



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			(  SCREAMING_SNAKE_CASE_=32    ,	SCREAMING_SNAKE_CASE_=10    ,	SCREAMING_SNAKE_CASE_=100    ,	SCREAMING_SNAKE_CASE_=1026    ,	SCREAMING_SNAKE_CASE_=True    ,	SCREAMING_SNAKE_CASE_="data/tokenized_stories_train_wikitext103.jbl"    ,	SCREAMING_SNAKE_CASE_="igf_context_pairs.jbl"    ,	):
			set_seed(3	)
			# generate train_data and objective_set
			lowercase__       	=					generate_datasets(
			    UpperCamelCase__    ,	UpperCamelCase__    ,	number=UpperCamelCase__    ,	min_len=1026    ,	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?
			lowercase__       	=					torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)

			# load pretrained model
			lowercase__       	=					load_gpta("gpt2"	).to(UpperCamelCase__	)
			print("computing perplexity on objective set"	)
			lowercase__       	=					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			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_=15    ,	SCREAMING_SNAKE_CASE_=128    ,	SCREAMING_SNAKE_CASE_=100    ,	SCREAMING_SNAKE_CASE_="igf_model.pt"    ,	):
			set_seed(42	)

			# Load pre-trained model
			lowercase__       	=					GPTaLMHeadModel.from_pretrained("gpt2"	)

			# Initialize secondary learner to use embedding weights of model
			lowercase__       	=					SecondaryLearner(UpperCamelCase__	)

			# Train secondary learner
			lowercase__       	=					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			(  SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_=32    ,	SCREAMING_SNAKE_CASE_=1000    ,	SCREAMING_SNAKE_CASE_=16    ,	SCREAMING_SNAKE_CASE_=1.0    ,	SCREAMING_SNAKE_CASE_=recopy_gpta    ,	SCREAMING_SNAKE_CASE_=None    ,	SCREAMING_SNAKE_CASE_=10    ,	SCREAMING_SNAKE_CASE_="gpt2_finetuned.pt"    ,	):
			lowercase__       	=					torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)
			lowercase__       	=					RandomSampler(UpperCamelCase__	)
			lowercase__       	=					DataLoader(UpperCamelCase__    ,	sampler=UpperCamelCase__	)

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

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

			lowercase__       	=					[]
			lowercase__       	=					[]

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

									if secondary_learner is not None:
												lowercase__       	=					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:
															lowercase__       	=					-1
												if predicted_q < threshold:
															lowercase__       	=					False

            # If we passed the filter, add the context to the batch!
									if do_backprop:
												contexts.append(np.array(context.cpu()	)	)
												lowercase__       	=					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__       	=					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__       	=					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			(  ):
			lowercase__       	=					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=1000    ,	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=1026    ,	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=1026    ,	trim=UpperCamelCase__    ,	data_file="data/tokenized_stories_train_wikitext103.jbl"    ,	igf_data_file="igf_context_pairs.jbl"    ,	)

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

			# Train secondary learner
			lowercase__       	=					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
			lowercase__       	=					GPTaLMHeadModel.from_pretrained("gpt2"	)
			set_seed(42	)

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

			# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
			finetune(
			    UpperCamelCase__    ,	UpperCamelCase__    ,	UpperCamelCase__    ,	context_len=32    ,	max_steps=1000    ,	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					)					)



