


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 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		"| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
											__snake_case				=		dict(zip(SCREAMING_SNAKE_CASE  ,     range(len(SCREAMING_SNAKE_CASE    )    )    )    )

											__snake_case				=		{
											    "unk_token": "<unk>",
											    "bos_token": "<s>",
											    "eos_token": "</s>",
											}
											__snake_case				=		{
											    "feature_size": 1,
											    "padding_value": 0.0,
											    "sampling_rate": 1_6_0_0_0,
											    "return_attention_mask": False,
											    "do_normalize": True,
											}

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

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

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     **SCREAMING_SNAKE_CASE						:		Optional[int]    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		self.add_kwargs_tokens_map.copy()
											kwargs.update(SCREAMING_SNAKE_CASE    )
											return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname  ,     **SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     **SCREAMING_SNAKE_CASE						:		Any    )      ->						Optional[int]:




											'''simple docstring'''

											return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname  ,     **SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     **SCREAMING_SNAKE_CASE						:		List[Any]    )      ->						Optional[int]:




											'''simple docstring'''

											return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name  ,     **SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						Union[str, Any]:




											'''simple docstring'''

											shutil.rmtree(self.tmpdirname    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		self.get_tokenizer()
											__snake_case				=		self.get_feature_extractor()
											__snake_case				=		self.get_decoder()

											__snake_case				=		WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE  ,     feature_extractor=SCREAMING_SNAKE_CASE  ,     decoder=SCREAMING_SNAKE_CASE    )

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

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

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

											# 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  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		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
											__snake_case				=		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 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		self.get_tokenizer()
											# add token to trigger raise
											tokenizer.add_tokens(["xx"]    )
											with self.assertRaisesRegex(SCREAMING_SNAKE_CASE  ,     "include"    ):
																	WavaVecaProcessorWithLM(
																	    tokenizer=SCREAMING_SNAKE_CASE  ,     feature_extractor=self.get_feature_extractor()  ,     decoder=self.get_decoder()    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		self.get_feature_extractor()
											__snake_case				=		self.get_tokenizer()
											__snake_case				=		self.get_decoder()

											__snake_case				=		WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE  ,     feature_extractor=SCREAMING_SNAKE_CASE  ,     decoder=SCREAMING_SNAKE_CASE    )

											__snake_case				=		floats_list((3, 1_0_0_0)    )

											__snake_case				=		feature_extractor(SCREAMING_SNAKE_CASE  ,     return_tensors="np"    )
											__snake_case				=		processor(SCREAMING_SNAKE_CASE  ,     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 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.get_feature_extractor()
											__snake_case				=		self.get_tokenizer()
											__snake_case				=		self.get_decoder()

											__snake_case				=		WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE  ,     feature_extractor=SCREAMING_SNAKE_CASE  ,     decoder=SCREAMING_SNAKE_CASE    )

											__snake_case				=		"This is a test string"

											__snake_case				=		processor(text=SCREAMING_SNAKE_CASE    )

											__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE    )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		List[Any]=(2, 1_0, 1_6)  ,     SCREAMING_SNAKE_CASE						:		List[Any]=7_7    )      ->						List[Any]:




											'''simple docstring'''

											np.random.seed(SCREAMING_SNAKE_CASE    )
											return np.random.rand(*SCREAMING_SNAKE_CASE    )


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




											'''simple docstring'''

											__snake_case				=		self.get_feature_extractor()
											__snake_case				=		self.get_tokenizer()
											__snake_case				=		self.get_decoder()

											__snake_case				=		WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE  ,     feature_extractor=SCREAMING_SNAKE_CASE  ,     decoder=SCREAMING_SNAKE_CASE    )

											__snake_case				=		self._get_dummy_logits(shape=(1_0, 1_6)  ,     seed=1_3    )

											__snake_case				=		processor.decode(SCREAMING_SNAKE_CASE    )

											__snake_case				=		decoder.decode_beams(SCREAMING_SNAKE_CASE    )[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 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.get_feature_extractor()
											__snake_case				=		self.get_tokenizer()
											__snake_case				=		self.get_decoder()

											__snake_case				=		WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE  ,     feature_extractor=SCREAMING_SNAKE_CASE  ,     decoder=SCREAMING_SNAKE_CASE    )

											__snake_case				=		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:
																	__snake_case				=		processor.batch_decode(SCREAMING_SNAKE_CASE    )
											else:
																	with get_context(SCREAMING_SNAKE_CASE    ).Pool() as pool:
																							__snake_case				=		processor.batch_decode(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

											__snake_case				=		list(SCREAMING_SNAKE_CASE    )

											with get_context("fork"    ).Pool() as p:
																	__snake_case				=		decoder.decode_beams_batch(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

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


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




											'''simple docstring'''

											__snake_case				=		self.get_feature_extractor()
											__snake_case				=		self.get_tokenizer()
											__snake_case				=		self.get_decoder()

											__snake_case				=		WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE  ,     feature_extractor=SCREAMING_SNAKE_CASE  ,     decoder=SCREAMING_SNAKE_CASE    )

											__snake_case				=		self._get_dummy_logits()

											__snake_case				=		1_5
											__snake_case				=		-20.0
											__snake_case				=		-4.0

											__snake_case				=		processor.batch_decode(
											    SCREAMING_SNAKE_CASE  ,     beam_width=SCREAMING_SNAKE_CASE  ,     beam_prune_logp=SCREAMING_SNAKE_CASE  ,     token_min_logp=SCREAMING_SNAKE_CASE  ,     )
											__snake_case				=		decoded_processor_out.text

											__snake_case				=		list(SCREAMING_SNAKE_CASE    )

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

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

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

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

											self.assertTrue(np.array_equal(SCREAMING_SNAKE_CASE  ,     decoded_processor_out.lm_score    )    )
											self.assertTrue(np.allclose([-15.554, -13.9474]  ,     SCREAMING_SNAKE_CASE  ,     atol=1e-3    )    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.get_feature_extractor()
											__snake_case				=		self.get_tokenizer()
											__snake_case				=		self.get_decoder()

											__snake_case				=		WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE  ,     feature_extractor=SCREAMING_SNAKE_CASE  ,     decoder=SCREAMING_SNAKE_CASE    )

											__snake_case				=		self._get_dummy_logits()

											__snake_case				=		2.0
											__snake_case				=		5.0
											__snake_case				=		-20.0
											__snake_case				=		True

											__snake_case				=		processor.batch_decode(
											    SCREAMING_SNAKE_CASE  ,     alpha=SCREAMING_SNAKE_CASE  ,     beta=SCREAMING_SNAKE_CASE  ,     unk_score_offset=SCREAMING_SNAKE_CASE  ,     lm_score_boundary=SCREAMING_SNAKE_CASE  ,     )
											__snake_case				=		decoded_processor_out.text

											__snake_case				=		list(SCREAMING_SNAKE_CASE    )
											decoder.reset_params(
											    alpha=SCREAMING_SNAKE_CASE  ,     beta=SCREAMING_SNAKE_CASE  ,     unk_score_offset=SCREAMING_SNAKE_CASE  ,     lm_score_boundary=SCREAMING_SNAKE_CASE  ,     )

											with get_context("fork"    ).Pool() as pool:
																	__snake_case				=		decoder.decode_beams_batch(
																	    SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     )

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

											self.assertListEqual(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )
											self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]  ,     SCREAMING_SNAKE_CASE    )
											__snake_case				=		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  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )

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

											__snake_case				=		os.listdir(SCREAMING_SNAKE_CASE    )
											__snake_case				=		["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(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		snapshot_download("hf-internal-testing/processor_with_lm"    )

											__snake_case				=		WavaVecaProcessorWithLM.from_pretrained(SCREAMING_SNAKE_CASE    )

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

											__snake_case				=		os.listdir(SCREAMING_SNAKE_CASE    )
											__snake_case				=		os.listdir(SCREAMING_SNAKE_CASE    )

											local_decoder_files.sort()
											expected_decoder_files.sort()

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )
											__snake_case				=		AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"    )

											__snake_case				=		floats_list((3, 1_0_0_0)    )

											__snake_case				=		processor_wavaveca(SCREAMING_SNAKE_CASE  ,     return_tensors="np"    )
											__snake_case				=		processor_auto(SCREAMING_SNAKE_CASE  ,     return_tensors="np"    )

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

											__snake_case				=		self._get_dummy_logits()

											__snake_case				=		processor_wavaveca.batch_decode(SCREAMING_SNAKE_CASE    )
											__snake_case				=		processor_auto.batch_decode(SCREAMING_SNAKE_CASE    )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.get_feature_extractor()
											__snake_case				=		self.get_tokenizer()
											__snake_case				=		self.get_decoder()

											__snake_case				=		WavaVecaProcessorWithLM(tokenizer=SCREAMING_SNAKE_CASE  ,     feature_extractor=SCREAMING_SNAKE_CASE  ,     decoder=SCREAMING_SNAKE_CASE    )

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


					@staticmethod
					def 				SCREAMING_SNAKE_CASE_	(    SCREAMING_SNAKE_CASE						:		List[str]  ,     SCREAMING_SNAKE_CASE						:		Tuple    )      ->						Optional[int]:




											'''simple docstring'''

											__snake_case				=		[d[key] for d in offsets]
											return retrieved_list


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )
											__snake_case				=		self._get_dummy_logits()[0]

											__snake_case				=		processor.decode(SCREAMING_SNAKE_CASE  ,     output_word_offsets=SCREAMING_SNAKE_CASE    )
											# 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(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )    )

											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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"    )
											__snake_case				=		self._get_dummy_logits()

											__snake_case				=		processor.batch_decode(SCREAMING_SNAKE_CASE  ,     output_word_offsets=SCREAMING_SNAKE_CASE    )

											# 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(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )    )

											self.assertListEqual(
											    [" ".join(self.get_from_offsets(SCREAMING_SNAKE_CASE  ,     "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 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Dict:




											'''simple docstring'''

											import torch

											__snake_case				=		load_dataset("common_voice"  ,     "en"  ,     split="train"  ,     streaming=SCREAMING_SNAKE_CASE    )
											__snake_case				=		ds.cast_column("audio"  ,     datasets.Audio(sampling_rate=1_6_0_0_0    )    )
											__snake_case				=		iter(SCREAMING_SNAKE_CASE    )
											__snake_case				=		next(SCREAMING_SNAKE_CASE    )

											__snake_case				=		AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"    )
											__snake_case				=		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
											__snake_case				=		processor(sample["audio"]["array"]  ,     return_tensors="pt"    ).input_values

											with torch.no_grad():
																	__snake_case				=		model(SCREAMING_SNAKE_CASE    ).logits.cpu().numpy()

											__snake_case				=		processor.decode(logits[0]  ,     output_word_offsets=SCREAMING_SNAKE_CASE    )

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

											__snake_case				=		"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(SCREAMING_SNAKE_CASE  ,     "word"    )    )  ,     SCREAMING_SNAKE_CASE    )
											self.assertEqual(" ".join(self.get_from_offsets(SCREAMING_SNAKE_CASE  ,     "word"    )    )  ,     output.text    )

											# output times
											__snake_case				=		torch.tensor(self.get_from_offsets(SCREAMING_SNAKE_CASE  ,     "start_time"    )    )
											__snake_case				=		torch.tensor(self.get_from_offsets(SCREAMING_SNAKE_CASE  ,     "end_time"    )    )

											# fmt: off
											__snake_case				=		torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]    )
											__snake_case				=		torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]    )
											# fmt: on

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









# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


A				:       Union[str, Any]      =  logging.get_logger(__name__)

A				:       Dict[Optional[str], Type[Formatter]]      =  {}
A				:       Dict[Optional[str], str]      =  {}
A				:       Dict[Optional[str], Exception]      =  {}





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None   ,						)    ->						str:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						if format_type in _FORMAT_TYPES:
												logger.warning(
												    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''			)
						__snake_case				=		formatter_cls
						for alias in set(aliases + [format_type]			):
												if alias in _FORMAT_TYPES_ALIASES:
																		logger.warning(
																		    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''			)
												__snake_case				=		format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						for alias in set(aliases + [format_type]			):
												__snake_case				=		unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
	A				:       Any      =  ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
	_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
	A				:       Tuple      =  ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
	_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
	A				:       Optional[int]      =  ValueError('JAX needs to be installed to be able to return JAX arrays.')
	_register_unavailable_formatter(_jax_error, 'jax', aliases=[])





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[str]:


						'''simple docstring'''






						if format_type in _FORMAT_TYPES_ALIASES:
												return _FORMAT_TYPES_ALIASES[format_type]
						else:
												return format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						**_lowerCAmelCase			)    ->						Formatter:


						'''simple docstring'''






						__snake_case				=		get_format_type_from_alias(_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES:
												return _FORMAT_TYPES[format_type](**_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
												raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
						else:
												raise ValueError(
												    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got \'{format_type}\''''			)





