


"""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 		lowercase							(     _snake_case						:      Union[str, Any]=32     ,   _snake_case						:      Optional[Any]=10     ,   _snake_case						:      Optional[Any]=100     ,   _snake_case						:      Tuple=1_026     ,   _snake_case						:      Any=True     ,   _snake_case						:      Optional[int]="data/tokenized_stories_train_wikitext103.jbl"     ,   _snake_case						:      List[Any]="igf_context_pairs.jbl"     ,   )     ->Union[str, Any]:
	"""simple docstring"""





	set_seed(3     )
	# generate train_data and objective_set
	__snake_case	:						int						=				generate_datasets(
	    SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   number=SCREAMING_SNAKE_CASE__     ,   min_len=1_026     ,   trim=SCREAMING_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?
	__snake_case	:						Tuple						=				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''     )

	# load pretrained model
	__snake_case	:						Dict						=				load_gpta('''gpt2'''     ).to(SCREAMING_SNAKE_CASE__     )
	print('''computing perplexity on objective set'''     )
	__snake_case	:						Optional[int]						=				compute_perplexity(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ).item()
	print('''perplexity on objective set:'''     ,   SCREAMING_SNAKE_CASE__     )

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

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






def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      List[str]=15     ,   _snake_case						:      List[Any]=128     ,   _snake_case						:      Any=100     ,   _snake_case						:      Union[str, Any]="igf_model.pt"     ,   )     ->List[str]:
	"""simple docstring"""





	set_seed(42     )

	# Load pre-trained model
	__snake_case	:						Dict						=				GPTaLMHeadModel.from_pretrained('''gpt2'''     )

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

	# Train secondary learner
	__snake_case	:						str						=				train_secondary_learner(
	    SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   max_epochs=SCREAMING_SNAKE_CASE__     ,   batch_size=SCREAMING_SNAKE_CASE__     ,   eval_freq=100     ,   igf_model_path=SCREAMING_SNAKE_CASE__     ,   )

	del model, secondary_learner_train_data
	torch.cuda.empty_cache()

	return secondary_learner






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





	__snake_case	:						str						=				torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''     )
	__snake_case	:						Optional[Any]						=				RandomSampler(SCREAMING_SNAKE_CASE__     )
	__snake_case	:						List[Any]						=				DataLoader(SCREAMING_SNAKE_CASE__     ,   sampler=SCREAMING_SNAKE_CASE__     )

	__snake_case	:						Tuple						=				max_steps // (len(SCREAMING_SNAKE_CASE__     )) + 1
	__snake_case	:						Optional[int]						=				0
	__snake_case	:						List[Any]						=				torch.zeros((1, context_len)     ,   dtype=torch.long     ,   device=SCREAMING_SNAKE_CASE__     )
	__snake_case	:						str						=				recopy_model(SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     ,   SCREAMING_SNAKE_CASE__     )

	model.train()
	if secondary_learner is not None:
		secondary_learner.to(SCREAMING_SNAKE_CASE__     )
		secondary_learner.eval()
	__snake_case	:						Optional[int]						=				[]
	__snake_case	:						Dict						=				0

	__snake_case	:						List[Any]						=				[]
	__snake_case	:						Optional[Any]						=				[]

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

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

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

					print('''Test perplexity, step'''     ,   SCREAMING_SNAKE_CASE__     ,   ''':'''     ,   SCREAMING_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()     ,   SCREAMING_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 		lowercase							(     )     ->List[str]:
	"""simple docstring"""





	__snake_case	:						List[str]						=				argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''     )

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

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

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

	parser.add_argument(
	    '''--context_len'''     ,   default=32     ,   type=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE__     ,   help='''secondary model evaluation is triggered at eval_freq'''     )

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

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

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

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

	parser.add_argument(
	    '''--min_len'''     ,   default=1_026     ,   type=SCREAMING_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=SCREAMING_SNAKE_CASE__     ,   help='''number of epochs to train secondary learner'''     )

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

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

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

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

	# Train secondary learner
	__snake_case	:						Optional[Any]						=				training_secondary_learner(
	    SCREAMING_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
	__snake_case	:						str						=				GPTaLMHeadModel.from_pretrained('''gpt2'''     )
	set_seed(42     )

	# Generate train and test data to train and evaluate gpt2 model
	__snake_case	:						Optional[Any]						=				generate_datasets(
	    context_len=32     ,   file='''data/tokenized_stories_train_wikitext103.jbl'''     ,   number=100     ,   min_len=1_026     ,   trim=SCREAMING_SNAKE_CASE__     )

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


if __name__ == "__main__":
			main()









import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase											=       get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
 from transformers.models.plbart.modeling_plbart import shift_tokens_right

lowerCamelCase											=       5_00_03
lowerCamelCase											=       5_00_02




@require_sentencepiece
@require_tokenizers
class A  (   UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  int      =PLBartTokenizer
      UpperCamelCase__							:  Dict      =None
      UpperCamelCase__							:  Optional[Any]      =False






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						List[Any]:
             """simple docstring"""




             super().setUp()

             # We have a SentencePiece fixture for testing
             _lowerCamelCase       :		Any		=PLBartTokenizer(lowercase_			,   language_codes='base'			,   keep_accents=lowercase_ )
             tokenizer.save_pretrained(self.tmpdirname )






      def        lowerCamelCase       (					self						:     List[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=PLBartTokenizer(lowercase_			,   language_codes='base'			,   keep_accents=lowercase_ )

             _lowerCamelCase       :		Optional[int]		=tokenizer.tokenize('This is a test' )
             self.assertListEqual(lowercase_			,   ['▁This', '▁is', '▁a', '▁t', 'est'] )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(lowercase_ )			,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,   )

             _lowerCamelCase       :		str		=tokenizer.tokenize('I was born in 92000, and this is falsé.' )
             self.assertListEqual(
                 lowercase_			,   [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '9',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     'é',
                     '.',
                 ]			,   )
             _lowerCamelCase       :		List[Any]		=tokenizer.convert_tokens_to_ids(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 ]			,   )

             _lowerCamelCase       :		Optional[Any]		=tokenizer.convert_ids_to_tokens(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '<unk>',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     '<unk>',
                     '.',
                 ]			,   )

             _lowerCamelCase       :		str		=tokenizer.vocab_size
             _lowerCamelCase       :		List[str]		=[tokenizer.convert_ids_to_tokens(lowercase_ ) for x in range(end - 4			,   lowercase_ )]

             self.assertListEqual(lowercase_			,   ['__java__', '__python__', '__en_XX__', '<mask>'] )

             _lowerCamelCase       :		Optional[Any]		='java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
             _lowerCamelCase       :		Dict		=tokenizer(lowercase_ ).input_ids
             self.assertEqual(
                 tokenizer.decode(lowercase_			,   skip_special_tokens=lowercase_			,   clean_up_tokenization_spaces=lowercase_ )			,   lowercase_			,   )






      def        lowerCamelCase       (					self						:     str )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=PLBartTokenizer(lowercase_			,   language_codes='multi'			,   keep_accents=lowercase_ )

             _lowerCamelCase       :		Any		=tokenizer.tokenize('This is a test' )
             self.assertListEqual(lowercase_			,   ['▁This', '▁is', '▁a', '▁t', 'est'] )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(lowercase_ )			,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]			,   )

             _lowerCamelCase       :		Optional[Any]		=tokenizer.tokenize('I was born in 92000, and this is falsé.' )
             self.assertListEqual(
                 lowercase_			,   [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '9',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     'é',
                     '.',
                 ]			,   )
             _lowerCamelCase       :		Dict		=tokenizer.convert_tokens_to_ids(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                 ]			,   )

             _lowerCamelCase       :		Optional[Any]		=tokenizer.convert_ids_to_tokens(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '<unk>',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     '<unk>',
                     '.',
                 ]			,   )
             _lowerCamelCase       :		Dict		=tokenizer.vocab_size
             _lowerCamelCase       :		Optional[int]		=[tokenizer.convert_ids_to_tokens(lowercase_ ) for x in range(end - 7			,   lowercase_ )]

             self.assertListEqual(
                 lowercase_			,   ['__java__', '__python__', '__en_XX__', '__javascript__', '__php__', '__ruby__', '__go__'] )
             _lowerCamelCase       :		int		='java.lang.Exception, python.lang.Exception, javascript, php, ruby, go'
             _lowerCamelCase       :		Any		=tokenizer(lowercase_ ).input_ids
             self.assertEqual(
                 tokenizer.decode(lowercase_			,   skip_special_tokens=lowercase_			,   clean_up_tokenization_spaces=lowercase_ )			,   lowercase_			,   )




@require_torch
@require_sentencepiece
@require_tokenizers
class A  (   unittest.TestCase     ):
      UpperCamelCase__							:  List[Any]      ='uclanlp/plbart-python-en_XX'
      UpperCamelCase__							:  List[str]      =[
          'def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])',
          'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])',
      ]
      UpperCamelCase__							:  Optional[int]      =[
          'Returns the maximum value of a b c.',
          'Sums the values of a b c.',
      ]
      UpperCamelCase__							:  str      =[
          134,
          5452,
          33460,
          33441,
          33463,
          33465,
          33463,
          33449,
          988,
          20,
          33456,
          19,
          33456,
          771,
          39,
          4258,
          889,
          3318,
          33441,
          33463,
          33465,
          33463,
          33449,
          2471,
          2,
          PYTHON_CODE,
      ]






      @classmethod
      def        lowerCamelCase       (					cls						:     str )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		PLBartTokenizer		=PLBartTokenizer.from_pretrained(
                 cls.checkpoint_name			,   language_codes='base'			,   src_lang='python'			,   tgt_lang='en_XX' )
             _lowerCamelCase       :		Any		=1
             return cls






      def        lowerCamelCase       (					self						:     List[str] )					->						Any:
             """simple docstring"""




             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__java__']			,   5_0001 )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__python__']			,   5_0002 )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['__en_XX__']			,   5_0003 )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
             self.assertListEqual(self.expected_src_tokens			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Tuple:
             """simple docstring"""




             self.assertIn(lowercase_			,   self.tokenizer.all_special_ids )
             _lowerCamelCase       :		Dict		=[EN_CODE, 9037, 3_3442, 57, 752, 153, 14, 56, 18, 9, 2]
             _lowerCamelCase       :		Optional[int]		=self.tokenizer.decode(lowercase_			,   skip_special_tokens=lowercase_ )
             _lowerCamelCase       :		int		=self.tokenizer.decode(generated_ids[1:]			,   skip_special_tokens=lowercase_ )
             self.assertEqual(lowercase_			,   lowercase_ )
             self.assertNotIn(self.tokenizer.eos_token			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		=['def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])' * 20]
             self.assertIsInstance(src_text[0]			,   lowercase_ )
             _lowerCamelCase       :		Tuple		=10
             _lowerCamelCase       :		Optional[int]		=self.tokenizer(lowercase_			,   max_length=lowercase_			,   truncation=lowercase_ ).input_ids[0]
             self.assertEqual(ids[-2]			,   2 )
             self.assertEqual(ids[-1]			,   lowercase_ )
             self.assertEqual(len(lowercase_ )			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Dict )					->						Optional[int]:
             """simple docstring"""




             self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', '__java__'] )			,   [5_0004, 5_0001] )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		=tempfile.mkdtemp()
             _lowerCamelCase       :		Dict		=self.tokenizer.fairseq_tokens_to_ids
             self.tokenizer.save_pretrained(lowercase_ )
             _lowerCamelCase       :		Any		=PLBartTokenizer.from_pretrained(lowercase_ )
             self.assertDictEqual(new_tok.fairseq_tokens_to_ids			,   lowercase_ )






      @require_torch
      def        lowerCamelCase       (					self						:     Optional[Any] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=self.tokenizer(self.src_text			,   text_target=self.tgt_text			,   padding=lowercase_			,   return_tensors='pt' )
             _lowerCamelCase       :		List[Any]		=shift_tokens_right(batch['labels']			,   self.tokenizer.pad_token_id )

             # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
             self.assertEqual(batch.input_ids[1][-2:].tolist()			,   [2, PYTHON_CODE] )
             self.assertEqual(batch.decoder_input_ids[1][0]			,   lowercase_ )
             self.assertEqual(batch.decoder_input_ids[1][-1]			,   2 )
             self.assertEqual(batch.labels[1][-2:].tolist()			,   [2, EN_CODE] )






      @require_torch
      def        lowerCamelCase       (					self						:     Tuple )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		int		=self.tokenizer(
                 self.src_text			,   text_target=self.tgt_text			,   padding=lowercase_			,   truncation=lowercase_			,   max_length=len(self.expected_src_tokens )			,   return_tensors='pt'			,   )
             _lowerCamelCase       :		List[Any]		=shift_tokens_right(batch['labels']			,   self.tokenizer.pad_token_id )

             self.assertIsInstance(lowercase_			,   lowercase_ )

             self.assertEqual((2, 26)			,   batch.input_ids.shape )
             self.assertEqual((2, 26)			,   batch.attention_mask.shape )
             _lowerCamelCase       :		List[Any]		=batch.input_ids.tolist()[0]
             self.assertListEqual(self.expected_src_tokens			,   lowercase_ )
             self.assertEqual(2			,   batch.decoder_input_ids[0, -1] )  # EOS
             # Test that special tokens are reset
             self.assertEqual(self.tokenizer.prefix_tokens			,   [] )
             self.assertEqual(self.tokenizer.suffix_tokens			,   [self.tokenizer.eos_token_id, PYTHON_CODE] )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=self.tokenizer(self.src_text			,   padding=lowercase_			,   truncation=lowercase_			,   max_length=3			,   return_tensors='pt' )
             _lowerCamelCase       :		Dict		=self.tokenizer(
                 text_target=self.tgt_text			,   padding=lowercase_			,   truncation=lowercase_			,   max_length=10			,   return_tensors='pt' )
             _lowerCamelCase       :		List[str]		=targets['input_ids']
             _lowerCamelCase       :		Optional[Any]		=shift_tokens_right(lowercase_			,   self.tokenizer.pad_token_id )

             self.assertEqual(batch.input_ids.shape[1]			,   3 )
             self.assertEqual(batch.decoder_input_ids.shape[1]			,   10 )






      @require_torch
      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             _lowerCamelCase       :		Any		=self.tokenizer._build_translation_inputs(
                 'A test'			,   return_tensors='pt'			,   src_lang='en_XX'			,   tgt_lang='java' )

             self.assertEqual(
                 nested_simplify(lowercase_ )			,   {
                     # A, test, EOS, en_XX
                     'input_ids': [[150, 242, 2, 5_0003]],
                     'attention_mask': [[1, 1, 1, 1]],
                     # java
                     'forced_bos_token_id': 5_0001,
                 }			,   )


