




"""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 				lowercase					(			unittest.TestCase):







				def     a_     (							self      :    Optional[Any]				):



					"""simple docstring"""

					A_     : List[Any]		      =						"| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
					A_     : Optional[Any]		      =						dict(zip(lowerCAmelCase__					,   range(len(lowerCAmelCase__				)				)				)				)

					A_     : Optional[Any]		      =						{
					    "unk_token": "<unk>",
					    "bos_token": "<s>",
					    "eos_token": "</s>",
					}
					A_     : Tuple		      =						{
					    "feature_size": 1,
					    "padding_value": 0.0,
					    "sampling_rate": 1_60_00,
					    "return_attention_mask": False,
					    "do_normalize": True,
					}

					A_     : List[str]		      =						tempfile.mkdtemp()
					A_     : List[Any]		      =						os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['''vocab_file''']				)
					A_     : int		      =						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_     : str		      =						"hf-internal-testing/ngram-beam-search-decoder"







				def     a_     (							self      :    Tuple					,   **_lowerCamelCase      :    List[str]				):



					"""simple docstring"""

					A_     : List[str]		      =						self.add_kwargs_tokens_map.copy()
					kwargs.update(lowerCAmelCase__				)
					return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname					,   **lowerCAmelCase__				)







				def     a_     (							self      :    Optional[int]					,   **_lowerCamelCase      :    Any				):



					"""simple docstring"""

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







				def     a_     (							self      :    str					,   **_lowerCamelCase      :    Tuple				):



					"""simple docstring"""

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







				def     a_     (							self      :    int				):



					"""simple docstring"""

					shutil.rmtree(self.tmpdirname				)







				def     a_     (							self      :    List[Any]				):



					"""simple docstring"""

					A_     : Any		      =						self.get_tokenizer()
					A_     : str		      =						self.get_feature_extractor()
					A_     : Tuple		      =						self.get_decoder()

					A_     : Optional[Any]		      =						WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__					,   decoder=lowerCAmelCase__				)

					processor.save_pretrained(self.tmpdirname				)
					A_     : List[Any]		      =						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     a_     (							self      :    Tuple				):



					"""simple docstring"""

					A_     : Optional[int]		      =						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      :    Any				):



					"""simple docstring"""

					A_     : Optional[Any]		      =						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     a_     (							self      :    Optional[int]				):



					"""simple docstring"""

					A_     : List[Any]		      =						self.get_feature_extractor()
					A_     : List[Any]		      =						self.get_tokenizer()
					A_     : List[str]		      =						self.get_decoder()

					A_     : Optional[Any]		      =						WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__					,   decoder=lowerCAmelCase__				)

					A_     : Optional[int]		      =						floats_list((3, 10_00)				)

					A_     : int		      =						feature_extractor(lowerCAmelCase__					,   return_tensors='''np'''				)
					A_     : Union[str, Any]		      =						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     a_     (							self      :    Union[str, Any]				):



					"""simple docstring"""

					A_     : str		      =						self.get_feature_extractor()
					A_     : int		      =						self.get_tokenizer()
					A_     : Any		      =						self.get_decoder()

					A_     : Tuple		      =						WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__					,   decoder=lowerCAmelCase__				)

					A_     : List[str]		      =						"This is a test string"

					A_     : Optional[int]		      =						processor(text=lowerCAmelCase__				)

					A_     : Any		      =						tokenizer(lowerCAmelCase__				)

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







				def     a_     (							self      :    str					,   _lowerCamelCase      :    Any=(2, 10, 16)					,   _lowerCamelCase      :    int=77				):



					"""simple docstring"""

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







				def     a_     (							self      :    List[Any]				):



					"""simple docstring"""

					A_     : Optional[int]		      =						self.get_feature_extractor()
					A_     : Optional[Any]		      =						self.get_tokenizer()
					A_     : Union[str, Any]		      =						self.get_decoder()

					A_     : Tuple		      =						WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__					,   decoder=lowerCAmelCase__				)

					A_     : int		      =						self._get_dummy_logits(shape=(10, 16)					,   seed=13				)

					A_     : int		      =						processor.decode(lowerCAmelCase__				)

					A_     : Optional[Any]		      =						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     a_     (							self      :    Optional[Any]					,   _lowerCamelCase      :    Tuple				):



					"""simple docstring"""

					A_     : Dict		      =						self.get_feature_extractor()
					A_     : Union[str, Any]		      =						self.get_tokenizer()
					A_     : Union[str, Any]		      =						self.get_decoder()

					A_     : Optional[int]		      =						WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__					,   decoder=lowerCAmelCase__				)

					A_     : List[Any]		      =						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_     : List[Any]		      =						processor.batch_decode(lowerCAmelCase__				)
					else:
						with get_context(lowerCAmelCase__				).Pool() as pool:
							A_     : Tuple		      =						processor.batch_decode(lowerCAmelCase__					,   lowerCAmelCase__				)

					A_     : Optional[int]		      =						list(lowerCAmelCase__				)

					with get_context('''fork'''				).Pool() as p:
						A_     : Any		      =						decoder.decode_beams_batch(lowerCAmelCase__					,   lowerCAmelCase__				)

					A_     : 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(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     a_     (							self      :    Tuple				):



					"""simple docstring"""

					A_     : Optional[Any]		      =						self.get_feature_extractor()
					A_     : Optional[Any]		      =						self.get_tokenizer()
					A_     : Union[str, Any]		      =						self.get_decoder()

					A_     : List[str]		      =						WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__					,   decoder=lowerCAmelCase__				)

					A_     : Optional[Any]		      =						self._get_dummy_logits()

					A_     : List[Any]		      =						15
					A_     : List[Any]		      =						-20.0
					A_     : str		      =						-4.0

					A_     : Union[str, Any]		      =						processor.batch_decode(
					    lowerCAmelCase__					,   beam_width=lowerCAmelCase__					,   beam_prune_logp=lowerCAmelCase__					,   token_min_logp=lowerCAmelCase__					,   )
					A_     : List[str]		      =						decoded_processor_out.text

					A_     : List[Any]		      =						list(lowerCAmelCase__				)

					with get_context('''fork'''				).Pool() as pool:
						A_     : Dict		      =						decoder.decode_beams_batch(
						    lowerCAmelCase__					,   lowerCAmelCase__					,   beam_width=lowerCAmelCase__					,   beam_prune_logp=lowerCAmelCase__					,   token_min_logp=lowerCAmelCase__					,   )

					A_     : str		      =						[d[0][0] for d in decoded_decoder_out]
					A_     : List[Any]		      =						[d[0][2] for d in decoded_decoder_out]
					A_     : List[Any]		      =						[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, -13.9474]					,   lowerCAmelCase__					,   atol=1E-3				)				)







				def     a_     (							self      :    Tuple				):



					"""simple docstring"""

					A_     : Optional[int]		      =						self.get_feature_extractor()
					A_     : List[Any]		      =						self.get_tokenizer()
					A_     : Optional[int]		      =						self.get_decoder()

					A_     : Dict		      =						WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase__					,   feature_extractor=lowerCAmelCase__					,   decoder=lowerCAmelCase__				)

					A_     : List[Any]		      =						self._get_dummy_logits()

					A_     : Dict		      =						2.0
					A_     : int		      =						5.0
					A_     : str		      =						-20.0
					A_     : Tuple		      =						True

					A_     : Tuple		      =						processor.batch_decode(
					    lowerCAmelCase__					,   alpha=lowerCAmelCase__					,   beta=lowerCAmelCase__					,   unk_score_offset=lowerCAmelCase__					,   lm_score_boundary=lowerCAmelCase__					,   )
					A_     : Union[str, Any]		      =						decoded_processor_out.text

					A_     : Tuple		      =						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_     : str		      =						decoder.decode_beams_batch(
						    lowerCAmelCase__					,   lowerCAmelCase__					,   )

					A_     : Optional[Any]		      =						[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_     : Dict		      =						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     a_     (							self      :    str				):



					"""simple docstring"""

					A_     : Any		      =						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''				)

					A_     : Dict		      =						processor.decoder.model_container[processor.decoder._model_key]
					A_     : Dict		      =						Path(language_model._kenlm_model.path.decode('''utf-8'''				)				).parent.parent.absolute()

					A_     : Union[str, Any]		      =						os.listdir(lowerCAmelCase__				)
					A_     : Tuple		      =						["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     a_     (							self      :    Dict				):



					"""simple docstring"""

					A_     : List[str]		      =						snapshot_download('''hf-internal-testing/processor_with_lm'''				)

					A_     : Union[str, Any]		      =						WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase__				)

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

					A_     : List[str]		      =						os.listdir(lowerCAmelCase__				)
					A_     : Optional[Any]		      =						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     a_     (							self      :    Dict				):



					"""simple docstring"""

					A_     : Dict		      =						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''				)
					A_     : str		      =						AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''				)

					A_     : str		      =						floats_list((3, 10_00)				)

					A_     : int		      =						processor_wavaveca(lowerCAmelCase__					,   return_tensors='''np'''				)
					A_     : List[Any]		      =						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_     : int		      =						self._get_dummy_logits()

					A_     : Tuple		      =						processor_wavaveca.batch_decode(lowerCAmelCase__				)
					A_     : int		      =						processor_auto.batch_decode(lowerCAmelCase__				)

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







				def     a_     (							self      :    Any				):



					"""simple docstring"""

					A_     : List[str]		      =						self.get_feature_extractor()
					A_     : List[Any]		      =						self.get_tokenizer()
					A_     : Dict		      =						self.get_decoder()

					A_     : Tuple		      =						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     a_     (							_lowerCamelCase      :    int					,   _lowerCamelCase      :    str				):



					"""simple docstring"""

					A_     : List[Any]		      =						[d[key] for d in offsets]
					return retrieved_list







				def     a_     (							self      :    Any				):



					"""simple docstring"""

					A_     : Any		      =						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''				)
					A_     : Tuple		      =						self._get_dummy_logits()[0]

					A_     : Union[str, Any]		      =						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     a_     (							self      :    Union[str, Any]				):



					"""simple docstring"""

					A_     : Dict		      =						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''				)
					A_     : List[str]		      =						self._get_dummy_logits()

					A_     : Union[str, Any]		      =						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     a_     (							self      :    Optional[int]				):



					"""simple docstring"""

					import torch

					A_     : Optional[Any]		      =						load_dataset('''common_voice'''					,   '''en'''					,   split='''train'''					,   streaming=lowerCAmelCase__				)
					A_     : int		      =						ds.cast_column('''audio'''					,   datasets.Audio(sampling_rate=1_60_00				)				)
					A_     : List[str]		      =						iter(lowerCAmelCase__				)
					A_     : Optional[int]		      =						next(lowerCAmelCase__				)

					A_     : Optional[Any]		      =						AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''				)
					A_     : Optional[int]		      =						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_     : Any		      =						processor(sample['''audio''']['''array''']					,   return_tensors='''pt'''				).input_values

					with torch.no_grad():
						A_     : Tuple		      =						model(lowerCAmelCase__				).logits.cpu().numpy()

					A_     : Optional[int]		      =						processor.decode(logits[0]					,   output_word_offsets=lowerCAmelCase__				)

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

					A_     : Tuple		      =						"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_     : Dict		      =						torch.tensor(self.get_from_offsets(lowerCAmelCase__					,   '''start_time'''				)				)
					A_     : str		      =						torch.tensor(self.get_from_offsets(lowerCAmelCase__					,   '''end_time'''				)				)

					# fmt: off
					A_     : Dict		      =						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]				)
					A_     : int		      =						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(lowerCAmelCase__					,   lowerCAmelCase__					,   atol=0.01				)				)
					self.assertTrue(torch.allclose(lowerCAmelCase__					,   lowerCAmelCase__					,   atol=0.01				)				)












from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
     from ..models.auto import AutoTokenizer







class 				__lowerCAmelCase       :







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        raise NotImplementedError()







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        raise NotImplementedError()







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False		,      **lowerCAmelCase__							)   ->						int:



        '''simple docstring'''




        a__	:					str										=tokenizer
        a__	:					List[str]										=skip_prompt
        a__	:					List[Any]										=decode_kwargs

        # variables used in the streaming process
        a__	:					Dict										=[]
        a__	:					int										=0
        a__	:					str										=True







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



        '''simple docstring'''




        if len(value.shape							) > 1 and value.shape[0] > 1:
          raise ValueError("TextStreamer only supports batch size 1"							)
        elif len(value.shape							) > 1:
          a__	:					Any										=value[0]

        if self.skip_prompt and self.next_tokens_are_prompt:
          a__	:					Dict										=False
          return

        # Add the new token to the cache and decodes the entire thing.
        self.token_cache.extend(value.tolist()							)
        a__	:					Union[str, Any]										=self.tokenizer.decode(self.token_cache		,      **self.decode_kwargs							)

        # After the symbol for a new line, we flush the cache.
        if text.endswith("\n"							):
          a__	:					List[Any]										=text[self.print_len :]
          a__	:					List[str]										=[]
          a__	:					Optional[int]										=0
        # If the last token is a CJK character, we print the characters.
        elif len(lowerCAmelCase__							) > 0 and self._is_chinese_char(ord(text[-1]							)							):
          a__	:					List[str]										=text[self.print_len :]
          self.print_len += len(lowerCAmelCase__							)
        # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
        # which may change with the subsequent token -- there are probably smarter ways to do this!)
        else:
          a__	:					str										=text[self.print_len : text.rfind(" "							) + 1]
          self.print_len += len(lowerCAmelCase__							)

        self.on_finalized_text(lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        if len(self.token_cache							) > 0:
          a__	:					Union[str, Any]										=self.tokenizer.decode(self.token_cache		,      **self.decode_kwargs							)
          a__	:					List[Any]										=text[self.print_len :]
          a__	:					List[str]										=[]
          a__	:					Optional[int]										=0
        else:
          a__	:					Union[str, Any]										=""

        a__	:					Any										=True
        self.on_finalized_text(lowerCAmelCase__		,      stream_end=lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False							)   ->						Optional[Any]:



        '''simple docstring'''




        print(lowerCAmelCase__		,      flush=lowerCAmelCase__		,      end="" if not stream_end else None							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						str:



        '''simple docstring'''




        if (
            (cp >= 0X4E_00 and cp <= 0X9F_FF)
            or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
            or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
            or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
            or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
            or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
            or (cp >= 0XF9_00 and cp <= 0XFA_FF)
            or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
        ):  #
          return True

        return False







class 				__lowerCAmelCase       (    UpperCamelCase__):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False		,      lowerCAmelCase__ = None		,      **lowerCAmelCase__							)   ->						Optional[Any]:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__		,      lowerCAmelCase__		,      **lowerCAmelCase__							)
        a__	:					str										=Queue()
        a__	:					Optional[Any]										=None
        a__	:					Any										=timeout







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__ = False							)   ->						List[str]:



        '''simple docstring'''




        self.text_queue.put(lowerCAmelCase__		,      timeout=self.timeout							)
        if stream_end:
          self.text_queue.put(self.stop_signal		,      timeout=self.timeout							)







      def __iter__(    self							)   ->						Dict:



        '''simple docstring'''




        return self







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					int										=self.text_queue.get(timeout=self.timeout							)
        if value == self.stop_signal:
          raise StopIteration()
        else:
          return value






