


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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



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

       snake_case__						= {
           '''unk_token''': '''<unk>''',
           '''bos_token''': '''<s>''',
           '''eos_token''': '''</s>''',
       }
       snake_case__						= {
           '''feature_size''': 1,
           '''padding_value''': 0.0,
           '''sampling_rate''': 1_60_00,
           '''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     ,		_a   )
       with open(self.vocab_file     ,		'''w'''     ,		encoding='''utf-8'''   ) as fp:
         fp.write(json.dumps(_a   ) + '''\n'''   )

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

       # load decoder from hub
       snake_case__						= '''hf-internal-testing/ngram-beam-search-decoder'''

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		**_a:List[Any]   ):
       snake_case__						= self.add_kwargs_tokens_map.copy()
       kwargs.update(_a   )
       return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict     ,		**_a:List[str]   ):
       return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		**_a:int   ):
       return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name     ,		**_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       shutil.rmtree(self.tmpdirname   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.get_tokenizer()
       snake_case__						= self.get_feature_extractor()
       snake_case__						= self.get_decoder()

       snake_case__						= WavaVecaProcessorWithLM(tokenizer=_a     ,		feature_extractor=_a     ,		decoder=_a   )

       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     ,		_a   )

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

       # 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     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       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:List[Any]   ):
       snake_case__						= self.get_tokenizer()
       # add token to trigger raise
       tokenizer.add_tokens(['''xx''']   )
       with self.assertRaisesRegex(_a     ,		'''include'''   ):
         WavaVecaProcessorWithLM(
             tokenizer=_a     ,		feature_extractor=self.get_feature_extractor()     ,		decoder=self.get_decoder()   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.get_feature_extractor()
       snake_case__						= self.get_tokenizer()
       snake_case__						= self.get_decoder()

       snake_case__						= WavaVecaProcessorWithLM(tokenizer=_a     ,		feature_extractor=_a     ,		decoder=_a   )

       snake_case__						= floats_list((3, 10_00)   )

       snake_case__						= feature_extractor(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor(_a     ,		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:Union[str, Any]   ):
       snake_case__						= self.get_feature_extractor()
       snake_case__						= self.get_tokenizer()
       snake_case__						= self.get_decoder()

       snake_case__						= WavaVecaProcessorWithLM(tokenizer=_a     ,		feature_extractor=_a     ,		decoder=_a   )

       snake_case__						= '''This is a test string'''

       snake_case__						= processor(text=_a   )

       snake_case__						= tokenizer(_a   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]     ,		_a:int=(2, 10, 16)     ,		_a:Dict=77   ):
       np.random.seed(_a   )
       return np.random.rand(*_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= self.get_feature_extractor()
       snake_case__						= self.get_tokenizer()
       snake_case__						= self.get_decoder()

       snake_case__						= WavaVecaProcessorWithLM(tokenizer=_a     ,		feature_extractor=_a     ,		decoder=_a   )

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

       snake_case__						= processor.decode(_a   )

       snake_case__						= decoder.decode_beams(_a   )[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:int     ,		_a:Union[str, Any]   ):
       snake_case__						= self.get_feature_extractor()
       snake_case__						= self.get_tokenizer()
       snake_case__						= self.get_decoder()

       snake_case__						= WavaVecaProcessorWithLM(tokenizer=_a     ,		feature_extractor=_a     ,		decoder=_a   )

       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(_a   )
       else:
         with get_context(_a   ).Pool() as pool:
           snake_case__						= processor.batch_decode(_a     ,		_a   )

       snake_case__						= list(_a   )

       with get_context('''fork'''   ).Pool() as p:
         snake_case__						= decoder.decode_beams_batch(_a     ,		_a   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.get_feature_extractor()
       snake_case__						= self.get_tokenizer()
       snake_case__						= self.get_decoder()

       snake_case__						= WavaVecaProcessorWithLM(tokenizer=_a     ,		feature_extractor=_a     ,		decoder=_a   )

       snake_case__						= self._get_dummy_logits()

       snake_case__						= 15
       snake_case__						= -20.0
       snake_case__						= -4.0

       snake_case__						= processor.batch_decode(
           _a     ,		beam_width=_a     ,		beam_prune_logp=_a     ,		token_min_logp=_a     ,		)
       snake_case__						= decoded_processor_out.text

       snake_case__						= list(_a   )

       with get_context('''fork'''   ).Pool() as pool:
         snake_case__						= decoder.decode_beams_batch(
             _a     ,		_a     ,		beam_width=_a     ,		beam_prune_logp=_a     ,		token_min_logp=_a     ,		)

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

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.get_feature_extractor()
       snake_case__						= self.get_tokenizer()
       snake_case__						= self.get_decoder()

       snake_case__						= WavaVecaProcessorWithLM(tokenizer=_a     ,		feature_extractor=_a     ,		decoder=_a   )

       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(
           _a     ,		alpha=_a     ,		beta=_a     ,		unk_score_offset=_a     ,		lm_score_boundary=_a     ,		)
       snake_case__						= decoded_processor_out.text

       snake_case__						= list(_a   )
       decoder.reset_params(
           alpha=_a     ,		beta=_a     ,		unk_score_offset=_a     ,		lm_score_boundary=_a     ,		)

       with get_context('''fork'''   ).Pool() as pool:
         snake_case__						= decoder.decode_beams_batch(
             _a     ,		_a     ,		)

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       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(_a   )
       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(_a     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= snapshot_download('''hf-internal-testing/processor_with_lm'''   )

       snake_case__						= WavaVecaProcessorWithLM.from_pretrained(_a   )

       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(_a   )
       snake_case__						= os.listdir(_a   )

       local_decoder_files.sort()
       expected_decoder_files.sort()

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       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, 10_00)   )

       snake_case__						= processor_wavaveca(_a     ,		return_tensors='''np'''   )
       snake_case__						= processor_auto(_a     ,		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(_a   )
       snake_case__						= processor_auto.batch_decode(_a   )

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Dict   ):
       snake_case__						= self.get_feature_extractor()
       snake_case__						= self.get_tokenizer()
       snake_case__						= self.get_decoder()

       snake_case__						= WavaVecaProcessorWithLM(tokenizer=_a     ,		feature_extractor=_a     ,		decoder=_a   )

       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__						(					_a:Union[str, Any]     ,		_a:Dict   ):
       snake_case__						= [d[key] for d in offsets]
       return retrieved_list

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''   )
       snake_case__						= self._get_dummy_logits()[0]

       snake_case__						= processor.decode(_a     ,		output_word_offsets=_a   )
       # 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(_a     ,		_a   )   )

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

       snake_case__						= processor.batch_decode(_a     ,		output_word_offsets=_a   )

       # 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(_a     ,		_a   )   )

       self.assertListEqual(
           [''' '''.join(self.get_from_offsets(_a     ,		'''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   ):
       import torch

       snake_case__						= load_dataset('''common_voice'''     ,		'''en'''     ,		split='''train'''     ,		streaming=_a   )
       snake_case__						= ds.cast_column('''audio'''     ,		datasets.Audio(sampling_rate=1_60_00   )   )
       snake_case__						= iter(_a   )
       snake_case__						= next(_a   )

       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(_a   ).logits.cpu().numpy()

       snake_case__						= processor.decode(logits[0]     ,		output_word_offsets=_a   )

       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(_a     ,		'''word'''   )   )     ,		_a   )
       self.assertEqual(''' '''.join(self.get_from_offsets(_a     ,		'''word'''   )   )     ,		output.text   )

       # output times
       snake_case__						= torch.tensor(self.get_from_offsets(_a     ,		'''start_time'''   )   )
       snake_case__						= torch.tensor(self.get_from_offsets(_a     ,		'''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(_a     ,		_a     ,		atol=0.01   )   )
       self.assertTrue(torch.allclose(_a     ,		_a     ,		atol=0.01   )   )



"""simple docstring"""
from __future__ import annotations

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  2
						__lowerCAmelCase     =  []
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(_lowerCAmelCase						)
						if n > 1:
												factors.append(_lowerCAmelCase						)
						return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()
