



'''simple docstring'''

import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= model.config

				SCREAMING_SNAKE_CASE  :						Dict      					= DonutSwinConfig(
				    image_size=original_config.input_size    ,	patch_size=4    ,	depths=original_config.encoder_layer    ,	num_heads=[4, 8, 16, 32]    ,	window_size=original_config.window_size    ,	embed_dim=1_28    ,	)
				SCREAMING_SNAKE_CASE  :						List[str]      					= MBartConfig(
				    is_decoder=_SCREAMING_SNAKE_CASE    ,	is_encoder_decoder=_SCREAMING_SNAKE_CASE    ,	add_cross_attention=_SCREAMING_SNAKE_CASE    ,	decoder_layers=original_config.decoder_layer    ,	max_position_embeddings=original_config.max_position_embeddings    ,	vocab_size=len(
				        model.decoder.tokenizer       )    ,	scale_embedding=_SCREAMING_SNAKE_CASE    ,	add_final_layer_norm=_SCREAMING_SNAKE_CASE    ,	)

				return encoder_config, decoder_config






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any       ):
				if "encoder.model" in name:
								SCREAMING_SNAKE_CASE  :						Tuple      					= name.replace('''encoder.model'''    ,	'''encoder'''       )
				if "decoder.model" in name:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= name.replace('''decoder.model'''    ,	'''decoder'''       )
				if "patch_embed.proj" in name:
								SCREAMING_SNAKE_CASE  :						Optional[Any]      					= name.replace('''patch_embed.proj'''    ,	'''embeddings.patch_embeddings.projection'''       )
				if "patch_embed.norm" in name:
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= name.replace('''patch_embed.norm'''    ,	'''embeddings.norm'''       )
				if name.startswith('''encoder'''       ):
								if "layers" in name:
												SCREAMING_SNAKE_CASE  :						List[str]      					= '''encoder.''' + name
								if "attn.proj" in name:
												SCREAMING_SNAKE_CASE  :						int      					= name.replace('''attn.proj'''    ,	'''attention.output.dense'''       )
								if "attn" in name and "mask" not in name:
												SCREAMING_SNAKE_CASE  :						List[Any]      					= name.replace('''attn'''    ,	'''attention.self'''       )
								if "norm1" in name:
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= name.replace('''norm1'''    ,	'''layernorm_before'''       )
								if "norm2" in name:
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= name.replace('''norm2'''    ,	'''layernorm_after'''       )
								if "mlp.fc1" in name:
												SCREAMING_SNAKE_CASE  :						Any      					= name.replace('''mlp.fc1'''    ,	'''intermediate.dense'''       )
								if "mlp.fc2" in name:
												SCREAMING_SNAKE_CASE  :						Any      					= name.replace('''mlp.fc2'''    ,	'''output.dense'''       )

								if name == "encoder.norm.weight":
												SCREAMING_SNAKE_CASE  :						List[Any]      					= '''encoder.layernorm.weight'''
								if name == "encoder.norm.bias":
												SCREAMING_SNAKE_CASE  :						str      					= '''encoder.layernorm.bias'''

				return name






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				for key in orig_state_dict.copy().keys():
								SCREAMING_SNAKE_CASE  :						Any      					= orig_state_dict.pop(_SCREAMING_SNAKE_CASE       )

								if "qkv" in key:
												SCREAMING_SNAKE_CASE  :						List[str]      					= key.split('''.'''       )
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= int(key_split[3]       )
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= int(key_split[5]       )
												SCREAMING_SNAKE_CASE  :						Tuple      					= model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

												if "weight" in key:
																SCREAMING_SNAKE_CASE  :						List[Any]      					= val[:dim, :]
																SCREAMING_SNAKE_CASE  :						Tuple      					= val[dim : dim * 2, :]
																SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= val[-dim:, :]
												else:
																SCREAMING_SNAKE_CASE  :						Dict      					= val[:dim]
																SCREAMING_SNAKE_CASE  :						List[Any]      					= val[dim : dim * 2]
																SCREAMING_SNAKE_CASE  :						str      					= val[-dim:]
								elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
												# HuggingFace implementation doesn't use attn_mask buffer
												# and model doesn't use final LayerNorms for the encoder
												pass
								else:
												SCREAMING_SNAKE_CASE  :						Tuple      					= val

				return orig_state_dict






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=None    ,	_SCREAMING_SNAKE_CASE      :List[Any]=False       ):
				# load original model
				SCREAMING_SNAKE_CASE  :						List[str]      					= DonutModel.from_pretrained(_SCREAMING_SNAKE_CASE       ).eval()

				# load HuggingFace model
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Tuple      					= get_configs(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= DonutSwinModel(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Any      					= MBartForCausalLM(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= VisionEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE    ,	decoder=_SCREAMING_SNAKE_CASE       )
				model.eval()

				SCREAMING_SNAKE_CASE  :						Any      					= original_model.state_dict()
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= convert_state_dict(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				model.load_state_dict(_SCREAMING_SNAKE_CASE       )

				# verify results on scanned document
				SCREAMING_SNAKE_CASE  :						Tuple      					= load_dataset('''hf-internal-testing/example-documents'''       )
				SCREAMING_SNAKE_CASE  :						Any      					= dataset['''test'''][0]['''image'''].convert('''RGB'''       )

				SCREAMING_SNAKE_CASE  :						str      					= XLMRobertaTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE    ,	from_slow=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= DonutImageProcessor(
				    do_align_long_axis=original_model.config.align_long_axis    ,	size=original_model.config.input_size[::-1]       )
				SCREAMING_SNAKE_CASE  :						int      					= DonutProcessor(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= processor(_SCREAMING_SNAKE_CASE    ,	return_tensors='''pt'''       ).pixel_values

				if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
								SCREAMING_SNAKE_CASE  :						int      					= '''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
								SCREAMING_SNAKE_CASE  :						str      					= '''When is the coffee break?'''
								SCREAMING_SNAKE_CASE  :						int      					= task_prompt.replace('''{user_input}'''    ,	_SCREAMING_SNAKE_CASE       )
				elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
								SCREAMING_SNAKE_CASE  :						List[Any]      					= '''<s_rvlcdip>'''
				elif model_name in [
				    "naver-clova-ix/donut-base-finetuned-cord-v1",
				    "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
				]:
								SCREAMING_SNAKE_CASE  :						List[str]      					= '''<s_cord>'''
				elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
								SCREAMING_SNAKE_CASE  :						Dict      					= '''s_cord-v2>'''
				elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
								SCREAMING_SNAKE_CASE  :						Tuple      					= '''<s_zhtrainticket>'''
				elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
								# use a random prompt
								SCREAMING_SNAKE_CASE  :						int      					= '''hello world'''
				else:
								raise ValueError('''Model name not supported'''       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= original_model.decoder.tokenizer(_SCREAMING_SNAKE_CASE    ,	add_special_tokens=_SCREAMING_SNAKE_CASE    ,	return_tensors='''pt'''       )[
				    '''input_ids'''
				]

				SCREAMING_SNAKE_CASE  :						str      					= original_model.encoder.model.patch_embed(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Dict      					= model.encoder.embeddings(_SCREAMING_SNAKE_CASE       )
				assert torch.allclose(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	atol=1E-3       )

				# verify encoder hidden states
				SCREAMING_SNAKE_CASE  :						Any      					= original_model.encoder(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Optional[int]      					= model.encoder(_SCREAMING_SNAKE_CASE       ).last_hidden_state
				assert torch.allclose(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	atol=1E-2       )

				# verify decoder hidden states
				SCREAMING_SNAKE_CASE  :						Tuple      					= original_model(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ).logits
				SCREAMING_SNAKE_CASE  :						Tuple      					= model(_SCREAMING_SNAKE_CASE    ,	decoder_input_ids=_SCREAMING_SNAKE_CASE       ).logits
				assert torch.allclose(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	atol=1E-3       )
				print('''Looks ok!'''       )

				if pytorch_dump_folder_path is not None:
								print(F'''Saving model and processor to {pytorch_dump_folder_path}'''       )
								model.save_pretrained(_SCREAMING_SNAKE_CASE       )
								processor.save_pretrained(_SCREAMING_SNAKE_CASE       )

				if push_to_hub:
								model.push_to_hub('''nielsr/''' + model_name.split('''/'''       )[-1]    ,	commit_message='''Update model'''       )
								processor.push_to_hub('''nielsr/''' + model_name.split('''/'''       )[-1]    ,	commit_message='''Update model'''       )


if __name__ == "__main__":
	snake_case_			=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""naver-clova-ix/donut-base-finetuned-docvqa""",
	    required=False,
	    type=str,
	    help="""Name of the original model you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    required=False,
	    type=str,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	    help="""Whether or not to push the converted model and processor to the 🤗 hub.""",
	)

	snake_case_			=     parser.parse_args()
	convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''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       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]=32    ,	_SCREAMING_SNAKE_CASE      :List[Any]=10    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=1_00    ,	_SCREAMING_SNAKE_CASE      :int=10_26    ,	_SCREAMING_SNAKE_CASE      :List[Any]=True    ,	_SCREAMING_SNAKE_CASE      :str="data/tokenized_stories_train_wikitext103.jbl"    ,	_SCREAMING_SNAKE_CASE      :List[Any]="igf_context_pairs.jbl"    ,	):
				set_seed(3       )
				# generate train_data and objective_set
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= generate_datasets(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	number=_SCREAMING_SNAKE_CASE    ,	min_len=10_26    ,	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?
				SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''       )

				# load pretrained model
				SCREAMING_SNAKE_CASE  :						Any      					= load_gpta('''gpt2'''       ).to(_SCREAMING_SNAKE_CASE       )
				print('''computing perplexity on objective set'''       )
				SCREAMING_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       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]=15    ,	_SCREAMING_SNAKE_CASE      :Any=1_28    ,	_SCREAMING_SNAKE_CASE      :Any=1_00    ,	_SCREAMING_SNAKE_CASE      :List[str]="igf_model.pt"    ,	):
				set_seed(42       )

				# Load pre-trained model
				SCREAMING_SNAKE_CASE  :						List[Any]      					= GPTaLMHeadModel.from_pretrained('''gpt2'''       )

				# Initialize secondary learner to use embedding weights of model
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= SecondaryLearner(_SCREAMING_SNAKE_CASE       )

				# Train secondary learner
				SCREAMING_SNAKE_CASE  :						Any      					= train_secondary_learner(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	max_epochs=_SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	eval_freq=1_00    ,	igf_model_path=_SCREAMING_SNAKE_CASE    ,	)

				del model, secondary_learner_train_data
				torch.cuda.empty_cache()

				return secondary_learner






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=32    ,	_SCREAMING_SNAKE_CASE      :Tuple=10_00    ,	_SCREAMING_SNAKE_CASE      :int=16    ,	_SCREAMING_SNAKE_CASE      :List[str]=1.0    ,	_SCREAMING_SNAKE_CASE      :Any=recopy_gpta    ,	_SCREAMING_SNAKE_CASE      :Tuple=None    ,	_SCREAMING_SNAKE_CASE      :int=10    ,	_SCREAMING_SNAKE_CASE      :Optional[int]="gpt2_finetuned.pt"    ,	):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''       )
				SCREAMING_SNAKE_CASE  :						Any      					= RandomSampler(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= DataLoader(_SCREAMING_SNAKE_CASE    ,	sampler=_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Dict      					= max_steps // (len(_SCREAMING_SNAKE_CASE       )) + 1
				SCREAMING_SNAKE_CASE  :						str      					= 0
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.zeros((1, context_len)    ,	dtype=torch.long    ,	device=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_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()
				SCREAMING_SNAKE_CASE  :						str      					= []
				SCREAMING_SNAKE_CASE  :						List[str]      					= 0

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []

				# Compute the performance of the transformer model at the beginning
				SCREAMING_SNAKE_CASE  :						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()
												SCREAMING_SNAKE_CASE  :						Any      					= random.randint(0    ,	example.size(2       ) - context_len - 1       )
												SCREAMING_SNAKE_CASE  :						int      					= example[0, 0, start : start + context_len]
												lm_optimizer.zero_grad()
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						int      					= True

												if secondary_learner is not None:
																SCREAMING_SNAKE_CASE  :						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:
																				SCREAMING_SNAKE_CASE  :						Dict      					= -1
																if predicted_q < threshold:
																				SCREAMING_SNAKE_CASE  :						Dict      					= False

            # If we passed the filter, add the context to the batch!
												if do_backprop:
																contexts.append(np.array(context.cpu()       )       )
																SCREAMING_SNAKE_CASE  :						Tuple      					= 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()
																SCREAMING_SNAKE_CASE  :						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:
																				SCREAMING_SNAKE_CASE  :						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       )
            # 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       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						Dict      					= 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=1_00    ,	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=1_00    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''secondary model evaluation is triggered at eval_freq'''       )

				parser.add_argument('''--max_steps'''    ,	default=10_00    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''To calculate training epochs'''       )

				parser.add_argument(
				    '''--secondary_learner_batch_size'''    ,	default=1_28    ,	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=1_00    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''The number of examples split to be used as objective_set/test_data'''       )

				parser.add_argument(
				    '''--min_len'''    ,	default=10_26    ,	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=1_00    ,	min_len=10_26    ,	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
				SCREAMING_SNAKE_CASE  :						int      					= joblib.load('''data/IGF_values.jbl'''       )

				# Train secondary learner
				SCREAMING_SNAKE_CASE  :						List[Any]      					= training_secondary_learner(
				    _SCREAMING_SNAKE_CASE    ,	secondary_learner_max_epochs=15    ,	secondary_learner_batch_size=1_28    ,	eval_freq=1_00    ,	igf_model_path='''igf_model.pt'''    ,	)

				# load pretrained gpt2 model
				SCREAMING_SNAKE_CASE  :						List[Any]      					= GPTaLMHeadModel.from_pretrained('''gpt2'''       )
				set_seed(42       )

				# Generate train and test data to train and evaluate gpt2 model
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						str      					= generate_datasets(
				    context_len=32    ,	file='''data/tokenized_stories_train_wikitext103.jbl'''    ,	number=1_00    ,	min_len=10_26    ,	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=10_00    ,	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()




