



"""simple docstring"""

import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
     from huggingface_hub import snapshot_download
     from pyctcdecode import BeamSearchDecoderCTC

     from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
     from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
     from transformers import WavaVecaForCTC




@require_pyctcdecode
class        _UpperCAmelCase       (			unittest.TestCase):

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

     snake_case_ :     Any				=	{
         '''unk_token''': '''<unk>''',
         '''bos_token''': '''<s>''',
         '''eos_token''': '''</s>''',
     }
     snake_case_ :     str				=	{
         '''feature_size''': 1,
         '''padding_value''': 0.0,
         '''sampling_rate''': 16000,
         '''return_attention_mask''': False,
         '''do_normalize''': True,
     }

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

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

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

 def 						_snake_case					(     self		:       Any  ,     **lowercase_		:       Optional[Any]				):
     snake_case_ :     List[Any]				=	self.add_kwargs_tokens_map.copy()
     kwargs.update(lowercase_				)
     return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname  ,     **lowercase_				)

 def 						_snake_case					(     self		:       str  ,     **lowercase_		:       Dict				):
     return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname  ,     **lowercase_				)

 def 						_snake_case					(     self		:       Union[str, Any]  ,     **lowercase_		:       int				):
     return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name  ,     **lowercase_				)

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

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

     snake_case_ :     str				=	WavaVecaProcessorWithLM(tokenizer=lowercase_  ,     feature_extractor=lowercase_  ,     decoder=lowercase_				)

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

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

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

     # 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  ,     lowercase_				)

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Any				=	WavaVecaProcessorWithLM(
         tokenizer=self.get_tokenizer()  ,     feature_extractor=self.get_feature_extractor()  ,     decoder=self.get_decoder()				)
     processor.save_pretrained(self.tmpdirname				)

     # make sure that error is thrown when decoder alphabet doesn't match
     snake_case_ :     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 						_snake_case					(     self		:       Dict				):
     snake_case_ :     Any				=	self.get_tokenizer()
     # add token to trigger raise
     tokenizer.add_tokens(['''xx''']				)
     with self.assertRaisesRegex(lowercase_  ,     '''include'''				):
         WavaVecaProcessorWithLM(
             tokenizer=lowercase_  ,     feature_extractor=self.get_feature_extractor()  ,     decoder=self.get_decoder()				)

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     int				=	self.get_feature_extractor()
     snake_case_ :     str				=	self.get_tokenizer()
     snake_case_ :     Union[str, Any]				=	self.get_decoder()

     snake_case_ :     Tuple				=	WavaVecaProcessorWithLM(tokenizer=lowercase_  ,     feature_extractor=lowercase_  ,     decoder=lowercase_				)

     snake_case_ :     str				=	floats_list((3, 1000)				)

     snake_case_ :     List[str]				=	feature_extractor(lowercase_  ,     return_tensors='''np'''				)
     snake_case_ :     int				=	processor(lowercase_  ,     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		:       Optional[int]				):
     snake_case_ :     Tuple				=	self.get_feature_extractor()
     snake_case_ :     str				=	self.get_tokenizer()
     snake_case_ :     Union[str, Any]				=	self.get_decoder()

     snake_case_ :     int				=	WavaVecaProcessorWithLM(tokenizer=lowercase_  ,     feature_extractor=lowercase_  ,     decoder=lowercase_				)

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

     snake_case_ :     Dict				=	processor(text=lowercase_				)

     snake_case_ :     Tuple				=	tokenizer(lowercase_				)

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

 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Any=(2, 10, 16)  ,     lowercase_		:       str=77				):
     np.random.seed(lowercase_				)
     return np.random.rand(*lowercase_				)

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Optional[int]				=	self.get_feature_extractor()
     snake_case_ :     str				=	self.get_tokenizer()
     snake_case_ :     int				=	self.get_decoder()

     snake_case_ :     str				=	WavaVecaProcessorWithLM(tokenizer=lowercase_  ,     feature_extractor=lowercase_  ,     decoder=lowercase_				)

     snake_case_ :     Optional[int]				=	self._get_dummy_logits(shape=(10, 16)  ,     seed=13				)

     snake_case_ :     Optional[Any]				=	processor.decode(lowercase_				)

     snake_case_ :     Union[str, Any]				=	decoder.decode_beams(lowercase_				)[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		:       Optional[Any]  ,     lowercase_		:       Union[str, Any]				):
     snake_case_ :     Any				=	self.get_feature_extractor()
     snake_case_ :     List[str]				=	self.get_tokenizer()
     snake_case_ :     List[str]				=	self.get_decoder()

     snake_case_ :     str				=	WavaVecaProcessorWithLM(tokenizer=lowercase_  ,     feature_extractor=lowercase_  ,     decoder=lowercase_				)

     snake_case_ :     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:
         snake_case_ :     str				=	processor.batch_decode(lowercase_				)
     else:
         with get_context(lowercase_				).Pool() as pool:
             snake_case_ :     Any				=	processor.batch_decode(lowercase_  ,     lowercase_				)

     snake_case_ :     int				=	list(lowercase_				)

     with get_context('''fork'''				).Pool() as p:
         snake_case_ :     Union[str, Any]				=	decoder.decode_beams_batch(lowercase_  ,     lowercase_				)

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

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Any				=	self.get_feature_extractor()
     snake_case_ :     Any				=	self.get_tokenizer()
     snake_case_ :     str				=	self.get_decoder()

     snake_case_ :     Any				=	WavaVecaProcessorWithLM(tokenizer=lowercase_  ,     feature_extractor=lowercase_  ,     decoder=lowercase_				)

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

     snake_case_ :     str				=	15
     snake_case_ :     Tuple				=	-20.0
     snake_case_ :     Dict				=	-4.0

     snake_case_ :     Optional[Any]				=	processor.batch_decode(
         lowercase_  ,     beam_width=lowercase_  ,     beam_prune_logp=lowercase_  ,     token_min_logp=lowercase_  ,     )
     snake_case_ :     Union[str, Any]				=	decoded_processor_out.text

     snake_case_ :     str				=	list(lowercase_				)

     with get_context('''fork'''				).Pool() as pool:
         snake_case_ :     str				=	decoder.decode_beams_batch(
             lowercase_  ,     lowercase_  ,     beam_width=lowercase_  ,     beam_prune_logp=lowercase_  ,     token_min_logp=lowercase_  ,     )

     snake_case_ :     List[str]				=	[d[0][0] for d in decoded_decoder_out]
     snake_case_ :     Dict				=	[d[0][2] for d in decoded_decoder_out]
     snake_case_ :     int				=	[d[0][3] for d in decoded_decoder_out]

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

     self.assertTrue(np.array_equal(lowercase_  ,     decoded_processor_out.logit_score				)				)
     self.assertTrue(np.allclose([-20.0_54, -18.4_47]  ,     lowercase_  ,     atol=1E-3				)				)

     self.assertTrue(np.array_equal(lowercase_  ,     decoded_processor_out.lm_score				)				)
     self.assertTrue(np.allclose([-15.5_54, -13.94_74]  ,     lowercase_  ,     atol=1E-3				)				)

 def 						_snake_case					(     self		:       List[Any]				):
     snake_case_ :     Optional[int]				=	self.get_feature_extractor()
     snake_case_ :     Dict				=	self.get_tokenizer()
     snake_case_ :     Optional[Any]				=	self.get_decoder()

     snake_case_ :     Dict				=	WavaVecaProcessorWithLM(tokenizer=lowercase_  ,     feature_extractor=lowercase_  ,     decoder=lowercase_				)

     snake_case_ :     str				=	self._get_dummy_logits()

     snake_case_ :     Optional[int]				=	2.0
     snake_case_ :     str				=	5.0
     snake_case_ :     Tuple				=	-20.0
     snake_case_ :     Tuple				=	True

     snake_case_ :     List[Any]				=	processor.batch_decode(
         lowercase_  ,     alpha=lowercase_  ,     beta=lowercase_  ,     unk_score_offset=lowercase_  ,     lm_score_boundary=lowercase_  ,     )
     snake_case_ :     Any				=	decoded_processor_out.text

     snake_case_ :     Union[str, Any]				=	list(lowercase_				)
     decoder.reset_params(
         alpha=lowercase_  ,     beta=lowercase_  ,     unk_score_offset=lowercase_  ,     lm_score_boundary=lowercase_  ,     )

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

     snake_case_ :     Union[str, Any]				=	[d[0][0] for d in decoded_decoder_out]

     self.assertListEqual(lowercase_  ,     lowercase_				)
     self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']  ,     lowercase_				)
     snake_case_ :     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  ,     lowercase_				)

 def 						_snake_case					(     self		:       Dict				):
     snake_case_ :     Union[str, Any]				=	WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''				)

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

     snake_case_ :     List[Any]				=	os.listdir(lowercase_				)
     snake_case_ :     int				=	['''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(lowercase_  ,     lowercase_				)

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

     snake_case_ :     Optional[Any]				=	WavaVecaProcessorWithLM.from_pretrained(lowercase_				)

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

     snake_case_ :     Optional[Any]				=	os.listdir(lowercase_				)
     snake_case_ :     List[str]				=	os.listdir(lowercase_				)

     local_decoder_files.sort()
     expected_decoder_files.sort()

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

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     Union[str, Any]				=	WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''				)
     snake_case_ :     Any				=	AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''				)

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

     snake_case_ :     List[Any]				=	processor_wavaveca(lowercase_  ,     return_tensors='''np'''				)
     snake_case_ :     str				=	processor_auto(lowercase_  ,     return_tensors='''np'''				)

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

     snake_case_ :     int				=	self._get_dummy_logits()

     snake_case_ :     List[str]				=	processor_wavaveca.batch_decode(lowercase_				)
     snake_case_ :     int				=	processor_auto.batch_decode(lowercase_				)

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

 def 						_snake_case					(     self		:       Union[str, Any]				):
     snake_case_ :     str				=	self.get_feature_extractor()
     snake_case_ :     List[str]				=	self.get_tokenizer()
     snake_case_ :     List[Any]				=	self.get_decoder()

     snake_case_ :     Tuple				=	WavaVecaProcessorWithLM(tokenizer=lowercase_  ,     feature_extractor=lowercase_  ,     decoder=lowercase_				)

     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					(     lowercase_		:       Tuple  ,     lowercase_		:       str				):
     snake_case_ :     int				=	[d[key] for d in offsets]
     return retrieved_list

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     int				=	WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''				)
     snake_case_ :     Dict				=	self._get_dummy_logits()[0]

     snake_case_ :     int				=	processor.decode(lowercase_  ,     output_word_offsets=lowercase_				)
     # 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(lowercase_  ,     lowercase_				)				)

     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		:       Optional[int]				):
     snake_case_ :     Union[str, Any]				=	WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''				)
     snake_case_ :     Optional[Any]				=	self._get_dummy_logits()

     snake_case_ :     int				=	processor.batch_decode(lowercase_  ,     output_word_offsets=lowercase_				)

     # 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(lowercase_  ,     lowercase_				)				)

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

     snake_case_ :     List[Any]				=	load_dataset('''common_voice'''  ,     '''en'''  ,     split='''train'''  ,     streaming=lowercase_				)
     snake_case_ :     Any				=	ds.cast_column('''audio'''  ,     datasets.Audio(sampling_rate=16000				)				)
     snake_case_ :     Dict				=	iter(lowercase_				)
     snake_case_ :     List[Any]				=	next(lowercase_				)

     snake_case_ :     int				=	AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''				)
     snake_case_ :     str				=	WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''				)

     # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
     snake_case_ :     Tuple				=	processor(sample['''audio''']['''array''']  ,     return_tensors='''pt'''				).input_values

     with torch.no_grad():
         snake_case_ :     Optional[int]				=	model(lowercase_				).logits.cpu().numpy()

     snake_case_ :     List[str]				=	processor.decode(logits[0]  ,     output_word_offsets=lowercase_				)

     snake_case_ :     str				=	model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
     snake_case_ :     Optional[int]				=	[
         {
             '''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_ :     Union[str, Any]				=	'''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

     # output words
     self.assertEqual(''' '''.join(self.get_from_offsets(lowercase_  ,     '''word'''				)				)  ,     lowercase_				)
     self.assertEqual(''' '''.join(self.get_from_offsets(lowercase_  ,     '''word'''				)				)  ,     output.text				)

     # output times
     snake_case_ :     Tuple				=	torch.tensor(self.get_from_offsets(lowercase_  ,     '''start_time'''				)				)
     snake_case_ :     Dict				=	torch.tensor(self.get_from_offsets(lowercase_  ,     '''end_time'''				)				)

     # fmt: off
     snake_case_ :     Tuple				=	torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]				)
     snake_case_ :     int				=	torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]				)
     # fmt: on

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










"""simple docstring"""

import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
     import torch

if is_soundfile_availble():
     import soundfile as sf

if is_vision_available():
     from PIL import Image




def __lowercase			(							_a=""    ):
    snake_case_ :     List[str]				=	tempfile.mkdtemp()
    return os.path.join(_a		,			str(uuid.uuida()    ) + suffix    )




@require_soundfile
@require_torch
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     int				=	torch.rand(12  ,     dtype=torch.floataa				) - 0.5
     snake_case_ :     Optional[int]				=	AgentAudio(lowercase_				)
     snake_case_ :     List[str]				=	str(agent_type.to_string()				)

     # Ensure that the tensor and the agent_type's tensor are the same
     self.assertTrue(torch.allclose(lowercase_  ,     agent_type.to_raw()  ,     atol=1E-4				)				)

     del agent_type

     # Ensure the path remains even after the object deletion
     self.assertTrue(os.path.exists(lowercase_				)				)

     # Ensure that the file contains the same value as the original tensor
     snake_case_,	snake_case_ :     int				=	sf.read(lowercase_				)
     self.assertTrue(torch.allclose(lowercase_  ,     torch.tensor(lowercase_				)  ,     atol=1E-4				)				)






 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Any				=	torch.rand(12  ,     dtype=torch.floataa				) - 0.5
     snake_case_ :     List[str]				=	get_new_path(suffix='''.wav'''				)
     sf.write(lowercase_  ,     lowercase_  ,     16000				)

     snake_case_ :     Tuple				=	AgentAudio(lowercase_				)

     self.assertTrue(torch.allclose(lowercase_  ,     agent_type.to_raw()  ,     atol=1E-4				)				)
     self.assertEqual(agent_type.to_string()  ,     lowercase_				)





@require_vision
@require_torch
class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       Tuple				):
     snake_case_ :     List[Any]				=	torch.randint(0  ,     256  ,     (64, 64, 3)				)
     snake_case_ :     str				=	AgentImage(lowercase_				)
     snake_case_ :     Union[str, Any]				=	str(agent_type.to_string()				)

     # Ensure that the tensor and the agent_type's tensor are the same
     self.assertTrue(torch.allclose(lowercase_  ,     agent_type._tensor  ,     atol=1E-4				)				)

     self.assertIsInstance(agent_type.to_raw()  ,     Image.Image				)

     # Ensure the path remains even after the object deletion
     del agent_type
     self.assertTrue(os.path.exists(lowercase_				)				)

 def 						_snake_case					(     self		:       str				):
     snake_case_ :     Any				=	Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
     snake_case_ :     Optional[int]				=	Image.open(lowercase_				)
     snake_case_ :     Tuple				=	AgentImage(lowercase_				)

     self.assertTrue(path.samefile(agent_type.to_string()				)				)
     self.assertTrue(image == agent_type.to_raw()				)

     # Ensure the path remains even after the object deletion
     del agent_type
     self.assertTrue(os.path.exists(lowercase_				)				)






 def 						_snake_case					(     self		:       str				):
     snake_case_ :     int				=	Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''
     snake_case_ :     Dict				=	Image.open(lowercase_				)
     snake_case_ :     List[str]				=	AgentImage(lowercase_				)

     self.assertFalse(path.samefile(agent_type.to_string()				)				)
     self.assertTrue(image == agent_type.to_raw()				)

     # Ensure the path remains even after the object deletion
     del agent_type
     self.assertTrue(os.path.exists(lowercase_				)				)





class        _UpperCAmelCase       (			unittest.TestCase):

 def 						_snake_case					(     self		:       Any				):
     snake_case_ :     Tuple				=	'''Hey!'''
     snake_case_ :     Optional[Any]				=	AgentText(lowercase_				)

     self.assertEqual(lowercase_  ,     agent_type.to_string()				)
     self.assertEqual(lowercase_  ,     agent_type.to_raw()				)
     self.assertEqual(lowercase_  ,     lowercase_				)





