


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      _a					( SCREAMING_SNAKE_CASE=32		,							SCREAMING_SNAKE_CASE=10		,							SCREAMING_SNAKE_CASE=1_00		,							SCREAMING_SNAKE_CASE=10_26		,							SCREAMING_SNAKE_CASE=True		,							SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"		,							SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"		,							):


		"""simple docstring"""




		set_seed(3	)
		# generate train_data and objective_set
		lowercase__	,						lowercase__				= generate_datasets(
		    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							number=SCREAMING_SNAKE_CASE		,							min_len=10_26		,							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?
		lowercase__				= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)

		# load pretrained model
		lowercase__				= load_gpta('''gpt2'''	).to(SCREAMING_SNAKE_CASE	)
		print('''computing perplexity on objective set'''	)
		lowercase__				= 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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=15		,							SCREAMING_SNAKE_CASE=1_28		,							SCREAMING_SNAKE_CASE=1_00		,							SCREAMING_SNAKE_CASE="igf_model.pt"		,							):


		"""simple docstring"""




		set_seed(42	)

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

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

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

		del model, secondary_learner_train_data
		torch.cuda.empty_cache()

		return secondary_learner
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=32		,							SCREAMING_SNAKE_CASE=10_00		,							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"		,							):


		"""simple docstring"""




		lowercase__				= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''	)
		lowercase__				= RandomSampler(SCREAMING_SNAKE_CASE	)
		lowercase__				= DataLoader(SCREAMING_SNAKE_CASE		,							sampler=SCREAMING_SNAKE_CASE	)

		lowercase__				= max_steps // (len(SCREAMING_SNAKE_CASE	)) + 1
		lowercase__				= 0
		lowercase__				= torch.zeros((1, context_len)		,							dtype=torch.long		,							device=SCREAMING_SNAKE_CASE	)
		lowercase__	,						lowercase__	,						lowercase__				= 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()
		lowercase__				= []
		lowercase__				= 0

		lowercase__				= []
		lowercase__				= []

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

						if secondary_learner is not None:
								lowercase__				= 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:
										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(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      _a					( ):


		"""simple docstring"""




		lowercase__				= 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=1_00		,							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=1_00		,							type=SCREAMING_SNAKE_CASE		,							help='''secondary model evaluation is triggered at eval_freq'''	)

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

		parser.add_argument(
		    '''--secondary_learner_batch_size'''		,							default=1_28		,							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=1_00		,							type=SCREAMING_SNAKE_CASE		,							help='''The number of examples split to be used as objective_set/test_data'''	)

		parser.add_argument(
		    '''--min_len'''		,							default=10_26		,							type=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=1_00		,							min_len=10_26		,							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
		lowercase__				= joblib.load('''data/IGF_values.jbl'''	)

		# Train secondary learner
		lowercase__				= training_secondary_learner(
		    SCREAMING_SNAKE_CASE		,							secondary_learner_max_epochs=15		,							secondary_learner_batch_size=1_28		,							eval_freq=1_00		,							igf_model_path='''igf_model.pt'''		,							)

		# load pretrained gpt2 model
		lowercase__				= GPTaLMHeadModel.from_pretrained('''gpt2'''	)
		set_seed(42	)

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






'''simple docstring'''

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     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Optional[int]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       str=13  ,		UpperCamelCase       :       Optional[int]=7  ,		UpperCamelCase       :       Union[str, Any]=False  ,		UpperCamelCase       :       Dict=True  ,		UpperCamelCase       :       Optional[int]=False  ,		UpperCamelCase       :       Optional[Any]=True  ,		UpperCamelCase       :       Union[str, Any]=33  ,		UpperCamelCase       :       List[Any]=32  ,		UpperCamelCase       :       Dict=5  ,		UpperCamelCase       :       str=4  ,		UpperCamelCase       :       str=37  ,		UpperCamelCase       :       List[Any]="gelu"  ,		UpperCamelCase       :       List[str]=0.1  ,		UpperCamelCase       :       Dict=0.1  ,		UpperCamelCase       :       Dict=512  ,		UpperCamelCase       :       Any=16  ,		UpperCamelCase       :       List[Any]=2  ,		UpperCamelCase       :       List[Any]=0.02  ,		UpperCamelCase       :       Tuple=3  ,		UpperCamelCase       :       Optional[Any]=4  ,		UpperCamelCase       :       int=None  ,		):

             '''simple docstring'''







             lowercase__      =					parent
             lowercase__      =					batch_size
             lowercase__      =					seq_length
             lowercase__      =					is_training
             lowercase__      =					use_input_mask
             lowercase__      =					use_token_type_ids
             lowercase__      =					use_labels
             lowercase__      =					vocab_size
             lowercase__      =					hidden_size
             lowercase__      =					num_hidden_layers
             lowercase__      =					num_attention_heads
             lowercase__      =					intermediate_size
             lowercase__      =					hidden_act
             lowercase__      =					hidden_dropout_prob
             lowercase__      =					attention_probs_dropout_prob
             lowercase__      =					max_position_embeddings
             lowercase__      =					type_vocab_size
             lowercase__      =					type_sequence_label_size
             lowercase__      =					initializer_range
             lowercase__      =					num_labels
             lowercase__      =					num_choices
             lowercase__      =					scope



      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







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

             lowercase__      =					None
             if self.use_input_mask:
                    lowercase__      =					random_attention_mask([self.batch_size, self.seq_length]					)

             lowercase__      =					None
             lowercase__      =					None
             lowercase__      =					None
             if self.use_labels:
                    lowercase__      =					ids_tensor([self.batch_size]  ,		self.type_sequence_label_size					)
                    lowercase__      =					ids_tensor([self.batch_size, self.seq_length]  ,		self.num_labels					)
                    lowercase__      =					ids_tensor([self.batch_size]  ,		self.num_choices					)

             lowercase__      =					self.get_config()

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



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''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  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       Dict					):

             '''simple docstring'''







             lowercase__      =					EsmModel(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase					)
             lowercase__      =					model(UpperCamelCase					)
             lowercase__      =					model(UpperCamelCase					)

             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  UpperCamelCase__						(self       :       str  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Any  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Optional[Any]  ,		UpperCamelCase       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					EsmForMaskedLM(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		labels=UpperCamelCase					)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.vocab_size)					)



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       str  ,		UpperCamelCase       :       List[Any]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       int  ,		UpperCamelCase       :       Union[str, Any]  ,		UpperCamelCase       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					self.num_labels
             lowercase__      =					EsmForTokenClassification(config=UpperCamelCase					)
             model.to(UpperCamelCase					)
             model.eval()
             lowercase__      =					model(UpperCamelCase  ,		attention_mask=UpperCamelCase  ,		labels=UpperCamelCase					)
             self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length, self.num_labels)					)






      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					self.prepare_config_and_inputs()
             (
                 (
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,(
                 lowercase__
             )     ,
             )      =					config_and_inputs
             lowercase__      =					{'''input_ids''': input_ids, '''attention_mask''': input_mask}
             return config, inputs_dict






@require_torch
class     __lowerCAmelCase		(lowercase_    ,	lowercase_    ,	unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	Dict       =				False

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



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					EsmModelTester(self					)
             lowercase__      =					ConfigTester(self  ,		config_class=UpperCamelCase  ,		hidden_size=37					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    lowercase__      =					type
                    self.model_tester.create_and_check_model(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*UpperCamelCase					)



      @slow
      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      =					EsmModel.from_pretrained(UpperCamelCase					)
                    self.assertIsNotNone(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()[0]
             lowercase__      =					EsmEmbeddings(config=UpperCamelCase					)

             lowercase__      =					torch.as_tensor([[12, 31, 13, model.padding_idx]]					)
             lowercase__      =					torch.as_tensor(
                 [
                     [
                         0 + model.padding_idx + 1,
                         1 + model.padding_idx + 1,
                         2 + model.padding_idx + 1,
                         model.padding_idx,
                     ]
                 ]					)
             lowercase__      =					create_position_ids_from_input_ids(UpperCamelCase  ,		model.padding_idx					)
             self.assertEqual(position_ids.shape  ,		expected_positions.shape					)
             self.assertTrue(torch.all(torch.eq(UpperCamelCase  ,		UpperCamelCase					)					)					)



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					self.model_tester.prepare_config_and_inputs()[0]
             lowercase__      =					EsmEmbeddings(config=UpperCamelCase					)

             lowercase__      =					torch.empty(2  ,		4  ,		30					)
             lowercase__      =					[
                 0 + embeddings.padding_idx + 1,
                 1 + embeddings.padding_idx + 1,
                 2 + embeddings.padding_idx + 1,
                 3 + embeddings.padding_idx + 1,
             ]
             lowercase__      =					torch.as_tensor([expected_single_positions, expected_single_positions]					)
             lowercase__      =					embeddings.create_position_ids_from_inputs_embeds(UpperCamelCase					)
             self.assertEqual(position_ids.shape  ,		expected_positions.shape					)
             self.assertTrue(torch.all(torch.eq(UpperCamelCase  ,		UpperCamelCase					)					)					)



      @unittest.skip('''Esm does not support embedding resizing'''					)
      def  UpperCamelCase__						(self       :       Optional[int]					):

             '''simple docstring'''







             pass



      @unittest.skip('''Esm does not support embedding resizing'''					)
      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             pass






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

             '''simple docstring'''







             pass






@require_torch
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      @slow
      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







             with torch.no_grad():
                    lowercase__      =					EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''					)
                    model.eval()
                    lowercase__      =					torch.tensor([[0, 1, 2, 3, 4, 5]]					)
                    lowercase__      =					model(UpperCamelCase					)[0]

                    lowercase__      =					33

                    lowercase__      =					torch.Size((1, 6, vocab_size)					)
                    self.assertEqual(output.shape  ,		UpperCamelCase					)

                    lowercase__      =					torch.tensor(
                        [[[8.92_15, -10.58_98, -6.46_71], [-6.39_67, -13.91_14, -1.12_12], [-7.78_12, -13.95_16, -3.74_06]]]					)
                    self.assertTrue(torch.allclose(output[:, :3, :3]  ,		UpperCamelCase  ,		atol=1E-4					)					)






      @slow
      def  UpperCamelCase__						(self       :       Optional[Any]					):

             '''simple docstring'''







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

                    lowercase__      =					torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]					)
                    lowercase__      =					model(UpperCamelCase					)[0]
                    # compare the actual values for a slice.
                    lowercase__      =					torch.tensor(
                        [[[0.14_44, 0.54_13, 0.32_48], [0.30_34, 0.00_53, 0.31_08], [0.32_28, -0.24_99, 0.34_15]]]					)
                    self.assertTrue(torch.allclose(output[:, :3, :3]  ,		UpperCamelCase  ,		atol=1E-4					)					)




