      type=_A			,      help='where to save'			,      default='tmp_gen'    )
  parser.add_argument('--max_source_length'			,      type=_A			,      default=_A    )
  parser.add_argument(
      '--type_path'			,      type=_A			,      default='test'			,      help='which subset to evaluate typically train/val/test'    )
  parser.add_argument('--task'			,      type=_A			,      default='summarization'			,      help='used for task_specific_params + metrics'    )
  parser.add_argument('--bs'			,      type=_A			,      default=8			,      required=_A			,      help='batch size'    )
  parser.add_argument(
      '--local_rank'			,      type=_A			,      default=-1			,      required=_A			,      help='should be passed by distributed.launch'    )

  parser.add_argument(
      '--n_obs'			,      type=_A			,      default=_A			,      required=_A			,      help='How many observations. Defaults to all.'    )
  parser.add_argument(
      '--num_return_sequences'			,      type=_A			,      default=1			,      required=_A			,      help='How many sequences to return'    )
  parser.add_argument(
      '--sync_timeout'			,      type=_A			,      default=600			,      required=_A			,      help='How long should master process wait for other processes to finish.'			,      )
  parser.add_argument('--src_lang'			,      type=_A			,      default=_A			,      required=_A    )
  parser.add_argument('--tgt_lang'			,      type=_A			,      default=_A			,      required=_A    )
  parser.add_argument(
      '--prefix'			,      type=_A			,      required=_A			,      default=_A			,      help='will be added to the begininng of src examples'    )
  parser.add_argument('--fp16'			,      action='store_true'    )
  parser.add_argument('--debug'			,      action='store_true'    )
  a						: Optional[Any]					       =					time.time()
  a       ,     a						: int					       =					parser.parse_known_args()
  a						: Tuple					       =					parse_numeric_n_bool_cl_kwargs(_A    )
  if generate_kwargs and args.local_rank <= 0:
    print(f"""parsed the following generate kwargs: {generate_kwargs}"""    )
  a						: Tuple					       =					Path(args.save_dir + '_tmp'    )
  Path(_A    ).mkdir(exist_ok=_A    )  # this handles locking.
  a						: str					       =					list(json_save_dir.glob('rank_*.json'    )    )
  if intermediate_files:
    raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""    )
    # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
  a						: Tuple					       =					{}
  if args.src_lang is not None:
    a						: Optional[Any]					       =					args.src_lang
  if args.tgt_lang is not None:
    a						: Union[str, Any]					       =					args.tgt_lang

  Path(args.save_dir    ).mkdir(exist_ok=_A    )
  a       ,     a						: int					       =					eval_data_dir(
      args.data_dir			,      _A			,      args.model_name			,      type_path=args.type_path			,      bs=args.bs			,      fpaa=args.fpaa			,      task=args.task			,      local_rank=args.local_rank			,      n_obs=args.n_obs			,      max_source_length=args.max_source_length			,      num_return_sequences=args.num_return_sequences			,      prefix=args.prefix			,      dataset_kwargs=_A			,      **_A			,      )

  if args.local_rank <= 0:
    a						: Tuple					       =					Path(args.save_dir    )
    save_dir.mkdir(exist_ok=_A    )
    a						: Dict					       =					gather_results_from_each_node(_A			,      _A			,      args.sync_timeout    )
    a						: List[str]					       =					combine_partial_results(_A    )
    if args.num_return_sequences > 1:
      a						: Optional[Any]					       =					save_dir.joinpath('pseudolabel_results.json'    )
      print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""    )
      save_json(_A			,      _A    )
      return
    a						: int					       =					Path(args.data_dir    ).joinpath(args.type_path + '.target'    )
    with open(_A    ) as f:
      a						: List[Any]					       =					[x.rstrip() for x in f.readlines()][: len(_A    )]

    # Calculate metrics, save metrics,  and save _generations.txt
    a						: int					       =					'translation' in args.task
    a						: List[str]					       =					calculate_bleu if calc_bleu else calculate_rouge
    a						: int					       =					'bleu' if calc_bleu else 'rouge'
    a						: Dict					       =					score_fn(_A			,      _A    )
    a						: Optional[int]					       =					len(_A    )
    a						: List[str]					       =					time.time() - start_time
    a						: Dict					       =					round(runtime / metrics['n_obs']			,      4    )
    a						: Dict					       =					num_replicas
    # TODO(@stas00): add whatever metadata to metrics
    a						: Optional[Any]					       =					save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""    )
    save_json(_A			,      _A			,      indent=_A    )
    print(_A    )
    write_txt_file(_A			,      save_dir.joinpath(f"""{args.type_path}_generations.txt"""    )    )
    if args.debug:
      write_txt_file(_A			,      save_dir.joinpath(f"""{args.type_path}.target"""    )    )
    else:
      shutil.rmtree(_A    )
def 				lowerCamelCase__	(						_A    ):
  a						: List[str]					       =					[]
  for partial_result in partial_results:
    records.extend(_A    )
  a						: List[Any]					       =					sorted(_A			,      key=lambda _A    : x["id"]    )
  a						: Optional[Any]					       =					[x['pred'] for x in records]
  return preds
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  # WAIT FOR lots of .json files
  a						: Union[str, Any]					       =					time.time()
  logger.info('waiting for all nodes to finish'    )
  a						: List[Any]					       =					None
  while (time.time() - start_wait) < timeout:
    a						: Any					       =					list(save_dir.glob('rank_*.json'    )    )
    if len(_A    ) < num_replicas:
      continue
    try:
      # make sure all json files are fully saved
      a						: Union[str, Any]					       =					lmap(_A			,      _A    )
      return json_data
    except JSONDecodeError:
      continue
  else:
    raise TimeoutError('Rank 0 gave up on waiting for other processes'    )
  # Unreachable


if __name__ == "__main__":
     # Usage for MT:
     run_generate()




'''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


lowerCAmelCase: List[Any]							=						logging.get_logger(__name__)

# General docstring
lowerCAmelCase: Optional[int]							=						'RegNetConfig'

# Base docstring
lowerCAmelCase: Tuple							=						'facebook/regnet-y-040'
lowerCAmelCase: str							=						[1, 1_0_8_8, 7, 7]

# Image classification docstring
lowerCAmelCase: Any							=						'facebook/regnet-y-040'
lowerCAmelCase: List[Any]							=						'tabby, tabby cat'

lowerCAmelCase: Optional[Any]							=						[
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]

class 							a__( nn.Module     ):







    def __init__(				self			:  str						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 3						,      __snake_case			:  int = 1						,      __snake_case			:  int = 1						,      __snake_case			:  Optional[str] = "relu"						,      ):
      super().__init__()
      a						: Tuple					       =					nn.Convad(
          __snake_case						,      __snake_case						,      kernel_size=__snake_case						,      stride=__snake_case						,      padding=kernel_size // 2						,      groups=__snake_case						,      bias=__snake_case						,      )
      a						: Dict					       =					nn.BatchNormad(__snake_case     )
      a						: Tuple					       =					ACTaFN[activation] if activation is not None else nn.Identity()







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  int     ):
      a						: int					       =					self.convolution(__snake_case     )
      a						: int					       =					self.normalization(__snake_case     )
      a						: Any					       =					self.activation(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[str]						,      __snake_case			:  RegNetConfig     ):
      super().__init__()
      a						: Optional[int]					       =					RegNetConvLayer(
          config.num_channels						,      config.embedding_size						,      kernel_size=3						,      stride=2						,      activation=config.hidden_act     )
      a						: Optional[int]					       =					config.num_channels







    def       lowercase_	(				self			:  int						,      __snake_case			:  List[str]     ):
      a						: List[Any]					       =					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						: List[str]					       =					self.embedder(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  Tuple						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 2     ):
      super().__init__()
      a						: Optional[int]					       =					nn.Convad(__snake_case						,      __snake_case						,      kernel_size=1						,      stride=__snake_case						,      bias=__snake_case     )
      a						: Optional[Any]					       =					nn.BatchNormad(__snake_case     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Tensor     ):
      a						: Any					       =					self.convolution(__snake_case     )
      a						: Dict					       =					self.normalization(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  Union[str, Any]						,      __snake_case			:  int						,      __snake_case			:  int     ):
      super().__init__()

      a						: Any					       =					nn.AdaptiveAvgPoolad((1, 1)     )
      a						: Dict					       =					nn.Sequential(
          nn.Convad(__snake_case						,      __snake_case						,      kernel_size=1     )						,      nn.ReLU()						,      nn.Convad(__snake_case						,      __snake_case						,      kernel_size=1     )						,      nn.Sigmoid()						,      )







    def       lowercase_	(				self			:  List[str]						,      __snake_case			:  List[Any]     ):
      # b c h w -> b c 1 1
      a						: Any					       =					self.pooler(__snake_case     )
      a						: Optional[Any]					       =					self.attention(__snake_case     )
      a						: Tuple					       =					hidden_state * attention
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[str]						,      __snake_case			:  RegNetConfig						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 1     ):
      super().__init__()
      a						: Union[str, Any]					       =					in_channels != out_channels or stride != 1
      a						: Dict					       =					max(1						,      out_channels // config.groups_width     )
      a						: List[str]					       =					(
          RegNetShortCut(__snake_case						,      __snake_case						,      stride=__snake_case     ) if should_apply_shortcut else nn.Identity()
      )
      a						: int					       =					nn.Sequential(
          RegNetConvLayer(__snake_case						,      __snake_case						,      kernel_size=1						,      activation=config.hidden_act     )						,      RegNetConvLayer(__snake_case						,      __snake_case						,      stride=__snake_case						,      groups=__snake_case						,      activation=config.hidden_act     )						,      RegNetConvLayer(__snake_case						,      __snake_case						,      kernel_size=1						,      activation=__snake_case     )						,      )
      a						: int					       =					ACTaFN[config.hidden_act]







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Tuple     ):
      a						: List[str]					       =					hidden_state
      a						: Dict					       =					self.layer(__snake_case     )
      a						: Tuple					       =					self.shortcut(__snake_case     )
      hidden_state += residual
      a						: Optional[int]					       =					self.activation(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[str]						,      __snake_case			:  RegNetConfig						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 1     ):
      super().__init__()
      a						: Union[str, Any]					       =					in_channels != out_channels or stride != 1
      a						: Dict					       =					max(1						,      out_channels // config.groups_width     )
      a						: Dict					       =					(
          RegNetShortCut(__snake_case						,      __snake_case						,      stride=__snake_case     ) if should_apply_shortcut else nn.Identity()
      )
      a						: List[str]					       =					nn.Sequential(
          RegNetConvLayer(__snake_case						,      __snake_case						,      kernel_size=1						,      activation=config.hidden_act     )						,      RegNetConvLayer(__snake_case						,      __snake_case						,      stride=__snake_case						,      groups=__snake_case						,      activation=config.hidden_act     )						,      RegNetSELayer(__snake_case						,      reduced_channels=int(round(in_channels / 4     )     )     )						,      RegNetConvLayer(__snake_case						,      __snake_case						,      kernel_size=1						,      activation=__snake_case     )						,      )
      a						: Union[str, Any]					       =					ACTaFN[config.hidden_act]







    def       lowercase_	(				self			:  Any						,      __snake_case			:  int     ):
      a						: Tuple					       =					hidden_state
      a						: Tuple					       =					self.layer(__snake_case     )
      a						: Optional[int]					       =					self.shortcut(__snake_case     )
      hidden_state += residual
      a						: Any					       =					self.activation(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  int						,      __snake_case			:  RegNetConfig						,      __snake_case			:  int						,      __snake_case			:  int						,      __snake_case			:  int = 2						,      __snake_case			:  int = 2						,      ):
      super().__init__()

      a						: int					       =					RegNetXLayer if config.layer_type == 'x' else RegNetYLayer

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







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  str     ):
      a						: Dict					       =					self.layers(__snake_case     )
      return hidden_state

class 							a__( nn.Module     ):







    def __init__(				self			:  List[Any]						,      __snake_case			:  RegNetConfig     ):
      super().__init__()
      a						: Dict					       =					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(
              __snake_case						,      config.embedding_size						,      config.hidden_sizes[0]						,      stride=2 if config.downsample_in_first_stage else 1						,      depth=config.depths[0]						,      )     )
      a						: str					       =					zip(config.hidden_sizes						,      config.hidden_sizes[1:]     )
      for (in_channels, out_channels), depth in zip(__snake_case						,      config.depths[1:]     ):
        self.stages.append(RegNetStage(__snake_case						,      __snake_case						,      __snake_case						,      depth=__snake_case     )     )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  Tensor						,      __snake_case			:  bool = False						,      __snake_case			:  bool = True     ):
      a						: Union[str, Any]					       =					() if output_hidden_states else None

      for stage_module in self.stages:
        if output_hidden_states:
          a						: List[str]					       =					hidden_states + (hidden_state,)

        a						: int					       =					stage_module(__snake_case     )

      if output_hidden_states:
        a						: Union[str, Any]					       =					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=__snake_case						,      hidden_states=__snake_case     )

class 							a__( lowerCamelCase__     ):
    lowercase__			=   RegNetConfig
    lowercase__			=   """regnet"""
    lowercase__			=   """pixel_values"""
    lowercase__			=   True







    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Optional[int]     ):
      if isinstance(__snake_case						,      nn.Convad     ):
        nn.init.kaiming_normal_(module.weight						,      mode='fan_out'						,      nonlinearity='relu'     )
      elif isinstance(__snake_case						,      (nn.BatchNormad, nn.GroupNorm)     ):
        nn.init.constant_(module.weight						,      1     )
        nn.init.constant_(module.bias						,      0     )







    def       lowercase_	(				self			:  Dict						,      __snake_case			:  List[str]						,      __snake_case			:  Dict=False     ):
      if isinstance(__snake_case						,      __snake_case     ):
        a						: Optional[Any]					       =					value
lowerCAmelCase: Union[str, Any]							=						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'

lowerCAmelCase: 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."""		,			lowerCamelCase__		,			)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 							a__( lowerCamelCase__     ):







    def __init__(				self			:  Dict						,      __snake_case			:  Dict     ):
      super().__init__(__snake_case     )
      a						: str					       =					config
      a						: str					       =					RegNetEmbeddings(__snake_case     )
      a						: Dict					       =					RegNetEncoder(__snake_case     )
      a						: int					       =					nn.AdaptiveAvgPoolad((1, 1)     )
      # Initialize weights and apply final processing
      self.post_init()







    @add_start_docstrings_to_model_forward(__snake_case     )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC						,      output_type=__snake_case						,      config_class=_CONFIG_FOR_DOC						,      modality='vision'						,      expected_output=_EXPECTED_OUTPUT_SHAPE						,      )
    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Tensor						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[bool] = None     ):
      a						: Dict					       =					(
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      a						: List[Any]					       =					return_dict if return_dict is not None else self.config.use_return_dict

      a						: int					       =					self.embedder(__snake_case     )

      a						: Optional[int]					       =					self.encoder(
          __snake_case						,      output_hidden_states=__snake_case						,      return_dict=__snake_case     )

      a						: List[Any]					       =					encoder_outputs[0]

      a						: Any					       =					self.pooler(__snake_case     )

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

      return BaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=__snake_case						,      pooler_output=__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.
    """		,			lowerCamelCase__		,			)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 							a__( lowerCamelCase__     ):







    def __init__(				self			:  int						,      __snake_case			:  Optional[int]     ):
      super().__init__(__snake_case     )
      a						: Optional[Any]					       =					config.num_labels
      a						: Tuple					       =					RegNetModel(__snake_case     )
      # classification head
      a						: List[Any]					       =					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(__snake_case     )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT						,      output_type=__snake_case						,      config_class=_CONFIG_FOR_DOC						,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,      )
    def       lowercase_	(				self			:  Optional[Any]						,      __snake_case			:  Optional[torch.FloatTensor] = None						,      __snake_case			:  Optional[torch.LongTensor] = None						,      __snake_case			:  Optional[bool] = None						,      __snake_case			:  Optional[bool] = None						,      ):
      a						: str					       =					return_dict if return_dict is not None else self.config.use_return_dict

      a						: Optional[Any]					       =					self.regnet(__snake_case						,      output_hidden_states=__snake_case						,      return_dict=__snake_case     )

      a						: Any					       =					outputs.pooler_output if return_dict else outputs[1]

      a						: str					       =					self.classifier(__snake_case     )

      a						: Optional[int]					       =					None

      if labels is not None:
        if self.config.problem_type is None:
          if self.num_labels == 1:
            a						: List[str]					       =					'regression'
          elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
            a						: int					       =					'single_label_classification'
          else:
            a						: Dict					       =					'multi_label_classification'
        if self.config.problem_type == "regression":
          a						: Any					       =					MSELoss()
          if self.num_labels == 1:
            a						: int					       =					loss_fct(logits.squeeze()						,      labels.squeeze()     )
          else:
            a						: List[str]					       =					loss_fct(__snake_case						,      __snake_case     )
        elif self.config.problem_type == "single_label_classification":
          a						: Union[str, Any]					       =					CrossEntropyLoss()
          a						: Tuple					       =					loss_fct(logits.view(-1						,      self.num_labels     )						,      labels.view(-1     )     )
        elif self.config.problem_type == "multi_label_classification":
          a						: Any					       =					BCEWithLogitsLoss()
          a						: Tuple					       =					loss_fct(__snake_case						,      __snake_case     )

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

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