
'''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    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__=32 ,       SCREAMING_SNAKE_CASE__=10 ,       SCREAMING_SNAKE_CASE__=100 ,       SCREAMING_SNAKE_CASE__=1026 ,       SCREAMING_SNAKE_CASE__=True ,       SCREAMING_SNAKE_CASE__="data/tokenized_stories_train_wikitext103.jbl" ,       SCREAMING_SNAKE_CASE__="igf_context_pairs.jbl" ,       )  ->							Optional[Any]:


		'''simple docstring'''


		set_seed(3			)
		# generate train_data and objective_set
		snake_case			,snake_case  :       Any     			=						generate_datasets(
		    SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       number=SCREAMING_SNAKE_CASE__ ,       min_len=1026 ,       trim=SCREAMING_SNAKE_CASE__			)
		# 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?
		snake_case  :       Tuple     			=						torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''			)

		# load pretrained model
		snake_case  :       int     			=						load_gpta('''gpt2'''			).to(SCREAMING_SNAKE_CASE__			)
		print('''computing perplexity on objective set'''			)
		snake_case  :       List[Any]     			=						compute_perplexity(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			).item()
		print('''perplexity on objective set:''' ,       SCREAMING_SNAKE_CASE__			)

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

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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=15 ,       SCREAMING_SNAKE_CASE__=128 ,       SCREAMING_SNAKE_CASE__=100 ,       SCREAMING_SNAKE_CASE__="igf_model.pt" ,       )  ->							List[str]:


		'''simple docstring'''


		set_seed(42			)

		# Load pre-trained model
		snake_case  :       Optional[int]     			=						GPTaLMHeadModel.from_pretrained('''gpt2'''			)

		# Initialize secondary learner to use embedding weights of model
		snake_case  :       List[str]     			=						SecondaryLearner(SCREAMING_SNAKE_CASE__			)

		# Train secondary learner
		snake_case  :       Any     			=						train_secondary_learner(
		    SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       max_epochs=SCREAMING_SNAKE_CASE__ ,       batch_size=SCREAMING_SNAKE_CASE__ ,       eval_freq=100 ,       igf_model_path=SCREAMING_SNAKE_CASE__ ,       )

		del model, secondary_learner_train_data
		torch.cuda.empty_cache()

		return secondary_learner




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=32 ,       SCREAMING_SNAKE_CASE__=1000 ,       SCREAMING_SNAKE_CASE__=16 ,       SCREAMING_SNAKE_CASE__=1.0 ,       SCREAMING_SNAKE_CASE__=recopy_gpta ,       SCREAMING_SNAKE_CASE__=None ,       SCREAMING_SNAKE_CASE__=10 ,       SCREAMING_SNAKE_CASE__="gpt2_finetuned.pt" ,       )  ->							Optional[Any]:


		'''simple docstring'''


		snake_case  :       List[Any]     			=						torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''			)
		snake_case  :       Any     			=						RandomSampler(SCREAMING_SNAKE_CASE__			)
		snake_case  :       int     			=						DataLoader(SCREAMING_SNAKE_CASE__ ,       sampler=SCREAMING_SNAKE_CASE__			)

		snake_case  :       Any     			=						max_steps // (len(SCREAMING_SNAKE_CASE__			)) + 1
		snake_case  :       str     			=						0
		snake_case  :       List[str]     			=						torch.zeros((1, context_len) ,       dtype=torch.long ,       device=SCREAMING_SNAKE_CASE__			)
		snake_case			,snake_case			,snake_case  :       List[Any]     			=						recopy_model(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

		model.train()
		if secondary_learner is not None:
				secondary_learner.to(SCREAMING_SNAKE_CASE__			)
				secondary_learner.eval()
		snake_case  :       Dict     			=						[]
		snake_case  :       str     			=						0

		snake_case  :       List[str]     			=						[]
		snake_case  :       int     			=						[]

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

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

								# 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:
										snake_case  :       Dict     			=						-1
								if predicted_q < threshold:
										snake_case  :       Optional[Any]     			=						False

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu()			)			)
								snake_case  :       List[str]     			=						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()
								snake_case  :       Optional[int]     			=						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:
										snake_case  :       Optional[int]     			=						compute_perplexity(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
										test_perps.append(SCREAMING_SNAKE_CASE__			)

										print('''Test perplexity, step''' ,       SCREAMING_SNAKE_CASE__ ,       ''':''' ,       SCREAMING_SNAKE_CASE__			)
            # 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() ,       SCREAMING_SNAKE_CASE__			)
		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    _UpperCamelCase						(       )  ->							List[Any]:


		'''simple docstring'''


		snake_case  :       Tuple     			=						argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''			)

		# Required parameters
		parser.add_argument(
		    '''--data_dir''' ,       default=SCREAMING_SNAKE_CASE__ ,       type=SCREAMING_SNAKE_CASE__ ,       required=SCREAMING_SNAKE_CASE__ ,       help='''The input data dir. Should contain data files for WikiText.''' ,       )
		parser.add_argument(
		    '''--model_name_or_path''' ,       default=SCREAMING_SNAKE_CASE__ ,       type=SCREAMING_SNAKE_CASE__ ,       required=SCREAMING_SNAKE_CASE__ ,       help='''Path to pretrained model or model identifier from huggingface.co/models''' ,       )
		parser.add_argument(
		    '''--data_file''' ,       type=SCREAMING_SNAKE_CASE__ ,       default=SCREAMING_SNAKE_CASE__ ,       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=SCREAMING_SNAKE_CASE__ ,       default=SCREAMING_SNAKE_CASE__ ,       help='''A jbl file containing the context and information gain pairs to train secondary learner.''' ,       )

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

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

		parser.add_argument(
		    '''--context_len''' ,       default=32 ,       type=SCREAMING_SNAKE_CASE__ ,       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=SCREAMING_SNAKE_CASE__ ,       help='''number of articles that are long enough to be used as our objective set''' ,       )
		parser.add_argument(
		    '''--eval_freq''' ,       default=100 ,       type=SCREAMING_SNAKE_CASE__ ,       help='''secondary model evaluation is triggered at eval_freq'''			)

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

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

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

		parser.add_argument(
		    '''--eval_interval''' ,       default=10 ,       type=SCREAMING_SNAKE_CASE__ ,       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=SCREAMING_SNAKE_CASE__ ,       help='''The number of examples split to be used as objective_set/test_data'''			)

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

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

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

		parser.add_argument(
		    '''--threshold''' ,       default=1.0 ,       type=SCREAMING_SNAKE_CASE__ ,       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=SCREAMING_SNAKE_CASE__ ,       help='''finetuned_model_name'''			)

		parser.add_argument(
		    '''--recopy_model''' ,       default=SCREAMING_SNAKE_CASE__ ,       type=SCREAMING_SNAKE_CASE__ ,       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=SCREAMING_SNAKE_CASE__ ,       data_file='''data/tokenized_stories_train_wikitext103.jbl''' ,       igf_data_file='''igf_context_pairs.jbl''' ,       )

		# Load train data for secondary learner
		snake_case  :       Optional[Any]     			=						joblib.load('''data/IGF_values.jbl'''			)

		# Train secondary learner
		snake_case  :       List[str]     			=						training_secondary_learner(
		    SCREAMING_SNAKE_CASE__ ,       secondary_learner_max_epochs=15 ,       secondary_learner_batch_size=128 ,       eval_freq=100 ,       igf_model_path='''igf_model.pt''' ,       )

		# load pretrained gpt2 model
		snake_case  :       Optional[int]     			=						GPTaLMHeadModel.from_pretrained('''gpt2'''			)
		set_seed(42			)

		# Generate train and test data to train and evaluate gpt2 model
		snake_case			,snake_case  :       Tuple     			=						generate_datasets(
		    context_len=32 ,       file='''data/tokenized_stories_train_wikitext103.jbl''' ,       number=100 ,       min_len=1026 ,       trim=SCREAMING_SNAKE_CASE__			)

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


if __name__ == "__main__":
						main()




'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""swin"""

		lowerCamelCase     							=						{
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			int=224  ,	UpperCamelCase__				:			Optional[int]=4  ,	UpperCamelCase__				:			str=3  ,	UpperCamelCase__				:			str=96  ,	UpperCamelCase__				:			List[Any]=[2, 2, 6, 2]  ,	UpperCamelCase__				:			Optional[Any]=[3, 6, 12, 24]  ,	UpperCamelCase__				:			Optional[Any]=7  ,	UpperCamelCase__				:			Tuple=4.0  ,	UpperCamelCase__				:			Tuple=True  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.0  ,	UpperCamelCase__				:			List[str]=0.1  ,	UpperCamelCase__				:			Optional[Any]="gelu"  ,	UpperCamelCase__				:			Optional[Any]=False  ,	UpperCamelCase__				:			Tuple=0.02  ,	UpperCamelCase__				:			List[Any]=1e-5  ,	UpperCamelCase__				:			Any=32  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			int=None  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Optional[int]:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       Any     			=						image_size
				snake_case  :       Optional[int]     			=						patch_size
				snake_case  :       List[Any]     			=						num_channels
				snake_case  :       Union[str, Any]     			=						embed_dim
				snake_case  :       str     			=						depths
				snake_case  :       str     			=						len(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						num_heads
				snake_case  :       List[Any]     			=						window_size
				snake_case  :       Optional[int]     			=						mlp_ratio
				snake_case  :       Union[str, Any]     			=						qkv_bias
				snake_case  :       Optional[int]     			=						hidden_dropout_prob
				snake_case  :       Optional[int]     			=						attention_probs_dropout_prob
				snake_case  :       Optional[int]     			=						drop_path_rate
				snake_case  :       List[Any]     			=						hidden_act
				snake_case  :       int     			=						use_absolute_embeddings
				snake_case  :       str     			=						layer_norm_eps
				snake_case  :       Optional[Any]     			=						initializer_range
				snake_case  :       Any     			=						encoder_stride
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				snake_case  :       str     			=						int(embed_dim * 2 ** (len(UpperCamelCase__      ) - 1)      )
				snake_case  :       int     			=						['''stem'''] + [f'stage{idx}' for idx in range(1  ,	len(UpperCamelCase__      ) + 1      )]
				snake_case			,snake_case  :       List[Any]     			=						get_aligned_output_features_output_indices(
				    out_features=UpperCamelCase__  ,	out_indices=UpperCamelCase__  ,	stage_names=self.stage_names      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


