



"""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    __magic_name__     ( _lowerCamelCase					:			Tuple=3_2	,							_lowerCamelCase					:			Optional[int]=1_0	,							_lowerCamelCase					:			int=1_0_0	,							_lowerCamelCase					:			Tuple=1_0_2_6	,							_lowerCamelCase					:			Optional[Any]=True	,							_lowerCamelCase					:			Optional[int]="data/tokenized_stories_train_wikitext103.jbl"	,							_lowerCamelCase					:			Tuple="igf_context_pairs.jbl"	,							):
							set_seed(3       )
							# generate train_data and objective_set
							__a		:		Optional[int]						=							generate_datasets(
							    snake_case__	,							snake_case__	,							number=snake_case__	,							min_len=1_0_2_6	,							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?
							__a		:		str						=							torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""       )

							# load pretrained model
							__a		:		List[str]						=							load_gpta("""gpt2"""       ).to(snake_case__       )
							print("""computing perplexity on objective set"""       )
							__a		:		Optional[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    __magic_name__     ( _lowerCamelCase					:			Dict	,							_lowerCamelCase					:			int=1_5	,							_lowerCamelCase					:			Optional[int]=1_2_8	,							_lowerCamelCase					:			Dict=1_0_0	,							_lowerCamelCase					:			Tuple="igf_model.pt"	,							):
							set_seed(4_2       )

							# Load pre-trained model
							__a		:		Dict						=							GPTaLMHeadModel.from_pretrained("""gpt2"""       )

							# Initialize secondary learner to use embedding weights of model
							__a		:		str						=							SecondaryLearner(snake_case__       )

							# Train secondary learner
							__a		:		Any						=							train_secondary_learner(
							    snake_case__	,							snake_case__	,							max_epochs=snake_case__	,							batch_size=snake_case__	,							eval_freq=1_0_0	,							igf_model_path=snake_case__	,							)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner





def    __magic_name__     ( _lowerCamelCase					:			str	,							_lowerCamelCase					:			List[str]	,							_lowerCamelCase					:			str	,							_lowerCamelCase					:			Optional[Any]=3_2	,							_lowerCamelCase					:			str=1_0_0_0	,							_lowerCamelCase					:			List[Any]=1_6	,							_lowerCamelCase					:			Dict=1.0	,							_lowerCamelCase					:			Dict=recopy_gpta	,							_lowerCamelCase					:			Any=None	,							_lowerCamelCase					:			List[Any]=1_0	,							_lowerCamelCase					:			List[Any]="gpt2_finetuned.pt"	,							):
							__a		:		List[str]						=							torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""       )
							__a		:		Tuple						=							RandomSampler(snake_case__       )
							__a		:		Optional[Any]						=							DataLoader(snake_case__	,							sampler=snake_case__       )

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

							__a		:		Any						=							[]
							__a		:		List[str]						=							[]

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

																					if secondary_learner is not None:
																												__a		:		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 == 1_0:
																																			__a		:		Optional[Any]						=							-1
																												if predicted_q < threshold:
																																			__a		:		Tuple						=							False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()       )       )
																												__a		:		int						=							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()
																												__a		:		Optional[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:
																																			__a		:		List[Any]						=							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 > 6_0:
																												break
														if max_steps > 0 and global_step > 6_0:
																					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    __magic_name__     ( ):
							__a		:		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=3_2	,							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_0_0	,							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_0_0	,							type=snake_case__	,							help="""secondary model evaluation is triggered at eval_freq"""       )

							parser.add_argument("""--max_steps"""	,							default=1_0_0_0	,							type=snake_case__	,							help="""To calculate training epochs"""       )

							parser.add_argument(
							    """--secondary_learner_batch_size"""	,							default=1_2_8	,							type=snake_case__	,							help="""batch size of training data for secondary learner"""	,							)

							parser.add_argument(
							    """--batch_size"""	,							default=1_6	,							type=snake_case__	,							help="""batch size of training data of language model(gpt2) """       )

							parser.add_argument(
							    """--eval_interval"""	,							default=1_0	,							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_0_0	,							type=snake_case__	,							help="""The number of examples split to be used as objective_set/test_data"""       )

							parser.add_argument(
							    """--min_len"""	,							default=1_0_2_6	,							type=snake_case__	,							help="""The minimum length of the article to be used as objective set"""       )

							parser.add_argument(
							    """--secondary_learner_max_epochs"""	,							default=1_5	,							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=3_2	,							max_steps=1_0	,							size_objective_set=1_0_0	,							min_len=1_0_2_6	,							trim=snake_case__	,							data_file="""data/tokenized_stories_train_wikitext103.jbl"""	,							igf_data_file="""igf_context_pairs.jbl"""	,							)

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

							# Train secondary learner
							__a		:		Dict						=							training_secondary_learner(
							    snake_case__	,							secondary_learner_max_epochs=1_5	,							secondary_learner_batch_size=1_2_8	,							eval_freq=1_0_0	,							igf_model_path="""igf_model.pt"""	,							)

							# load pretrained gpt2 model
							__a		:		Union[str, Any]						=							GPTaLMHeadModel.from_pretrained("""gpt2"""       )
							set_seed(4_2       )

							# Generate train and test data to train and evaluate gpt2 model
							__a		:		Tuple						=							generate_datasets(
							    context_len=3_2	,							file="""data/tokenized_stories_train_wikitext103.jbl"""	,							number=1_0_0	,							min_len=1_0_2_6	,							trim=snake_case__       )

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    snake_case__	,							snake_case__	,							snake_case__	,							context_len=3_2	,							max_steps=1_0_0_0	,							batch_size=1_6	,							threshold=1.0	,							recopy_model=snake_case__	,							secondary_learner=snake_case__	,							eval_interval=1_0	,							finetuned_model_name="""gpt2_finetuned.pt"""	,							)


if __name__ == "__main__":
	main()









import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase__       		=  get_tests_dir("""fixtures/test_sentencepiece_no_bos.model""")




@require_sentencepiece
@require_tokenizers
class       snake_case__ (				lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[str]								=			PegasusTokenizer
       lowerCamelCase				:					Optional[int]								=			PegasusTokenizerFast
       lowerCamelCase				:					int								=			True
       lowerCamelCase				:					List[Any]								=			True



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            super().setUp()

            # We have a SentencePiece fixture for testing
            __snake_case      :Optional[int] 							=    PegasusTokenizer(a__     )
            tokenizer.save_pretrained(self.tmpdirname     )



       @cached_property
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained("""google/pegasus-large"""     )



       def        __lowercase    (				self      ,    **a__     )			->   PegasusTokenizer:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            return ("This is a test", "This is a test")



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Dict 							=    """</s>"""
            __snake_case      :Union[str, Any] 							=    1

            self.assertEqual(self.get_tokenizer()._convert_token_to_id(a__     )      ,    a__     )
            self.assertEqual(self.get_tokenizer()._convert_id_to_token(a__     )      ,    a__     )



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






            '''simple docstring'''





            __snake_case      :Any 							=    list(self.get_tokenizer().get_vocab().keys()     )

            self.assertEqual(vocab_keys[0]      ,    """<pad>"""     )
            self.assertEqual(vocab_keys[1]      ,    """</s>"""     )
            self.assertEqual(vocab_keys[-1]      ,    """v"""     )
            self.assertEqual(len(a__     )      ,    11_03     )



       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            self.assertEqual(self.get_tokenizer().vocab_size      ,    11_03     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :int 							=    self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :Tuple 							=    self.tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :List[Any] 							=    (
                """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
                """ </s> <pad> <pad> <pad>"""
            )
            __snake_case      :Tuple 							=    rust_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            __snake_case      :Union[str, Any] 							=    py_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )



       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self._large_tokenizer
            # <mask_1> masks whole sentence while <mask_2> masks single word
            __snake_case      :Any 							=    """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
            __snake_case      :List[Any] 							=    [2, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
            __snake_case      :Tuple 							=    tokenizer([raw_input_str]      ,    return_tensors=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )



       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    self._large_tokenizer
            # The tracebacks for the following asserts are **better** without messages or self.assertEqual
            assert tokenizer.vocab_size == 9_61_03
            assert tokenizer.pad_token_id == 0
            assert tokenizer.eos_token_id == 1
            assert tokenizer.offset == 1_03
            assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
            assert tokenizer.unk_token == "<unk>"
            assert tokenizer.model_max_length == 10_24
            __snake_case      :Any 							=    """To ensure a smooth flow of bank resolutions."""
            __snake_case      :int 							=    [4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
            __snake_case      :Any 							=    tokenizer([raw_input_str]      ,    return_tensors=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )
            assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]     ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]



       @require_torch
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :str 							=    ["""This is going to be way too long.""" * 1_50, """short example"""]
            __snake_case      :int 							=    ["""not super long but more than 5 tokens""", """tiny"""]
            __snake_case      :str 							=    self._large_tokenizer(a__      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )
            __snake_case      :Optional[int] 							=    self._large_tokenizer(
                text_target=a__      ,    max_length=5      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )

            assert batch.input_ids.shape == (2, 10_24)
            assert batch.attention_mask.shape == (2, 10_24)
            assert targets["input_ids"].shape == (2, 5)
            assert len(a__     ) == 2  # input_ids, attention_mask.



       @slow
       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    {"""input_ids""": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
            # fmt: on

            self.tokenizer_integration_test_util(
                expected_encoding=a__      ,    model_name="""google/bigbird-pegasus-large-arxiv"""      ,    revision="""ba85d0851d708441f91440d509690f1ab6353415"""      ,    )




@require_sentencepiece
@require_tokenizers
class       snake_case__ (				lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[Any]								=			PegasusTokenizer
       lowerCamelCase				:					List[Any]								=			PegasusTokenizerFast
       lowerCamelCase				:					List[str]								=			True
       lowerCamelCase				:					List[Any]								=			True



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            super().setUp()

            # We have a SentencePiece fixture for testing
            __snake_case      :Optional[int] 							=    PegasusTokenizer(a__      ,    offset=0      ,    mask_token_sent=a__      ,    mask_token="""[MASK]"""     )
            tokenizer.save_pretrained(self.tmpdirname     )



       @cached_property
       def        __lowercase    (				self     )			->   Union[str, Any]:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""     )



       def        __lowercase    (				self      ,    **a__     )			->   PegasusTokenizer:






            '''simple docstring'''





            return PegasusTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return ("This is a test", "This is a test")



       def        __lowercase    (				self     )			->   Optional[Any]:






            '''simple docstring'''





            __snake_case      :Tuple 							=    self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :Optional[int] 							=    self.tokenizer_class.from_pretrained(self.tmpdirname     )
            __snake_case      :Union[str, Any] 							=    (
                """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
                """ <pad> <pad> <pad>"""
            )
            __snake_case      :Dict 							=    rust_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            __snake_case      :List[Any] 							=    py_tokenizer([raw_input_str]      ,    return_tensors=a__      ,    add_special_tokens=a__     ).input_ids[0]
            self.assertListEqual(a__      ,    a__     )



       @require_torch
       def        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            __snake_case      :Any 							=    ["""This is going to be way too long.""" * 10_00, """short example"""]
            __snake_case      :str 							=    ["""not super long but more than 5 tokens""", """tiny"""]
            __snake_case      :Optional[int] 							=    self._large_tokenizer(a__      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )
            __snake_case      :Union[str, Any] 							=    self._large_tokenizer(
                text_target=a__      ,    max_length=5      ,    padding=a__      ,    truncation=a__      ,    return_tensors="""pt"""     )

            assert batch.input_ids.shape == (2, 40_96)
            assert batch.attention_mask.shape == (2, 40_96)
            assert targets["input_ids"].shape == (2, 5)
            assert len(a__     ) == 2  # input_ids, attention_mask.



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






            '''simple docstring'''





            __snake_case      :Tuple 							=    (
                """This is an example string that is used to test the original TF implementation against the HF"""
                """ implementation"""
            )

            __snake_case      :Dict 							=    self._large_tokenizer(a__     ).input_ids

            self.assertListEqual(
                a__      ,    [1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]      ,    )


