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


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

transformers_logging.set_verbosity_info()




def   _UpperCamelCase   (			snake_case__							)						->   Optional[int]:
			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   (			snake_case__,    snake_case__,    snake_case__							)						->   Dict:
			return max(metric_fn(snake_case__,    snake_case__							) for gt in ground_truths							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   List[Any]:
			__UpperCAmelCase						:			Tuple         =   [line.strip() for line in open(snake_case__,    "r"							).readlines()]
			__UpperCAmelCase						:			List[str]         =   []

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

			__UpperCAmelCase						:			Any         =   0
			for prediction, ground_truths in zip(snake_case__,    snake_case__							):
						total += 1
						em += metric_max_over_ground_truths(snake_case__,    snake_case__,    snake_case__							)
						fa += metric_max_over_ground_truths(snake_case__,    snake_case__,    snake_case__							)

			__UpperCAmelCase						:			Tuple         =   100.0 * em / total
			__UpperCAmelCase						:			Optional[Any]         =   100.0 * fa / total

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




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   str:
			__UpperCAmelCase						:			Dict         =   args.k
			__UpperCAmelCase						:			Union[str, Any]         =   [line.strip() for line in open(snake_case__,    "r"							).readlines()]
			__UpperCAmelCase						:			Dict         =   [line.strip() for line in open(snake_case__,    "r"							).readlines()]

			__UpperCAmelCase						:			Tuple         =   0
			for hypo, reference in zip(snake_case__,    snake_case__							):
						__UpperCAmelCase						:			List[Any]         =   set(hypo.split("\t"							)[:k]							)
						__UpperCAmelCase						:			List[str]         =   set(reference.split("\t"							)							)
						total += 1
						em += len(hypo_provenance & ref_provenance							) / k

			__UpperCAmelCase						:			Optional[Any]         =   100.0 * em / total
			logger.info(f'''Precision@{k}: {em: .2f}'''							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   str:
			def strip_title(snake_case__							):
						if title.startswith("\""							):
									__UpperCAmelCase						:			int         =   title[1:]
						if title.endswith("\""							):
									__UpperCAmelCase						:			int         =   title[:-1]
						return title

			__UpperCAmelCase						:			Optional[Any]         =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
			    snake_case__,    return_tensors="pt",    padding=snake_case__,    truncation=snake_case__,    )["input_ids"].to(args.device							)

			__UpperCAmelCase						:			str         =   rag_model.rag.question_encoder(snake_case__							)
			__UpperCAmelCase						:			Tuple         =   question_enc_outputs[0]

			__UpperCAmelCase						:			List[Any]         =   rag_model.retriever(
			    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",    )
			__UpperCAmelCase						:			int         =   rag_model.retriever.index.get_doc_dicts(result.doc_ids							)
			__UpperCAmelCase						:			Any         =   []
			for docs in all_docs:
						__UpperCAmelCase						:			Optional[Any]         =   [strip_title(snake_case__							) for title in docs["title"]]
						provenance_strings.append("\t".join(snake_case__							)							)
			return provenance_strings




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   Optional[Any]:
			with torch.no_grad():
						__UpperCAmelCase						:			Union[str, Any]         =   rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
						    snake_case__,    return_tensors="pt",    padding=snake_case__,    truncation=snake_case__							)

						__UpperCAmelCase						:			Dict         =   inputs_dict.input_ids.to(args.device							)
						__UpperCAmelCase						:			List[Any]         =   inputs_dict.attention_mask.to(args.device							)
						__UpperCAmelCase						:			Any         =   rag_model.generate(  # rag_model overwrites generate
						    snake_case__,    attention_mask=snake_case__,    num_beams=args.num_beams,    min_length=args.min_length,    max_length=args.max_length,    early_stopping=snake_case__,    num_return_sequences=1,    bad_words_ids=[[0, 0]],    )
						__UpperCAmelCase						:			Tuple         =   rag_model.retriever.generator_tokenizer.batch_decode(snake_case__,    skip_special_tokens=snake_case__							)

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

						return answers




def   _UpperCamelCase   (			)						->   List[str]:
			__UpperCAmelCase						:			List[Any]         =   argparse.ArgumentParser()
			parser.add_argument(
			    "--model_type",    choices=["rag_sequence", "rag_token", "bart"],    type=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=snake_case__,    choices=["exact", "compressed", "legacy"],    type=snake_case__,    help="RAG model retriever type",    )
			parser.add_argument(
			    "--index_path",    default=snake_case__,    type=snake_case__,    help="Path to the retrieval index",    )
			parser.add_argument("--n_docs",    default=5,    type=snake_case__,    help="Number of retrieved docs"							)
			parser.add_argument(
			    "--model_name_or_path",    default=snake_case__,    type=snake_case__,    required=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=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=snake_case__,    help="k for the precision@k calculation"							)
			parser.add_argument(
			    "--evaluation_set",    default=snake_case__,    type=snake_case__,    required=snake_case__,    help="Path to a file containing evaluation samples",    )
			parser.add_argument(
			    "--gold_data_path",    default=snake_case__,    type=snake_case__,    required=snake_case__,    help="Path to a tab-separated file with gold samples",    )
			parser.add_argument(
			    "--gold_data_mode",    default="qa",    type=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=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=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=snake_case__,    help="Number of beams to be used when generating answers",    )
			parser.add_argument("--min_length",    default=1,    type=snake_case__,    help="Min length of the generated answers"							)
			parser.add_argument("--max_length",    default=50,    type=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.",    )
			__UpperCAmelCase						:			Union[str, Any]         =   parser.parse_args()
			__UpperCAmelCase						:			List[str]         =   torch.device("cuda" if torch.cuda.is_available() else "cpu"							)
			return args




def   _UpperCamelCase   (			snake_case__							)						->   List[str]:
			__UpperCAmelCase						:			int         =   {}
			if args.model_type is None:
						__UpperCAmelCase						:			str         =   infer_model_type(args.model_name_or_path							)
						assert args.model_type is not None
			if args.model_type.startswith("rag"							):
						__UpperCAmelCase						:			Optional[Any]         =   RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
						__UpperCAmelCase						:			List[str]         =   args.n_docs
						if args.index_name is not None:
									__UpperCAmelCase						:			Union[str, Any]         =   args.index_name
						if args.index_path is not None:
									__UpperCAmelCase						:			str         =   args.index_path
			else:
						__UpperCAmelCase						:			Optional[Any]         =   BartForConditionalGeneration

			__UpperCAmelCase						:			Tuple         =   (
			    [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",    snake_case__							)

			__UpperCAmelCase						:			Union[str, Any]         =   get_scores if args.eval_mode == "e2e" else get_precision_at_k
			__UpperCAmelCase						:			List[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(snake_case__,    args.predictions_path,    args.gold_data_path							)
									continue

						logger.info("***** Running evaluation for {} *****".format(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"							):
									__UpperCAmelCase						:			Union[str, Any]         =   RagRetriever.from_pretrained(snake_case__,    **snake_case__							)
									__UpperCAmelCase						:			Union[str, Any]         =   model_class.from_pretrained(snake_case__,    retriever=snake_case__,    **snake_case__							)
									model.retriever.init_retrieval()
						else:
									__UpperCAmelCase						:			str         =   model_class.from_pretrained(snake_case__,    **snake_case__							)
						model.to(args.device							)

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

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


if __name__ == "__main__":
	_snake_case			=						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 					_snake_case						(						unittest.TestCase				):
					def    _lowerCamelCase (  self:				str	,  __lowerCamelCase:				int     )    ->				List[Any]:
												__UpperCAmelCase						:			List[str]         =   list(tokenizer_class.max_model_input_sizes.keys()     )
												for model_name in sa_models[:1]:
																			__UpperCAmelCase						:			Any         =   tokenizer_class.from_pretrained(__lowerCamelCase     )
																			self.assertIsNotNone(__lowerCamelCase     )
																			self.assertIsInstance(__lowerCamelCase	,  __lowerCamelCase     )
																			self.assertIsInstance(__lowerCamelCase	,  __lowerCamelCase     )

																			for special_tok in tokenizer.all_special_tokens:
																										self.assertIsInstance(__lowerCamelCase	,  __lowerCamelCase     )
																										__UpperCAmelCase						:			str         =   tokenizer.convert_tokens_to_ids(__lowerCamelCase     )
																										self.assertIsInstance(__lowerCamelCase	,  __lowerCamelCase     )
					def    _lowerCamelCase (  self:				List[Any]	,  __lowerCamelCase:				BatchEncoding	,  __lowerCamelCase:				Optional[Callable] = None     )    ->				Dict:
												__UpperCAmelCase						:			int         =   pickle.dumps(__lowerCamelCase     )
												self.assertIsNotNone(__lowerCamelCase     )

												__UpperCAmelCase						:			Dict         =   pickle.loads(__lowerCamelCase     )

												# 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(__lowerCamelCase	,  __lowerCamelCase     )     )
																			else:
																										self.assertEqual(__lowerCamelCase	,  __lowerCamelCase     )
					@slow
					def    _lowerCamelCase (  self:				str     )    ->				Optional[Any]:
												self.check_tokenizer_from_pretrained(__lowerCamelCase     )
					def    _lowerCamelCase (  self:				str     )    ->				Dict:
												self.assertEqual(TensorType("tf"     )	,  TensorType.TENSORFLOW     )
												self.assertEqual(TensorType("pt"     )	,  TensorType.PYTORCH     )
												self.assertEqual(TensorType("np"     )	,  TensorType.NUMPY     )
					@require_tokenizers
					def    _lowerCamelCase (  self:				Union[str, Any]     )    ->				int:
												import numpy as np

												__UpperCAmelCase						:			str         =   BertTokenizer.from_pretrained("bert-base-cased"     )
												__UpperCAmelCase						:			Any         =   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    _lowerCamelCase (  self:				Tuple     )    ->				str:
												import tensorflow as tf

												def tf_array_equals(__lowerCamelCase:				Dict	,  __lowerCamelCase:				Tuple     ):
																			return tf.reduce_all(tf.equal(__lowerCamelCase	,  __lowerCamelCase     )     )

												__UpperCAmelCase						:			List[str]         =   BertTokenizer.from_pretrained("bert-base-cased"     )
												__UpperCAmelCase						:			Optional[int]         =   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     )	,  __lowerCamelCase     )

												with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"     ):
																			self.assert_dump_and_restore(
																			    tokenizer_r("Small example to encode"	,  return_tensors=TensorType.TENSORFLOW     )	,  __lowerCamelCase     )
					@require_torch
					@require_tokenizers
					def    _lowerCamelCase (  self:				Tuple     )    ->				Union[str, Any]:
												import torch

												__UpperCAmelCase						:			Optional[int]         =   BertTokenizer.from_pretrained("bert-base-cased"     )
												__UpperCAmelCase						:			Optional[Any]         =   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    _lowerCamelCase (  self:				int     )    ->				Any:
												__UpperCAmelCase						:			Tuple         =   BertTokenizer.from_pretrained("bert-base-cased"     )
												__UpperCAmelCase						:			Tuple         =   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    _lowerCamelCase (  self:				Union[str, Any]     )    ->				List[str]:
												__UpperCAmelCase						:			Tuple         =   BertTokenizerFast.from_pretrained("bert-base-cased"     )
												__UpperCAmelCase						:			Tuple         =   tokenizer_r(["Test", "\xad", "test"]	,  is_split_into_words=__lowerCamelCase     )

												self.assertEqual(encoded.word_to_tokens(0     )	,  TokenSpan(start=1	,  end=2     )     )
												self.assertEqual(encoded.word_to_tokens(1     )	,  __lowerCamelCase     )
												self.assertEqual(encoded.word_to_tokens(2     )	,  TokenSpan(start=2	,  end=3     )     )
					def    _lowerCamelCase (  self:				List[str]     )    ->				List[Any]:
												__UpperCAmelCase						:			str         =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}     )
												__UpperCAmelCase						:			str         =   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:
																			__UpperCAmelCase						:			Tuple         =   batch.convert_to_tensors(tensor_type="np"     )
												self.assertFalse(len(cs.err     )	,  msg=f'''should have no warning, but got {cs.err}'''     )

												__UpperCAmelCase						:			Optional[Any]         =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}     )
												__UpperCAmelCase						:			Any         =   batch.convert_to_tensors(tensor_type="np"	,  prepend_batch_axis=__lowerCamelCase     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (1, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (1,)     )
					@require_torch
					def    _lowerCamelCase (  self:				Dict     )    ->				int:
												__UpperCAmelCase						:			str         =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}     )
												__UpperCAmelCase						:			Union[str, Any]         =   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:
																			__UpperCAmelCase						:			Optional[int]         =   batch.convert_to_tensors(tensor_type="pt"     )
												self.assertFalse(len(cs.err     )	,  msg=f'''should have no warning, but got {cs.err}'''     )

												__UpperCAmelCase						:			Optional[Any]         =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}     )
												__UpperCAmelCase						:			Union[str, Any]         =   batch.convert_to_tensors(tensor_type="pt"	,  prepend_batch_axis=__lowerCamelCase     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (1, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (1,)     )
					@require_tf
					def    _lowerCamelCase (  self:				Any     )    ->				Dict:
												__UpperCAmelCase						:			Any         =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}     )
												__UpperCAmelCase						:			Union[str, Any]         =   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:
																			__UpperCAmelCase						:			Tuple         =   batch.convert_to_tensors(tensor_type="tf"     )
												self.assertFalse(len(cs.err     )	,  msg=f'''should have no warning, but got {cs.err}'''     )

												__UpperCAmelCase						:			Tuple         =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}     )
												__UpperCAmelCase						:			int         =   batch.convert_to_tensors(tensor_type="tf"	,  prepend_batch_axis=__lowerCamelCase     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (1, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (1,)     )
					@require_flax
					def    _lowerCamelCase (  self:				Any     )    ->				Tuple:
												__UpperCAmelCase						:			Optional[Any]         =   BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}     )
												__UpperCAmelCase						:			List[Any]         =   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:
																			__UpperCAmelCase						:			Tuple         =   batch.convert_to_tensors(tensor_type="jax"     )
												self.assertFalse(len(cs.err     )	,  msg=f'''should have no warning, but got {cs.err}'''     )

												__UpperCAmelCase						:			str         =   BatchEncoding({"inputs": [1, 2, 3], "labels": 0}     )
												__UpperCAmelCase						:			str         =   batch.convert_to_tensors(tensor_type="jax"	,  prepend_batch_axis=__lowerCamelCase     )
												self.assertEqual(tensor_batch["inputs"].shape	,  (1, 3)     )
												self.assertEqual(tensor_batch["labels"].shape	,  (1,)     )
					def    _lowerCamelCase (  self:				Optional[int]     )    ->				Optional[int]:
												__UpperCAmelCase						:			List[Any]         =   [{"input_ids": np.array([0, 1, 2]     )}, {"input_ids": np.array([0, 1, 2, 3]     )}]
												__UpperCAmelCase						:			Any         =   BertTokenizer.from_pretrained("bert-base-cased"     )

												__UpperCAmelCase						:			Union[str, Any]         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase     )
												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]]     )
												__UpperCAmelCase						:			Union[str, Any]         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase	,  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    _lowerCamelCase (  self:				List[Any]     )    ->				Optional[Any]:
												import torch

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

												__UpperCAmelCase						:			str         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase     )
												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]]     )
												__UpperCAmelCase						:			Union[str, Any]         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase	,  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    _lowerCamelCase (  self:				int     )    ->				Union[str, Any]:
												import tensorflow as tf

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

												__UpperCAmelCase						:			str         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase     )
												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]]     )
												__UpperCAmelCase						:			Dict         =   tokenizer.pad(__lowerCamelCase	,  padding=__lowerCamelCase	,  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    _lowerCamelCase (  self:				Dict     )    ->				int:
												__UpperCAmelCase						:			Optional[int]         =   Tokenizer(WordPiece(unk_token="[UNK]"     )     )
												PreTrainedTokenizerFast(tokenizer_object=__lowerCamelCase     )





					@require_tokenizers
					def    _lowerCamelCase (  self:				str     )    ->				Dict:
												__UpperCAmelCase						:			Tuple         =   Tokenizer(WordPiece(unk_token="[UNK]"     )     )
												with tempfile.TemporaryDirectory() as tmpdirname:
																			bert_tokenizer.save(os.path.join(__lowerCamelCase	,  "tokenizer.json"     )     )
																			PreTrainedTokenizerFast(tokenizer_file=os.path.join(__lowerCamelCase	,  "tokenizer.json"     )     )




