'''simple docstring'''




import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


__snake_case   :      Optional[int]						    =  logging.getLogger(__name__)





def      _UpperCAmelCase    (							_UpperCamelCase    :	int   )	->      str:
    random.seed(_UpperCamelCase   )
    np.random.seed(_UpperCamelCase   )
    torch.manual_seed(_UpperCamelCase   )
    torch.cuda.manual_seed_all(_UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Optional[int]   )	->      Optional[Any]:
    model.eval()
    A_     =   next(model.parameters()   ).device
    A_     =   1
    A_     =   torch.zeros((eval_batch_size, context_len),   dtype=torch.long,   device=_UpperCamelCase   )
    A_     =   DataLoader(_UpperCamelCase,   shuffle=_UpperCamelCase,   batch_size=_UpperCamelCase   )
    A_     =   torch.zeros(1,   device=_UpperCamelCase   )
    A_     =   0
    for batch in eval_dataloader:
        batch.to(_UpperCamelCase   )
        # pad
        context.zero_()
        for i in range(_UpperCamelCase   ):
            A_     =   batch[i]
        A_     =   model(_UpperCamelCase,   labels=_UpperCamelCase   )
        eval_loss += outputs[0].sum().item()
        nb_eval_examples += batch.size(0   )
    A_     =   eval_loss / nb_eval_examples
    A_     =   torch.exp(_UpperCamelCase   )
    model.train()
    return perplexity





def      _UpperCAmelCase    (							_UpperCamelCase    :	Any="gpt2"   )	->      Optional[int]:
    A_     =   GPTaLMHeadModel.from_pretrained(_UpperCamelCase,   output_hidden_states=_UpperCamelCase   )
    torch.save(model.state_dict(),   model_name + '''local.pt'''   )
    return model





def      _UpperCAmelCase    (							_UpperCamelCase    :	Dict,   _UpperCamelCase    :	str,   _UpperCamelCase    :	List[str]   )	->      Tuple:
    A_     =   copy.deepcopy(_UpperCamelCase   )
    model.to(_UpperCamelCase   )

    A_     =   ['''bias''', '''LayerNorm.weight''']
    A_     =   [
        {
            '''params''': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay   )],
            '''weight_decay''': 0.0,
        },
        {'''params''': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay   )], '''weight_decay''': 0.0},
    ]
    A_     =   AdamW(_UpperCamelCase,   lr=5E-5,   eps=1E-8   )
    A_     =   get_linear_schedule_with_warmup(_UpperCamelCase,   0,   _UpperCamelCase   )
    torch.cuda.empty_cache()
    return model, lm_optimizer, lm_scheduler





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Dict,   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Union[str, Any]   )	->      Dict:
    A_     =   np.array(_UpperCamelCase   ).mean()
    A_     =   np.array(_UpperCamelCase   ).std()
    A_     =   (real_perps - avg) / std
    A_     =   list(zip(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )   )
    joblib.dump(_UpperCamelCase,   _UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	str,   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	List[str],   _UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	Tuple,   _UpperCamelCase    :	str="dev.jbl",   _UpperCamelCase    :	Dict=recopy_gpta,   )	->      List[Any]:
    A_     =   []
    A_     =   []
    A_     =   []

    # Initialize the transformer model
    A_     =   copy.deepcopy(_UpperCamelCase   )
    orig_model.to(device='''cpu'''   )
    torch.cuda.empty_cache()

    # Compute perplexity of initial transformer model for comparison
    model.train()
    A_   ,A_   ,A_     =   recopy_model(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

    for step in tqdm(range(_UpperCamelCase   )   ):
        A_     =   torch.zeros((1, context_len),   dtype=torch.long,   device=_UpperCamelCase   )
        A_     =   random.choice(_UpperCamelCase   )
        A_     =   random.randint(0,   len(story[0]   ) - context_len - 1   )
        A_     =   story[0][start : start + context_len]
        lm_optimizer.zero_grad()
        A_     =   model(_UpperCamelCase,   labels=_UpperCamelCase   )
        A_     =   outputs[0]
        A_     =   compute_perplexity(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )
        model.train()
        lm_loss.backward()
        # Do LM backprop
        torch.nn.utils.clip_grad_norm_(model.parameters(),   3.0   )
        lm_optimizer.step()
        lm_scheduler.step()  # Update learning rate schedule

        # Compute perplexity after back-propagating on the selected context
        A_     =   compute_perplexity(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

        # Periodically save the stored (X, IG(X)) pairs
        if step % 10_00 == 0 and step > 1:
            intermittent_save(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

        # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
        A_   ,A_   ,A_     =   recopy_model(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )

        past_perps.append(past_perp.item()   )
        real_perps.append(orig_perp - real_perp.item()   )
        contexts.append(np.array(context.cpu()   )   )

    intermittent_save(_UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase,   _UpperCamelCase   )





def      _UpperCAmelCase    (							_UpperCamelCase    :	Any,   _UpperCamelCase    :	List[Any]="data/tokenized_stories_train_wikitext103.jbl",   _UpperCamelCase    :	List[str]=1_00,   _UpperCamelCase    :	Optional[int]=10_26,   _UpperCamelCase    :	Dict=True   )	->      Optional[Any]:
    A_     =   joblib.load(_UpperCamelCase   )
    print('''data loaded'''   )
    A_     =   []
    if trim:
        for i, example in enumerate(_UpperCamelCase   ):
            if len(example[0]   ) > min_len:
                A_     =   random.randint(0,   len(example[0]   ) - context_len - 1   )
                objective_set.append(example[0, start : start + context_len]   )
            if len(_UpperCamelCase   ) >= number:
                break
        A_     =   []
        for j in range(i + 1,   len(_UpperCamelCase   )   ):
            if len(data[j][0]   ) > min_len:
                train_data.append(data[j]   )
    else:
        A_     =   data[0:number]
        A_     =   data[number:]

    joblib.dump(_UpperCamelCase,   '''objective_set.jbl'''   )
    print('''objective set saved'''   )
    return train_data, objective_set





def      _UpperCAmelCase    (							_UpperCamelCase    :	Optional[int],   _UpperCamelCase    :	Union[str, Any],   _UpperCamelCase    :	str,   _UpperCamelCase    :	Tuple,   _UpperCamelCase    :	Union[str, Any]=50,   _UpperCamelCase    :	Tuple="secondary_learner.pt"   )	->      List[Any]:
    A_     =   torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''   )
    # We will use the first 512 pairs from our dataset as a test set for
    # our secondary learner and the rest to train
    A_     =   train_dataset[:5_12]
    A_     =   train_dataset[5_12:]
    A_     =   DataLoader(_UpperCamelCase,   shuffle=_UpperCamelCase,   batch_size=_UpperCamelCase   )
    A_     =   DataLoader(_UpperCamelCase,   shuffle=_UpperCamelCase,   batch_size=_UpperCamelCase   )

    # secondary learner model set up
    A_     =   nn.MSELoss()
    A_     =   nn.MSELoss(reduction='''sum'''   )
    secondary_learner.to(_UpperCamelCase   )
    A_     =   torch.optim.Adam(secondary_learner.parameters(),   lr=0.0_0_0_0_1   )
    secondary_learner.train()

    # TODO in original code this is written as number of actual batches seen
    # not number of items seen but other places it is number of items instead.
    # improve consistency! changed this to epochs for clarity
    A_     =   float('''inf'''   )
    # Iterate through batches until we've used max_steps batches
    for epoch in range(int(_UpperCamelCase   )   ):
        A_     =   0.0
        secondary_learner.train()
        for step, batch in enumerate(_UpperCamelCase   ):
            A_     =   batch[0].to(_UpperCamelCase   )
            A_     =   batch[1].to(_UpperCamelCase   )
            A_     =   secondary_learner(_UpperCamelCase   )
            q_optimizer.zero_grad()
            A_     =   loss(_UpperCamelCase,   real_q.float()   )
            q_loss.backward()
            q_optimizer.step()
            tr_q_loss += q_loss.item()

            # model trains fairly quickly so we won't wait for a full epoch
            # eval is triggered at eval_freq and end of epochs
            if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(_UpperCamelCase   )):
                A_     =   tr_q_loss / (step + 1)

                secondary_learner.eval()
                A_     =   0.0
                A_     =   0.0
                A_     =   []
                A_     =   []
                # Compute performance of the secondary learner after this batch
                for stepa, batcha in enumerate(_UpperCamelCase   ):
                    A_     =   batcha[0].to(_UpperCamelCase   )
                    A_     =   batcha[1].to(_UpperCamelCase   )
                    A_     =   secondary_learner(_UpperCamelCase   )
                    q_lossa += test_loss(_UpperCamelCase,   _UpperCamelCase   ).item()
                    sum_qa += torch.sum(_UpperCamelCase   ).item()
                    for ei, i in enumerate(predicted_qa.cpu().detach().numpy()   ):
                        predicted.append(i.item()   )
                    for ei, i in enumerate(real_qa.cpu().detach().numpy()   ):
                        actual.append(i.item()   )

                q_lossa /= len(_UpperCamelCase   )
                print(
                    '''Epoch: ''',   _UpperCamelCase,   '''step: ''',   _UpperCamelCase,   '''Avg. q:''',   sum_qa / len(_UpperCamelCase   ),   '''Train Loss: ''',   _UpperCamelCase,   '''Test Loss: ''',   _UpperCamelCase,   )
                if q_lossa < best_test_loss:
                    joblib.dump((predicted, actual),   '''pred_vs_actual.jbl'''   )
                    torch.save(secondary_learner.state_dict(),   _UpperCamelCase   )
                    A_     =   q_lossa

            secondary_learner.train()
    return secondary_learner




class 	__UpperCAmelCase				(				nn.Module     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE )		->     Any:
           super(_SCREAMING_SNAKE_CASE    ,     self ).__init__()
           A_     =   model.transformer.wte
           A_     =   copy.deepcopy(model.transformer.wte.weight )
           A_     =   nn.Convad(self.embeddings.weight.size(1 )    ,     256    ,     3    ,     padding=1 )
           A_     =   nn.Sequential(nn.Linear(256    ,     32 )    ,     nn.Dropout(p=0.1 )    ,     nn.Linear(32    ,     32 )    ,     nn.Linear(32    ,     1 ) )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE )		->     Optional[int]:
           A_     =   torch.max(self.conv(self.embeddings(_SCREAMING_SNAKE_CASE ).squeeze(1 ).transpose(1    ,     2 ) )    ,     2 )[0]
           A_     =   self.fc(_SCREAMING_SNAKE_CASE )
           return qs.squeeze(1 )

       @classmethod
       def 					__A       (      cls    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Dict:
           A_     =   cls(_SCREAMING_SNAKE_CASE )  # this calls __init__
           A_     =   torch.load(_SCREAMING_SNAKE_CASE )
           secondary_learner.load_state_dict(_SCREAMING_SNAKE_CASE )
           A_     =   model.transformer.wte
           A_     =   copy.deepcopy(model.transformer.wte.weight )
           return secondary_learner











"""simple docstring"""
import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import 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 (
			    ErnieMForInformationExtraction,
			    ErnieMForMultipleChoice,
			    ErnieMForQuestionAnswering,
			    ErnieMForSequenceClassification,
			    ErnieMForTokenClassification,
			    ErnieMModel,
			)
			from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=1_3 ,				lowerCamelCase__=7 ,				lowerCamelCase__=True ,				lowerCamelCase__=True ,				lowerCamelCase__=True ,				lowerCamelCase__=9_9 ,				lowerCamelCase__=3_2 ,				lowerCamelCase__=5 ,				lowerCamelCase__=4 ,				lowerCamelCase__=3_7 ,				lowerCamelCase__="gelu" ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=0.1 ,				lowerCamelCase__=5_1_2 ,				lowerCamelCase__=1_6 ,				lowerCamelCase__=2 ,				lowerCamelCase__=0.0_2 ,				lowerCamelCase__=3 ,				lowerCamelCase__=4 ,				lowerCamelCase__=None ,				):
									_lowerCamelCase					     = parent
									_lowerCamelCase					     = batch_size
									_lowerCamelCase					     = seq_length
									_lowerCamelCase					     = is_training
									_lowerCamelCase					     = use_input_mask
									_lowerCamelCase					     = use_labels
									_lowerCamelCase					     = vocab_size
									_lowerCamelCase					     = hidden_size
									_lowerCamelCase					     = num_hidden_layers
									_lowerCamelCase					     = num_attention_heads
									_lowerCamelCase					     = intermediate_size
									_lowerCamelCase					     = hidden_act
									_lowerCamelCase					     = hidden_dropout_prob
									_lowerCamelCase					     = attention_probs_dropout_prob
									_lowerCamelCase					     = max_position_embeddings
									_lowerCamelCase					     = type_vocab_size
									_lowerCamelCase					     = type_sequence_label_size
									_lowerCamelCase					     = initializer_range
									_lowerCamelCase					     = num_labels
									_lowerCamelCase					     = num_choices
									_lowerCamelCase					     = scope

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

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

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

									_lowerCamelCase					     = self.get_config()

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

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size      )

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

									return config, input_ids, input_mask

					def 	snake_case__					(			self      ):
									return ErnieMConfig(
									    vocab_size=self.vocab_size ,				hidden_size=self.hidden_size ,				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__      ):
									_lowerCamelCase					     = ErnieMModel(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = model(lowerCamelCase__ ,				return_dict=lowerCamelCase__      )
									self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = ErnieMForQuestionAnswering(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = model(
									    lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				start_positions=lowerCamelCase__ ,				end_positions=lowerCamelCase__ ,				)
									self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)      )
									self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = ErnieMForInformationExtraction(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = torch.ones_like(lowerCamelCase__ ,				dtype=torch.floataa      )
									_lowerCamelCase					     = model(
									    lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				start_positions=lowerCamelCase__ ,				end_positions=lowerCamelCase__ ,				)
									self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)      )
									self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.num_labels
									_lowerCamelCase					     = ErnieMForSequenceClassification(lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = model(lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				labels=lowerCamelCase__      )
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.num_labels
									_lowerCamelCase					     = ErnieMForTokenClassification(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									input_ids.to(lowerCamelCase__      )
									input_mask.to(lowerCamelCase__      )
									token_labels.to(lowerCamelCase__      )

									_lowerCamelCase					     = 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 ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.num_choices
									_lowerCamelCase					     = ErnieMForMultipleChoice(config=lowerCamelCase__      )
									model.to(lowerCamelCase__      )
									model.eval()
									_lowerCamelCase					     = input_ids.unsqueeze(1      ).expand(-1 ,				self.num_choices ,				-1      ).contiguous()
									_lowerCamelCase					     = input_mask.unsqueeze(1      ).expand(-1 ,				self.num_choices ,				-1      ).contiguous()
									_lowerCamelCase					     = model(
									    lowerCamelCase__ ,				attention_mask=lowerCamelCase__ ,				labels=lowerCamelCase__ ,				)
									self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_choices)      )





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


@require_torch
class 							lowerCamelCase_(      A__, A__, unittest.TestCase			):





					'''simple docstring'''



					lowercase__      :				Optional[Any]        =       (
					    (
					        ErnieMModel,
					        ErnieMForMultipleChoice,
					        ErnieMForQuestionAnswering,
					        ErnieMForSequenceClassification,
					        ErnieMForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					lowercase__      :				List[Any]        =       ()
					lowercase__      :				Union[str, Any]        =       (
					    {
					        'feature-extraction': ErnieMModel,
					        'question-answering': ErnieMForQuestionAnswering,
					        'text-classification': ErnieMForSequenceClassification,
					        'token-classification': ErnieMForTokenClassification,
					        'zero-shot': ErnieMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					lowercase__      :				Optional[int]        =       False

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									if pipeline_test_casse_name == "QAPipelineTests":
													return True

									return False

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = ErnieMModelTester(self      )
									_lowerCamelCase					     = ConfigTester(self ,				config_class=lowerCamelCase__ ,				hidden_size=3_7      )

					def 	snake_case__					(			self      ):
									self.config_tester.run_common_tests()

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													_lowerCamelCase					     = type
													self.model_tester.create_and_check_model(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_information_extraction(*lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__      )





					@slow
					def 	snake_case__					(			self      ):
									for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													_lowerCamelCase					     = ErnieMModel.from_pretrained(lowerCamelCase__      )
													self.assertIsNotNone(lowerCamelCase__      )


@require_torch
class 							lowerCamelCase_(      unittest.TestCase			):





					'''simple docstring'''



					@slow
					def 	snake_case__					(			self      ):
									_lowerCamelCase					     = ErnieMModel.from_pretrained('''susnato/ernie-m-base_pytorch'''      )
									model.eval()
									_lowerCamelCase					     = torch.tensor([[0, 1, 2, 3, 4, 5]]      )
									_lowerCamelCase					     = model(lowerCamelCase__      )[0]

									# TODO Replace vocab size
									_lowerCamelCase					     = 7_6_8

									_lowerCamelCase					     = torch.Size((1, 6, hidden_size)      )
									self.assertEqual(output.shape ,				lowerCamelCase__      )

									_lowerCamelCase					     = torch.tensor(
									    [[[-0.0_0_1_2, 0.1_2_4_5, -0.0_2_1_4], [-0.0_7_4_2, 0.0_2_4_4, -0.0_7_7_1], [-0.0_3_3_3, 0.1_1_6_4, -0.1_5_5_4]]]      )

									self.assertTrue(torch.allclose(output[:, :3, :3] ,				lowerCamelCase__ ,				atol=1e-3      )      )
