
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


_lowercase											=    logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int				)			->						Union[str, 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 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     List[str]			,							UpperCAmelCase_	:     List[str]				)			->						Optional[Any]:
       return max(metric_fn(UpperCAmelCase_			,							UpperCAmelCase_				) for gt in ground_truths				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Optional[int]				)			->						Dict:
       SCREAMING_SNAKE_CASE_							:				Tuple		=[line.strip() for line in open(UpperCAmelCase_			,							'''r'''				).readlines()]
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=[]

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

       SCREAMING_SNAKE_CASE_							:				Tuple		=0
       for prediction, ground_truths in zip(UpperCAmelCase_			,							UpperCAmelCase_				):
              total += 1
              em += metric_max_over_ground_truths(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)
              fa += metric_max_over_ground_truths(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				int		=100.0 * em / total
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=100.0 * fa / total

       logger.info(f'F1: {fa:.2f}'				)
       logger.info(f'EM: {em:.2f}'				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[str]			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Any				)			->						Optional[int]:
       SCREAMING_SNAKE_CASE_							:				int		=args.k
       SCREAMING_SNAKE_CASE_							:				Dict		=[line.strip() for line in open(UpperCAmelCase_			,							'''r'''				).readlines()]
       SCREAMING_SNAKE_CASE_							:				Tuple		=[line.strip() for line in open(UpperCAmelCase_			,							'''r'''				).readlines()]

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

       SCREAMING_SNAKE_CASE_							:				Any		=100.0 * em / total
       logger.info(f'Precision@{k}: {em: .2f}'				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     List[Any]				)			->						int:
       def strip_title(UpperCAmelCase_	:     List[Any]				):
              if title.startswith('''"'''				):
                     SCREAMING_SNAKE_CASE_							:				Optional[int]		=title[1:]
              if title.endswith('''"'''				):
                     SCREAMING_SNAKE_CASE_							:				Tuple		=title[:-1]
              return title

       SCREAMING_SNAKE_CASE_							:				List[Any]		=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           UpperCAmelCase_			,							return_tensors='''pt'''			,							padding=UpperCAmelCase_			,							truncation=UpperCAmelCase_			,							)['''input_ids'''].to(args.device				)

       SCREAMING_SNAKE_CASE_							:				List[Any]		=rag_model.rag.question_encoder(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Tuple		=question_enc_outputs[0]

       SCREAMING_SNAKE_CASE_							:				Tuple		=rag_model.retriever(
           UpperCAmelCase_			,							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[Any]		=rag_model.retriever.index.get_doc_dicts(result.doc_ids				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[]
       for docs in all_docs:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[strip_title(UpperCAmelCase_				) for title in docs['''title''']]
              provenance_strings.append('''\t'''.join(UpperCAmelCase_				)				)
       return provenance_strings
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     List[Any]				)			->						List[Any]:
       with torch.no_grad():
              SCREAMING_SNAKE_CASE_							:				Dict		=rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  UpperCAmelCase_			,							return_tensors='''pt'''			,							padding=UpperCAmelCase_			,							truncation=UpperCAmelCase_				)

              SCREAMING_SNAKE_CASE_							:				Optional[int]		=inputs_dict.input_ids.to(args.device				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=inputs_dict.attention_mask.to(args.device				)
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=rag_model.generate(  # rag_model overwrites generate
                  UpperCAmelCase_			,							attention_mask=UpperCAmelCase_			,							num_beams=args.num_beams			,							min_length=args.min_length			,							max_length=args.max_length			,							early_stopping=UpperCAmelCase_			,							num_return_sequences=1			,							bad_words_ids=[[0, 0]]			,							)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=rag_model.retriever.generator_tokenizer.batch_decode(UpperCAmelCase_			,							skip_special_tokens=UpperCAmelCase_				)

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

              return answers
def 							SCREAMING_SNAKE_CASE_	(					)			->						int:
       SCREAMING_SNAKE_CASE_							:				Any		=argparse.ArgumentParser()
       parser.add_argument(
           '''--model_type'''			,							choices=['''rag_sequence''', '''rag_token''', '''bart''']			,							type=UpperCAmelCase_			,							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=UpperCAmelCase_			,							choices=['''exact''', '''compressed''', '''legacy''']			,							type=UpperCAmelCase_			,							help='''RAG model retriever type'''			,							)
       parser.add_argument(
           '''--index_path'''			,							default=UpperCAmelCase_			,							type=UpperCAmelCase_			,							help='''Path to the retrieval index'''			,							)
       parser.add_argument('''--n_docs'''			,							default=5			,							type=UpperCAmelCase_			,							help='''Number of retrieved docs'''				)
       parser.add_argument(
           '''--model_name_or_path'''			,							default=UpperCAmelCase_			,							type=UpperCAmelCase_			,							required=UpperCAmelCase_			,							help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''			,							)
       parser.add_argument(
           '''--eval_mode'''			,							choices=['''e2e''', '''retrieval''']			,							default='''e2e'''			,							type=UpperCAmelCase_			,							help=(
               '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
               ''' precision@k.'''
           )			,							)
       parser.add_argument('''--k'''			,							default=1			,							type=UpperCAmelCase_			,							help='''k for the precision@k calculation'''				)
       parser.add_argument(
           '''--evaluation_set'''			,							default=UpperCAmelCase_			,							type=UpperCAmelCase_			,							required=UpperCAmelCase_			,							help='''Path to a file containing evaluation samples'''			,							)
       parser.add_argument(
           '''--gold_data_path'''			,							default=UpperCAmelCase_			,							type=UpperCAmelCase_			,							required=UpperCAmelCase_			,							help='''Path to a tab-separated file with gold samples'''			,							)
       parser.add_argument(
           '''--gold_data_mode'''			,							default='''qa'''			,							type=UpperCAmelCase_			,							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=UpperCAmelCase_			,							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=UpperCAmelCase_			,							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=UpperCAmelCase_			,							help='''Number of beams to be used when generating answers'''			,							)
       parser.add_argument('''--min_length'''			,							default=1			,							type=UpperCAmelCase_			,							help='''Min length of the generated answers'''				)
       parser.add_argument('''--max_length'''			,							default=5_0			,							type=UpperCAmelCase_			,							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_							:				List[Any]		=parser.parse_args()
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)
       return args
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[Any]				)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				int		={}
       if args.model_type is None:
              SCREAMING_SNAKE_CASE_							:				Tuple		=infer_model_type(args.model_name_or_path				)
              assert args.model_type is not None
       if args.model_type.startswith('''rag'''				):
              SCREAMING_SNAKE_CASE_							:				Dict		=RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
              SCREAMING_SNAKE_CASE_							:				List[Any]		=args.n_docs
              if args.index_name is not None:
                     SCREAMING_SNAKE_CASE_							:				Dict		=args.index_name
              if args.index_path is not None:
                     SCREAMING_SNAKE_CASE_							:				Tuple		=args.index_path
       else:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=BartForConditionalGeneration

       SCREAMING_SNAKE_CASE_							:				Dict		=(
           [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'''			,							UpperCAmelCase_				)

       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(UpperCAmelCase_			,							args.predictions_path			,							args.gold_data_path				)
                     continue

              logger.info('''***** Running evaluation for {} *****'''.format(UpperCAmelCase_				)				)
              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_							:				int		=RagRetriever.from_pretrained(UpperCAmelCase_			,							**UpperCAmelCase_				)
                     SCREAMING_SNAKE_CASE_							:				str		=model_class.from_pretrained(UpperCAmelCase_			,							retriever=UpperCAmelCase_			,							**UpperCAmelCase_				)
                     model.retriever.init_retrieval()
              else:
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=model_class.from_pretrained(UpperCAmelCase_			,							**UpperCAmelCase_				)
              model.to(args.device				)

              with open(args.evaluation_set			,							'''r'''				) as eval_file, open(args.predictions_path			,							'''w'''				) as preds_file:
                     SCREAMING_SNAKE_CASE_							:				str		=[]
                     for line in tqdm(UpperCAmelCase_				):
                            questions.append(line.strip()				)
                            if len(UpperCAmelCase_				) == args.eval_batch_size:
                                   SCREAMING_SNAKE_CASE_							:				Dict		=evaluate_batch_fn(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)
                                   preds_file.write('''\n'''.join(UpperCAmelCase_				) + '''\n'''				)
                                   preds_file.flush()
                                   SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=[]
                     if len(UpperCAmelCase_				) > 0:
                            SCREAMING_SNAKE_CASE_							:				Optional[int]		=evaluate_batch_fn(UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_				)
                            preds_file.write('''\n'''.join(UpperCAmelCase_				)				)
                            preds_file.flush()

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


if __name__ == "__main__":
 _lowercase											=    get_args()
 main(args)


import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase											=    """▁"""

_lowercase											=    get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class 			lowercase_		(      A  ,      unittest.TestCase       ):
       __lowerCamelCase				       =						BigBirdTokenizer
       __lowerCamelCase				       =						BigBirdTokenizerFast
       __lowerCamelCase				       =						True
       __lowerCamelCase				       =						True


       def 	_snake_case				(    self				)						->    Tuple:
              super().setUp()

              SCREAMING_SNAKE_CASE_							:				str		=self.tokenizer_class(__A						,       keep_accents=__A				)
              tokenizer.save_pretrained(self.tmpdirname				)


       def 	_snake_case				(    self				)						->    List[Any]:
              SCREAMING_SNAKE_CASE_							:				str		='''<s>'''
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=1

              self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A				)						,       __A				)
              self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A				)						,       __A				)


       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				str		=list(self.get_tokenizer().get_vocab().keys()				)

              self.assertEqual(vocab_keys[0]						,       '''<unk>'''				)
              self.assertEqual(vocab_keys[1]						,       '''<s>'''				)
              self.assertEqual(vocab_keys[-1]						,       '''[MASK]'''				)
              self.assertEqual(len(__A				)						,       1_004				)


       def 	_snake_case				(    self				)						->    Optional[Any]:
              self.assertEqual(self.get_tokenizer().vocab_size						,       1_000				)


       def 	_snake_case				(    self				)						->    str:
              if not self.test_rust_tokenizer:
                     return

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_tokenizer()
              SCREAMING_SNAKE_CASE_							:				List[Any]		=self.get_rust_tokenizer()

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		='''I was born in 92000, and this is falsé.'''

              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.tokenize(__A				)
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=rust_tokenizer.tokenize(__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=tokenizer.encode(__A						,       add_special_tokens=__A				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=rust_tokenizer.encode(__A						,       add_special_tokens=__A				)
              self.assertListEqual(__A						,       __A				)

              SCREAMING_SNAKE_CASE_							:				Dict		=self.get_rust_tokenizer()
              SCREAMING_SNAKE_CASE_							:				int		=tokenizer.encode(__A				)
              SCREAMING_SNAKE_CASE_							:				Dict		=rust_tokenizer.encode(__A				)
              self.assertListEqual(__A						,       __A				)


       def 	_snake_case				(    self				)						->    str:
              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=BigBirdTokenizer(__A						,       keep_accents=__A				)

              SCREAMING_SNAKE_CASE_							:				str		=tokenizer.tokenize('''This is a test'''				)
              self.assertListEqual(__A						,       ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

              self.assertListEqual(
                  tokenizer.convert_tokens_to_ids(__A				)						,       [285, 46, 10, 170, 382]						,       )

              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
              self.assertListEqual(
                  __A						,       [
                      SPIECE_UNDERLINE + '''I''',
                      SPIECE_UNDERLINE + '''was''',
                      SPIECE_UNDERLINE + '''b''',
                      '''or''',
                      '''n''',
                      SPIECE_UNDERLINE + '''in''',
                      SPIECE_UNDERLINE + '''''',
                      '''9''',
                      '''2''',
                      '''0''',
                      '''0''',
                      '''0''',
                      ''',''',
                      SPIECE_UNDERLINE + '''and''',
                      SPIECE_UNDERLINE + '''this''',
                      SPIECE_UNDERLINE + '''is''',
                      SPIECE_UNDERLINE + '''f''',
                      '''al''',
                      '''s''',
                      '''é''',
                      '''.''',
                  ]						,       )
              SCREAMING_SNAKE_CASE_							:				Dict		=tokenizer.convert_tokens_to_ids(__A				)
              self.assertListEqual(
                  __A						,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]						,       )

              SCREAMING_SNAKE_CASE_							:				Optional[Any]		=tokenizer.convert_ids_to_tokens(__A				)
              self.assertListEqual(
                  __A						,       [
                      SPIECE_UNDERLINE + '''I''',
                      SPIECE_UNDERLINE + '''was''',
                      SPIECE_UNDERLINE + '''b''',
                      '''or''',
                      '''n''',
                      SPIECE_UNDERLINE + '''in''',
                      SPIECE_UNDERLINE + '''''',
                      '''<unk>''',
                      '''2''',
                      '''0''',
                      '''0''',
                      '''0''',
                      ''',''',
                      SPIECE_UNDERLINE + '''and''',
                      SPIECE_UNDERLINE + '''this''',
                      SPIECE_UNDERLINE + '''is''',
                      SPIECE_UNDERLINE + '''f''',
                      '''al''',
                      '''s''',
                      '''<unk>''',
                      '''.''',
                  ]						,       )


       @cached_property
       def 	_snake_case				(    self				)						->    Union[str, Any]:
              return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)


       @slow
       def 	_snake_case				(    self				)						->    Any:
              SCREAMING_SNAKE_CASE_							:				str		='''Hello World!'''
              SCREAMING_SNAKE_CASE_							:				Dict		=[65, 18_536, 2_260, 101, 66]

              self.assertListEqual(__A						,       self.big_tokenizer.encode(__A				)				)


       @slow
       def 	_snake_case				(    self				)						->    Optional[int]:
              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=(
                  '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
                  ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
              )
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				List[Any]		=[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
              # fmt: on
              self.assertListEqual(__A						,       self.big_tokenizer.encode(__A				)				)


       @require_torch
       @slow
       def 	_snake_case				(    self				)						->    int:
              import torch

              from transformers import BigBirdConfig, BigBirdModel

              # Build sequence
              SCREAMING_SNAKE_CASE_							:				Optional[int]		=list(self.big_tokenizer.get_vocab().keys()				)[:10]
              SCREAMING_SNAKE_CASE_							:				Any		=''' '''.join(__A				)
              SCREAMING_SNAKE_CASE_							:				int		=self.big_tokenizer.encode_plus(__A						,       return_tensors='''pt'''						,       return_token_type_ids=__A				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=self.big_tokenizer.batch_encode_plus(
                  [sequence + ''' ''' + sequence]						,       return_tensors='''pt'''						,       return_token_type_ids=__A				)

              SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=BigBirdConfig(attention_type='''original_full'''				)
              SCREAMING_SNAKE_CASE_							:				Tuple		=BigBirdModel(__A				)

              assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

              with torch.no_grad():
                     model(**__A				)
                     model(**__A				)


       @slow
       def 	_snake_case				(    self				)						->    Tuple:
              SCREAMING_SNAKE_CASE_							:				Any		=BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=tokenizer.decode(tokenizer('''Paris is the [MASK].'''				).input_ids				)

              self.assertTrue(decoded_text == '''[CLS] Paris is the[MASK].[SEP]'''				)





       @slow
       def 	_snake_case				(    self				)						->    Any:
              # fmt: off
              SCREAMING_SNAKE_CASE_							:				Optional[int]		={'''input_ids''': [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
              # fmt: on

              self.tokenizer_integration_test_util(
                  expected_encoding=__A						,       model_name='''google/bigbird-roberta-base'''						,       revision='''215c99f1600e06f83acce68422f2035b2b5c3510'''						,       )
