


"""simple docstring"""

import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel


def lowerCAmelCase_   (					UpperCamelCase__						:			int=32					, UpperCamelCase__						:			Tuple=10					, UpperCamelCase__						:			Union[str, Any]=100					, UpperCamelCase__						:			Optional[Any]=1026					, UpperCamelCase__						:			Tuple=True					, UpperCamelCase__						:			Tuple="data/tokenized_stories_train_wikitext103.jbl"					, UpperCamelCase__						:			List[str]="igf_context_pairs.jbl"					, ):






 """simple docstring"""







 set_seed(3			)
 # generate train_data and objective_set
 __lowercase				,		__lowercase						=						generate_datasets(
     UpperCamelCase__					, UpperCamelCase__					, number=UpperCamelCase__					, min_len=1026					, trim=UpperCamelCase__			)
 # keeps model same across runs
 set_seed(4			)
 # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
 # can we train on GPU?
 __lowercase						=						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""			)

 # load pretrained model
 __lowercase						=						load_gpta("""gpt2"""			).to(UpperCamelCase__			)
 print("""computing perplexity on objective set"""			)
 __lowercase						=						compute_perplexity(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			).item()
 print("""perplexity on objective set:"""					, UpperCamelCase__			)

 # collect igf pairs and save to file demo.jbl
 collect_objective_set(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)

 # clean up, delete model and data we don't need anymore
 del model, train_data, objective_set
 torch.cuda.empty_cache()


def lowerCAmelCase_   (					UpperCamelCase__						:			Dict					, UpperCamelCase__						:			Optional[int]=15					, UpperCamelCase__						:			List[Any]=128					, UpperCamelCase__						:			Tuple=100					, UpperCamelCase__						:			Dict="igf_model.pt"					, ):






 """simple docstring"""







 set_seed(42			)

 # Load pre-trained model
 __lowercase						=						GPTaLMHeadModel.from_pretrained("""gpt2"""			)

 # Initialize secondary learner to use embedding weights of model
 __lowercase						=						SecondaryLearner(UpperCamelCase__			)

 # Train secondary learner
 __lowercase						=						train_secondary_learner(
     UpperCamelCase__					, UpperCamelCase__					, max_epochs=UpperCamelCase__					, batch_size=UpperCamelCase__					, eval_freq=100					, igf_model_path=UpperCamelCase__					, )

 del model, secondary_learner_train_data
 torch.cuda.empty_cache()

 return secondary_learner


def lowerCAmelCase_   (					UpperCamelCase__						:			Any					, UpperCamelCase__						:			Union[str, Any]					, UpperCamelCase__						:			Union[str, Any]					, UpperCamelCase__						:			Union[str, Any]=32					, UpperCamelCase__						:			Optional[int]=1000					, UpperCamelCase__						:			List[str]=16					, UpperCamelCase__						:			Union[str, Any]=1.0					, UpperCamelCase__						:			Tuple=recopy_gpta					, UpperCamelCase__						:			str=None					, UpperCamelCase__						:			Optional[int]=10					, UpperCamelCase__						:			Union[str, Any]="gpt2_finetuned.pt"					, ):






 """simple docstring"""







 __lowercase						=						torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""			)
 __lowercase						=						RandomSampler(UpperCamelCase__			)
 __lowercase						=						DataLoader(UpperCamelCase__					, sampler=UpperCamelCase__			)

 __lowercase						=						max_steps // (len(UpperCamelCase__			)) + 1
 __lowercase						=						0
 __lowercase						=						torch.zeros((1, context_len)					, dtype=torch.long					, device=UpperCamelCase__			)
 __lowercase				,		__lowercase				,		__lowercase						=						recopy_model(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)

 model.train()
 if secondary_learner is not None:
  secondary_learner.to(UpperCamelCase__			)
  secondary_learner.eval()
 __lowercase						=						[]
 __lowercase						=						0

 __lowercase						=						[]
 __lowercase						=						[]

 # Compute the performance of the transformer model at the beginning
 __lowercase						=						compute_perplexity(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
 test_perps.append(UpperCamelCase__			)
 print("""Test perplexity, step"""					, UpperCamelCase__					, """:"""					, UpperCamelCase__			)
 for epoch in range(int(UpperCamelCase__			)			):
  for step, example in enumerate(UpperCamelCase__			):
   torch.cuda.empty_cache()
   __lowercase						=						random.randint(0					, example.size(2			) - context_len - 1			)
   __lowercase						=						example[0, 0, start : start + context_len]
   lm_optimizer.zero_grad()
   __lowercase						=						model(UpperCamelCase__					, labels=UpperCamelCase__			)
   __lowercase						=						True

   if secondary_learner is not None:
    __lowercase						=						secondary_learner.forward(
        torch.tensor(UpperCamelCase__					, dtype=torch.long					, device=UpperCamelCase__			).unsqueeze(0			)			)[0].item()
    observed_qs.append(float(UpperCamelCase__			)			)

    # Here we implement the simple non-constant threshold for the predicted IG(X) value
    # We will decay the selectivity of our secondary learner filter from
    # 1 standard deviation above average to 1 below average after 10 batches.

    if global_step == 10:
     __lowercase						=						-1
    if predicted_q < threshold:
     __lowercase						=						False

            # If we passed the filter, add the context to the batch!
   if do_backprop:
    contexts.append(np.array(context.cpu()			)			)
    __lowercase						=						outputs[0]
    lm_loss.backward()
    examples += 1

   del outputs

   # Once the batch is filled with enough contexts, backprop on the batch.
   if examples == batch_size:
    torch.cuda.empty_cache()
    __lowercase						=						0
    # Do LM backprop
    torch.nn.utils.clip_grad_norm_(model.parameters()					, 3.0			)
    lm_optimizer.step()
    lm_scheduler.step()  # Update learning rate schedule
    global_step += 1
    # Compute the performance of the transformer model at this batch
    if global_step % eval_interval == 0:
     __lowercase						=						compute_perplexity(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
     test_perps.append(UpperCamelCase__			)

     print("""Test perplexity, step"""					, UpperCamelCase__					, """:"""					, UpperCamelCase__			)
            # Break out of the loop after 60 batches
   if max_steps > 0 and global_step > 60:
    break
  if max_steps > 0 and global_step > 60:
   break

    # save finetuned transformer model
 torch.save(model.state_dict()					, UpperCamelCase__			)
 torch.cuda.empty_cache()
 # Do some cleaning up so we can reinitialize for the next run of this function
 del lm_optimizer
 del lm_scheduler
 return model


def lowerCAmelCase_   (					):






 """simple docstring"""







 __lowercase						=						argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""			)

 # Required parameters
 parser.add_argument(
     """--data_dir"""					, default=UpperCamelCase__					, type=UpperCamelCase__					, required=UpperCamelCase__					, help="""The input data dir. Should contain data files for WikiText."""					, )
 parser.add_argument(
     """--model_name_or_path"""					, default=UpperCamelCase__					, type=UpperCamelCase__					, required=UpperCamelCase__					, help="""Path to pretrained model or model identifier from huggingface.co/models"""					, )
 parser.add_argument(
     """--data_file"""					, type=UpperCamelCase__					, default=UpperCamelCase__					, help=(
         """A jbl file containing tokenized data which can be split as objective dataset, """
         """train_dataset and test_dataset."""
     )					, )

 parser.add_argument(
     """--igf_data_file"""					, type=UpperCamelCase__					, default=UpperCamelCase__					, help="""A jbl file containing the context and information gain pairs to train secondary learner."""					, )

 parser.add_argument(
     """--output_dir"""					, default=UpperCamelCase__					, type=UpperCamelCase__					, required=UpperCamelCase__					, help="""The output directory where the final fine-tuned model is stored."""					, )

 parser.add_argument(
     """--tokenizer_name"""					, default=UpperCamelCase__					, type=UpperCamelCase__					, help="""Pretrained tokenizer name or path if not the same as model_name"""					, )
 parser.add_argument("""--seed"""					, type=UpperCamelCase__					, default=UpperCamelCase__					, help="""A seed for reproducible training."""			)

 parser.add_argument(
     """--context_len"""					, default=32					, type=UpperCamelCase__					, help=(
         """The maximum total input sequence length after tokenization. Sequences longer """
         """than this will be truncated, sequences shorter will be padded."""
     )					, )

 parser.add_argument(
     """--size_objective_set"""					, default=100					, type=UpperCamelCase__					, help="""number of articles that are long enough to be used as our objective set"""					, )
 parser.add_argument(
     """--eval_freq"""					, default=100					, type=UpperCamelCase__					, help="""secondary model evaluation is triggered at eval_freq"""			)

 parser.add_argument("""--max_steps"""					, default=1000					, type=UpperCamelCase__					, help="""To calculate training epochs"""			)

 parser.add_argument(
     """--secondary_learner_batch_size"""					, default=128					, type=UpperCamelCase__					, help="""batch size of training data for secondary learner"""					, )

 parser.add_argument(
     """--batch_size"""					, default=16					, type=UpperCamelCase__					, help="""batch size of training data of language model(gpt2) """			)

 parser.add_argument(
     """--eval_interval"""					, default=10					, type=UpperCamelCase__					, help=(
         """decay the selectivity of our secondary learner filter from"""
         """1 standard deviation above average to 1 below average after 10 batches"""
     )					, )

 parser.add_argument(
     """--number"""					, default=100					, type=UpperCamelCase__					, help="""The number of examples split to be used as objective_set/test_data"""			)

 parser.add_argument(
     """--min_len"""					, default=1026					, type=UpperCamelCase__					, help="""The minimum length of the article to be used as objective set"""			)

 parser.add_argument(
     """--secondary_learner_max_epochs"""					, default=15					, type=UpperCamelCase__					, help="""number of epochs to train secondary learner"""			)

 parser.add_argument("""--trim"""					, default=UpperCamelCase__					, type=UpperCamelCase__					, help="""truncate the example if it exceeds context length"""			)

 parser.add_argument(
     """--threshold"""					, default=1.0					, type=UpperCamelCase__					, help=(
         """The threshold value used by secondary learner to filter the train_data and allow only"""
         """ informative data as input to the model"""
     )					, )

 parser.add_argument("""--finetuned_model_name"""					, default="""gpt2_finetuned.pt"""					, type=UpperCamelCase__					, help="""finetuned_model_name"""			)

 parser.add_argument(
     """--recopy_model"""					, default=UpperCamelCase__					, type=UpperCamelCase__					, help="""Reset the model to the original pretrained GPT-2 weights after each iteration"""					, )

 # function calls
 # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
 generate_n_pairs(
     context_len=32					, max_steps=10					, size_objective_set=100					, min_len=1026					, trim=UpperCamelCase__					, data_file="""data/tokenized_stories_train_wikitext103.jbl"""					, igf_data_file="""igf_context_pairs.jbl"""					, )

 # Load train data for secondary learner
 __lowercase						=						joblib.load("""data/IGF_values.jbl"""			)

 # Train secondary learner
 __lowercase						=						training_secondary_learner(
     UpperCamelCase__					, secondary_learner_max_epochs=15					, secondary_learner_batch_size=128					, eval_freq=100					, igf_model_path="""igf_model.pt"""					, )

 # load pretrained gpt2 model
 __lowercase						=						GPTaLMHeadModel.from_pretrained("""gpt2"""			)
 set_seed(42			)

 # Generate train and test data to train and evaluate gpt2 model
 __lowercase				,		__lowercase						=						generate_datasets(
     context_len=32					, file="""data/tokenized_stories_train_wikitext103.jbl"""					, number=100					, min_len=1026					, trim=UpperCamelCase__			)

 # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
 finetune(
     UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, context_len=32					, max_steps=1000					, batch_size=16					, threshold=1.0					, recopy_model=UpperCamelCase__					, secondary_learner=UpperCamelCase__					, eval_interval=10					, finetuned_model_name="""gpt2_finetuned.pt"""					, )


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				)
