

import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


__snake_case							:     Optional[Any] 						=	get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")



@require_torch
class    A__(unittest.TestCase    ):
						"""simple docstring"""






						def 	UpperCamelCase__			(  self  )      ->				Dict:
									a_		:		int				    =				tempfile.mkdtemp()

									a_		:		Tuple				    =				SpeechTaTokenizer(_lowercase  )
									tokenizer.save_pretrained(self.tmpdirname  )

									a_		:		List[str]				    =				{
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 16_000,
									    """do_normalize""": False,
									    """num_mel_bins""": 80,
									    """hop_length""": 16,
									    """win_length""": 64,
									    """win_function""": """hann_window""",
									    """fmin""": 80,
									    """fmax""": 7_600,
									    """mel_floor""": 1e-10,
									    """reduction_factor""": 2,
									    """return_attention_mask""": True,
									}

									a_		:		Optional[int]				    =				os.path.join(self.tmpdirname       ,     _lowercase  )
									with open(self.feature_extraction_file       ,     """w"""       ,     encoding="""utf-8"""  ) as fp:
												fp.write(json.dumps(_lowercase  ) + """\n"""  )





						def 	UpperCamelCase__			(  self       ,     **_lowercase  )      ->				str:
									return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self       ,     **_lowercase  )      ->				Any:
									return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,     **_lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				List[str]:
									shutil.rmtree(self.tmpdirname  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									a_		:		Dict				    =				self.get_tokenizer()
									a_		:		str				    =				self.get_feature_extractor()

									a_		:		List[str]				    =				SpeechTaProcessor(tokenizer=_lowercase       ,     feature_extractor=_lowercase  )

									processor.save_pretrained(self.tmpdirname  )
									a_		:		Optional[int]				    =				SpeechTaProcessor.from_pretrained(self.tmpdirname  )

									self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer.get_vocab()  )
									self.assertIsInstance(processor.tokenizer       ,     _lowercase  )

									self.assertEqual(processor.feature_extractor.to_json_string()       ,     feature_extractor.to_json_string()  )
									self.assertIsInstance(processor.feature_extractor       ,     _lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Any:
									a_		:		Dict				    =				SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,     feature_extractor=self.get_feature_extractor()  )
									processor.save_pretrained(self.tmpdirname  )

									a_		:		Tuple				    =				self.get_tokenizer(bos_token="""(BOS)"""       ,     eos_token="""(EOS)"""  )
									a_		:		Optional[int]				    =				self.get_feature_extractor(do_normalize=_lowercase       ,     padding_value=1.0  )

									a_		:		Any				    =				SpeechTaProcessor.from_pretrained(
									    self.tmpdirname       ,     bos_token="""(BOS)"""       ,     eos_token="""(EOS)"""       ,     do_normalize=_lowercase       ,     padding_value=1.0  )

									self.assertEqual(processor.tokenizer.get_vocab()       ,     tokenizer_add_kwargs.get_vocab()  )
									self.assertIsInstance(processor.tokenizer       ,     _lowercase  )

									self.assertEqual(processor.feature_extractor.to_json_string()       ,     feature_extractor_add_kwargs.to_json_string()  )
									self.assertIsInstance(processor.feature_extractor       ,     _lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				Dict:
									a_		:		List[Any]				    =				self.get_feature_extractor()
									a_		:		Optional[int]				    =				self.get_tokenizer()

									a_		:		Any				    =				SpeechTaProcessor(tokenizer=_lowercase       ,     feature_extractor=_lowercase  )

									a_		:		Tuple				    =				floats_list((3, 1_000)  )

									a_		:		Any				    =				feature_extractor(audio=_lowercase       ,     return_tensors="""np"""  )
									a_		:		Dict				    =				processor(audio=_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 	UpperCamelCase__			(  self  )      ->				Tuple:
									a_		:		Any				    =				self.get_feature_extractor()
									a_		:		Any				    =				self.get_tokenizer()

									a_		:		Any				    =				SpeechTaProcessor(tokenizer=_lowercase       ,     feature_extractor=_lowercase  )

									a_		:		Union[str, Any]				    =				floats_list((3, 1_000)  )

									a_		:		Optional[Any]				    =				feature_extractor(audio_target=_lowercase       ,     return_tensors="""np"""  )
									a_		:		Any				    =				processor(audio_target=_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 	UpperCamelCase__			(  self  )      ->				str:
									a_		:		Optional[Any]				    =				self.get_feature_extractor()
									a_		:		Optional[int]				    =				self.get_tokenizer()

									a_		:		Union[str, Any]				    =				SpeechTaProcessor(tokenizer=_lowercase       ,     feature_extractor=_lowercase  )

									a_		:		int				    =				"""This is a test string"""

									a_		:		Union[str, Any]				    =				processor(text=_lowercase  )
									a_		:		Optional[int]				    =				tokenizer(_lowercase  )

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





						def 	UpperCamelCase__			(  self  )      ->				Optional[Any]:
									a_		:		int				    =				self.get_feature_extractor()
									a_		:		Optional[Any]				    =				self.get_tokenizer()

									a_		:		int				    =				SpeechTaProcessor(tokenizer=_lowercase       ,     feature_extractor=_lowercase  )

									a_		:		str				    =				"""This is a test string"""

									a_		:		Union[str, Any]				    =				processor(text_target=_lowercase  )
									a_		:		List[str]				    =				tokenizer(_lowercase  )

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





						def 	UpperCamelCase__			(  self  )      ->				str:
									a_		:		Optional[int]				    =				self.get_feature_extractor()
									a_		:		List[str]				    =				self.get_tokenizer()

									a_		:		List[str]				    =				SpeechTaProcessor(tokenizer=_lowercase       ,     feature_extractor=_lowercase  )

									a_		:		int				    =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									a_		:		str				    =				processor.batch_decode(_lowercase  )
									a_		:		Optional[int]				    =				tokenizer.batch_decode(_lowercase  )

									self.assertListEqual(_lowercase       ,     _lowercase  )





						def 	UpperCamelCase__			(  self  )      ->				List[Any]:
									a_		:		Optional[int]				    =				self.get_feature_extractor()
									a_		:		Optional[int]				    =				self.get_tokenizer()

									a_		:		int				    =				SpeechTaProcessor(tokenizer=_lowercase       ,     feature_extractor=_lowercase  )

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





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case							:     Optional[Any] 						=	logging.get_logger(__name__)

__snake_case							:     str 						=	{
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}



class    A__(a_    ):
						"""simple docstring"""






						_A  :							Any   			=	'''tvlt'''





						def __init__(  self       ,     _lowercase=224       ,     _lowercase=2_048       ,     _lowercase=128       ,     _lowercase=[16, 16]       ,     _lowercase=[16, 16]       ,     _lowercase=3       ,     _lowercase=1       ,     _lowercase=8       ,     _lowercase=768       ,     _lowercase=12       ,     _lowercase=12       ,     _lowercase=3_072       ,     _lowercase="gelu"       ,     _lowercase=0.0       ,     _lowercase=0.0       ,     _lowercase=0.0_2       ,     _lowercase=1e-6       ,     _lowercase=True       ,     _lowercase=False       ,     _lowercase=16       ,     _lowercase=512       ,     _lowercase=8       ,     _lowercase=2_048       ,     _lowercase=0.7_5       ,     _lowercase=0.1_5       ,     _lowercase="frame-level"       ,     _lowercase=True       ,     _lowercase=True       ,     _lowercase="classification"       ,     **_lowercase       ,     )      ->				Tuple:
									super().__init__(**_lowercase  )

									if audio_mask_type not in ("frame-level", "patch_level"):
												raise ValueError(
												    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
												    F'''got {audio_mask_type}'''  )

									a_		:		int				    =				image_size
									a_		:		str				    =				spectrogram_length
									a_		:		int				    =				frequency_length
									a_		:		str				    =				image_patch_size
									a_		:		int				    =				audio_patch_size
									a_		:		Any				    =				num_image_channels
									a_		:		str				    =				num_audio_channels
									a_		:		int				    =				num_frames

									a_		:		Optional[int]				    =				hidden_size
									a_		:		Tuple				    =				num_hidden_layers
									a_		:		Any				    =				num_attention_heads
									a_		:		Union[str, Any]				    =				intermediate_size
									a_		:		Tuple				    =				hidden_act
									a_		:		Union[str, Any]				    =				hidden_dropout_prob
									a_		:		int				    =				attention_probs_dropout_prob
									a_		:		Any				    =				initializer_range
									a_		:		int				    =				layer_norm_eps
									a_		:		Union[str, Any]				    =				qkv_bias
									a_		:		str				    =				use_mean_pooling

									a_		:		List[Any]				    =				decoder_num_attention_heads
									a_		:		Optional[Any]				    =				decoder_hidden_size
									a_		:		Dict				    =				decoder_num_hidden_layers
									a_		:		Optional[Any]				    =				decoder_intermediate_size
									a_		:		Dict				    =				pixel_mask_ratio
									a_		:		int				    =				audio_mask_ratio
									a_		:		Optional[Any]				    =				audio_mask_type

									a_		:		Union[str, Any]				    =				task_matching
									a_		:		Dict				    =				task_mae
									a_		:		Union[str, Any]				    =				loss_type


