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   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:Optional[int]						  =     '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
										lowerCamelCase						:Any						  =     dict(zip(__snake_case	,					range(len(__snake_case		)		)		)		)

										lowerCamelCase						:Optional[Any]						  =     {
										    '''unk_token''': '''<unk>''',
										    '''bos_token''': '''<s>''',
										    '''eos_token''': '''</s>''',
										}
										lowerCamelCase						:List[Any]						  =     {
										    '''feature_size''': 1,
										    '''padding_value''': 0.0,
										    '''sampling_rate''': 16000,
										    '''return_attention_mask''': False,
										    '''do_normalize''': True,
										}

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

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

										# load decoder from hub
										lowerCamelCase						:Dict						  =     '''hf-internal-testing/ngram-beam-search-decoder'''







							def 				snake_case  (				self     :    Optional[int]	,					**__snake_case     :    Any		):
										lowerCamelCase						:Dict						  =     self.add_kwargs_tokens_map.copy()
										kwargs.update(__snake_case		)
										return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Optional[int]	,					**__snake_case     :    List[Any]		):
										return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Union[str, Any]	,					**__snake_case     :    List[Any]		):
										return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name	,					**__snake_case		)







							def 				snake_case  (				self     :    Any		):
										shutil.rmtree(self.tmpdirname		)







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Dict						  =     self.get_tokenizer()
										lowerCamelCase						:Tuple						  =     self.get_feature_extractor()
										lowerCamelCase						:Optional[int]						  =     self.get_decoder()

										lowerCamelCase						:str						  =     WavaVecaProcessorWithLM(tokenizer=__snake_case	,					feature_extractor=__snake_case	,					decoder=__snake_case		)

										processor.save_pretrained(self.tmpdirname		)
										lowerCamelCase						:Union[str, Any]						  =     WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname		)

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

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







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:Dict						  =     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
										lowerCamelCase						:Any						  =     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 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Optional[Any]						  =     self.get_tokenizer()
										# add token to trigger raise
										tokenizer.add_tokens(['''xx''']		)
										with self.assertRaisesRegex(__snake_case	,					'''include'''		):
													WavaVecaProcessorWithLM(
													    tokenizer=__snake_case	,					feature_extractor=self.get_feature_extractor()	,					decoder=self.get_decoder()		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[Any]						  =     self.get_feature_extractor()
										lowerCamelCase						:List[Any]						  =     self.get_tokenizer()
										lowerCamelCase						:Any						  =     self.get_decoder()

										lowerCamelCase						:Any						  =     WavaVecaProcessorWithLM(tokenizer=__snake_case	,					feature_extractor=__snake_case	,					decoder=__snake_case		)

										lowerCamelCase						:Tuple						  =     floats_list((3, 1000)		)

										lowerCamelCase						:Union[str, Any]						  =     feature_extractor(__snake_case	,					return_tensors='''np'''		)
										lowerCamelCase						:Optional[Any]						  =     processor(__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 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Union[str, Any]						  =     self.get_feature_extractor()
										lowerCamelCase						:List[Any]						  =     self.get_tokenizer()
										lowerCamelCase						:Optional[Any]						  =     self.get_decoder()

										lowerCamelCase						:List[str]						  =     WavaVecaProcessorWithLM(tokenizer=__snake_case	,					feature_extractor=__snake_case	,					decoder=__snake_case		)

										lowerCamelCase						:Union[str, Any]						  =     '''This is a test string'''

										lowerCamelCase						:Optional[int]						  =     processor(text=__snake_case		)

										lowerCamelCase						:Dict						  =     tokenizer(__snake_case		)

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







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    List[str]=(2, 10, 16)	,					__snake_case     :    Tuple=77		):
										np.random.seed(__snake_case		)
										return np.random.rand(*__snake_case		)







							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Any						  =     self.get_feature_extractor()
										lowerCamelCase						:int						  =     self.get_tokenizer()
										lowerCamelCase						:int						  =     self.get_decoder()

										lowerCamelCase						:int						  =     WavaVecaProcessorWithLM(tokenizer=__snake_case	,					feature_extractor=__snake_case	,					decoder=__snake_case		)

										lowerCamelCase						:Dict						  =     self._get_dummy_logits(shape=(10, 16)	,					seed=13		)

										lowerCamelCase						:Optional[Any]						  =     processor.decode(__snake_case		)

										lowerCamelCase						:Optional[int]						  =     decoder.decode_beams(__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 				snake_case  (				self     :    Any	,					__snake_case     :    str		):
										lowerCamelCase						:str						  =     self.get_feature_extractor()
										lowerCamelCase						:str						  =     self.get_tokenizer()
										lowerCamelCase						:Any						  =     self.get_decoder()

										lowerCamelCase						:Any						  =     WavaVecaProcessorWithLM(tokenizer=__snake_case	,					feature_extractor=__snake_case	,					decoder=__snake_case		)

										lowerCamelCase						: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:
													lowerCamelCase						:int						  =     processor.batch_decode(__snake_case		)
										else:
													with get_context(__snake_case		).Pool() as pool:
																lowerCamelCase						:List[str]						  =     processor.batch_decode(__snake_case	,					__snake_case		)

										lowerCamelCase						:List[Any]						  =     list(__snake_case		)

										with get_context('''fork'''		).Pool() as p:
													lowerCamelCase						:int						  =     decoder.decode_beams_batch(__snake_case	,					__snake_case		)

										lowerCamelCase						:Optional[int]						  =     [], [], []
										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(__snake_case	,					decoded_processor.text		)
										self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']	,					decoded_processor.text		)
										self.assertListEqual(__snake_case	,					decoded_processor.logit_score		)
										self.assertListEqual(__snake_case	,					decoded_processor.lm_score		)







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:str						  =     self.get_feature_extractor()
										lowerCamelCase						:int						  =     self.get_tokenizer()
										lowerCamelCase						:List[Any]						  =     self.get_decoder()

										lowerCamelCase						:str						  =     WavaVecaProcessorWithLM(tokenizer=__snake_case	,					feature_extractor=__snake_case	,					decoder=__snake_case		)

										lowerCamelCase						:Dict						  =     self._get_dummy_logits()

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

										lowerCamelCase						:int						  =     processor.batch_decode(
										    __snake_case	,					beam_width=__snake_case	,					beam_prune_logp=__snake_case	,					token_min_logp=__snake_case	,					)
										lowerCamelCase						:List[str]						  =     decoded_processor_out.text

										lowerCamelCase						:List[str]						  =     list(__snake_case		)

										with get_context('''fork'''		).Pool() as pool:
													lowerCamelCase						:List[Any]						  =     decoder.decode_beams_batch(
													    __snake_case	,					__snake_case	,					beam_width=__snake_case	,					beam_prune_logp=__snake_case	,					token_min_logp=__snake_case	,					)

										lowerCamelCase						:Dict						  =     [d[0][0] for d in decoded_decoder_out]
										lowerCamelCase						:Optional[int]						  =     [d[0][2] for d in decoded_decoder_out]
										lowerCamelCase						:Tuple						  =     [d[0][3] for d in decoded_decoder_out]

										self.assertListEqual(__snake_case	,					__snake_case		)
										self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']	,					__snake_case		)

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

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







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:List[str]						  =     self.get_feature_extractor()
										lowerCamelCase						:List[Any]						  =     self.get_tokenizer()
										lowerCamelCase						:List[str]						  =     self.get_decoder()

										lowerCamelCase						:str						  =     WavaVecaProcessorWithLM(tokenizer=__snake_case	,					feature_extractor=__snake_case	,					decoder=__snake_case		)

										lowerCamelCase						:Dict						  =     self._get_dummy_logits()

										lowerCamelCase						:str						  =     2.0
										lowerCamelCase						:Any						  =     5.0
										lowerCamelCase						:Optional[Any]						  =     -20.0
										lowerCamelCase						:List[Any]						  =     True

										lowerCamelCase						:List[Any]						  =     processor.batch_decode(
										    __snake_case	,					alpha=__snake_case	,					beta=__snake_case	,					unk_score_offset=__snake_case	,					lm_score_boundary=__snake_case	,					)
										lowerCamelCase						:Dict						  =     decoded_processor_out.text

										lowerCamelCase						:List[Any]						  =     list(__snake_case		)
										decoder.reset_params(
										    alpha=__snake_case	,					beta=__snake_case	,					unk_score_offset=__snake_case	,					lm_score_boundary=__snake_case	,					)

										with get_context('''fork'''		).Pool() as pool:
													lowerCamelCase						:Dict						  =     decoder.decode_beams_batch(
													    __snake_case	,					__snake_case	,					)

										lowerCamelCase						:Optional[Any]						  =     [d[0][0] for d in decoded_decoder_out]

										self.assertListEqual(__snake_case	,					__snake_case		)
										self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']	,					__snake_case		)
										lowerCamelCase						:List[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	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Dict						  =     WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''		)

										lowerCamelCase						:Optional[Any]						  =     processor.decoder.model_container[processor.decoder._model_key]
										lowerCamelCase						:Tuple						  =     Path(language_model._kenlm_model.path.decode('''utf-8'''		)		).parent.parent.absolute()

										lowerCamelCase						:Any						  =     os.listdir(__snake_case		)
										lowerCamelCase						: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(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Union[str, Any]						  =     snapshot_download('''hf-internal-testing/processor_with_lm'''		)

										lowerCamelCase						:Tuple						  =     WavaVecaProcessorWithLM.from_pretrained(__snake_case		)

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

										lowerCamelCase						:List[Any]						  =     os.listdir(__snake_case		)
										lowerCamelCase						:Any						  =     os.listdir(__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(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:Tuple						  =     WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''		)
										lowerCamelCase						:List[Any]						  =     AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''		)

										lowerCamelCase						:List[Any]						  =     floats_list((3, 1000)		)

										lowerCamelCase						:Optional[Any]						  =     processor_wavaveca(__snake_case	,					return_tensors='''np'''		)
										lowerCamelCase						:int						  =     processor_auto(__snake_case	,					return_tensors='''np'''		)

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

										lowerCamelCase						:Union[str, Any]						  =     self._get_dummy_logits()

										lowerCamelCase						:Optional[Any]						  =     processor_wavaveca.batch_decode(__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     processor_auto.batch_decode(__snake_case		)

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







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:Optional[int]						  =     self.get_feature_extractor()
										lowerCamelCase						:int						  =     self.get_tokenizer()
										lowerCamelCase						:Dict						  =     self.get_decoder()

										lowerCamelCase						:str						  =     WavaVecaProcessorWithLM(tokenizer=__snake_case	,					feature_extractor=__snake_case	,					decoder=__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 				snake_case  (				__snake_case     :    Optional[Any]	,					__snake_case     :    Union[str, Any]		):
										lowerCamelCase						:Union[str, Any]						  =     [d[key] for d in offsets]
										return retrieved_list







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Dict						  =     WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''		)
										lowerCamelCase						:List[str]						  =     self._get_dummy_logits()[0]

										lowerCamelCase						:Any						  =     processor.decode(__snake_case	,					output_word_offsets=__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(__snake_case	,					__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 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:List[str]						  =     WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''		)
										lowerCamelCase						:Optional[int]						  =     self._get_dummy_logits()

										lowerCamelCase						:List[str]						  =     processor.batch_decode(__snake_case	,					output_word_offsets=__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(__snake_case	,					__snake_case		)		)

										self.assertListEqual(
										    [''' '''.join(self.get_from_offsets(__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 				snake_case  (				self     :    Dict		):
										import torch

										lowerCamelCase						:Union[str, Any]						  =     load_dataset('''common_voice'''	,					'''en'''	,					split='''train'''	,					streaming=__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     ds.cast_column('''audio'''	,					datasets.Audio(sampling_rate=16000		)		)
										lowerCamelCase						:Dict						  =     iter(__snake_case		)
										lowerCamelCase						:Tuple						  =     next(__snake_case		)

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

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

										lowerCamelCase						:Dict						  =     processor.decode(logits[0]	,					output_word_offsets=__snake_case		)

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

										lowerCamelCase						:Dict						  =     '''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(__snake_case	,					'''word'''		)		)	,					__snake_case		)
										self.assertEqual(''' '''.join(self.get_from_offsets(__snake_case	,					'''word'''		)		)	,					output.text		)

										# output times
										lowerCamelCase						:List[Any]						  =     torch.tensor(self.get_from_offsets(__snake_case	,					'''start_time'''		)		)
										lowerCamelCase						:Any						  =     torch.tensor(self.get_from_offsets(__snake_case	,					'''end_time'''		)		)

										# fmt: off
										lowerCamelCase						:Union[str, Any]						  =     torch.tensor([1.4_1_9_9, 1.6_5_9_9, 2.2_5_9_9, 3.0, 3.2_4, 3.5_9_9_9, 3.7_9_9_9, 4.0_9_9_9, 4.2_6, 4.9_4, 5.2_8, 5.6_5_9_9, 5.7_8, 5.9_4, 6.3_2, 6.5_3_9_9, 6.6_5_9_9]		)
										lowerCamelCase						:Optional[Any]						  =     torch.tensor([1.5_3_9_9, 1.8_9_9_9, 2.9, 3.1_6, 3.5_3_9_9, 3.7_2, 4.0_1_9_9, 4.1_7_9_9, 4.7_6, 5.1_5_9_9, 5.5_5_9_9, 5.6_9_9_9, 5.8_6, 6.1_9_9_9, 6.3_8, 6.6_1_9_9, 6.9_4]		)
										# fmt: on

										self.assertTrue(torch.allclose(__snake_case	,					__snake_case	,					atol=0.0_1		)		)
										self.assertTrue(torch.allclose(__snake_case	,					__snake_case	,					atol=0.0_1		)		)




import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
			import torch

			from transformers import AutoModelForCausalLM
@require_torch
class   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:List[Any]						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     -1

										lowerCamelCase						:List[str]						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Tuple						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:str						  =     tokenizer.decode(greedy_ids[0]		)

										with CaptureStdout() as cs:
													lowerCamelCase						:str						  =     TextStreamer(__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case	,					streamer=__snake_case		)
										# The greedy text should be printed to stdout, except for the final "\n" in the streamer
										lowerCamelCase						:Optional[int]						  =     cs.out[:-1]

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Tuple						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:int						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     -1

										lowerCamelCase						:Dict						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Tuple						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:List[Any]						  =     tokenizer.decode(greedy_ids[0]		)

										lowerCamelCase						:List[str]						  =     TextIteratorStreamer(__snake_case		)
										lowerCamelCase						:List[str]						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     Thread(target=model.generate	,					kwargs=__snake_case		)
										thread.start()
										lowerCamelCase						:Any						  =     ''''''
										for new_text in streamer:
													streamer_text += new_text

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:int						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Dict						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:List[str]						  =     -1

										lowerCamelCase						:Optional[Any]						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:Optional[Any]						  =     model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case		)
										lowerCamelCase						:List[str]						  =     greedy_ids[:, input_ids.shape[1] :]
										lowerCamelCase						:Union[str, Any]						  =     tokenizer.decode(new_greedy_ids[0]		)

										with CaptureStdout() as cs:
													lowerCamelCase						:List[str]						  =     TextStreamer(__snake_case	,					skip_prompt=__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=10	,					do_sample=__snake_case	,					streamer=__snake_case		)
										# The greedy text should be printed to stdout, except for the final "\n" in the streamer
										lowerCamelCase						:int						  =     cs.out[:-1]

										self.assertEqual(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
										# with actual models -- the dummy models' tokenizers are not aligned with their models, and
										# `skip_special_tokens=True` has no effect on them
										lowerCamelCase						:List[Any]						  =     AutoTokenizer.from_pretrained('''distilgpt2'''		)
										lowerCamelCase						:Union[str, Any]						  =     AutoModelForCausalLM.from_pretrained('''distilgpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

										lowerCamelCase						:Union[str, Any]						  =     torch.ones((1, 5)	,					device=__snake_case		).long() * model.config.bos_token_id
										with CaptureStdout() as cs:
													lowerCamelCase						:Dict						  =     TextStreamer(__snake_case	,					skip_special_tokens=__snake_case		)
													model.generate(__snake_case	,					max_new_tokens=1	,					do_sample=__snake_case	,					streamer=__snake_case		)

										# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
										# re-tokenized, must only contain one token
										lowerCamelCase						:Tuple						  =     cs.out[:-1]  # Remove the final "\n"
										lowerCamelCase						:int						  =     tokenizer(__snake_case	,					return_tensors='''pt'''		)
										self.assertEqual(streamer_text_tokenized.input_ids.shape	,					(1, 1)		)







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:List[str]						  =     AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		)
										lowerCamelCase						:Optional[int]						  =     AutoModelForCausalLM.from_pretrained('''hf-internal-testing/tiny-random-gpt2'''		).to(__snake_case		)
										lowerCamelCase						:Optional[int]						  =     -1

										lowerCamelCase						:Tuple						  =     ids_tensor((1, 5)	,					vocab_size=model.config.vocab_size		).to(__snake_case		)
										lowerCamelCase						:List[Any]						  =     TextIteratorStreamer(__snake_case	,					timeout=0.0_0_1		)
										lowerCamelCase						:Dict						  =     {'''input_ids''': input_ids, '''max_new_tokens''': 10, '''do_sample''': False, '''streamer''': streamer}
										lowerCamelCase						:Tuple						  =     Thread(target=model.generate	,					kwargs=__snake_case		)
										thread.start()

										# The streamer will timeout after 0.001 seconds, so an exception will be raised
										with self.assertRaises(__snake_case		):
													lowerCamelCase						:Dict						  =     ''''''
													for new_text in streamer:
																streamer_text += new_text



