

"""simple docstring"""





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel




@require_tf
class 					_lowerCAmelCase  :







 """simple docstring"""







 __UpperCAmelCase	:					List[Any]   		=	PegasusConfig
 __UpperCAmelCase	:					Optional[int]   		=	{}
 __UpperCAmelCase	:					Any   		=	"gelu"


 def __init__(			self							:			str,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Any=1_3,					UpperCAmelCase__							:			Optional[int]=7,					UpperCAmelCase__							:			Tuple=True,					UpperCAmelCase__							:			Any=False,					UpperCAmelCase__							:			Dict=9_9,					UpperCAmelCase__							:			List[str]=3_2,					UpperCAmelCase__							:			Tuple=2,					UpperCAmelCase__							:			Any=4,					UpperCAmelCase__							:			Union[str, Any]=3_7,					UpperCAmelCase__							:			str=0.1,					UpperCAmelCase__							:			Optional[Any]=0.1,					UpperCAmelCase__							:			List[str]=4_0,					UpperCAmelCase__							:			Union[str, Any]=2,					UpperCAmelCase__							:			List[str]=1,					UpperCAmelCase__							:			Tuple=0,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						is_training
    __lowercase								=						use_labels
    __lowercase								=						vocab_size
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size

    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						max_position_embeddings
    __lowercase								=						eos_token_id
    __lowercase								=						pad_token_id
    __lowercase								=						bos_token_id


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						ids_tensor([self.batch_size, self.seq_length - 1],					self.vocab_size	)
    __lowercase								=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size	),					1	)
    __lowercase								=						tf.concat([input_ids, eos_tensor],					axis=1	)

    __lowercase								=						ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	)

    __lowercase								=						self.config_cls(
        vocab_size=self.vocab_size,					d_model=self.hidden_size,					encoder_layers=self.num_hidden_layers,					decoder_layers=self.num_hidden_layers,					encoder_attention_heads=self.num_attention_heads,					decoder_attention_heads=self.num_attention_heads,					encoder_ffn_dim=self.intermediate_size,					decoder_ffn_dim=self.intermediate_size,					dropout=self.hidden_dropout_prob,					attention_dropout=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					eos_token_ids=[2],					bos_token_id=self.bos_token_id,					pad_token_id=self.pad_token_id,					decoder_start_token_id=self.pad_token_id,					**self.config_updates,					)
    __lowercase								=						prepare_pegasus_inputs_dict(UpperCAmelCase__,					UpperCAmelCase__,					UpperCAmelCase__	)
    return config, inputs_dict



 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			Tuple	):
    __lowercase								=						TFPegasusModel(config=UpperCAmelCase__	).get_decoder()
    __lowercase								=						inputs_dict["input_ids"]

    __lowercase								=						input_ids[:1, :]
    __lowercase								=						inputs_dict["attention_mask"][:1, :]
    __lowercase								=						inputs_dict["head_mask"]
    __lowercase								=						1

    # first forward pass
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					head_mask=UpperCAmelCase__,					use_cache=UpperCAmelCase__	)

    __lowercase		,__lowercase								=						outputs.to_tuple()

    # create hypothetical next token and extent to next_input_ids
    __lowercase								=						ids_tensor((self.batch_size, 3),					config.vocab_size	)
    __lowercase								=						tf.cast(ids_tensor((self.batch_size, 3),					2	),					tf.inta	)

    # append to next input_ids and
    __lowercase								=						tf.concat([input_ids, next_tokens],					axis=-1	)
    __lowercase								=						tf.concat([attention_mask, next_attn_mask],					axis=-1	)

    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__	)[0]
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					past_key_values=UpperCAmelCase__	)[0]

    self.parent.assertEqual(next_tokens.shape[1],					output_from_past.shape[1]	)

    # select random slice
    __lowercase								=						int(ids_tensor((1,),					output_from_past.shape[-1]	)	)
    __lowercase								=						output_from_no_past[:, -3:, random_slice_idx]
    __lowercase								=						output_from_past[:, :, random_slice_idx]

    # test that outputs are equal for slice
    tf.debugging.assert_near(UpperCAmelCase__,					UpperCAmelCase__,					rtol=1E-3	)







def   _A  (  UpperCamelCase_     :       Any,			UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       Any=None,			UpperCamelCase_     :       Union[str, Any]=None,			UpperCamelCase_     :       List[str]=None,			UpperCamelCase_     :       List[Any]=None,			UpperCamelCase_     :       Dict=None,			)	->						Tuple:
   '''simple docstring'''


   if attention_mask is None:
      __lowercase								=						tf.cast(tf.math.not_equal(UpperCamelCase_,			config.pad_token_id),			tf.inta)
   if decoder_attention_mask is None:
      __lowercase								=						tf.concat(
          [
              tf.ones(decoder_input_ids[:, :1].shape,			dtype=tf.inta),
              tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],			config.pad_token_id),			tf.inta),
          ],			axis=-1,			)
   if head_mask is None:
      __lowercase								=						tf.ones((config.encoder_layers, config.encoder_attention_heads))
   if decoder_head_mask is None:
      __lowercase								=						tf.ones((config.decoder_layers, config.decoder_attention_heads))
   if cross_attn_head_mask is None:
      __lowercase								=						tf.ones((config.decoder_layers, config.decoder_attention_heads))
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
       "cross_attn_head_mask": cross_attn_head_mask,
   }




@require_tf
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Dict   		=	(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
 __UpperCAmelCase	:					str   		=	(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
 __UpperCAmelCase	:					Optional[int]   		=	(
     {
         "conversational": TFPegasusForConditionalGeneration,
         "feature-extraction": TFPegasusModel,
         "summarization": TFPegasusForConditionalGeneration,
         "text2text-generation": TFPegasusForConditionalGeneration,
         "translation": TFPegasusForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 __UpperCAmelCase	:					List[Any]   		=	True
 __UpperCAmelCase	:					Union[str, Any]   		=	False
 __UpperCAmelCase	:					Dict   		=	False


 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						TFPegasusModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[Any]	):
    self.config_tester.run_common_tests()



 def 					_lowercase (			self							:			Tuple	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase__	)







@require_sentencepiece
@require_tokenizers
@require_tf
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Tuple   		=	[
     " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
     " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
 ]
 __UpperCAmelCase	:					List[str]   		=	[
     "California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
     " reduce the risk of wildfires.",
     "N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
 ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
 __UpperCAmelCase	:					Dict   		=	"google/pegasus-xsum"


 @cached_property
 def 					_lowercase (			self							:			int	):
    return AutoTokenizer.from_pretrained(self.model_name	)


 @cached_property
 def 					_lowercase (			self							:			int	):
    __lowercase								=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name	)
    return model


 def 					_lowercase (			self							:			str,					**UpperCAmelCase__							:			Tuple	):
    __lowercase								=						self.translate_src_text(**UpperCAmelCase__	)
    assert self.expected_text == generated_words


 def 					_lowercase (			self							:			Tuple,					**UpperCAmelCase__							:			Any	):
    __lowercase								=						self.tokenizer(self.src_text,					**UpperCAmelCase__,					padding=UpperCAmelCase__,					return_tensors="tf"	)
    __lowercase								=						self.model.generate(
        model_inputs.input_ids,					attention_mask=model_inputs.attention_mask,					num_beams=2,					use_cache=UpperCAmelCase__,					)
    __lowercase								=						self.tokenizer.batch_decode(generated_ids.numpy(),					skip_special_tokens=UpperCAmelCase__	)
    return generated_words



 @slow
 def 					_lowercase (			self							:			Dict	):
    self._assert_generated_batch_equal_expected()








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

       # load pretrained model
       lowercase__:   str  			=   load_gpta('gpt2'     ).to(snake_case     )
       print('computing perplexity on objective set'     )
       lowercase__:   int  			=   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    snake_case_     (  snake_case			,							snake_case=15			,							snake_case=1_28			,							snake_case=1_00			,							snake_case="igf_model.pt"			,							)  ->				Optional[Any]:
       set_seed(42     )

       # Load pre-trained model
       lowercase__:   Any  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )

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

       # Train secondary learner
       lowercase__:   Tuple  			=   train_secondary_learner(
           snake_case			,							snake_case			,							max_epochs=snake_case			,							batch_size=snake_case			,							eval_freq=1_00			,							igf_model_path=snake_case			,							)

       del model, secondary_learner_train_data
       torch.cuda.empty_cache()

       return secondary_learner




def    snake_case_     (  snake_case			,							snake_case			,							snake_case			,							snake_case=32			,							snake_case=10_00			,							snake_case=16			,							snake_case=1.0			,							snake_case=recopy_gpta			,							snake_case=None			,							snake_case=10			,							snake_case="gpt2_finetuned.pt"			,							)  ->				Tuple:
       lowercase__:   Dict  			=   torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'     )
       lowercase__:   Optional[int]  			=   RandomSampler(snake_case     )
       lowercase__:   Optional[int]  			=   DataLoader(snake_case			,							sampler=snake_case     )

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

       model.train()
       if secondary_learner is not None:
              secondary_learner.to(snake_case     )
              secondary_learner.eval()
       lowercase__:   List[Any]  			=   []
       lowercase__:   str  			=   0

       lowercase__:   Tuple  			=   []
       lowercase__:   Dict  			=   []

       # Compute the performance of the transformer model at the beginning
       lowercase__:   Optional[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()
                     lowercase__:   Union[str, Any]  			=   random.randint(0			,							example.size(2     ) - context_len - 1     )
                     lowercase__:   Dict  			=   example[0, 0, start : start + context_len]
                     lm_optimizer.zero_grad()
                     lowercase__:   Union[str, Any]  			=   model(snake_case			,							labels=snake_case     )
                     lowercase__:   Tuple  			=   True

                     if secondary_learner is not None:
                            lowercase__:   Optional[Any]  			=   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:
                                   lowercase__:   Optional[Any]  			=   -1
                            if predicted_q < threshold:
                                   lowercase__:   str  			=   False

            # If we passed the filter, add the context to the batch!
                     if do_backprop:
                            contexts.append(np.array(context.cpu()     )     )
                            lowercase__:   List[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()
                            lowercase__:   Any  			=   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:
                                   lowercase__:   int  			=   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    snake_case_     (  )  ->				str:
       lowercase__:   Tuple  			=   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=1_00			,							type=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=snake_case			,							help='secondary model evaluation is triggered at eval_freq'     )

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

       parser.add_argument(
           '--secondary_learner_batch_size'			,							default=1_28			,							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=1_00			,							type=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=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=1_00			,							min_len=10_26			,							trim=snake_case			,							data_file='data/tokenized_stories_train_wikitext103.jbl'			,							igf_data_file='igf_context_pairs.jbl'			,							)

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

       # Train secondary learner
       lowercase__:   List[str]  			=   training_secondary_learner(
           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
       lowercase__:   Dict  			=   GPTaLMHeadModel.from_pretrained('gpt2'     )
       set_seed(42     )

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


