
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
lowercase						:      Any									= logging.get_logger(__name__)

lowercase						:      Any									= {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowercase						:      List[Any]									= [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any])      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      {}
							with open(__A		,						"r") as file:
														for line_number, line in enumerate(__A):
																					__UpperCamelCase			:     Optional[Any]         =      line.strip()
																					if line:
																												__UpperCamelCase			:     str         =      line.split()
																												__UpperCamelCase			:     str         =      line_number
																												__UpperCamelCase			:     Optional[Any]         =      words[0]
																												__UpperCamelCase			:     Union[str, Any]         =      value
							return result




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any		,						_lowerCamelCase				:    int		,						_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    str		,						_lowerCamelCase				:    List[Any])      ->					Optional[int]:






							'''simple docstring'''





							for attribute in key.split("."):
														__UpperCamelCase			:     Any         =      getattr(__A		,						__A)

							__UpperCamelCase			:     Optional[int]         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(__A):
																					__UpperCamelCase			:     Tuple         =      PARAM_MAPPING[full_name.split(".")[-1]]
																					__UpperCamelCase			:     Dict         =      "param"

							if weight_type is not None and weight_type != "param":
														__UpperCamelCase			:     str         =      getattr(__A		,						__A).shape
							elif weight_type is not None and weight_type == "param":
														__UpperCamelCase			:     Union[str, Any]         =      hf_pointer
														for attribute in hf_param_name.split("."):
																					__UpperCamelCase			:     List[Any]         =      getattr(__A		,						__A)
														__UpperCamelCase			:     Optional[Any]         =      shape_pointer.shape

														# let's reduce dimension
														__UpperCamelCase			:     int         =      value[0]
							else:
														__UpperCamelCase			:     str         =      hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
														    F' {value.shape} for {full_name}')

							if weight_type == "weight":
														__UpperCamelCase			:     str         =      value
							elif weight_type == "weight_g":
														__UpperCamelCase			:     List[Any]         =      value
							elif weight_type == "weight_v":
														__UpperCamelCase			:     List[Any]         =      value
							elif weight_type == "bias":
														__UpperCamelCase			:     str         =      value
							elif weight_type == "param":
														for attribute in hf_param_name.split("."):
																					__UpperCamelCase			:     Any         =      getattr(__A		,						__A)
														__UpperCamelCase			:     List[str]         =      value
							else:
														__UpperCamelCase			:     Union[str, Any]         =      value

							logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.')




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Any		,						_lowerCamelCase				:    List[Any])      ->					Tuple:






							'''simple docstring'''





							__UpperCamelCase			:     str         =      None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(__A):
																					__UpperCamelCase			:     Optional[Any]         =      PARAM_MAPPING[full_name.split(".")[-1]]
																					__UpperCamelCase			:     Any         =      "param"

							if weight_type is not None and weight_type != "param":
														__UpperCamelCase			:     Optional[Any]         =      ".".join([key, weight_type])
							elif weight_type is not None and weight_type == "param":
														__UpperCamelCase			:     Optional[Any]         =      ".".join([key, hf_param_name])
							else:
														__UpperCamelCase			:     Optional[int]         =      key

							__UpperCamelCase			:     Optional[int]         =      value if "lm_head" in full_key else value[0]


lowercase						:      List[Any]									= {
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Any		,						_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    List[str]=None		,						_lowerCamelCase				:    Optional[int]=None)      ->					Optional[int]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      False
							for key, mapped_key in MAPPING.items():
														__UpperCamelCase			:     Tuple         =      "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
														if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																					__UpperCamelCase			:     List[str]         =      True
																					if "*" in mapped_key:
																												__UpperCamelCase			:     int         =      name.split(__A)[0].split(".")[-2]
																												__UpperCamelCase			:     Tuple         =      mapped_key.replace("*"		,						__A)
																					if "weight_g" in name:
																												__UpperCamelCase			:     Union[str, Any]         =      "weight_g"
																					elif "weight_v" in name:
																												__UpperCamelCase			:     int         =      "weight_v"
																					elif "bias" in name:
																												__UpperCamelCase			:     Optional[int]         =      "bias"
																					elif "weight" in name:
																												# TODO: don't match quantizer.weight_proj
																												__UpperCamelCase			:     Tuple         =      "weight"
																					else:
																												__UpperCamelCase			:     str         =      None
																					if hf_dict is not None:
																												rename_dict(__A		,						__A		,						__A		,						__A		,						__A)
																					else:
																												set_recursively(__A		,						__A		,						__A		,						__A		,						__A)
																					return is_used
							return is_used




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    Any)      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      []
							__UpperCamelCase			:     Dict         =      fairseq_model.state_dict()

							__UpperCamelCase			:     List[str]         =      hf_model.wavaveca.feature_extractor

							for name, value in fairseq_dict.items():
														__UpperCamelCase			:     Union[str, Any]         =      False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __A		,						__A		,						__A		,						__A		,						hf_model.config.feat_extract_norm == "group"		,						)
																					__UpperCamelCase			:     List[str]         =      True
														else:
																					__UpperCamelCase			:     Dict         =      load_wavaveca_layer(__A		,						__A		,						__A)
														if not is_used:
																					unused_weights.append(__A)

							logger.warning(F'Unused weights: {unused_weights}')




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int		,						_lowerCamelCase				:    Union[str, Any]		,						_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Optional[Any])      ->					Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      full_name.split("conv_layers.")[-1]
							__UpperCamelCase			:     List[str]         =      name.split(".")
							__UpperCamelCase			:     List[str]         =      int(items[0])
							__UpperCamelCase			:     int         =      int(items[1])

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F'{full_name} has size {value.shape}, but'
																												    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.')
																					__UpperCamelCase			:     List[Any]         =      value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.')
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F'{full_name} has size {value.shape}, but'
																												    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.')
																					__UpperCamelCase			:     Optional[Any]         =      value
																					logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.')
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F'{full_name} has size {value.shape}, but'
																												    F' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.')
																					__UpperCamelCase			:     List[Any]         =      value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F'{full_name} has size {value.shape}, but'
																												    F' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.')
																					__UpperCamelCase			:     Union[str, Any]         =      value
																					logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.')
							else:
														unused_weights.append(__A)




@torch.no_grad()
def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Optional[int]		,						_lowerCamelCase				:    str=None		,						_lowerCamelCase				:    Optional[Any]=None		,						_lowerCamelCase				:    str=True		,						_lowerCamelCase				:    int=False)      ->					List[Any]:






							'''simple docstring'''





							if config_path is not None:
														__UpperCamelCase			:     str         =      WavaVecaConfig.from_pretrained(__A)
							else:
														__UpperCamelCase			:     Optional[int]         =      WavaVecaConfig()

							if is_seq_class:
														__UpperCamelCase			:     Optional[Any]         =      read_txt_into_dict(__A)
														__UpperCamelCase			:     List[str]         =      idalabel
														__UpperCamelCase			:     Any         =      WavaVecaForSequenceClassification(__A)
														__UpperCamelCase			:     Dict         =      WavaVecaFeatureExtractor(
														    feature_size=1		,						sampling_rate=16_000		,						padding_value=0		,						do_normalize=__A		,						return_attention_mask=__A		,						)
														feature_extractor.save_pretrained(__A)

							elif is_finetuned:
														if dict_path:
																					__UpperCamelCase			:     Tuple         =      Dictionary.load(__A)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					__UpperCamelCase			:     Optional[Any]         =      target_dict.pad_index
																					__UpperCamelCase			:     Tuple         =      target_dict.bos_index
																					__UpperCamelCase			:     str         =      target_dict.eos_index
																					__UpperCamelCase			:     Dict         =      len(target_dict.symbols)
																					__UpperCamelCase			:     Optional[Any]         =      os.path.join(__A		,						"vocab.json")
																					if not os.path.isdir(__A):
																												logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(__A))
																												return
																					os.makedirs(__A		,						exist_ok=__A)
																					__UpperCamelCase			:     List[Any]         =      target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					__UpperCamelCase			:     Optional[int]         =      0
																					__UpperCamelCase			:     Any         =      1
																					with open(__A		,						"w"		,						encoding="utf-8") as vocab_handle:
																												json.dump(__A		,						__A)
																					__UpperCamelCase			:     List[str]         =      WavaVecaCTCTokenizer(
																					    __A		,						unk_token=target_dict.unk_word		,						pad_token=target_dict.pad_word		,						bos_token=target_dict.bos_word		,						eos_token=target_dict.eos_word		,						word_delimiter_token="|"		,						do_lower_case=__A		,						)
																					__UpperCamelCase			:     Dict         =      True if config.feat_extract_norm == "layer" else False
																					__UpperCamelCase			:     int         =      WavaVecaFeatureExtractor(
																					    feature_size=1		,						sampling_rate=16_000		,						padding_value=0		,						do_normalize=__A		,						return_attention_mask=__A		,						)
																					__UpperCamelCase			:     Optional[int]         =      WavaVecaProcessor(feature_extractor=__A		,						tokenizer=__A)
																					processor.save_pretrained(__A)

														__UpperCamelCase			:     Dict         =      WavaVecaForCTC(__A)
							else:
														__UpperCamelCase			:     Tuple         =      WavaVecaForPreTraining(__A)

							if is_finetuned or is_seq_class:
														__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]		,						arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
							else:
														__UpperCamelCase			:     Any         =      argparse.Namespace(task="audio_pretraining")
														__UpperCamelCase			:     Any         =      fairseq.tasks.setup_task(__A)

														__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]		,						task=__A)

							__UpperCamelCase			:     Union[str, Any]         =      model[0].eval()

							recursively_load_weights(__A		,						__A		,						not is_finetuned)

							hf_wavavec.save_pretrained(__A)


if __name__ == "__main__":
		lowercase						:      int									= argparse.ArgumentParser()
		parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
		parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
		parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
		)
		parser.add_argument(
		    '--is_seq_class',
		    action='store_true',
		    help='Whether the model to convert is a fine-tuned sequence classification model or not',
		)
		lowercase						:      str									= parser.parse_args()

		lowercase						:      Union[str, Any]									= not args.not_finetuned and not args.is_seq_class
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.dict_path,
		    is_finetuned,
		    args.is_seq_class,
		)








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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int=32		,						_lowerCamelCase				:    str=10		,						_lowerCamelCase				:    Dict=100		,						_lowerCamelCase				:    int=1_026		,						_lowerCamelCase				:    Union[str, Any]=True		,						_lowerCamelCase				:    str="data/tokenized_stories_train_wikitext103.jbl"		,						_lowerCamelCase				:    Any="igf_context_pairs.jbl"		,						)      ->					str:






							'''simple docstring'''





							set_seed(3)
							# generate train_data and objective_set
							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      generate_datasets(
							    _lowerCamelCase		,						_lowerCamelCase		,						number=_lowerCamelCase		,						min_len=1_026		,						trim=_lowerCamelCase)
							# 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?
							__UpperCamelCase			:     Dict         =      torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

							# load pretrained model
							__UpperCamelCase			:     str         =      load_gpta("gpt2").to(_lowerCamelCase)
							print("computing perplexity on objective set")
							__UpperCamelCase			:     Union[str, Any]         =      compute_perplexity(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase).item()
							print("perplexity on objective set:"		,						_lowerCamelCase)

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

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




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    Any=15		,						_lowerCamelCase				:    Union[str, Any]=128		,						_lowerCamelCase				:    Any=100		,						_lowerCamelCase				:    List[Any]="igf_model.pt"		,						)      ->					Any:






							'''simple docstring'''





							set_seed(42)

							# Load pre-trained model
							__UpperCamelCase			:     int         =      GPTaLMHeadModel.from_pretrained("gpt2")

							# Initialize secondary learner to use embedding weights of model
							__UpperCamelCase			:     Any         =      SecondaryLearner(_lowerCamelCase)

							# Train secondary learner
							__UpperCamelCase			:     Union[str, Any]         =      train_secondary_learner(
							    _lowerCamelCase		,						_lowerCamelCase		,						max_epochs=_lowerCamelCase		,						batch_size=_lowerCamelCase		,						eval_freq=100		,						igf_model_path=_lowerCamelCase		,						)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    int=32		,						_lowerCamelCase				:    Tuple=1_000		,						_lowerCamelCase				:    Dict=16		,						_lowerCamelCase				:    Union[str, Any]=1.0		,						_lowerCamelCase				:    Optional[Any]=recopy_gpta		,						_lowerCamelCase				:    List[Any]=None		,						_lowerCamelCase				:    Optional[Any]=10		,						_lowerCamelCase				:    Union[str, Any]="gpt2_finetuned.pt"		,						)      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
							__UpperCamelCase			:     List[Any]         =      RandomSampler(_lowerCamelCase)
							__UpperCamelCase			:     Any         =      DataLoader(_lowerCamelCase		,						sampler=_lowerCamelCase)

							__UpperCamelCase			:     Tuple         =      max_steps // (len(_lowerCamelCase)) + 1
							__UpperCamelCase			:     List[Any]         =      0
							__UpperCamelCase			:     List[Any]         =      torch.zeros((1, context_len)		,						dtype=torch.long		,						device=_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      recopy_model(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(_lowerCamelCase)
														secondary_learner.eval()
							__UpperCamelCase			:     Union[str, Any]         =      []
							__UpperCamelCase			:     Any         =      0

							__UpperCamelCase			:     List[Any]         =      []
							__UpperCamelCase			:     Any         =      []

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

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

																												# 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:
																																			__UpperCamelCase			:     List[str]         =      -1
																												if predicted_q < threshold:
																																			__UpperCamelCase			:     Optional[int]         =      False

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

																																			print("Test perplexity, step"		,						_lowerCamelCase		,						":"		,						_lowerCamelCase)
            # 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()		,						_lowerCamelCase)
							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 			_SCREAMING_SNAKE_CASE      (	)      ->					Optional[Any]:






							'''simple docstring'''





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

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

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

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

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

							parser.add_argument("--max_steps"		,						default=1_000		,						type=_lowerCamelCase		,						help="To calculate training epochs")

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

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

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

							parser.add_argument(
							    "--min_len"		,						default=1_026		,						type=_lowerCamelCase		,						help="The minimum length of the article to be used as objective set")

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

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

							parser.add_argument(
							    "--threshold"		,						default=1.0		,						type=_lowerCamelCase		,						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=_lowerCamelCase		,						help="finetuned_model_name")

							parser.add_argument(
							    "--recopy_model"		,						default=_lowerCamelCase		,						type=_lowerCamelCase		,						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=1_026		,						trim=_lowerCamelCase		,						data_file="data/tokenized_stories_train_wikitext103.jbl"		,						igf_data_file="igf_context_pairs.jbl"		,						)

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

							# Train secondary learner
							__UpperCamelCase			:     Optional[Any]         =      training_secondary_learner(
							    _lowerCamelCase		,						secondary_learner_max_epochs=15		,						secondary_learner_batch_size=128		,						eval_freq=100		,						igf_model_path="igf_model.pt"		,						)

							# load pretrained gpt2 model
							__UpperCamelCase			:     int         =      GPTaLMHeadModel.from_pretrained("gpt2")
							set_seed(42)

							# Generate train and test data to train and evaluate gpt2 model
							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      generate_datasets(
							    context_len=32		,						file="data/tokenized_stories_train_wikitext103.jbl"		,						number=100		,						min_len=1_026		,						trim=_lowerCamelCase)

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    _lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase		,						context_len=32		,						max_steps=1_000		,						batch_size=16		,						threshold=1.0		,						recopy_model=_lowerCamelCase		,						secondary_learner=_lowerCamelCase		,						eval_interval=10		,						finetuned_model_name="gpt2_finetuned.pt"		,						)


if __name__ == "__main__":
		main()