
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_						(					_snake_case      :  List[Any]=32      ,   _snake_case      :  int=10      ,   _snake_case      :  Dict=100      ,   _snake_case      :  Optional[int]=1026      ,   _snake_case      :  Any=True      ,   _snake_case      :  List[Any]="data/tokenized_stories_train_wikitext103.jbl"      ,   _snake_case      :  int="igf_context_pairs.jbl"      ,   )				->		Union[str, Any]:







	'''simple docstring'''


	set_seed(3   )
	# generate train_data and objective_set
	__magic_name__   ,    __magic_name__		:		Tuple	   =     generate_datasets(
	    _snake_case      ,   _snake_case      ,   number=_snake_case      ,   min_len=1026      ,   trim=_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?
	__magic_name__		:		Dict	   =     torch.device("cuda:0" if torch.cuda.is_available() else "cpu"   )

	# load pretrained model
	__magic_name__		:		List[Any]	   =     load_gpta("gpt2"   ).to(_snake_case   )
	print("computing perplexity on objective set"   )
	__magic_name__		:		Dict	   =     compute_perplexity(_snake_case      ,   _snake_case      ,   _snake_case   ).item()
	print("perplexity on objective set:"      ,   _snake_case   )

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

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






def   lowerCAmelCase_						(					_snake_case      :  Dict      ,   _snake_case      :  Tuple=15      ,   _snake_case      :  List[str]=128      ,   _snake_case      :  Tuple=100      ,   _snake_case      :  Union[str, Any]="igf_model.pt"      ,   )				->		int:







	'''simple docstring'''


	set_seed(42   )

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

	# Initialize secondary learner to use embedding weights of model
	__magic_name__		:		List[Any]	   =     SecondaryLearner(_snake_case   )

	# Train secondary learner
	__magic_name__		:		Optional[int]	   =     train_secondary_learner(
	    _snake_case      ,   _snake_case      ,   max_epochs=_snake_case      ,   batch_size=_snake_case      ,   eval_freq=100      ,   igf_model_path=_snake_case      ,   )

	del model, secondary_learner_train_data
	torch.cuda.empty_cache()

	return secondary_learner






def   lowerCAmelCase_						(					_snake_case      :  Any      ,   _snake_case      :  Any      ,   _snake_case      :  Dict      ,   _snake_case      :  int=32      ,   _snake_case      :  Optional[int]=1000      ,   _snake_case      :  List[str]=16      ,   _snake_case      :  List[str]=1.0      ,   _snake_case      :  Optional[int]=recopy_gpta      ,   _snake_case      :  Optional[Any]=None      ,   _snake_case      :  Any=10      ,   _snake_case      :  Optional[int]="gpt2_finetuned.pt"      ,   )				->		List[Any]:







	'''simple docstring'''


	__magic_name__		:		Optional[Any]	   =     torch.device("cuda:0" if torch.cuda.is_available() else "cpu"   )
	__magic_name__		:		Dict	   =     RandomSampler(_snake_case   )
	__magic_name__		:		List[str]	   =     DataLoader(_snake_case      ,   sampler=_snake_case   )

	__magic_name__		:		List[str]	   =     max_steps // (len(_snake_case   )) + 1
	__magic_name__		:		int	   =     0
	__magic_name__		:		int	   =     torch.zeros((1, context_len)      ,   dtype=torch.long      ,   device=_snake_case   )
	__magic_name__   ,    __magic_name__   ,    __magic_name__		:		List[str]	   =     recopy_model(_snake_case      ,   _snake_case      ,   _snake_case   )

	model.train()
	if secondary_learner is not None:
		secondary_learner.to(_snake_case   )
		secondary_learner.eval()
	__magic_name__		:		Tuple	   =     []
	__magic_name__		:		Any	   =     0

	__magic_name__		:		Dict	   =     []
	__magic_name__		:		Optional[Any]	   =     []

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

			if secondary_learner is not None:
				__magic_name__		:		int	   =     secondary_learner.forward(
				    torch.tensor(_snake_case      ,   dtype=torch.long      ,   device=_snake_case   ).unsqueeze(0   )   )[0].item()
				observed_qs.append(float(_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:
					__magic_name__		:		List[Any]	   =     -1
				if predicted_q < threshold:
					__magic_name__		:		int	   =     False

            # If we passed the filter, add the context to the batch!
			if do_backprop:
				contexts.append(np.array(context.cpu()   )   )
				__magic_name__		:		Dict	   =     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()
				__magic_name__		:		List[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:
					__magic_name__		:		Dict	   =     compute_perplexity(_snake_case      ,   _snake_case      ,   _snake_case   )
					test_perps.append(_snake_case   )

					print("Test perplexity, step"      ,   _snake_case      ,   ":"      ,   _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()      ,   _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   lowerCAmelCase_						(					)				->		Optional[int]:







	'''simple docstring'''


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

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

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

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

	parser.add_argument(
	    "--context_len"      ,   default=32      ,   type=_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=_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=_snake_case      ,   help="secondary model evaluation is triggered at eval_freq"   )

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

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

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

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

	parser.add_argument(
	    "--min_len"      ,   default=1026      ,   type=_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=_snake_case      ,   help="number of epochs to train secondary learner"   )

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

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

	parser.add_argument(
	    "--recopy_model"      ,   default=_snake_case      ,   type=_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=_snake_case      ,   data_file="data/tokenized_stories_train_wikitext103.jbl"      ,   igf_data_file="igf_context_pairs.jbl"      ,   )

	# Load train data for secondary learner
	__magic_name__		:		List[Any]	   =     joblib.load("data/IGF_values.jbl"   )

	# Train secondary learner
	__magic_name__		:		List[str]	   =     training_secondary_learner(
	    _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
	__magic_name__		:		Union[str, Any]	   =     GPTaLMHeadModel.from_pretrained("gpt2"   )
	set_seed(42   )

	# Generate train and test data to train and evaluate gpt2 model
	__magic_name__   ,    __magic_name__		:		Optional[Any]	   =     generate_datasets(
	    context_len=32      ,   file="data/tokenized_stories_train_wikitext103.jbl"      ,   number=100      ,   min_len=1026      ,   trim=_snake_case   )

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


if __name__ == "__main__":
				main()


import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


snake_case :		int							      =  logging.get_logger(__name__)
snake_case :		List[str]							      =  {"vocab_file": "spiece.model"}

snake_case :		List[str]							      =  {
    "vocab_file": {
        "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
        "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
        "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
        "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
        "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
        "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
        "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
        "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
    }
}

snake_case :		Tuple							      =  {
    "albert-base-v1": 512,
    "albert-large-v1": 512,
    "albert-xlarge-v1": 512,
    "albert-xxlarge-v1": 512,
    "albert-base-v2": 512,
    "albert-large-v2": 512,
    "albert-xlarge-v2": 512,
    "albert-xxlarge-v2": 512,
}

snake_case :		List[str]							      =  "▁"





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







			def __init__(				self						,   _a						,   _a=True						,   _a=True						,   _a=False						,   _a="[CLS]"						,   _a="[SEP]"						,   _a="<unk>"						,   _a="[SEP]"						,   _a="<pad>"						,   _a="[CLS]"						,   _a="[MASK]"						,   _a = None						,   **_a						,   ):
				# Mask token behave like a normal word, i.e. include the space before it and
				# is included in the raw text, there should be a match in a non-normalized sentence.
				__magic_name__		:		str	   =     (
				    AddedToken(_a						,   lstrip=_a						,   rstrip=_a						,   normalized=_a		)
				    if isinstance(_a						,   _a		)
				    else mask_token
				)

				__magic_name__		:		Union[str, Any]	   =     {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=_a						,   remove_space=_a						,   keep_accents=_a						,   bos_token=_a						,   eos_token=_a						,   unk_token=_a						,   sep_token=_a						,   pad_token=_a						,   cls_token=_a						,   mask_token=_a						,   sp_model_kwargs=self.sp_model_kwargs						,   **_a						,   )

				__magic_name__		:		Dict	   =     do_lower_case
				__magic_name__		:		Tuple	   =     remove_space
				__magic_name__		:		Union[str, Any]	   =     keep_accents
				__magic_name__		:		Tuple	   =     vocab_file

				__magic_name__		:		int	   =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(_a		)







			@property
			def 			SCREAMING_SNAKE_CASE    (				self		):
				return len(self.sp_model		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		List[str]	   =     {self.convert_ids_to_tokens(_a		): i for i in range(self.vocab_size		)}
				vocab.update(self.added_tokens_encoder		)
				return vocab







			def __getstate__(				self		):
				__magic_name__		:		List[str]	   =     self.__dict__.copy()
				__magic_name__		:		Any	   =     None
				return state







			def __setstate__(				self						,   _a		):
				__magic_name__		:		Union[str, Any]	   =     d

				# for backward compatibility
				if not hasattr(self						,   "sp_model_kwargs"		):
					__magic_name__		:		str	   =     {}

				__magic_name__		:		Optional[int]	   =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
				self.sp_model.Load(self.vocab_file		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				if self.remove_space:
					__magic_name__		:		List[Any]	   =     " ".join(inputs.strip().split()		)
				else:
					__magic_name__		:		str	   =     inputs
				__magic_name__		:		int	   =     outputs.replace("``"						,   "\""		).replace("''"						,   "\""		)

				if not self.keep_accents:
					__magic_name__		:		str	   =     unicodedata.normalize("NFKD"						,   _a		)
					__magic_name__		:		Tuple	   =     "".join([c for c in outputs if not unicodedata.combining(_a		)]		)
				if self.do_lower_case:
					__magic_name__		:		int	   =     outputs.lower()

				return outputs







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     self.preprocess_text(_a		)
				__magic_name__		:		Dict	   =     self.sp_model.encode(_a						,   out_type=_a		)
				__magic_name__		:		Any	   =     []
				for piece in pieces:
					if len(_a		) > 1 and piece[-1] == str(","		) and piece[-2].isdigit():
						__magic_name__		:		Optional[Any]	   =     self.sp_model.EncodeAsPieces(piece[:-1].replace(_a						,   ""		)		)
						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
							if len(cur_pieces[0]		) == 1:
								__magic_name__		:		List[str]	   =     cur_pieces[1:]
							else:
								__magic_name__		:		Optional[int]	   =     cur_pieces[0][1:]
						cur_pieces.append(piece[-1]		)
						new_pieces.extend(_a		)
					else:
						new_pieces.append(_a		)

				return new_pieces







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.sp_model.PieceToId(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				return self.sp_model.IdToPiece(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a		):
				__magic_name__		:		Any	   =     []
				__magic_name__		:		Union[str, Any]	   =     ""
				__magic_name__		:		int	   =     False
				for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
						if not prev_is_special:
							out_string += " "
						out_string += self.sp_model.decode(_a		) + token
						__magic_name__		:		List[Any]	   =     True
						__magic_name__		:		Optional[int]	   =     []
					else:
						current_sub_tokens.append(_a		)
						__magic_name__		:		Optional[Any]	   =     False
				out_string += self.sp_model.decode(_a		)
				return out_string.strip()







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		List[str]	   =     [self.sep_token_id]
				__magic_name__		:		Union[str, Any]	   =     [self.cls_token_id]
				if token_ids_a is None:
					return cls + token_ids_a + sep
				return cls + token_ids_a + sep + token_ids_a + sep







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None						,   _a = False		):

				if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=_a						,   token_ids_a=_a						,   already_has_special_tokens=_a		)

				if token_ids_a is not None:
					return [1] + ([0] * len(_a		)) + [1] + ([0] * len(_a		)) + [1]
				return [1] + ([0] * len(_a		)) + [1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		Optional[int]	   =     [self.sep_token_id]
				__magic_name__		:		Union[str, Any]	   =     [self.cls_token_id]

				if token_ids_a is None:
					return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				if not os.path.isdir(_a		):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
					return
				__magic_name__		:		List[str]	   =     os.path.join(
				    _a						,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(_a		) and os.path.isfile(self.vocab_file		):
					copyfile(self.vocab_file						,   _a		)
				elif not os.path.isfile(self.vocab_file		):
					with open(_a						,   "wb"		) as fi:
						__magic_name__		:		Union[str, Any]	   =     self.sp_model.serialized_model_proto()
						fi.write(_a		)

				return (out_vocab_file,)
