




import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
		from huggingface_hub import snapshot_download
		from pyctcdecode import BeamSearchDecoderCTC

		from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
		from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
		from transformers import WavaVecaForCTC




@require_pyctcdecode
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  List[Any]:
							a					=    "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
							a					=    dict(zip(__lowerCamelCase      ,   range(len(__lowerCamelCase							)							)							)							)

							a					=    {
							    "unk_token": "<unk>",
							    "bos_token": "<s>",
							    "eos_token": "</s>",
							}
							a					=    {
							    "feature_size": 1,
							    "padding_value": 0.0,
							    "sampling_rate": 1_60_00,
							    "return_attention_mask": False,
							    "do_normalize": True,
							}

							a					=    tempfile.mkdtemp()
							a					=    os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES["vocab_file"]							)
							a					=    os.path.join(self.tmpdirname      ,   __lowerCamelCase							)
							with open(self.vocab_file      ,   "w"      ,   encoding="utf-8"							) as fp:
										fp.write(json.dumps(__lowerCamelCase							) + "\n"							)

							with open(self.feature_extraction_file      ,   "w"      ,   encoding="utf-8"							) as fp:
										fp.write(json.dumps(__lowerCamelCase							) + "\n"							)

							# load decoder from hub
							a					=    "hf-internal-testing/ngram-beam-search-decoder"





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   **__lowerCamelCase       :  Union[str, Any]							)      ->  int:
							a					=    self.add_kwargs_tokens_map.copy()
							kwargs.update(__lowerCamelCase							)
							return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  str      ,   **__lowerCamelCase       :  Optional[int]							)      ->  int:
							return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   **__lowerCamelCase       :  Union[str, Any]							)      ->  int:
							return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  List[str]:
							shutil.rmtree(self.tmpdirname							)





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  str:
							a					=    self.get_tokenizer()
							a					=    self.get_feature_extractor()
							a					=    self.get_decoder()

							a					=    WavaVecaProcessorWithLM(tokenizer=__lowerCamelCase      ,   feature_extractor=__lowerCamelCase      ,   decoder=__lowerCamelCase							)

							processor.save_pretrained(self.tmpdirname							)
							a					=    WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname							)

							# tokenizer
							self.assertEqual(processor.tokenizer.get_vocab()      ,   tokenizer.get_vocab()							)
							self.assertIsInstance(processor.tokenizer      ,   __lowerCamelCase							)

							# feature extractor
							self.assertEqual(processor.feature_extractor.to_json_string()      ,   feature_extractor.to_json_string()							)
							self.assertIsInstance(processor.feature_extractor      ,   __lowerCamelCase							)

							# decoder
							self.assertEqual(processor.decoder._alphabet.labels      ,   decoder._alphabet.labels							)
							self.assertEqual(
							    processor.decoder.model_container[decoder._model_key]._unigram_set      ,   decoder.model_container[decoder._model_key]._unigram_set      ,   )
							self.assertIsInstance(processor.decoder      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Optional[int]:
							a					=    WavaVecaProcessorWithLM(
							    tokenizer=self.get_tokenizer()      ,   feature_extractor=self.get_feature_extractor()      ,   decoder=self.get_decoder()							)
							processor.save_pretrained(self.tmpdirname							)

							# make sure that error is thrown when decoder alphabet doesn't match
							a					=    WavaVecaProcessorWithLM.from_pretrained(
							    self.tmpdirname      ,   alpha=5.0      ,   beta=3.0      ,   score_boundary=-7.0      ,   unk_score_offset=3							)

							# decoder
							self.assertEqual(processor.language_model.alpha      ,   5.0							)
							self.assertEqual(processor.language_model.beta      ,   3.0							)
							self.assertEqual(processor.language_model.score_boundary      ,   -7.0							)
							self.assertEqual(processor.language_model.unk_score_offset      ,   3							)





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Dict:
							a					=    self.get_tokenizer()
							# add token to trigger raise
							tokenizer.add_tokens(["xx"]							)
							with self.assertRaisesRegex(__lowerCamelCase      ,   "include"							):
										WavaVecaProcessorWithLM(
										    tokenizer=__lowerCamelCase      ,   feature_extractor=self.get_feature_extractor()      ,   decoder=self.get_decoder()							)





				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Dict:
							a					=    self.get_feature_extractor()
							a					=    self.get_tokenizer()
							a					=    self.get_decoder()

							a					=    WavaVecaProcessorWithLM(tokenizer=__lowerCamelCase      ,   feature_extractor=__lowerCamelCase      ,   decoder=__lowerCamelCase							)

							a					=    floats_list((3, 10_00)							)

							a					=    feature_extractor(__lowerCamelCase      ,   return_tensors="np"							)
							a					=    processor(__lowerCamelCase      ,   return_tensors="np"							)

							for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()      ,   input_processor[key].sum()      ,   delta=1e-2							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  List[str]:
							a					=    self.get_feature_extractor()
							a					=    self.get_tokenizer()
							a					=    self.get_decoder()

							a					=    WavaVecaProcessorWithLM(tokenizer=__lowerCamelCase      ,   feature_extractor=__lowerCamelCase      ,   decoder=__lowerCamelCase							)

							a					=    "This is a test string"

							a					=    processor(text=__lowerCamelCase							)

							a					=    tokenizer(__lowerCamelCase							)

							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]      ,   encoded_processor[key]							)





				def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  Union[str, Any]=(2, 10, 16)      ,   __lowerCamelCase       :  List[str]=77							)      ->  List[str]:
							np.random.seed(__lowerCamelCase							)
							return np.random.rand(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Dict:
							a					=    self.get_feature_extractor()
							a					=    self.get_tokenizer()
							a					=    self.get_decoder()

							a					=    WavaVecaProcessorWithLM(tokenizer=__lowerCamelCase      ,   feature_extractor=__lowerCamelCase      ,   decoder=__lowerCamelCase							)

							a					=    self._get_dummy_logits(shape=(10, 16)      ,   seed=13							)

							a					=    processor.decode(__lowerCamelCase							)

							a					=    decoder.decode_beams(__lowerCamelCase							)[0]

							self.assertEqual(decoded_decoder[0]      ,   decoded_processor.text							)
							self.assertEqual("</s> <s> </s>"      ,   decoded_processor.text							)
							self.assertEqual(decoded_decoder[-2]      ,   decoded_processor.logit_score							)
							self.assertEqual(decoded_decoder[-1]      ,   decoded_processor.lm_score							)





				@parameterized.expand([[None], ["fork"], ["spawn"]]							)
				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  Tuple							)      ->  Dict:
							a					=    self.get_feature_extractor()
							a					=    self.get_tokenizer()
							a					=    self.get_decoder()

							a					=    WavaVecaProcessorWithLM(tokenizer=__lowerCamelCase      ,   feature_extractor=__lowerCamelCase      ,   decoder=__lowerCamelCase							)

							a					=    self._get_dummy_logits()

							# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
							#       otherwise, the LM won't be available to the pool's sub-processes.
							# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
							if pool_context is None:
										a					=    processor.batch_decode(__lowerCamelCase							)
							else:
										with get_context(__lowerCamelCase							).Pool() as pool:
													a					=    processor.batch_decode(__lowerCamelCase      ,   __lowerCamelCase							)

							a					=    list(__lowerCamelCase							)

							with get_context("fork"							).Pool() as p:
										a					=    decoder.decode_beams_batch(__lowerCamelCase      ,   __lowerCamelCase							)

							a					,	a					,	a					=    [], [], []
							for beams in decoded_beams:
										texts_decoder.append(beams[0][0]							)
										logit_scores_decoder.append(beams[0][-2]							)
										lm_scores_decoder.append(beams[0][-1]							)

							self.assertListEqual(__lowerCamelCase      ,   decoded_processor.text							)
							self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]      ,   decoded_processor.text							)
							self.assertListEqual(__lowerCamelCase      ,   decoded_processor.logit_score							)
							self.assertListEqual(__lowerCamelCase      ,   decoded_processor.lm_score							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  str:
							a					=    self.get_feature_extractor()
							a					=    self.get_tokenizer()
							a					=    self.get_decoder()

							a					=    WavaVecaProcessorWithLM(tokenizer=__lowerCamelCase      ,   feature_extractor=__lowerCamelCase      ,   decoder=__lowerCamelCase							)

							a					=    self._get_dummy_logits()

							a					=    15
							a					=    -20.0
							a					=    -4.0

							a					=    processor.batch_decode(
							    __lowerCamelCase      ,   beam_width=__lowerCamelCase      ,   beam_prune_logp=__lowerCamelCase      ,   token_min_logp=__lowerCamelCase      ,   )
							a					=    decoded_processor_out.text

							a					=    list(__lowerCamelCase							)

							with get_context("fork"							).Pool() as pool:
										a					=    decoder.decode_beams_batch(
										    __lowerCamelCase      ,   __lowerCamelCase      ,   beam_width=__lowerCamelCase      ,   beam_prune_logp=__lowerCamelCase      ,   token_min_logp=__lowerCamelCase      ,   )

							a					=    [d[0][0] for d in decoded_decoder_out]
							a					=    [d[0][2] for d in decoded_decoder_out]
							a					=    [d[0][3] for d in decoded_decoder_out]

							self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase							)
							self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]      ,   __lowerCamelCase							)

							self.assertTrue(np.array_equal(__lowerCamelCase      ,   decoded_processor_out.logit_score							)							)
							self.assertTrue(np.allclose([-20.054, -18.447]      ,   __lowerCamelCase      ,   atol=1e-3							)							)

							self.assertTrue(np.array_equal(__lowerCamelCase      ,   decoded_processor_out.lm_score							)							)
							self.assertTrue(np.allclose([-15.554, -13.9_474]      ,   __lowerCamelCase      ,   atol=1e-3							)							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  int:
							a					=    self.get_feature_extractor()
							a					=    self.get_tokenizer()
							a					=    self.get_decoder()

							a					=    WavaVecaProcessorWithLM(tokenizer=__lowerCamelCase      ,   feature_extractor=__lowerCamelCase      ,   decoder=__lowerCamelCase							)

							a					=    self._get_dummy_logits()

							a					=    2.0
							a					=    5.0
							a					=    -20.0
							a					=    True

							a					=    processor.batch_decode(
							    __lowerCamelCase      ,   alpha=__lowerCamelCase      ,   beta=__lowerCamelCase      ,   unk_score_offset=__lowerCamelCase      ,   lm_score_boundary=__lowerCamelCase      ,   )
							a					=    decoded_processor_out.text

							a					=    list(__lowerCamelCase							)
							decoder.reset_params(
							    alpha=__lowerCamelCase      ,   beta=__lowerCamelCase      ,   unk_score_offset=__lowerCamelCase      ,   lm_score_boundary=__lowerCamelCase      ,   )

							with get_context("fork"							).Pool() as pool:
										a					=    decoder.decode_beams_batch(
										    __lowerCamelCase      ,   __lowerCamelCase      ,   )

							a					=    [d[0][0] for d in decoded_decoder_out]

							self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase							)
							self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]      ,   __lowerCamelCase							)
							a					=    processor.decoder.model_container[processor.decoder._model_key]
							self.assertEqual(lm_model.alpha      ,   2.0							)
							self.assertEqual(lm_model.beta      ,   5.0							)
							self.assertEqual(lm_model.unk_score_offset      ,   -20.0							)
							self.assertEqual(lm_model.score_boundary      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  int:
							a					=    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"							)

							a					=    processor.decoder.model_container[processor.decoder._model_key]
							a					=    Path(language_model._kenlm_model.path.decode("utf-8"							)							).parent.parent.absolute()

							a					=    os.listdir(__lowerCamelCase							)
							a					=    ["alphabet.json", "language_model"]

							downloaded_decoder_files.sort()
							expected_decoder_files.sort()

							# test that only decoder relevant files from
							# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
							# are downloaded and none of the rest (e.g. README.md, ...)
							self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  Optional[Any]:
							a					=    snapshot_download("hf-internal-testing/processor_with_lm"							)

							a					=    WavaVecaProcessorWithLM.from_pretrained(__lowerCamelCase							)

							a					=    processor.decoder.model_container[processor.decoder._model_key]
							a					=    Path(language_model._kenlm_model.path.decode("utf-8"							)							).parent.parent.absolute()

							a					=    os.listdir(__lowerCamelCase							)
							a					=    os.listdir(__lowerCamelCase							)

							local_decoder_files.sort()
							expected_decoder_files.sort()

							# test that both decoder form hub and local files in cache are the same
							self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  List[str]:
							a					=    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"							)
							a					=    AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"							)

							a					=    floats_list((3, 10_00)							)

							a					=    processor_wavaveca(__lowerCamelCase      ,   return_tensors="np"							)
							a					=    processor_auto(__lowerCamelCase      ,   return_tensors="np"							)

							for key in input_wavaveca.keys():
										self.assertAlmostEqual(input_wavaveca[key].sum()      ,   input_auto[key].sum()      ,   delta=1e-2							)

							a					=    self._get_dummy_logits()

							a					=    processor_wavaveca.batch_decode(__lowerCamelCase							)
							a					=    processor_auto.batch_decode(__lowerCamelCase							)

							self.assertListEqual(decoded_wavaveca.text      ,   decoded_auto.text							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  int:
							a					=    self.get_feature_extractor()
							a					=    self.get_tokenizer()
							a					=    self.get_decoder()

							a					=    WavaVecaProcessorWithLM(tokenizer=__lowerCamelCase      ,   feature_extractor=__lowerCamelCase      ,   decoder=__lowerCamelCase							)

							self.assertListEqual(
							    processor.model_input_names      ,   feature_extractor.model_input_names      ,   msg="`processor` and `feature_extractor` model input names do not match"      ,   )





				@staticmethod
				def 							__UpperCAmelCase							(   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  int							)      ->  Union[str, Any]:
							a					=    [d[key] for d in offsets]
							return retrieved_list





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  int:
							a					=    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"							)
							a					=    self._get_dummy_logits()[0]

							a					=    processor.decode(__lowerCamelCase      ,   output_word_offsets=__lowerCamelCase							)
							# check Wav2Vec2CTCTokenizerOutput keys for word
							self.assertEqual(len(outputs.keys()							)      ,   4							)
							self.assertTrue("text" in outputs							)
							self.assertTrue("word_offsets" in outputs							)
							self.assertTrue(isinstance(__lowerCamelCase      ,   __lowerCamelCase							)							)

							self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"]      ,   "word"							)							)      ,   outputs.text							)
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]      ,   "word"							)      ,   ["<s>", "<s>", "</s>"]							)
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]      ,   "start_offset"							)      ,   [0, 2, 4]							)
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]      ,   "end_offset"							)      ,   [1, 3, 5]							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Tuple:
							a					=    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"							)
							a					=    self._get_dummy_logits()

							a					=    processor.batch_decode(__lowerCamelCase      ,   output_word_offsets=__lowerCamelCase							)

							# check Wav2Vec2CTCTokenizerOutput keys for word
							self.assertEqual(len(outputs.keys()							)      ,   4							)
							self.assertTrue("text" in outputs							)
							self.assertTrue("word_offsets" in outputs							)
							self.assertTrue(isinstance(__lowerCamelCase      ,   __lowerCamelCase							)							)

							self.assertListEqual(
							    [" ".join(self.get_from_offsets(__lowerCamelCase      ,   "word"							)							) for o in outputs["word_offsets"]]      ,   outputs.text							)
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]      ,   "word"							)      ,   ["<s>", "<s>", "</s>"]							)
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]      ,   "start_offset"							)      ,   [0, 2, 4]							)
							self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]      ,   "end_offset"							)      ,   [1, 3, 5]							)





				@slow
				@require_torch
				@require_torchaudio
				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[Any]:
							import torch

							a					=    load_dataset("common_voice"      ,   "en"      ,   split="train"      ,   streaming=__lowerCamelCase							)
							a					=    ds.cast_column("audio"      ,   datasets.Audio(sampling_rate=1_60_00							)							)
							a					=    iter(__lowerCamelCase							)
							a					=    next(__lowerCamelCase							)

							a					=    AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"							)
							a					=    WavaVecaForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"							)

							# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
							a					=    processor(sample["audio"]["array"]      ,   return_tensors="pt"							).input_values

							with torch.no_grad():
										a					=    model(__lowerCamelCase							).logits.cpu().numpy()

							a					=    processor.decode(logits[0]      ,   output_word_offsets=__lowerCamelCase							)

							a					=    model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
							a					=    [
							    {
							        "start_time": d["start_offset"] * time_offset,
							        "end_time": d["end_offset"] * time_offset,
							        "word": d["word"],
							    }
							    for d in output["word_offsets"]
							]

							a					=    "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"

							# output words
							self.assertEqual(" ".join(self.get_from_offsets(__lowerCamelCase      ,   "word"							)							)      ,   __lowerCamelCase							)
							self.assertEqual(" ".join(self.get_from_offsets(__lowerCamelCase      ,   "word"							)							)      ,   output.text							)

							# output times
							a					=    torch.tensor(self.get_from_offsets(__lowerCamelCase      ,   "start_time"							)							)
							a					=    torch.tensor(self.get_from_offsets(__lowerCamelCase      ,   "end_time"							)							)

							# fmt: off
							a					=    torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]							)
							a					=    torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]							)
							# fmt: on

							self.assertTrue(torch.allclose(__lowerCamelCase      ,   __lowerCamelCase      ,   atol=0.01							)							)
							self.assertTrue(torch.allclose(__lowerCamelCase      ,   __lowerCamelCase      ,   atol=0.01							)							)












import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__lowerCAmelCase	:       List[Any]     =	logging.getLogger(__name__)




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    argparse.ArgumentParser(
			    description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."     )
			parser.add_argument(
			    "--dataset_name",						type=A,						default="wikitext",						help="Name of the training. Explore datasets at: hf.co/datasets.",						)
			parser.add_argument(
			    "--dataset_config",						type=A,						default="wikitext-103-raw-v1",						help="Configuration name of the dataset."     )
			parser.add_argument(
			    "--tokenizer_name_or_path",						type=A,						default="sayakpaul/unigram-tokenizer-wikitext",						help="Tokenizer identifier. Can be a local filepath or a Hub identifier.",						)
			parser.add_argument(
			    "--shard_size",						type=A,						default=1000,						help="Number of entries to go in a single shard.",						)
			parser.add_argument("--split",						type=A,						default="train",						choices=["train", "test", "validation"]     )
			parser.add_argument(
			    "--limit",						default=A,						type=A,						help="Limit the number of shards (used for debugging).",						)
			parser.add_argument(
			    "--max_length",						type=A,						default=512,						help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
			    " sequence length that is a multiple of 8.",						)
			parser.add_argument(
			    "--output_dir",						default="tf-tpu",						type=A,						help="Output directory where the TFRecord shards will be saved. If the"
			    " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
			    " shards will be directly saved to a Google Cloud Storage bucket.",						)

			a					=    parser.parse_args()
			return args




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			def fn(A	:      Tuple     ):
						return tokenizer(examples["text"]     )

			return fn




def     __magic_name__       (				A	:      Any     ):






			'''simple docstring'''







			a					=    []
			for i in range(len(tokenized_data["input_ids"]     )     ):
						a					=    {
						    "input_ids": tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data["input_ids"][i]     )     ),
						    "attention_mask": tf.train.Feature(
						        intaa_list=tf.train.IntaaList(value=tokenized_data["attention_mask"][i]     )     ),
						}
						a					=    tf.train.Features(feature=A     )
						a					=    tf.train.Example(features=A     )
						a					=    example.SerializeToString()
						records.append(A     )
			return records




def     __magic_name__       (				A	:      Union[str, Any]     ):






			'''simple docstring'''







			a					=    datasets.load_dataset(args.dataset_name,						args.dataset_config,						split=args.split     )

			if args.limit is not None:
						a					=    min(len(A     ),						args.limit     )
						a					=    dataset.select(range(A     )     )
						print(F"""Limiting the dataset to {args.limit} entries."""     )

			a					=    AutoTokenizer.from_pretrained(args.tokenizer_name_or_path     )

			# Handle output directory creation.
			# For serializing into a Google Cloud Storage Bucket, one needs to first
			# create a bucket.
			if "gs" not in args.output_dir:
						if not os.path.exists(args.output_dir     ):
									os.makedirs(args.output_dir     )
						a					=    os.path.join(args.output_dir,						args.split     )
						if not os.path.exists(A     ):
									os.makedirs(A     )
			else:
						a					=    os.path.join(args.output_dir,						args.split     )

			# Tokenize the whole dataset at once.
			a					=    tokenize_function(A     )
			a					=    dataset.map(A,						batched=A,						num_proc=4,						remove_columns=["text"]     )

			# We need to concatenate all our texts together, and then split the result
			# into chunks of a fixed size, which we will call block_size. To do this, we
			# will use the map method again, with the option batched=True. When we use batched=True,
			# the function we pass to map() will be passed multiple inputs at once, allowing us
			# to group them into more or fewer examples than we had in the input.
			# This allows us to create our new fixed-length samples. The advantage of this
			# method is that we don't lose a whole lot of content from the dataset compared to the
			# case where we simply tokenize with a pre-defined max_length.

			def group_texts(A	:      List[Any]     ):
						# Concatenate all texts.
						a					=    {k: sum(examples[k],						[]     ) for k in examples.keys()}
						a					=    len(concatenated_examples[list(examples.keys()     )[0]]     )
						# We drop the small remainder, though you could add padding instead if the model supports it
						# In this, as in all things, we advise you to follow your heart 🫀
						a					=    (total_length // args.max_length) * args.max_length
						# Split by chunks of max_len.
						a					=    {
						    k: [t[i : i + args.max_length] for i in range(0,						A,						args.max_length     )]
						    for k, t in concatenated_examples.items()
						}
						return result

			a					=    dataset_tokenized.map(A,						batched=A,						batch_size=1000,						num_proc=4     )

			a					=    0
			a					=    0
			for shard in range(0,						len(A     ),						args.shard_size     ):
						a					=    grouped_dataset[shard : shard + args.shard_size]
						a					=    len(dataset_snapshot["input_ids"]     )
						a					=    os.path.join(A,						F"""dataset-{shard_count}-{records_containing}.tfrecord"""     )
						a					=    get_serialized_examples(A     )

						with tf.io.TFRecordWriter(A     ) as out_file:
									for i in range(len(A     )     ):
												a					=    serialized_examples[i]
												out_file.write(A     )
									print("Wrote file {} containing {} records".format(A,						A     )     )

						shard_count += 1
						total_records += records_containing

			with open(F"""split-{args.split}-records-count.txt""",						"w"     ) as f:
						print(F"""Total {args.split} records: {total_records}""",						file=A     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Optional[int]     =	parse_args()
		main(args)






