
"""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


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

transformers_logging.set_verbosity_info()




def __magic_name__  (							_lowerCamelCase:		Any )				->	Dict:

 '''simple docstring'''




 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 __magic_name__  (							_lowerCamelCase:		Tuple,				_lowerCamelCase:		Union[str, Any],				_lowerCamelCase:		Optional[Any] )				->	Optional[int]:

 '''simple docstring'''




 return max(metric_fn(_lowerCamelCase,				_lowerCamelCase ) for gt in ground_truths )




def __magic_name__  (							_lowerCamelCase:		Optional[Any],				_lowerCamelCase:		Tuple,				_lowerCamelCase:		List[Any] )				->	Optional[Any]:

 '''simple docstring'''




 lowerCAmelCase				       =     [line.strip() for line in open(_lowerCamelCase,				'''r''' ).readlines()]
 lowerCAmelCase				       =     []

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

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

 lowerCAmelCase				       =     100.0 * em / total
 lowerCAmelCase				       =     100.0 * fa / total

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




def __magic_name__  (							_lowerCamelCase:		List[Any],				_lowerCamelCase:		str,				_lowerCamelCase:		str )				->	Optional[Any]:

 '''simple docstring'''




 lowerCAmelCase				       =     args.k
 lowerCAmelCase				       =     [line.strip() for line in open(_lowerCamelCase,				'''r''' ).readlines()]
 lowerCAmelCase				       =     [line.strip() for line in open(_lowerCamelCase,				'''r''' ).readlines()]

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

 lowerCAmelCase				       =     100.0 * em / total
 logger.info(F"""Precision@{k}: {em: .2f}""" )




def __magic_name__  (							_lowerCamelCase:		int,				_lowerCamelCase:		Tuple,				_lowerCamelCase:		List[Any] )				->	Tuple:

 '''simple docstring'''




 def strip_title(_lowerCamelCase:		Union[str, Any] ):
  if title.startswith('''\"''' ):
   lowerCAmelCase				       =     title[1:]
  if title.endswith('''\"''' ):
   lowerCAmelCase				       =     title[:-1]
  return title

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

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

 lowerCAmelCase				       =     rag_model.retriever(
     _lowerCamelCase,				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(_lowerCamelCase ) for title in docs["title"]]
  provenance_strings.append('''\t'''.join(_lowerCamelCase ) )
 return provenance_strings




def __magic_name__  (							_lowerCamelCase:		Optional[int],				_lowerCamelCase:		Optional[int],				_lowerCamelCase:		int )				->	Any:

 '''simple docstring'''




 with torch.no_grad():
  lowerCAmelCase				       =     rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
      _lowerCamelCase,				return_tensors='''pt''',				padding=_lowerCamelCase,				truncation=_lowerCamelCase )

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

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

  return answers




def __magic_name__  (							)				->	List[str]:

 '''simple docstring'''




 lowerCAmelCase				       =     argparse.ArgumentParser()
 parser.add_argument(
     '''--model_type''',				choices=['''rag_sequence''', '''rag_token''', '''bart'''],				type=_lowerCamelCase,				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=_lowerCamelCase,				choices=['''exact''', '''compressed''', '''legacy'''],				type=_lowerCamelCase,				help='''RAG model retriever type''',				)
 parser.add_argument(
     '''--index_path''',				default=_lowerCamelCase,				type=_lowerCamelCase,				help='''Path to the retrieval index''',				)
 parser.add_argument('''--n_docs''',				default=5,				type=_lowerCamelCase,				help='''Number of retrieved docs''' )
 parser.add_argument(
     '''--model_name_or_path''',				default=_lowerCamelCase,				type=_lowerCamelCase,				required=_lowerCamelCase,				help='''Path to pretrained checkpoints or model identifier from huggingface.co/models''',				)
 parser.add_argument(
     '''--eval_mode''',				choices=['''e2e''', '''retrieval'''],				default='''e2e''',				type=_lowerCamelCase,				help=(
         '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
         ''' precision@k.'''
     ),				)
 parser.add_argument('''--k''',				default=1,				type=_lowerCamelCase,				help='''k for the precision@k calculation''' )
 parser.add_argument(
     '''--evaluation_set''',				default=_lowerCamelCase,				type=_lowerCamelCase,				required=_lowerCamelCase,				help='''Path to a file containing evaluation samples''',				)
 parser.add_argument(
     '''--gold_data_path''',				default=_lowerCamelCase,				type=_lowerCamelCase,				required=_lowerCamelCase,				help='''Path to a tab-separated file with gold samples''',				)
 parser.add_argument(
     '''--gold_data_mode''',				default='''qa''',				type=_lowerCamelCase,				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=_lowerCamelCase,				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=_lowerCamelCase,				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=_lowerCamelCase,				help='''Number of beams to be used when generating answers''',				)
 parser.add_argument('''--min_length''',				default=1,				type=_lowerCamelCase,				help='''Min length of the generated answers''' )
 parser.add_argument('''--max_length''',				default=50,				type=_lowerCamelCase,				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 __magic_name__  (							_lowerCamelCase:		List[str] )				->	List[str]:

 '''simple docstring'''




 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''',				_lowerCamelCase )

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

  logger.info('''***** Running evaluation for {} *****'''.format(_lowerCamelCase ) )
  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(_lowerCamelCase,				**_lowerCamelCase )
   lowerCAmelCase				       =     model_class.from_pretrained(_lowerCamelCase,				retriever=_lowerCamelCase,				**_lowerCamelCase )
   model.retriever.init_retrieval()
  else:
   lowerCAmelCase				       =     model_class.from_pretrained(_lowerCamelCase,				**_lowerCamelCase )
  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(_lowerCamelCase ):
    questions.append(line.strip() )
    if len(_lowerCamelCase ) == args.eval_batch_size:
     lowerCAmelCase				       =     evaluate_batch_fn(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase )
     preds_file.write('''\n'''.join(_lowerCamelCase ) + '''\n''' )
     preds_file.flush()
     lowerCAmelCase				       =     []
   if len(_lowerCamelCase ) > 0:
    lowerCAmelCase				       =     evaluate_batch_fn(_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase )
    preds_file.write('''\n'''.join(_lowerCamelCase ) )
    preds_file.flush()

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


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




"""simple docstring"""






import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
					from tokenizers import Tokenizer
					from tokenizers.models import WordPiece



class  lowercase				(   unittest.TestCase		):






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  Dict )					->  int:





				"""simple docstring"""


				lowerCAmelCase				       =     list(tokenizer_class.max_model_input_sizes.keys() )
				for model_name in sa_models[:1]:
					lowerCAmelCase				       =     tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
					self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					for special_tok in tokenizer.all_special_tokens:
						self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
						lowerCAmelCase				       =     tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
						self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  BatchEncoding			,SCREAMING_SNAKE_CASE_     :  Optional[Callable] = None )					->  Dict:





				"""simple docstring"""


				lowerCAmelCase				       =     pickle.dumps(SCREAMING_SNAKE_CASE_ )
				self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     pickle.loads(SCREAMING_SNAKE_CASE_ )

				# Ensure is_fast is correctly restored
				self.assertEqual(be_restored.is_fast			,be_original.is_fast )

				# Ensure encodings are potentially correctly restored
				if be_original.is_fast:
					self.assertIsNotNone(be_restored.encodings )
				else:
					self.assertIsNone(be_restored.encodings )

				# Ensure the keys are the same
				for original_v, restored_v in zip(be_original.values()			,be_restored.values() ):
					if equal_op:
						self.assertTrue(equal_op(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ ) )
					else:
						self.assertEqual(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )






			@slow
			def 				UpperCAmelCase       (self     :  List[str] )					->  int:





				"""simple docstring"""


				self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  str )					->  Dict:





				"""simple docstring"""


				self.assertEqual(TensorType('''tf''' )			,TensorType.TENSORFLOW )
				self.assertEqual(TensorType('''pt''' )			,TensorType.PYTORCH )
				self.assertEqual(TensorType('''np''' )			,TensorType.NUMPY )






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  int )					->  Tuple:





				"""simple docstring"""


				import numpy as np

				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     BertTokenizerFast.from_pretrained('''bert-base-cased''' )

				# Python no tensor
				with self.subTest('''BatchEncoding (Python, return_tensors=None)''' ):
					self.assert_dump_and_restore(tokenizer_p('''Small example to encode''' ) )

				with self.subTest('''BatchEncoding (Python, return_tensors=NUMPY)''' ):
					self.assert_dump_and_restore(
					    tokenizer_p('''Small example to encode'''			,return_tensors=TensorType.NUMPY )			,np.array_equal )

				with self.subTest('''BatchEncoding (Rust, return_tensors=None)''' ):
					self.assert_dump_and_restore(tokenizer_r('''Small example to encode''' ) )

				with self.subTest('''BatchEncoding (Rust, return_tensors=NUMPY)''' ):
					self.assert_dump_and_restore(
					    tokenizer_r('''Small example to encode'''			,return_tensors=TensorType.NUMPY )			,np.array_equal )






			@require_tf
			@require_tokenizers
			def 				UpperCAmelCase       (self     :  str )					->  int:





				"""simple docstring"""


				import tensorflow as tf

				def tf_array_equals(SCREAMING_SNAKE_CASE_     :  Optional[Any]			,SCREAMING_SNAKE_CASE_     :  List[Any] ):
					return tf.reduce_all(tf.equal(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ ) )

				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     BertTokenizerFast.from_pretrained('''bert-base-cased''' )

				with self.subTest('''BatchEncoding (Python, return_tensors=TENSORFLOW)''' ):
					self.assert_dump_and_restore(
					    tokenizer_p('''Small example to encode'''			,return_tensors=TensorType.TENSORFLOW )			,SCREAMING_SNAKE_CASE_ )

				with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)''' ):
					self.assert_dump_and_restore(
					    tokenizer_r('''Small example to encode'''			,return_tensors=TensorType.TENSORFLOW )			,SCREAMING_SNAKE_CASE_ )






			@require_torch
			@require_tokenizers
			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  Union[str, Any]:





				"""simple docstring"""


				import torch

				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     BertTokenizerFast.from_pretrained('''bert-base-cased''' )

				with self.subTest('''BatchEncoding (Python, return_tensors=PYTORCH)''' ):
					self.assert_dump_and_restore(
					    tokenizer_p('''Small example to encode'''			,return_tensors=TensorType.PYTORCH )			,torch.equal )

				with self.subTest('''BatchEncoding (Rust, return_tensors=PYTORCH)''' ):
					self.assert_dump_and_restore(
					    tokenizer_r('''Small example to encode'''			,return_tensors=TensorType.PYTORCH )			,torch.equal )






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  str )					->  Tuple:





				"""simple docstring"""


				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     BertTokenizerFast.from_pretrained('''bert-base-cased''' )

				with self.subTest('''Python Tokenizer''' ):
					self.assertFalse(tokenizer_p('''Small example to_encode''' ).is_fast )

				with self.subTest('''Rust Tokenizer''' ):
					self.assertTrue(tokenizer_r('''Small example to_encode''' ).is_fast )






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  Any )					->  Dict:





				"""simple docstring"""


				lowerCAmelCase				       =     BertTokenizerFast.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     tokenizer_r(['''Test''', '''\xad''', '''test''']			,is_split_into_words=SCREAMING_SNAKE_CASE_ )

				self.assertEqual(encoded.word_to_tokens(0 )			,TokenSpan(start=1			,end=2 ) )
				self.assertEqual(encoded.word_to_tokens(1 )			,SCREAMING_SNAKE_CASE_ )
				self.assertEqual(encoded.word_to_tokens(2 )			,TokenSpan(start=2			,end=3 ) )






			def 				UpperCAmelCase       (self     :  List[Any] )					->  int:





				"""simple docstring"""


				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''np''' )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(2, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(2,) )
				# test converting the converted
				with CaptureStderr() as cs:
					lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''np''' )
				self.assertFalse(len(cs.err )			,msg=F"""should have no warning, but got {cs.err}""" )

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''np'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






			@require_torch
			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Optional[int]:





				"""simple docstring"""


				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''pt''' )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(2, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(2,) )
				# test converting the converted
				with CaptureStderr() as cs:
					lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''pt''' )
				self.assertFalse(len(cs.err )			,msg=F"""should have no warning, but got {cs.err}""" )

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''pt'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






			@require_tf
			def 				UpperCAmelCase       (self     :  List[Any] )					->  List[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''tf''' )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(2, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(2,) )
				# test converting the converted
				with CaptureStderr() as cs:
					lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''tf''' )
				self.assertFalse(len(cs.err )			,msg=F"""should have no warning, but got {cs.err}""" )

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''tf'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






			@require_flax
			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Optional[int]:





				"""simple docstring"""


				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''jax''' )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(2, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(2,) )
				# test converting the converted
				with CaptureStderr() as cs:
					lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''jax''' )
				self.assertFalse(len(cs.err )			,msg=F"""should have no warning, but got {cs.err}""" )

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''jax'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     [{'''input_ids''': np.array([0, 1, 2] )}, {'''input_ids''': np.array([0, 1, 2, 3] )}]
				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )

				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_ )
				self.assertTrue(isinstance(batch['''input_ids''']			,np.ndarray ) )
				self.assertEqual(batch['''input_ids'''].tolist()			,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,return_tensors='''np''' )
				self.assertTrue(isinstance(batch['''input_ids''']			,np.ndarray ) )
				self.assertEqual(batch['''input_ids'''].tolist()			,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )






			@require_torch
			def 				UpperCAmelCase       (self     :  str )					->  Tuple:





				"""simple docstring"""


				import torch

				lowerCAmelCase				       =     [{'''input_ids''': torch.tensor([0, 1, 2] )}, {'''input_ids''': torch.tensor([0, 1, 2, 3] )}]
				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )

				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_ )
				self.assertTrue(isinstance(batch['''input_ids''']			,torch.Tensor ) )
				self.assertEqual(batch['''input_ids'''].tolist()			,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,return_tensors='''pt''' )
				self.assertTrue(isinstance(batch['''input_ids''']			,torch.Tensor ) )
				self.assertEqual(batch['''input_ids'''].tolist()			,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )






			@require_tf
			def 				UpperCAmelCase       (self     :  Tuple )					->  Any:





				"""simple docstring"""


				import tensorflow as tf

				lowerCAmelCase				       =     [{'''input_ids''': tf.constant([0, 1, 2] )}, {'''input_ids''': tf.constant([0, 1, 2, 3] )}]
				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )

				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_ )
				self.assertTrue(isinstance(batch['''input_ids''']			,tf.Tensor ) )
				self.assertEqual(batch['''input_ids'''].numpy().tolist()			,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )
				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,return_tensors='''tf''' )
				self.assertTrue(isinstance(batch['''input_ids''']			,tf.Tensor ) )
				self.assertEqual(batch['''input_ids'''].numpy().tolist()			,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]] )






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  List[str]:





				"""simple docstring"""


				lowerCAmelCase				       =     Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
				PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_ )






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  Tuple )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
				with tempfile.TemporaryDirectory() as tmpdirname:
					bert_tokenizer.save(os.path.join(SCREAMING_SNAKE_CASE_			,'''tokenizer.json''' ) )
					PreTrainedTokenizerFast(tokenizer_file=os.path.join(SCREAMING_SNAKE_CASE_			,'''tokenizer.json''' ) )


