





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 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							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__:							Any   ,				UpperCamelCase__:							Any   ,				UpperCamelCase__:							List[Any]      ):
		return max(metric_fn(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE      ) for gt in ground_truths      )






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Dict   ,				UpperCamelCase__:							int   ,				UpperCamelCase__:							Any      ):
		SCREAMING_SNAKE_CASE__								= [line.strip() for line in open(_SCREAMING_SNAKE_CASE   ,				"""r"""      ).readlines()]
		SCREAMING_SNAKE_CASE__								= []

		if args.gold_data_mode == "qa":
				SCREAMING_SNAKE_CASE__								= pd.read_csv(_SCREAMING_SNAKE_CASE   ,				sep="""\t"""   ,				header=_SCREAMING_SNAKE_CASE      )
				for answer_list in data[1]:
						SCREAMING_SNAKE_CASE__								= ast.literal_eval(_SCREAMING_SNAKE_CASE      )
						answers.append(_SCREAMING_SNAKE_CASE      )
		else:
				SCREAMING_SNAKE_CASE__								= [line.strip() for line in open(_SCREAMING_SNAKE_CASE   ,				"""r"""      ).readlines()]
				SCREAMING_SNAKE_CASE__								= [[reference] for reference in references]

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

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

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






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							List[str]   ,				UpperCamelCase__:							Dict   ,				UpperCamelCase__:							List[str]      ):
		SCREAMING_SNAKE_CASE__								= args.k
		SCREAMING_SNAKE_CASE__								= [line.strip() for line in open(_SCREAMING_SNAKE_CASE   ,				"""r"""      ).readlines()]
		SCREAMING_SNAKE_CASE__								= [line.strip() for line in open(_SCREAMING_SNAKE_CASE   ,				"""r"""      ).readlines()]

		SCREAMING_SNAKE_CASE__								= 0
		for hypo, reference in zip(_SCREAMING_SNAKE_CASE   ,				_SCREAMING_SNAKE_CASE      ):
				SCREAMING_SNAKE_CASE__								= set(hypo.split("""\t"""      )[:k]      )
				SCREAMING_SNAKE_CASE__								= set(reference.split("""\t"""      )      )
				total += 1
				em += len(hypo_provenance & ref_provenance      ) / k

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






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Dict   ,				UpperCamelCase__:							Optional[int]   ,				UpperCamelCase__:							List[str]      ):
		def strip_title(UpperCamelCase__:							Optional[Any]      ):
				if title.startswith("""\""""      ):
						SCREAMING_SNAKE_CASE__								= title[1:]
				if title.endswith("""\""""      ):
						SCREAMING_SNAKE_CASE__								= title[:-1]
				return title

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

		SCREAMING_SNAKE_CASE__								= rag_model.rag.question_encoder(_SCREAMING_SNAKE_CASE      )
		SCREAMING_SNAKE_CASE__								= question_enc_outputs[0]

		SCREAMING_SNAKE_CASE__								= rag_model.retriever(
		    _SCREAMING_SNAKE_CASE   ,				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__								= rag_model.retriever.index.get_doc_dicts(result.doc_ids      )
		SCREAMING_SNAKE_CASE__								= []
		for docs in all_docs:
				SCREAMING_SNAKE_CASE__								= [strip_title(_SCREAMING_SNAKE_CASE      ) for title in docs["title"]]
				provenance_strings.append("""\t""".join(_SCREAMING_SNAKE_CASE      )      )
		return provenance_strings






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Union[str, Any]   ,				UpperCamelCase__:							Union[str, Any]   ,				UpperCamelCase__:							Dict      ):
		with torch.no_grad():
				SCREAMING_SNAKE_CASE__								= rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
				    _SCREAMING_SNAKE_CASE   ,				return_tensors="""pt"""   ,				padding=_SCREAMING_SNAKE_CASE   ,				truncation=_SCREAMING_SNAKE_CASE      )

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

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

				return answers






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






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Union[str, Any]      ):
		SCREAMING_SNAKE_CASE__								= {}
		if args.model_type is None:
				SCREAMING_SNAKE_CASE__								= infer_model_type(args.model_name_or_path      )
				assert args.model_type is not None
		if args.model_type.startswith("""rag"""      ):
				SCREAMING_SNAKE_CASE__								= RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
				SCREAMING_SNAKE_CASE__								= args.n_docs
				if args.index_name is not None:
						SCREAMING_SNAKE_CASE__								= args.index_name
				if args.index_path is not None:
						SCREAMING_SNAKE_CASE__								= args.index_path
		else:
				SCREAMING_SNAKE_CASE__								= BartForConditionalGeneration

		SCREAMING_SNAKE_CASE__								= (
		    [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"""   ,				_SCREAMING_SNAKE_CASE      )

		SCREAMING_SNAKE_CASE__								= get_scores if args.eval_mode == "e2e" else get_precision_at_k
		SCREAMING_SNAKE_CASE__								= 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(_SCREAMING_SNAKE_CASE   ,				args.predictions_path   ,				args.gold_data_path      )
						continue

				logger.info("""***** Running evaluation for {} *****""".format(_SCREAMING_SNAKE_CASE      )      )
				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__								= RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE   ,				**_SCREAMING_SNAKE_CASE      )
						SCREAMING_SNAKE_CASE__								= model_class.from_pretrained(_SCREAMING_SNAKE_CASE   ,				retriever=_SCREAMING_SNAKE_CASE   ,				**_SCREAMING_SNAKE_CASE      )
						model.retriever.init_retrieval()
				else:
						SCREAMING_SNAKE_CASE__								= model_class.from_pretrained(_SCREAMING_SNAKE_CASE   ,				**_SCREAMING_SNAKE_CASE      )
				model.to(args.device      )

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

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


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






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 					UpperCamelCase_    (       unittest.TestCase      ):


							def        _snake_case					(							self      :str							,			__A      :str						)				->				Optional[int]:



									"""simple docstring"""



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

											for special_tok in tokenizer.all_special_tokens:
													self.assertIsInstance(__A							,			__A						)
													SCREAMING_SNAKE_CASE__								= tokenizer.convert_tokens_to_ids(__A						)
													self.assertIsInstance(__A							,			__A						)


							def        _snake_case					(							self      :List[str]							,			__A      :BatchEncoding							,			__A      :Optional[Callable] = None						)				->				Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= pickle.dumps(__A						)
									self.assertIsNotNone(__A						)

									SCREAMING_SNAKE_CASE__								= pickle.loads(__A						)

									# 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(__A							,			__A						)						)
											else:
													self.assertEqual(__A							,			__A						)


							@slow
							def        _snake_case					(							self      :Dict						)				->				Optional[int]:



									"""simple docstring"""



									self.check_tokenizer_from_pretrained(__A						)


							def        _snake_case					(							self      :List[Any]						)				->				Dict:



									"""simple docstring"""



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


							@require_tokenizers
							def        _snake_case					(							self      :Optional[Any]						)				->				List[Any]:



									"""simple docstring"""



									import numpy as np

									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= 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        _snake_case					(							self      :Optional[Any]						)				->				Optional[Any]:



									"""simple docstring"""



									import tensorflow as tf

									def tf_array_equals(__A      :Union[str, Any]							,			__A      :Any						):
											return tf.reduce_all(tf.equal(__A							,			__A						)						)

									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= 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						)							,			__A						)

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


							@require_torch
							@require_tokenizers
							def        _snake_case					(							self      :List[Any]						)				->				int:



									"""simple docstring"""



									import torch

									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= 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        _snake_case					(							self      :Optional[Any]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BertTokenizer.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= 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        _snake_case					(							self      :List[str]						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BertTokenizerFast.from_pretrained("""bert-base-cased"""						)
									SCREAMING_SNAKE_CASE__								= tokenizer_r(["""Test""", """\xad""", """test"""]							,			is_split_into_words=__A						)

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


							def        _snake_case					(							self      :Tuple						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= 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:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""np"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

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


							@require_torch
							def        _snake_case					(							self      :int						)				->				int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= 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:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""pt"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

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


							@require_tf
							def        _snake_case					(							self      :Optional[Any]						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= 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:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""tf"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

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


							@require_flax
							def        _snake_case					(							self      :Dict						)				->				Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}						)
									SCREAMING_SNAKE_CASE__								= 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:
											SCREAMING_SNAKE_CASE__								= batch.convert_to_tensors(tensor_type="""jax"""						)
									self.assertFalse(len(cs.err						)							,			msg=f'''should have no warning, but got {cs.err}'''						)

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


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



									"""simple docstring"""



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

									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A						)
									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]]						)
									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A							,			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        _snake_case					(							self      :int						)				->				Dict:



									"""simple docstring"""



									import torch

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

									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A						)
									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]]						)
									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A							,			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        _snake_case					(							self      :int						)				->				int:



									"""simple docstring"""



									import tensorflow as tf

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

									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A						)
									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]]						)
									SCREAMING_SNAKE_CASE__								= tokenizer.pad(__A							,			padding=__A							,			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        _snake_case					(							self      :Tuple						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= Tokenizer(WordPiece(unk_token="""[UNK]"""						)						)
									PreTrainedTokenizerFast(tokenizer_object=__A						)


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



									"""simple docstring"""



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