





import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel






def 		lowerCAmelCase  (	UpperCamelCase__   :   Any=32       ,			UpperCamelCase__   :   Optional[Any]=10       ,			UpperCamelCase__   :   Any=100       ,			UpperCamelCase__   :   List[str]=1_026       ,			UpperCamelCase__   :   Optional[Any]=True       ,			UpperCamelCase__   :   Optional[Any]="data/tokenized_stories_train_wikitext103.jbl"       ,			UpperCamelCase__   :   List[Any]="igf_context_pairs.jbl"       ,			)      ->    Union[str, Any]:

						"""simple docstring"""





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

						# load pretrained model
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   load_gpta('''gpt2'''					).to(UpperCamelCase__					)
						print('''computing perplexity on objective set'''					)
						__SCREAMING_SNAKE_CASE:				Any           =   compute_perplexity(UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__					).item()
						print('''perplexity on objective set:'''       ,			UpperCamelCase__					)

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

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






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[Any]       ,			UpperCamelCase__   :   Tuple=15       ,			UpperCamelCase__   :   Optional[int]=128       ,			UpperCamelCase__   :   int=100       ,			UpperCamelCase__   :   Union[str, Any]="igf_model.pt"       ,			)      ->    Any:

						"""simple docstring"""





						set_seed(42					)

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

						# Initialize secondary learner to use embedding weights of model
						__SCREAMING_SNAKE_CASE:				Dict           =   SecondaryLearner(UpperCamelCase__					)

						# Train secondary learner
						__SCREAMING_SNAKE_CASE:				Tuple           =   train_secondary_learner(
						    UpperCamelCase__       ,			UpperCamelCase__       ,			max_epochs=UpperCamelCase__       ,			batch_size=UpperCamelCase__       ,			eval_freq=100       ,			igf_model_path=UpperCamelCase__       ,			)

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner






def 		lowerCAmelCase  (	UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   int       ,			UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   List[Any]=32       ,			UpperCamelCase__   :   str=1_000       ,			UpperCamelCase__   :   Any=16       ,			UpperCamelCase__   :   Optional[int]=1.0       ,			UpperCamelCase__   :   str=recopy_gpta       ,			UpperCamelCase__   :   int=None       ,			UpperCamelCase__   :   Union[str, Any]=10       ,			UpperCamelCase__   :   Tuple="gpt2_finetuned.pt"       ,			)      ->    Optional[int]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   RandomSampler(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   DataLoader(UpperCamelCase__       ,			sampler=UpperCamelCase__					)

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

						model.train()
						if secondary_learner is not None:
												secondary_learner.to(UpperCamelCase__					)
												secondary_learner.eval()
						__SCREAMING_SNAKE_CASE:				List[str]           =   []
						__SCREAMING_SNAKE_CASE:				Tuple           =   0

						__SCREAMING_SNAKE_CASE:				Dict           =   []
						__SCREAMING_SNAKE_CASE:				List[str]           =   []

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

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

																								# 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:
																														__SCREAMING_SNAKE_CASE:				Any           =   -1
																								if predicted_q < threshold:
																														__SCREAMING_SNAKE_CASE:				Tuple           =   False

            # If we passed the filter, add the context to the batch!
																		if do_backprop:
																								contexts.append(np.array(context.cpu()					)					)
																								__SCREAMING_SNAKE_CASE:				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()
																								__SCREAMING_SNAKE_CASE:				str           =   0
																								# Do LM backprop
																								torch.nn.utils.clip_grad_norm_(model.parameters()       ,			3.0					)
																								lm_optimizer.step()
																								lm_scheduler.step()  # Update learning rate schedule
																								global_step += 1
																								# Compute the performance of the transformer model at this batch
																								if global_step % eval_interval == 0:
																														__SCREAMING_SNAKE_CASE:				str           =   compute_perplexity(UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__					)
																														test_perps.append(UpperCamelCase__					)

																														print('''Test perplexity, step'''       ,			UpperCamelCase__       ,			''':'''       ,			UpperCamelCase__					)
            # 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()       ,			UpperCamelCase__					)
						torch.cuda.empty_cache()
						# Do some cleaning up so we can reinitialize for the next run of this function
						del lm_optimizer
						del lm_scheduler
						return model






def 		lowerCAmelCase  (	)      ->    Any:

						"""simple docstring"""





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

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

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

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

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

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

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

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

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

						parser.add_argument(
						    '''--min_len'''       ,			default=1_026       ,			type=UpperCamelCase__       ,			help='''The minimum length of the article to be used as objective set'''					)

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

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

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

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

						# Load train data for secondary learner
						__SCREAMING_SNAKE_CASE:				List[str]           =   joblib.load('''data/IGF_values.jbl'''					)

						# Train secondary learner
						__SCREAMING_SNAKE_CASE:				Any           =   training_secondary_learner(
						    UpperCamelCase__       ,			secondary_learner_max_epochs=15       ,			secondary_learner_batch_size=128       ,			eval_freq=100       ,			igf_model_path='''igf_model.pt'''       ,			)

						# load pretrained gpt2 model
						__SCREAMING_SNAKE_CASE:				str           =   GPTaLMHeadModel.from_pretrained('''gpt2'''					)
						set_seed(42					)

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

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


if __name__ == "__main__":
							main()










import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
							from transformers.models.esm.modeling_esm import (
							    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
							    EsmEmbeddings,
							    create_position_ids_from_input_ids,
							)





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=13			, _lowerCAmelCase=7			, _lowerCAmelCase=False			, _lowerCAmelCase=True			, _lowerCAmelCase=False			, _lowerCAmelCase=True			, _lowerCAmelCase=33			, _lowerCAmelCase=32			, _lowerCAmelCase=5			, _lowerCAmelCase=4			, _lowerCAmelCase=37			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.1			, _lowerCAmelCase=512			, _lowerCAmelCase=16			, _lowerCAmelCase=2			, _lowerCAmelCase=0.02			, _lowerCAmelCase=3			, _lowerCAmelCase=4			, _lowerCAmelCase=None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   parent
										__SCREAMING_SNAKE_CASE:				Any           =   batch_size
										__SCREAMING_SNAKE_CASE:				Dict           =   seq_length
										__SCREAMING_SNAKE_CASE:				List[Any]           =   is_training
										__SCREAMING_SNAKE_CASE:				List[Any]           =   use_input_mask
										__SCREAMING_SNAKE_CASE:				str           =   use_token_type_ids
										__SCREAMING_SNAKE_CASE:				Any           =   use_labels
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   vocab_size
										__SCREAMING_SNAKE_CASE:				Dict           =   hidden_size
										__SCREAMING_SNAKE_CASE:				Dict           =   num_hidden_layers
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   num_attention_heads
										__SCREAMING_SNAKE_CASE:				str           =   intermediate_size
										__SCREAMING_SNAKE_CASE:				str           =   hidden_act
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   hidden_dropout_prob
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE:				Any           =   max_position_embeddings
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   type_vocab_size
										__SCREAMING_SNAKE_CASE:				List[Any]           =   type_sequence_label_size
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   initializer_range
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   num_labels
										__SCREAMING_SNAKE_CASE:				List[str]           =   num_choices
										__SCREAMING_SNAKE_CASE:				List[Any]           =   scope






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   ids_tensor([self.batch_size, self.seq_length]			, self.vocab_size					)

										__SCREAMING_SNAKE_CASE:				int           =   None
										if self.use_input_mask:
																__SCREAMING_SNAKE_CASE:				Tuple           =   random_attention_mask([self.batch_size, self.seq_length]					)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   None
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   None
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   None
										if self.use_labels:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   ids_tensor([self.batch_size]			, self.type_sequence_label_size					)
																__SCREAMING_SNAKE_CASE:				List[str]           =   ids_tensor([self.batch_size, self.seq_length]			, self.num_labels					)
																__SCREAMING_SNAKE_CASE:				str           =   ids_tensor([self.batch_size]			, self.num_choices					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_config()

										return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return EsmConfig(
										    vocab_size=self.vocab_size			, hidden_size=self.hidden_size			, pad_token_id=1			, num_hidden_layers=self.num_hidden_layers			, num_attention_heads=self.num_attention_heads			, intermediate_size=self.intermediate_size			, hidden_act=self.hidden_act			, hidden_dropout_prob=self.hidden_dropout_prob			, attention_probs_dropout_prob=self.attention_probs_dropout_prob			, max_position_embeddings=self.max_position_embeddings			, type_vocab_size=self.type_vocab_size			, initializer_range=self.initializer_range			, )






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   EsmModel(config=_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   model(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   model(_lowerCAmelCase					)

										self.parent.assertEqual(result.last_hidden_state.shape			, (self.batch_size, self.seq_length, self.hidden_size)					)
										self.parent.assertEqual(result.pooler_output.shape			, (self.batch_size, self.hidden_size)					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   EsmForMaskedLM(config=_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase			, labels=_lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.vocab_size)					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   self.num_labels
										__SCREAMING_SNAKE_CASE:				List[Any]           =   EsmForTokenClassification(config=_lowerCAmelCase					)
										model.to(_lowerCAmelCase					)
										model.eval()
										__SCREAMING_SNAKE_CASE:				int           =   model(_lowerCAmelCase			, attention_mask=_lowerCAmelCase			, labels=_lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.seq_length, self.num_labels)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.prepare_config_and_inputs()
										(
										    (
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,(
										    __SCREAMING_SNAKE_CASE
										)       ,
										):				int           =   config_and_inputs
										__SCREAMING_SNAKE_CASE:				Tuple           =   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
										return config, inputs_dict





@require_torch
class 				a			(      __lowercase   ,__lowercase   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     False

				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     (
				    (
				        EsmForMaskedLM,
				        EsmModel,
				        EsmForSequenceClassification,
				        EsmForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__     :   int		      =     ()
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     (
				    {
				        '''feature-extraction''': EsmModel,
				        '''fill-mask''': EsmForMaskedLM,
				        '''text-classification''': EsmForSequenceClassification,
				        '''token-classification''': EsmForTokenClassification,
				        '''zero-shot''': EsmForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE__     :   Any		      =     True






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[str]           =   EsmModelTester(self					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   ConfigTester(self			, config_class=_lowerCAmelCase			, hidden_size=37					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										self.config_tester.run_common_tests()






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																__SCREAMING_SNAKE_CASE:				Any           =   type
																self.model_tester.create_and_check_model(*_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase					)






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   EsmModel.from_pretrained(_lowerCAmelCase					)
																self.assertIsNotNone(_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.model_tester.prepare_config_and_inputs()[0]
										__SCREAMING_SNAKE_CASE:				int           =   EsmEmbeddings(config=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   torch.as_tensor([[12, 31, 13, model.padding_idx]]					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   torch.as_tensor(
										    [
										        [
										            0 + model.padding_idx + 1,
										            1 + model.padding_idx + 1,
										            2 + model.padding_idx + 1,
										            model.padding_idx,
										        ]
										    ]					)
										__SCREAMING_SNAKE_CASE:				Any           =   create_position_ids_from_input_ids(_lowerCAmelCase			, model.padding_idx					)
										self.assertEqual(position_ids.shape			, expected_positions.shape					)
										self.assertTrue(torch.all(torch.eq(_lowerCAmelCase			, _lowerCAmelCase					)					)					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   self.model_tester.prepare_config_and_inputs()[0]
										__SCREAMING_SNAKE_CASE:				Dict           =   EsmEmbeddings(config=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.empty(2			, 4			, 30					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   [
										    0 + embeddings.padding_idx + 1,
										    1 + embeddings.padding_idx + 1,
										    2 + embeddings.padding_idx + 1,
										    3 + embeddings.padding_idx + 1,
										]
										__SCREAMING_SNAKE_CASE:				str           =   torch.as_tensor([expected_single_positions, expected_single_positions]					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   embeddings.create_position_ids_from_inputs_embeds(_lowerCAmelCase					)
										self.assertEqual(position_ids.shape			, expected_positions.shape					)
										self.assertTrue(torch.all(torch.eq(_lowerCAmelCase			, _lowerCAmelCase					)					)					)






				@unittest.skip('''Esm does not support embedding resizing'''					)
				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass






				@unittest.skip('''Esm does not support embedding resizing'''					)
				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass






				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass





@require_torch
class 				a			(      __lowercase			):






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										with torch.no_grad():
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
																model.eval()
																__SCREAMING_SNAKE_CASE:				Tuple           =   torch.tensor([[0, 1, 2, 3, 4, 5]]					)
																__SCREAMING_SNAKE_CASE:				str           =   model(_lowerCAmelCase					)[0]

																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   33

																__SCREAMING_SNAKE_CASE:				Dict           =   torch.Size((1, 6, vocab_size)					)
																self.assertEqual(output.shape			, _lowerCAmelCase					)

																__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.tensor(
																    [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]					)
																self.assertTrue(torch.allclose(output[:, :3, :3]			, _lowerCAmelCase			, atol=1e-4					)					)






				@slow
				def 			snake_case_    (  self					):

										"""simple docstring"""




										with torch.no_grad():
																__SCREAMING_SNAKE_CASE:				Dict           =   EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
																model.eval()

																__SCREAMING_SNAKE_CASE:				Dict           =   torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]					)
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   model(_lowerCAmelCase					)[0]
																# compare the actual values for a slice.
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   torch.tensor(
																    [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]					)
																self.assertTrue(torch.allclose(output[:, :3, :3]			, _lowerCAmelCase			, atol=1e-4					)					)



