





"""simple docstring"""




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  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   )			->				int:
								a   :	str			=       '| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
								a   :	Dict			=       dict(zip(UpperCAmelCase_   ,      range(len(UpperCAmelCase_   )   )   )   )

								a   :	str			=       {
								    'unk_token': '<unk>',
								    'bos_token': '<s>',
								    'eos_token': '</s>',
								}
								a   :	str			=       {
								    'feature_size': 1,
								    'padding_value': 0.0,
								    'sampling_rate': 1_6000,
								    'return_attention_mask': False,
								    'do_normalize': True,
								}

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

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

								# load decoder from hub
								a   :	List[Any]			=       'hf-internal-testing/ngram-beam-search-decoder'






							def 					__a     (     self   ,      **lowerCAmelCase__   )			->				List[Any]:
								a   :	Dict			=       self.add_kwargs_tokens_map.copy()
								kwargs.update(UpperCAmelCase_   )
								return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname   ,      **UpperCAmelCase_   )






							def 					__a     (     self   ,      **lowerCAmelCase__   )			->				Dict:
								return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname   ,      **UpperCAmelCase_   )






							def 					__a     (     self   ,      **lowerCAmelCase__   )			->				List[str]:
								return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name   ,      **UpperCAmelCase_   )






							def 					__a     (     self   )			->				int:
								shutil.rmtree(self.tmpdirname   )






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	List[Any]			=       self.get_tokenizer()
								a   :	str			=       self.get_feature_extractor()
								a   :	Union[str, Any]			=       self.get_decoder()

								a   :	int			=       WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_   ,      feature_extractor=UpperCAmelCase_   ,      decoder=UpperCAmelCase_   )

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

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

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

								# 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   ,      UpperCAmelCase_   )






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






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       self.get_feature_extractor()
								a   :	Union[str, Any]			=       self.get_tokenizer()
								a   :	Tuple			=       self.get_decoder()

								a   :	Dict			=       WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_   ,      feature_extractor=UpperCAmelCase_   ,      decoder=UpperCAmelCase_   )

								a   :	Optional[int]			=       floats_list((3, 1000)   )

								a   :	List[Any]			=       feature_extractor(UpperCAmelCase_   ,      return_tensors="np"   )
								a   :	str			=       processor(UpperCAmelCase_   ,      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 					__a     (     self   )			->				Optional[Any]:
								a   :	Optional[int]			=       self.get_feature_extractor()
								a   :	Union[str, Any]			=       self.get_tokenizer()
								a   :	str			=       self.get_decoder()

								a   :	Any			=       WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_   ,      feature_extractor=UpperCAmelCase_   ,      decoder=UpperCAmelCase_   )

								a   :	Any			=       'This is a test string'

								a   :	List[Any]			=       processor(text=UpperCAmelCase_   )

								a   :	str			=       tokenizer(UpperCAmelCase_   )

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






							def 					__a     (     self   ,      lowerCAmelCase__=(2, 10, 16)   ,      lowerCAmelCase__=77   )			->				str:
								np.random.seed(UpperCAmelCase_   )
								return np.random.rand(*UpperCAmelCase_   )






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

								a   :	Any			=       WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_   ,      feature_extractor=UpperCAmelCase_   ,      decoder=UpperCAmelCase_   )

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

								a   :	List[str]			=       processor.decode(UpperCAmelCase_   )

								a   :	str			=       decoder.decode_beams(UpperCAmelCase_   )[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 					__a     (     self   ,      lowerCAmelCase__   )			->				Tuple:
								a   :	Union[str, Any]			=       self.get_feature_extractor()
								a   :	List[Any]			=       self.get_tokenizer()
								a   :	str			=       self.get_decoder()

								a   :	List[str]			=       WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_   ,      feature_extractor=UpperCAmelCase_   ,      decoder=UpperCAmelCase_   )

								a   :	Tuple			=       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   :	Optional[Any]			=       processor.batch_decode(UpperCAmelCase_   )
								else:
									with get_context(UpperCAmelCase_   ).Pool() as pool:
										a   :	Optional[int]			=       processor.batch_decode(UpperCAmelCase_   ,      UpperCAmelCase_   )

								a   :	Optional[Any]			=       list(UpperCAmelCase_   )

								with get_context("fork"   ).Pool() as p:
									a   :	Optional[Any]			=       decoder.decode_beams_batch(UpperCAmelCase_   ,      UpperCAmelCase_   )

								a   :	Optional[Any]			=       [], [], []
								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(UpperCAmelCase_   ,      decoded_processor.text   )
								self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]   ,      decoded_processor.text   )
								self.assertListEqual(UpperCAmelCase_   ,      decoded_processor.logit_score   )
								self.assertListEqual(UpperCAmelCase_   ,      decoded_processor.lm_score   )






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

								a   :	Dict			=       WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_   ,      feature_extractor=UpperCAmelCase_   ,      decoder=UpperCAmelCase_   )

								a   :	Any			=       self._get_dummy_logits()

								a   :	Optional[Any]			=       15
								a   :	Optional[int]			=       -20.0
								a   :	int			=       -4.0

								a   :	List[str]			=       processor.batch_decode(
								    UpperCAmelCase_   ,      beam_width=UpperCAmelCase_   ,      beam_prune_logp=UpperCAmelCase_   ,      token_min_logp=UpperCAmelCase_   ,      )
								a   :	Any			=       decoded_processor_out.text

								a   :	List[Any]			=       list(UpperCAmelCase_   )

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

								a   :	List[str]			=       [d[0][0] for d in decoded_decoder_out]
								a   :	Union[str, Any]			=       [d[0][2] for d in decoded_decoder_out]
								a   :	Union[str, Any]			=       [d[0][3] for d in decoded_decoder_out]

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

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

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






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

								a   :	List[str]			=       WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_   ,      feature_extractor=UpperCAmelCase_   ,      decoder=UpperCAmelCase_   )

								a   :	int			=       self._get_dummy_logits()

								a   :	int			=       2.0
								a   :	List[Any]			=       5.0
								a   :	Union[str, Any]			=       -20.0
								a   :	Any			=       True

								a   :	Tuple			=       processor.batch_decode(
								    UpperCAmelCase_   ,      alpha=UpperCAmelCase_   ,      beta=UpperCAmelCase_   ,      unk_score_offset=UpperCAmelCase_   ,      lm_score_boundary=UpperCAmelCase_   ,      )
								a   :	List[str]			=       decoded_processor_out.text

								a   :	Optional[int]			=       list(UpperCAmelCase_   )
								decoder.reset_params(
								    alpha=UpperCAmelCase_   ,      beta=UpperCAmelCase_   ,      unk_score_offset=UpperCAmelCase_   ,      lm_score_boundary=UpperCAmelCase_   ,      )

								with get_context("fork"   ).Pool() as pool:
									a   :	List[str]			=       decoder.decode_beams_batch(
									    UpperCAmelCase_   ,      UpperCAmelCase_   ,      )

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

								self.assertListEqual(UpperCAmelCase_   ,      UpperCAmelCase_   )
								self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]   ,      UpperCAmelCase_   )
								a   :	Any			=       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   ,      UpperCAmelCase_   )






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

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

								a   :	List[str]			=       os.listdir(UpperCAmelCase_   )
								a   :	Any			=       ['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(UpperCAmelCase_   ,      UpperCAmelCase_   )






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

								a   :	str			=       WavaVecaProcessorWithLM.from_pretrained(UpperCAmelCase_   )

								a   :	Union[str, Any]			=       processor.decoder.model_container[processor.decoder._model_key]
								a   :	Optional[int]			=       Path(language_model._kenlm_model.path.decode("utf-8"   )   ).parent.parent.absolute()

								a   :	str			=       os.listdir(UpperCAmelCase_   )
								a   :	str			=       os.listdir(UpperCAmelCase_   )

								local_decoder_files.sort()
								expected_decoder_files.sort()

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






							def 					__a     (     self   )			->				List[Any]:
								a   :	int			=       WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"   )
								a   :	Optional[int]			=       AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"   )

								a   :	List[str]			=       floats_list((3, 1000)   )

								a   :	Union[str, Any]			=       processor_wavaveca(UpperCAmelCase_   ,      return_tensors="np"   )
								a   :	Dict			=       processor_auto(UpperCAmelCase_   ,      return_tensors="np"   )

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

								a   :	Any			=       self._get_dummy_logits()

								a   :	int			=       processor_wavaveca.batch_decode(UpperCAmelCase_   )
								a   :	Union[str, Any]			=       processor_auto.batch_decode(UpperCAmelCase_   )

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






							def 					__a     (     self   )			->				List[Any]:
								a   :	Dict			=       self.get_feature_extractor()
								a   :	Union[str, Any]			=       self.get_tokenizer()
								a   :	Optional[Any]			=       self.get_decoder()

								a   :	List[str]			=       WavaVecaProcessorWithLM(tokenizer=UpperCAmelCase_   ,      feature_extractor=UpperCAmelCase_   ,      decoder=UpperCAmelCase_   )

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






							@staticmethod
							def 					__a     (     lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Union[str, Any]:
								a   :	Tuple			=       [d[key] for d in offsets]
								return retrieved_list






							def 					__a     (     self   )			->				Tuple:
								a   :	List[Any]			=       WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"   )
								a   :	Dict			=       self._get_dummy_logits()[0]

								a   :	Any			=       processor.decode(UpperCAmelCase_   ,      output_word_offsets=UpperCAmelCase_   )
								# 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(UpperCAmelCase_   ,      UpperCAmelCase_   )   )

								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 					__a     (     self   )			->				Optional[Any]:
								a   :	List[str]			=       WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"   )
								a   :	Dict			=       self._get_dummy_logits()

								a   :	Optional[int]			=       processor.batch_decode(UpperCAmelCase_   ,      output_word_offsets=UpperCAmelCase_   )

								# 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(UpperCAmelCase_   ,      UpperCAmelCase_   )   )

								self.assertListEqual(
								    [" ".join(self.get_from_offsets(UpperCAmelCase_   ,      "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 					__a     (     self   )			->				Optional[int]:
								import torch

								a   :	List[str]			=       load_dataset("common_voice"   ,      "en"   ,      split="train"   ,      streaming=UpperCAmelCase_   )
								a   :	int			=       ds.cast_column("audio"   ,      datasets.Audio(sampling_rate=1_6000   )   )
								a   :	Any			=       iter(UpperCAmelCase_   )
								a   :	int			=       next(UpperCAmelCase_   )

								a   :	List[Any]			=       AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"   )
								a   :	List[str]			=       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   :	int			=       processor(sample["audio"]["array"]   ,      return_tensors="pt"   ).input_values

								with torch.no_grad():
									a   :	Union[str, Any]			=       model(UpperCAmelCase_   ).logits.cpu().numpy()

								a   :	Optional[Any]			=       processor.decode(logits[0]   ,      output_word_offsets=UpperCAmelCase_   )

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

								a   :	Union[str, Any]			=       '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(UpperCAmelCase_   ,      "word"   )   )   ,      UpperCAmelCase_   )
								self.assertEqual(" ".join(self.get_from_offsets(UpperCAmelCase_   ,      "word"   )   )   ,      output.text   )

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

								# fmt: off
								a   :	Any			=       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   :	Union[str, Any]			=       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(UpperCAmelCase_   ,      UpperCAmelCase_   ,      atol=0.01   )   )
								self.assertTrue(torch.allclose(UpperCAmelCase_   ,      UpperCAmelCase_   ,      atol=0.01   )   )









"""simple docstring"""




import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


a		:				Optional[Any]  			=    datasets.utils.logging.get_logger(__name__)

a		:				Union[str, Any]  			=    ['''names''', '''prefix''']
a		:				Any  			=    ['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
a		:				Any  			=    ['''encoding_errors''', '''on_bad_lines''']
a		:				List[str]  			=    ['''date_format''']


@dataclass
class  __UpperCamelCase       (		datasets.BuilderConfig		):
							lowerCamelCase     :      str         =","
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      Optional[Union[int, List[int], str]]         ="infer"
							lowerCamelCase     :      Optional[List[str]]         =None
							lowerCamelCase     :      Optional[List[str]]         =None
							lowerCamelCase     :      Optional[Union[int, str, List[int], List[str]]]         =None
							lowerCamelCase     :      Optional[Union[List[int], List[str]]]         =None
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      Optional[Literal["c", "python", "pyarrow"]]         =None
							lowerCamelCase     :      Dict[Union[int, str], Callable[[Any], Any]]         =None
							lowerCamelCase     :      Optional[list]         =None
							lowerCamelCase     :      Optional[list]         =None
							lowerCamelCase     :      bool         =False
							lowerCamelCase     :      Optional[Union[int, List[int]]]         =None
							lowerCamelCase     :      Optional[int]         =None
							lowerCamelCase     :      Optional[Union[str, List[str]]]         =None
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      bool         =False
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      str         ="."
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      str         ='"'
							lowerCamelCase     :      int         =0
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      int         =0
							lowerCamelCase     :      bool         =True
							lowerCamelCase     :      bool         =False
							lowerCamelCase     :      Optional[str]         =None
							lowerCamelCase     :      int         =1_0000
							lowerCamelCase     :      Optional[datasets.Features]         =None
							lowerCamelCase     :      Optional[str]         ="strict"
							lowerCamelCase     :      Literal["error", "warn", "skip"]         ="error"
							lowerCamelCase     :      Optional[str]         =None






							def 					__a     (     self   )			->				Dict:
								if self.delimiter is not None:
									a   :	int			=       self.delimiter
								if self.column_names is not None:
									a   :	Any			=       self.column_names






							@property
							def 					__a     (     self   )			->				List[str]:
								a   :	Dict			=       {
								    "sep": self.sep,
								    "header": self.header,
								    "names": self.names,
								    "index_col": self.index_col,
								    "usecols": self.usecols,
								    "prefix": self.prefix,
								    "mangle_dupe_cols": self.mangle_dupe_cols,
								    "engine": self.engine,
								    "converters": self.converters,
								    "true_values": self.true_values,
								    "false_values": self.false_values,
								    "skipinitialspace": self.skipinitialspace,
								    "skiprows": self.skiprows,
								    "nrows": self.nrows,
								    "na_values": self.na_values,
								    "keep_default_na": self.keep_default_na,
								    "na_filter": self.na_filter,
								    "verbose": self.verbose,
								    "skip_blank_lines": self.skip_blank_lines,
								    "thousands": self.thousands,
								    "decimal": self.decimal,
								    "lineterminator": self.lineterminator,
								    "quotechar": self.quotechar,
								    "quoting": self.quoting,
								    "escapechar": self.escapechar,
								    "comment": self.comment,
								    "encoding": self.encoding,
								    "dialect": self.dialect,
								    "error_bad_lines": self.error_bad_lines,
								    "warn_bad_lines": self.warn_bad_lines,
								    "skipfooter": self.skipfooter,
								    "doublequote": self.doublequote,
								    "memory_map": self.memory_map,
								    "float_precision": self.float_precision,
								    "chunksize": self.chunksize,
								    "encoding_errors": self.encoding_errors,
								    "on_bad_lines": self.on_bad_lines,
								    "date_format": self.date_format,
								}

								# some kwargs must not be passed if they don't have a default value
								# some others are deprecated and we can also not pass them if they are the default value
								for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
									if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()   ,      lowerCAmelCase__   ):
										del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
								if not (datasets.config.PANDAS_VERSION.major >= 2):
									for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
										del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
								if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
									for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
										del pd_read_csv_kwargs[pd_read_csv_parameter]

								return pd_read_csv_kwargs


class  __UpperCamelCase       (		datasets.ArrowBasedBuilder		):
							lowerCamelCase     :      Union[str, Any]         =CsvConfig






							def 					__a     (     self   )			->				Optional[Any]:
								return datasets.DatasetInfo(features=self.config.features   )






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								if not self.config.data_files:
									raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""   )
								a   :	Optional[Any]			=       dl_manager.download_and_extract(self.config.data_files   )
								if isinstance(lowerCAmelCase__   ,      (str, list, tuple)   ):
									a   :	Tuple			=       data_files
									if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ):
										a   :	Tuple			=       [files]
									a   :	int			=       [dl_manager.iter_files(lowerCAmelCase__   ) for file in files]
									return [datasets.SplitGenerator(name=datasets.Split.TRAIN   ,      gen_kwargs={"files": files}   )]
								a   :	int			=       []
								for split_name, files in data_files.items():
									if isinstance(lowerCAmelCase__   ,      lowerCAmelCase__   ):
										a   :	Any			=       [files]
									a   :	List[str]			=       [dl_manager.iter_files(lowerCAmelCase__   ) for file in files]
									splits.append(datasets.SplitGenerator(name=lowerCAmelCase__   ,      gen_kwargs={"files": files}   )   )
								return splits






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				pa.Table:
								if self.config.features is not None:
									a   :	Optional[Any]			=       self.config.features.arrow_schema
									if all(not require_storage_cast(lowerCAmelCase__   ) for feature in self.config.features.values()   ):
										# cheaper cast
										a   :	Dict			=       pa.Table.from_arrays([pa_table[field.name] for field in schema]   ,      schema=lowerCAmelCase__   )
									else:
										# more expensive cast; allows str <-> int/float or str to Audio for example
										a   :	Union[str, Any]			=       table_cast(lowerCAmelCase__   ,      lowerCAmelCase__   )
								return pa_table






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Any:
								a   :	Tuple			=       self.config.features.arrow_schema if self.config.features else None
								# dtype allows reading an int column as str
								a   :	Any			=       (
								    {
								        name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCAmelCase__   ) else object
								        for name, dtype, feature in zip(schema.names   ,      schema.types   ,      self.config.features.values()   )
								    }
								    if schema is not None
								    else None
								)
								for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCAmelCase__   )   ):
									a   :	Tuple			=       pd.read_csv(lowerCAmelCase__   ,      iterator=lowerCAmelCase__   ,      dtype=lowerCAmelCase__   ,      **self.config.pd_read_csv_kwargs   )
									try:
										for batch_idx, df in enumerate(lowerCAmelCase__   ):
											a   :	Any			=       pa.Table.from_pandas(lowerCAmelCase__   )
											# Uncomment for debugging (will print the Arrow table size and elements)
											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
											yield (file_idx, batch_idx), self._cast_table(lowerCAmelCase__   )
									except ValueError as e:
										logger.error(f"""Failed to read file '{file}' with error {type(lowerCAmelCase__   )}: {e}"""   )
										raise


