


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


__lowerCAmelCase     :			Any      =logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def 	_UpperCamelCase							(				lowercase__				):
						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 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						return max(metric_fn(lowercase__   ,	lowercase__				) for gt in ground_truths				)


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		[line.strip() for line in open(lowercase__   ,	'''r'''				).readlines()]
						__SCREAMING_SNAKE_CASE						:					Any		      =		[]

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

						__SCREAMING_SNAKE_CASE						:					Tuple		      =		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__				)

						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		100.0 * em / total
						__SCREAMING_SNAKE_CASE						:					int		      =		100.0 * fa / total

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


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Tuple		      =		args.k
						__SCREAMING_SNAKE_CASE						:					Any		      =		[line.strip() for line in open(lowercase__   ,	'''r'''				).readlines()]
						__SCREAMING_SNAKE_CASE						:					int		      =		[line.strip() for line in open(lowercase__   ,	'''r'''				).readlines()]

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

						__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		100.0 * em / total
						logger.info(F'''Precision@{k}: {em: .2f}'''				)


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						def strip_title(lowercase__				):
												if title.startswith('''"'''				):
																		__SCREAMING_SNAKE_CASE						:					Tuple		      =		title[1:]
												if title.endswith('''"'''				):
																		__SCREAMING_SNAKE_CASE						:					Any		      =		title[:-1]
												return title

						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    lowercase__   ,	return_tensors='''pt'''   ,	padding=lowercase__   ,	truncation=lowercase__   ,	)['''input_ids'''].to(args.device				)

						__SCREAMING_SNAKE_CASE						:					str		      =		rag_model.rag.question_encoder(lowercase__				)
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		question_enc_outputs[0]

						__SCREAMING_SNAKE_CASE						:					Tuple		      =		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'''   ,	)
						__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		[]
						for docs in all_docs:
												__SCREAMING_SNAKE_CASE						:					str		      =		[strip_title(lowercase__				) for title in docs['''title''']]
												provenance_strings.append('''\t'''.join(lowercase__				)				)
						return provenance_strings


def 	_UpperCamelCase							(				lowercase__   ,	lowercase__   ,	lowercase__				):
						with torch.no_grad():
												__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
												    lowercase__   ,	return_tensors='''pt'''   ,	padding=lowercase__   ,	truncation=lowercase__				)

												__SCREAMING_SNAKE_CASE						:					str		      =		inputs_dict.input_ids.to(args.device				)
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		inputs_dict.attention_mask.to(args.device				)
												__SCREAMING_SNAKE_CASE						:					List[Any]		      =		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]]   ,	)
												__SCREAMING_SNAKE_CASE						:					str		      =		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 	_UpperCamelCase							(				):
						__SCREAMING_SNAKE_CASE						:					str		      =		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.'''   ,	)
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		parser.parse_args()
						__SCREAMING_SNAKE_CASE						:					int		      =		torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)
						return args


def 	_UpperCamelCase							(				lowercase__				):
						__SCREAMING_SNAKE_CASE						:					Dict		      =		{}
						if args.model_type is None:
												__SCREAMING_SNAKE_CASE						:					int		      =		infer_model_type(args.model_name_or_path				)
												assert args.model_type is not None
						if args.model_type.startswith('''rag'''				):
												__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
												__SCREAMING_SNAKE_CASE						:					int		      =		args.n_docs
												if args.index_name is not None:
																		__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		args.index_name
												if args.index_path is not None:
																		__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		args.index_path
						else:
												__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		BartForConditionalGeneration

						__SCREAMING_SNAKE_CASE						:					List[Any]		      =		(
						    [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__				)

						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		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'''				):
																		__SCREAMING_SNAKE_CASE						:					str		      =		RagRetriever.from_pretrained(lowercase__   ,	**lowercase__				)
																		__SCREAMING_SNAKE_CASE						:					int		      =		model_class.from_pretrained(lowercase__   ,	retriever=lowercase__   ,	**lowercase__				)
																		model.retriever.init_retrieval()
												else:
																		__SCREAMING_SNAKE_CASE						:					str		      =		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:
																		__SCREAMING_SNAKE_CASE						:					List[str]		      =		[]
																		for line in tqdm(lowercase__				):
																								questions.append(line.strip()				)
																								if len(lowercase__				) == args.eval_batch_size:
																														__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		evaluate_batch_fn(lowercase__   ,	lowercase__   ,	lowercase__				)
																														preds_file.write('''\n'''.join(lowercase__				) + '''\n'''				)
																														preds_file.flush()
																														__SCREAMING_SNAKE_CASE						:					str		      =		[]
																		if len(lowercase__				) > 0:
																								__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		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__":
						__lowerCAmelCase     :			Tuple      =get_args()
						main(args)




import unittest

from transformers import 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    RemBertConfig,
						    RemBertForCausalLM,
						    RemBertForMaskedLM,
						    RemBertForMultipleChoice,
						    RemBertForQuestionAnswering,
						    RemBertForSequenceClassification,
						    RemBertForTokenClassification,
						    RemBertModel,
						)
						from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class 	_lowercase				:






				'''simple docstring'''



				def __init__(	self      :int      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Optional[Any]=13      ,      lowerCAmelCase__      :int=7      ,      lowerCAmelCase__      :Any=True      ,      lowerCAmelCase__      :Optional[int]=True      ,      lowerCAmelCase__      :Union[str, Any]=True      ,      lowerCAmelCase__      :Union[str, Any]=True      ,      lowerCAmelCase__      :List[str]=99      ,      lowerCAmelCase__      :Tuple=32      ,      lowerCAmelCase__      :List[str]=18      ,      lowerCAmelCase__      :List[Any]=43      ,      lowerCAmelCase__      :int=5      ,      lowerCAmelCase__      :Any=4      ,      lowerCAmelCase__      :List[str]=37      ,      lowerCAmelCase__      :List[Any]="gelu"      ,      lowerCAmelCase__      :Union[str, Any]=0.1      ,      lowerCAmelCase__      :Union[str, Any]=0.1      ,      lowerCAmelCase__      :Optional[int]=512      ,      lowerCAmelCase__      :Union[str, Any]=16      ,      lowerCAmelCase__      :Optional[Any]=2      ,      lowerCAmelCase__      :Any=0.02      ,      lowerCAmelCase__      :Optional[int]=3      ,      lowerCAmelCase__      :str=4      ,      lowerCAmelCase__      :str=None      ,      )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		parent
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		batch_size
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		seq_length
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		is_training
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		use_input_mask
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		use_token_type_ids
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		use_labels
										__SCREAMING_SNAKE_CASE						:					int		      =		vocab_size
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		hidden_size
										__SCREAMING_SNAKE_CASE						:					int		      =		input_embedding_size
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		output_embedding_size
										__SCREAMING_SNAKE_CASE						:					int		      =		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:					int		      =		num_attention_heads
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		intermediate_size
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		hidden_act
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		max_position_embeddings
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		type_vocab_size
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		type_sequence_label_size
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		initializer_range
										__SCREAMING_SNAKE_CASE						:					Any		      =		num_labels
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		num_choices
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		scope


				def 		__magic_name__(	self      :Any     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size     )

										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		None
										if self.use_input_mask:
																__SCREAMING_SNAKE_CASE						:					List[str]		      =		random_attention_mask([self.batch_size, self.seq_length]     )

										__SCREAMING_SNAKE_CASE						:					Dict		      =		None
										if self.use_token_type_ids:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		ids_tensor([self.batch_size, self.seq_length]      ,      self.type_vocab_size     )

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

										__SCREAMING_SNAKE_CASE						:					str		      =		RemBertConfig(
										    vocab_size=self.vocab_size      ,      hidden_size=self.hidden_size      ,      input_embedding_size=self.input_embedding_size      ,      output_embedding_size=self.output_embedding_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      ,      is_decoder=lowerCAmelCase__      ,      initializer_range=self.initializer_range      ,      )

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


				def 		__magic_name__(	self      :int     )		->      Any:
										(
										    (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      
										)						:					Dict		      =		self.prepare_config_and_inputs()

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		True
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		ids_tensor([self.batch_size, self.seq_length]      ,      vocab_size=2     )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :List[Any]     )		->      Optional[int]:
										__SCREAMING_SNAKE_CASE						:					str		      =		RemBertModel(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					int		      =		model(lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model(lowerCAmelCase__     )
										self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)     )


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :List[str]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Any      ,      )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		True
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		RemBertModel(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		model(
										    lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      encoder_attention_mask=lowerCAmelCase__      ,      )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model(
										    lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__     )
										self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)     )


				def 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :int      ,      )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		RemBertForCausalLM(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Any		      =		model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)     )


				def 		__magic_name__(	self      :Optional[int]      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :List[str]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					int		      =		RemBertForMaskedLM(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Dict		      =		model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.vocab_size)     )


				def 		__magic_name__(	self      :Tuple      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :int      ,      lowerCAmelCase__      :str      ,      )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		True
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		True
										__SCREAMING_SNAKE_CASE						:					str		      =		RemBertForCausalLM(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()

										# first forward pass
										__SCREAMING_SNAKE_CASE						:					Dict		      =		model(
										    lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      encoder_attention_mask=lowerCAmelCase__      ,      use_cache=lowerCAmelCase__      ,      )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		outputs.past_key_values

										# create hypothetical multiple next token and extent to next_input_ids
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		ids_tensor((self.batch_size, 3)      ,      config.vocab_size     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		ids_tensor((self.batch_size, 3)      ,      vocab_size=2     )

										# append to next input_ids and
										__SCREAMING_SNAKE_CASE						:					str		      =		torch.cat([input_ids, next_tokens]      ,      dim=-1     )
										__SCREAMING_SNAKE_CASE						:					str		      =		torch.cat([input_mask, next_mask]      ,      dim=-1     )

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model(
										    lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      encoder_attention_mask=lowerCAmelCase__      ,      output_hidden_states=lowerCAmelCase__      ,      )['''hidden_states'''][0]
										__SCREAMING_SNAKE_CASE						:					int		      =		model(
										    lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      encoder_hidden_states=lowerCAmelCase__      ,      encoder_attention_mask=lowerCAmelCase__      ,      past_key_values=lowerCAmelCase__      ,      output_hidden_states=lowerCAmelCase__      ,      )['''hidden_states'''][0]

										# select random slice
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		ids_tensor((1,)      ,      output_from_past.shape[-1]     ).item()
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		output_from_no_past[:, -3:, random_slice_idx].detach()
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		output_from_past[:, :, random_slice_idx].detach()

										self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

										# test that outputs are equal for slice
										self.parent.assertTrue(torch.allclose(lowerCAmelCase__      ,      lowerCAmelCase__      ,      atol=1E-3     )     )


				def 		__magic_name__(	self      :Any      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		RemBertForQuestionAnswering(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		model(
										    lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=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 		__magic_name__(	self      :Optional[Any]      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Optional[Any]     )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.num_labels
										__SCREAMING_SNAKE_CASE						:					Any		      =		RemBertForSequenceClassification(lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Dict		      =		model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_labels)     )


				def 		__magic_name__(	self      :int      ,      lowerCAmelCase__      :str      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Any      ,      lowerCAmelCase__      :Optional[Any]      ,      lowerCAmelCase__      :List[str]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.num_labels
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		RemBertForTokenClassification(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		model(lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      labels=lowerCAmelCase__     )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.seq_length, self.num_labels)     )


				def 		__magic_name__(	self      :List[str]      ,      lowerCAmelCase__      :Dict      ,      lowerCAmelCase__      :Tuple      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :List[Any]      ,      lowerCAmelCase__      :Union[str, Any]      ,      lowerCAmelCase__      :Optional[int]      ,      lowerCAmelCase__      :Union[str, Any]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.num_choices
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		RemBertForMultipleChoice(config=lowerCAmelCase__     )
										model.to(lowerCAmelCase__     )
										model.eval()
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		input_ids.unsqueeze(1     ).expand(-1      ,      self.num_choices      ,      -1     ).contiguous()
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		token_type_ids.unsqueeze(1     ).expand(-1      ,      self.num_choices      ,      -1     ).contiguous()
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		input_mask.unsqueeze(1     ).expand(-1      ,      self.num_choices      ,      -1     ).contiguous()
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		model(
										    lowerCAmelCase__      ,      attention_mask=lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      labels=lowerCAmelCase__      ,      )
										self.parent.assertEqual(result.logits.shape      ,      (self.batch_size, self.num_choices)     )



				def 		__magic_name__(	self      :Union[str, Any]     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					int		      =		self.prepare_config_and_inputs()
										(
										    (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __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						:					Any		      =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
										return config, inputs_dict




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






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Dict      =		(
				    (
				        RemBertModel,
				        RemBertForMaskedLM,
				        RemBertForCausalLM,
				        RemBertForMultipleChoice,
				        RemBertForQuestionAnswering,
				        RemBertForSequenceClassification,
				        RemBertForTokenClassification,
				    )
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE__    :      Tuple      =		(RemBertForCausalLM,) if is_torch_available() else ()
				SCREAMING_SNAKE_CASE__    :      Tuple      =		(
				    {
				        '''feature-extraction''': RemBertModel,
				        '''fill-mask''': RemBertForMaskedLM,
				        '''question-answering''': RemBertForQuestionAnswering,
				        '''text-classification''': RemBertForSequenceClassification,
				        '''text-generation''': RemBertForCausalLM,
				        '''token-classification''': RemBertForTokenClassification,
				        '''zero-shot''': RemBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)


				def 		__magic_name__(	self      :List[str]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		RemBertModelTester(self     )
										__SCREAMING_SNAKE_CASE						:					Dict		      =		ConfigTester(self      ,      config_class=lowerCAmelCase__      ,      hidden_size=37     )


				def 		__magic_name__(	self      :Dict     )		->      int:
										self.config_tester.run_common_tests()


				def 		__magic_name__(	self      :Any     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					int		      =		self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		type
																self.model_tester.create_and_check_model(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[Any]     )		->      int:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :Dict     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]     )		->      Optional[Any]:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :Any     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]     )		->      List[str]:
										__SCREAMING_SNAKE_CASE						:					str		      =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :str     )		->      Dict:
										__SCREAMING_SNAKE_CASE						:					int		      =		self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(*lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[int]     )		->      Optional[int]:
										# This regression test was failing with PyTorch < 1.3
										(
										    (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      (
										    __SCREAMING_SNAKE_CASE
										)      ,      
										)						:					Optional[Any]		      =		self.model_tester.prepare_config_and_inputs_for_decoder()

										__SCREAMING_SNAKE_CASE						:					Any		      =		None

										self.model_tester.create_and_check_model_as_decoder(
										    lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      )



				@slow
				def 		__magic_name__(	self      :Any     )		->      Union[str, Any]:
										for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		RemBertModel.from_pretrained(lowerCAmelCase__     )
																self.assertIsNotNone(lowerCAmelCase__     )




@require_torch
class 	_lowercase				(      unittest.TestCase	):






				'''simple docstring'''



				@slow
				def 		__magic_name__(	self      :str     )		->      List[Any]:
										# Test exact values at the last hidden layer
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		RemBertModel.from_pretrained('''google/rembert'''     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.tensor([[312, 56_498, 313, 2_125, 313]]     )
										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.tensor([[0, 0, 0, 1, 1]]     )
										with torch.no_grad():
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model(lowerCAmelCase__      ,      token_type_ids=lowerCAmelCase__      ,      output_hidden_states=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		1_152

										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.Size((1, 5, hidden_size)     )
										self.assertEqual(output['''last_hidden_state'''].shape      ,      lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					str		      =		torch.tensor(
										    [
										        [
										            [0.0754, -0.2022, 0.1904],
										            [-0.3354, -0.3692, -0.4791],
										            [-0.2314, -0.6729, -0.0749],
										            [-0.0396, -0.3105, -0.4234],
										            [-0.1571, -0.0525, 0.5353],
										        ]
										    ]     )

										# Running on the original tf implementation gives slightly different results here.
										# Not clear why this variations is present
										# TODO: Find reason for discrepancy
										# expected_original_implementation = [[
										#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
										#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
										#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
										#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
										#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
										# ]]

										self.assertTrue(torch.allclose(output['''last_hidden_state'''][:, :, :3]      ,      lowerCAmelCase__      ,      atol=1E-4     )     )
