



"""simple docstring"""
import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


__SCREAMING_SNAKE_CASE     :  Optional[Any]										=					logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def 				lowerCAmelCase_(      lowercase_   :							List[str]							)		->				List[Any]:
				if "token" in model_name_or_path:
								return "rag_token"
				if "sequence" in model_name_or_path:
								return "rag_sequence"
				if "bart" in model_name_or_path:
								return "bart"
				return None



def 				lowerCAmelCase_(      lowercase_   :							int  ,      lowercase_   :							List[Any]  ,      lowercase_   :							Dict							)		->				Union[str, Any]:
				return max(metric_fn(lowercase_  ,      lowercase_							) for gt in ground_truths							)



def 				lowerCAmelCase_(      lowercase_   :							List[Any]  ,      lowercase_   :							List[str]  ,      lowercase_   :							Optional[int]							)		->				List[Any]:
				_lowerCamelCase					     = [line.strip() for line in open(lowercase_  ,      '''r'''							).readlines()]
				_lowerCamelCase					     = []

				if args.gold_data_mode == "qa":
								_lowerCamelCase					     = pd.read_csv(lowercase_  ,      sep='''\t'''  ,      header=lowercase_							)
								for answer_list in data[1]:
												_lowerCamelCase					     = ast.literal_eval(lowercase_							)
												answers.append(lowercase_							)
				else:
								_lowerCamelCase					     = [line.strip() for line in open(lowercase_  ,      '''r'''							).readlines()]
								_lowerCamelCase					     = [[reference] for reference in references]

				_lowerCamelCase					     = _lowerCamelCase					     = _lowerCamelCase					     = 0
				for prediction, ground_truths in zip(lowercase_  ,      lowercase_							):
								total += 1
								em += metric_max_over_ground_truths(lowercase_  ,      lowercase_  ,      lowercase_							)
								fa += metric_max_over_ground_truths(lowercase_  ,      lowercase_  ,      lowercase_							)

				_lowerCamelCase					     = 1_0_0.0 * em / total
				_lowerCamelCase					     = 1_0_0.0 * fa / total

				logger.info(F"""F1: {fa:.2f}"""							)
				logger.info(F"""EM: {em:.2f}"""							)



def 				lowerCAmelCase_(      lowercase_   :							int  ,      lowercase_   :							Optional[int]  ,      lowercase_   :							Optional[Any]							)		->				Optional[int]:
				_lowerCamelCase					     = args.k
				_lowerCamelCase					     = [line.strip() for line in open(lowercase_  ,      '''r'''							).readlines()]
				_lowerCamelCase					     = [line.strip() for line in open(lowercase_  ,      '''r'''							).readlines()]

				_lowerCamelCase					     = _lowerCamelCase					     = 0
				for hypo, reference in zip(lowercase_  ,      lowercase_							):
								_lowerCamelCase					     = set(hypo.split('''\t'''							)[:k]							)
								_lowerCamelCase					     = set(reference.split('''\t'''							)							)
								total += 1
								em += len(hypo_provenance & ref_provenance							) / k

				_lowerCamelCase					     = 1_0_0.0 * em / total
				logger.info(F"""Precision@{k}: {em: .2f}"""							)



def 				lowerCAmelCase_(      lowercase_   :							Union[str, Any]  ,      lowercase_   :							Tuple  ,      lowercase_   :							Dict							)		->				Any:
				def strip_title(lowercase_   :							str							):
								if title.startswith('''"'''							):
												_lowerCamelCase					     = title[1:]
								if title.endswith('''"'''							):
												_lowerCamelCase					     = title[:-1]
								return title

				_lowerCamelCase					     = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    lowercase_  ,      return_tensors='''pt'''  ,      padding=lowercase_  ,      truncation=lowercase_  ,      )['''input_ids'''].to(args.device							)

				_lowerCamelCase					     = rag_model.rag.question_encoder(lowercase_							)
				_lowerCamelCase					     = question_enc_outputs[0]

				_lowerCamelCase					     = rag_model.retriever(
				    lowercase_  ,      question_enc_pool_output.cpu().detach().to(torch.floataa							).numpy()  ,      prefix=rag_model.rag.generator.config.prefix  ,      n_docs=rag_model.config.n_docs  ,      return_tensors='''pt'''  ,      )
				_lowerCamelCase					     = rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
				_lowerCamelCase					     = []
				for docs in all_docs:
								_lowerCamelCase					     = [strip_title(lowercase_							) for title in docs['''title''']]
								provenance_strings.append('''\t'''.join(lowercase_							)							)
				return provenance_strings



def 				lowerCAmelCase_(      lowercase_   :							Tuple  ,      lowercase_   :							Optional[Any]  ,      lowercase_   :							str							)		->				Tuple:
				with torch.no_grad():
								_lowerCamelCase					     = rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
								    lowercase_  ,      return_tensors='''pt'''  ,      padding=lowercase_  ,      truncation=lowercase_							)

								_lowerCamelCase					     = inputs_dict.input_ids.to(args.device							)
								_lowerCamelCase					     = inputs_dict.attention_mask.to(args.device							)
								_lowerCamelCase					     = rag_model.generate(  # rag_model overwrites generate
								    lowercase_  ,      attention_mask=lowercase_  ,      num_beams=args.num_beams  ,      min_length=args.min_length  ,      max_length=args.max_length  ,      early_stopping=lowercase_  ,      num_return_sequences=1  ,      bad_words_ids=[[0, 0]]  ,      )
								_lowerCamelCase					     = rag_model.retriever.generator_tokenizer.batch_decode(lowercase_  ,      skip_special_tokens=lowercase_							)

								if args.print_predictions:
												for q, a in zip(lowercase_  ,      lowercase_							):
																logger.info('''Q: {} - A: {}'''.format(lowercase_  ,      lowercase_							)							)

								return answers



def 				lowerCAmelCase_(      )		->				int:
				_lowerCamelCase					     = argparse.ArgumentParser()
				parser.add_argument(
				    '''--model_type'''  ,      choices=['''rag_sequence''', '''rag_token''', '''bart''']  ,      type=lowercase_  ,      help=(
				        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
				        ''' model_name_or_path'''
				    )  ,      )
				parser.add_argument(
				    '''--index_name'''  ,      default=lowercase_  ,      choices=['''exact''', '''compressed''', '''legacy''']  ,      type=lowercase_  ,      help='''RAG model retriever type'''  ,      )
				parser.add_argument(
				    '''--index_path'''  ,      default=lowercase_  ,      type=lowercase_  ,      help='''Path to the retrieval index'''  ,      )
				parser.add_argument('''--n_docs'''  ,      default=5  ,      type=lowercase_  ,      help='''Number of retrieved docs'''							)
				parser.add_argument(
				    '''--model_name_or_path'''  ,      default=lowercase_  ,      type=lowercase_  ,      required=lowercase_  ,      help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''  ,      )
				parser.add_argument(
				    '''--eval_mode'''  ,      choices=['''e2e''', '''retrieval''']  ,      default='''e2e'''  ,      type=lowercase_  ,      help=(
				        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
				        ''' precision@k.'''
				    )  ,      )
				parser.add_argument('''--k'''  ,      default=1  ,      type=lowercase_  ,      help='''k for the precision@k calculation'''							)
				parser.add_argument(
				    '''--evaluation_set'''  ,      default=lowercase_  ,      type=lowercase_  ,      required=lowercase_  ,      help='''Path to a file containing evaluation samples'''  ,      )
				parser.add_argument(
				    '''--gold_data_path'''  ,      default=lowercase_  ,      type=lowercase_  ,      required=lowercase_  ,      help='''Path to a tab-separated file with gold samples'''  ,      )
				parser.add_argument(
				    '''--gold_data_mode'''  ,      default='''qa'''  ,      type=lowercase_  ,      choices=['''qa''', '''ans''']  ,      help=(
				        '''Format of the gold data file'''
				        '''qa - a single line in the following format: question [tab] answer_list'''
				        '''ans - a single line of the gold file contains the expected answer string'''
				    )  ,      )
				parser.add_argument(
				    '''--predictions_path'''  ,      type=lowercase_  ,      default='''predictions.txt'''  ,      help='''Name of the predictions file, to be stored in the checkpoints directory'''  ,      )
				parser.add_argument(
				    '''--eval_all_checkpoints'''  ,      action='''store_true'''  ,      help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''  ,      )
				parser.add_argument(
				    '''--eval_batch_size'''  ,      default=8  ,      type=lowercase_  ,      help='''Batch size per GPU/CPU for evaluation.'''  ,      )
				parser.add_argument(
				    '''--recalculate'''  ,      help='''Recalculate predictions even if the prediction file exists'''  ,      action='''store_true'''  ,      )
				parser.add_argument(
				    '''--num_beams'''  ,      default=4  ,      type=lowercase_  ,      help='''Number of beams to be used when generating answers'''  ,      )
				parser.add_argument('''--min_length'''  ,      default=1  ,      type=lowercase_  ,      help='''Min length of the generated answers'''							)
				parser.add_argument('''--max_length'''  ,      default=50  ,      type=lowercase_  ,      help='''Max length of the generated answers'''							)

				parser.add_argument(
				    '''--print_predictions'''  ,      action='''store_true'''  ,      help='''If True, prints predictions while evaluating.'''  ,      )
				parser.add_argument(
				    '''--print_docs'''  ,      action='''store_true'''  ,      help='''If True, prints docs retried while generating.'''  ,      )
				_lowerCamelCase					     = parser.parse_args()
				_lowerCamelCase					     = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''							)
				return args



def 				lowerCAmelCase_(      lowercase_   :							str							)		->				int:
				_lowerCamelCase					     = {}
				if args.model_type is None:
								_lowerCamelCase					     = infer_model_type(args.model_name_or_path							)
								assert args.model_type is not None
				if args.model_type.startswith('''rag'''							):
								_lowerCamelCase					     = RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
								_lowerCamelCase					     = args.n_docs
								if args.index_name is not None:
												_lowerCamelCase					     = args.index_name
								if args.index_path is not None:
												_lowerCamelCase					     = args.index_path
				else:
								_lowerCamelCase					     = BartForConditionalGeneration

				_lowerCamelCase					     = (
				    [f.path for f in os.scandir(args.model_name_or_path							) if f.is_dir()]
				    if args.eval_all_checkpoints
				    else [args.model_name_or_path]
				)

				logger.info('''Evaluate the following checkpoints: %s'''  ,      lowercase_							)

				_lowerCamelCase					     = get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
				_lowerCamelCase					     = evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

				for checkpoint in checkpoints:
								if os.path.exists(args.predictions_path							) and (not args.recalculate):
												logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path							)							)
												score_fn(lowercase_  ,      args.predictions_path  ,      args.gold_data_path							)
												continue

								logger.info('''***** Running evaluation for {} *****'''.format(lowercase_							)							)
								logger.info('''  Batch size = %d'''  ,      args.eval_batch_size							)
								logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path							)							)

								if args.model_type.startswith('''rag'''							):
												_lowerCamelCase					     = RagRetriever.from_pretrained(lowercase_  ,      **lowercase_							)
												_lowerCamelCase					     = model_class.from_pretrained(lowercase_  ,      retriever=lowercase_  ,      **lowercase_							)
												model.retriever.init_retrieval()
								else:
												_lowerCamelCase					     = model_class.from_pretrained(lowercase_  ,      **lowercase_							)
								model.to(args.device							)

								with open(args.evaluation_set  ,      '''r'''							) as eval_file, open(args.predictions_path  ,      '''w'''							) as preds_file:
												_lowerCamelCase					     = []
												for line in tqdm(lowercase_							):
																questions.append(line.strip()							)
																if len(lowercase_							) == args.eval_batch_size:
																				_lowerCamelCase					     = evaluate_batch_fn(lowercase_  ,      lowercase_  ,      lowercase_							)
																				preds_file.write('''\n'''.join(lowercase_							) + '''\n'''							)
																				preds_file.flush()
																				_lowerCamelCase					     = []
												if len(lowercase_							) > 0:
																_lowerCamelCase					     = evaluate_batch_fn(lowercase_  ,      lowercase_  ,      lowercase_							)
																preds_file.write('''\n'''.join(lowercase_							)							)
																preds_file.flush()

												score_fn(lowercase_  ,      args.predictions_path  ,      args.gold_data_path							)


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  str										=					get_args()
			main(args)





"""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      )      )
