
'''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 		A_(     A						:		Optional[Any]=32		,     A						:		List[Any]=10		,     A						:		Union[str, Any]=100		,     A						:		List[str]=1026		,     A						:		List[Any]=True		,     A						:		Dict="data/tokenized_stories_train_wikitext103.jbl"		,     A						:		Dict="igf_context_pairs.jbl"		,     ):
							set_seed(3)
							# generate train_data and objective_set
							UpperCamelCase			,	UpperCamelCase   			=     generate_datasets(
							    A		,     A		,     number=A		,     min_len=1026		,     trim=A)
							# 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   			=     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

							# load pretrained model
							UpperCamelCase   			=     load_gpta('gpt2').to(A)
							print('computing perplexity on objective set')
							UpperCamelCase   			=     compute_perplexity(A		,     A		,     A).item()
							print('perplexity on objective set:'		,     A)

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

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







def 		A_(     A						:		Union[str, Any]		,     A						:		Any=15		,     A						:		List[str]=128		,     A						:		List[str]=100		,     A						:		Tuple="igf_model.pt"		,     ):
							set_seed(42)

							# Load pre-trained model
							UpperCamelCase   			=     GPTaLMHeadModel.from_pretrained('gpt2')

							# Initialize secondary learner to use embedding weights of model
							UpperCamelCase   			=     SecondaryLearner(A)

							# Train secondary learner
							UpperCamelCase   			=     train_secondary_learner(
							    A		,     A		,     max_epochs=A		,     batch_size=A		,     eval_freq=100		,     igf_model_path=A		,     )

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner







def 		A_(     A						:		Tuple		,     A						:		List[Any]		,     A						:		int		,     A						:		Optional[int]=32		,     A						:		Any=1000		,     A						:		List[str]=16		,     A						:		List[str]=1.0		,     A						:		Union[str, Any]=recopy_gpta		,     A						:		Any=None		,     A						:		Dict=10		,     A						:		Any="gpt2_finetuned.pt"		,     ):
							UpperCamelCase   			=     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
							UpperCamelCase   			=     RandomSampler(A)
							UpperCamelCase   			=     DataLoader(A		,     sampler=A)

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

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

							UpperCamelCase   			=     []
							UpperCamelCase   			=     []

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

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

																												# 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   			=     -1
																												if predicted_q < threshold:
																																			UpperCamelCase   			=     False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()))
																												UpperCamelCase   			=     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   			=     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   			=     compute_perplexity(A		,     A		,     A)
																																			test_perps.append(A)

																																			print('Test perplexity, step'		,     A		,     ':'		,     A)
            # 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()		,     A)
							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 		A_(     ):
							UpperCamelCase   			=     argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task')

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

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

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

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

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

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

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

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

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

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

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

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

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

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

							# Train secondary learner
							UpperCamelCase   			=     training_secondary_learner(
							    A		,     secondary_learner_max_epochs=15		,     secondary_learner_batch_size=128		,     eval_freq=100		,     igf_model_path='igf_model.pt'		,     )

							# load pretrained gpt2 model
							UpperCamelCase   			=     GPTaLMHeadModel.from_pretrained('gpt2')
							set_seed(42)

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

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


if __name__ == "__main__":
						main()






'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
						import tensorflow as tf

if is_torch_available():
						import torch


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_ = None						,				A_ = None						,				A_=None						,				A_=None     )->		Optional[Any]:







											'''simple docstring'''






											if not conversation_id:
																		UpperCamelCase   			=     uuid.uuida()
											if past_user_inputs is None:
																		UpperCamelCase   			=     []
											if generated_responses is None:
																		UpperCamelCase   			=     []

											UpperCamelCase   			=     conversation_id
											UpperCamelCase   			=     past_user_inputs
											UpperCamelCase   			=     generated_responses
											UpperCamelCase   			=     text







				def __eq__(       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											if not isinstance(A_						,				A_     ):
																		return False
											if self.uuid == other.uuid:
																		return True
											return (
											    self.new_user_input == other.new_user_input
											    and self.past_user_inputs == other.past_user_inputs
											    and self.generated_responses == other.generated_responses
											)







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = False     )->		int:







											'''simple docstring'''






											if self.new_user_input:
																		if overwrite:
																									logger.warning(
																									    F'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
																									    F'''with: "{text}".'''     )
																									UpperCamelCase   			=     text
																		else:
																									logger.warning(
																									    F'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
																									    F'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''     )
											else:
																		UpperCamelCase   			=     text







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											if self.new_user_input:
																		self.past_user_inputs.append(self.new_user_input     )
											UpperCamelCase   			=     None







				def 						UpperCAmelCase_    (       self						,				A_     )->		int:







											'''simple docstring'''






											self.generated_responses.append(A_     )







				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											for user_input, generated_response in zip(self.past_user_inputs						,				self.generated_responses     ):
																		yield True, user_input
																		yield False, generated_response
											if self.new_user_input:
																		yield True, self.new_user_input







				def __repr__(       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     F'''Conversation id: {self.uuid} \n'''
											for is_user, text in self.iter_texts():
																		UpperCamelCase   			=     'user' if is_user else 'bot'
																		output += F'''{name} >> {text} \n'''
											return output


@add_end_docstrings(
    snake_case_							,       R"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """							,       )
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				*A_						,				**A_     )->		Any:







											'''simple docstring'''






											super().__init__(*A_						,				**A_     )
											if self.tokenizer.pad_token_id is None:
																		UpperCamelCase   			=     self.tokenizer.eos_token







				def 						UpperCAmelCase_    (       self						,				A_=None						,				A_=None						,				A_=None						,				**A_     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     {}
											UpperCamelCase   			=     {}
											UpperCamelCase   			=     {}

											if min_length_for_response is not None:
																		UpperCamelCase   			=     min_length_for_response
											if minimum_tokens is not None:
																		UpperCamelCase   			=     minimum_tokens

											if "max_length" in generate_kwargs:
																		UpperCamelCase   			=     generate_kwargs['max_length']
																		# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
											if clean_up_tokenization_spaces is not None:
																		UpperCamelCase   			=     clean_up_tokenization_spaces

											if generate_kwargs:
																		forward_params.update(A_     )
											return preprocess_params, forward_params, postprocess_params







				def __call__(       self						,				A_						,				A_=0						,				**A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     super().__call__(A_						,				num_workers=A_						,				**A_     )
											if isinstance(A_						,				A_     ) and len(A_     ) == 1:
																		return outputs[0]
											return outputs







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=32     )->		Dict[str, Any]:







											'''simple docstring'''






											if not isinstance(A_						,				A_     ):
																		raise ValueError('ConversationalPipeline, expects Conversation as inputs'     )
											if conversation.new_user_input is None:
																		raise ValueError(
																		    F'''Conversation with UUID {type(conversation.uuid     )} does not contain new user input to process. '''
																		    'Add user inputs with the conversation\'s `add_user_input` method'     )
											if hasattr(self.tokenizer						,				'_build_conversation_input_ids'     ):
																		UpperCamelCase   			=     self.tokenizer._build_conversation_input_ids(A_     )
											else:
																		# If the tokenizer cannot handle conversations, we default to only the old version
																		UpperCamelCase   			=     self._legacy_parse_and_tokenize(A_     )

											if self.framework == "pt":
																		UpperCamelCase   			=     torch.LongTensor([input_ids]     )
											elif self.framework == "tf":
																		UpperCamelCase   			=     tf.constant([input_ids]     )
											return {"input_ids": input_ids, "conversation": conversation}







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=10						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     generate_kwargs.get('max_length'						,				self.model.config.max_length     )

											UpperCamelCase   			=     model_inputs['input_ids'].shape[1]
											if max_length - minimum_tokens < n:
																		logger.warning(F'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''     )
																		UpperCamelCase   			=     max_length - minimum_tokens
																		UpperCamelCase   			=     model_inputs['input_ids'][:, -trim:]
																		if "attention_mask" in model_inputs:
																									UpperCamelCase   			=     model_inputs['attention_mask'][:, -trim:]
											UpperCamelCase   			=     model_inputs.pop('conversation'     )
											UpperCamelCase   			=     max_length
											UpperCamelCase   			=     self.model.generate(**A_						,				**A_     )
											if self.model.config.is_encoder_decoder:
																		UpperCamelCase   			=     1
											else:
																		UpperCamelCase   			=     n
											return {"output_ids": output_ids[:, start_position:], "conversation": conversation}







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=True     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     model_outputs['output_ids']
											UpperCamelCase   			=     self.tokenizer.decode(
											    output_ids[0]						,				skip_special_tokens=A_						,				clean_up_tokenization_spaces=A_						,				)
											UpperCamelCase   			=     model_outputs['conversation']
											conversation.mark_processed()
											conversation.append_response(A_     )
											return conversation







				def 						UpperCAmelCase_    (       self						,				A_     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.tokenizer.eos_token_id
											UpperCamelCase   			=     []
											for is_user, text in conversation.iter_texts():
																		if eos_token_id is not None:
																									input_ids.extend(self.tokenizer.encode(A_						,				add_special_tokens=A_     ) + [eos_token_id]     )
																		else:
																									input_ids.extend(self.tokenizer.encode(A_						,				add_special_tokens=A_     )     )

											if len(A_     ) > self.tokenizer.model_max_length:
																		UpperCamelCase   			=     input_ids[-self.tokenizer.model_max_length :]
											return input_ids




