
"""simple docstring"""


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


A_						       =				get_tests_dir("""fixtures/test_sentencepiece_bpe_char.model""")




@require_torch
class    __lowerCamelCase     (   unittest.TestCase ):




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     tempfile.mkdtemp()

							lowerCamelCase_ 		=     SpeechTaTokenizer(UpperCAmelCase      )
							tokenizer.save_pretrained(self.tmpdirname      )

							lowerCamelCase_ 		=     {
							    '''feature_size''': 1,
							    '''padding_value''': 0.0,
							    '''sampling_rate''': 1_6000,
							    '''do_normalize''': False,
							    '''num_mel_bins''': 80,
							    '''hop_length''': 16,
							    '''win_length''': 64,
							    '''win_function''': '''hann_window''',
							    '''fmin''': 80,
							    '''fmax''': 7600,
							    '''mel_floor''': 1e-1_0,
							    '''reduction_factor''': 2,
							    '''return_attention_mask''': True,
							}

							lowerCamelCase_ 		=     os.path.join(self.tmpdirname       ,	UpperCAmelCase      )
							with open(self.feature_extraction_file       ,	'''w'''       ,	encoding='''utf-8'''      ) as fp:
										fp.write(json.dumps(UpperCAmelCase      ) + '''\n'''      )




				def 					UpperCAmelCase__	(							self       ,	**UpperCAmelCase      ):
							return SpeechTaTokenizer.from_pretrained(self.tmpdirname       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self       ,	**UpperCAmelCase      ):
							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname       ,	**UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							shutil.rmtree(self.tmpdirname      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.get_tokenizer()
							lowerCamelCase_ 		=     self.get_feature_extractor()

							lowerCamelCase_ 		=     SpeechTaProcessor(tokenizer=UpperCAmelCase       ,	feature_extractor=UpperCAmelCase      )

							processor.save_pretrained(self.tmpdirname      )
							lowerCamelCase_ 		=     SpeechTaProcessor.from_pretrained(self.tmpdirname      )

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

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




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     SpeechTaProcessor(tokenizer=self.get_tokenizer()       ,	feature_extractor=self.get_feature_extractor()      )
							processor.save_pretrained(self.tmpdirname      )

							lowerCamelCase_ 		=     self.get_tokenizer(bos_token='''(BOS)'''       ,	eos_token='''(EOS)'''      )
							lowerCamelCase_ 		=     self.get_feature_extractor(do_normalize=UpperCAmelCase       ,	padding_value=1.0      )

							lowerCamelCase_ 		=     SpeechTaProcessor.from_pretrained(
							    self.tmpdirname       ,	bos_token='''(BOS)'''       ,	eos_token='''(EOS)'''       ,	do_normalize=UpperCAmelCase       ,	padding_value=1.0      )

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

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




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.get_feature_extractor()
							lowerCamelCase_ 		=     self.get_tokenizer()

							lowerCamelCase_ 		=     SpeechTaProcessor(tokenizer=UpperCAmelCase       ,	feature_extractor=UpperCAmelCase      )

							lowerCamelCase_ 		=     floats_list((3, 1000)      )

							lowerCamelCase_ 		=     feature_extractor(audio=UpperCAmelCase       ,	return_tensors='''np'''      )
							lowerCamelCase_ 		=     processor(audio=UpperCAmelCase       ,	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      ):
							lowerCamelCase_ 		=     self.get_feature_extractor()
							lowerCamelCase_ 		=     self.get_tokenizer()

							lowerCamelCase_ 		=     SpeechTaProcessor(tokenizer=UpperCAmelCase       ,	feature_extractor=UpperCAmelCase      )

							lowerCamelCase_ 		=     floats_list((3, 1000)      )

							lowerCamelCase_ 		=     feature_extractor(audio_target=UpperCAmelCase       ,	return_tensors='''np'''      )
							lowerCamelCase_ 		=     processor(audio_target=UpperCAmelCase       ,	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      ):
							lowerCamelCase_ 		=     self.get_feature_extractor()
							lowerCamelCase_ 		=     self.get_tokenizer()

							lowerCamelCase_ 		=     SpeechTaProcessor(tokenizer=UpperCAmelCase       ,	feature_extractor=UpperCAmelCase      )

							lowerCamelCase_ 		=     '''This is a test string'''

							lowerCamelCase_ 		=     processor(text=UpperCAmelCase      )
							lowerCamelCase_ 		=     tokenizer(UpperCAmelCase      )

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




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.get_feature_extractor()
							lowerCamelCase_ 		=     self.get_tokenizer()

							lowerCamelCase_ 		=     SpeechTaProcessor(tokenizer=UpperCAmelCase       ,	feature_extractor=UpperCAmelCase      )

							lowerCamelCase_ 		=     '''This is a test string'''

							lowerCamelCase_ 		=     processor(text_target=UpperCAmelCase      )
							lowerCamelCase_ 		=     tokenizer(UpperCAmelCase      )

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




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.get_feature_extractor()
							lowerCamelCase_ 		=     self.get_tokenizer()

							lowerCamelCase_ 		=     SpeechTaProcessor(tokenizer=UpperCAmelCase       ,	feature_extractor=UpperCAmelCase      )

							lowerCamelCase_ 		=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCamelCase_ 		=     processor.batch_decode(UpperCAmelCase      )
							lowerCamelCase_ 		=     tokenizer.batch_decode(UpperCAmelCase      )

							self.assertListEqual(UpperCAmelCase       ,	UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.get_feature_extractor()
							lowerCamelCase_ 		=     self.get_tokenizer()

							lowerCamelCase_ 		=     SpeechTaProcessor(tokenizer=UpperCAmelCase       ,	feature_extractor=UpperCAmelCase      )

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





"""simple docstring"""


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


A_						       =				logging.get_logger(__name__)

A_						       =				{
    """uw-madison/yoso-4096""": """https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json""",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			Dict									=						'yoso'




				def __init__(							self       ,	UpperCAmelCase=5_0265       ,	UpperCAmelCase=768       ,	UpperCAmelCase=12       ,	UpperCAmelCase=12       ,	UpperCAmelCase=3072       ,	UpperCAmelCase="gelu"       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=4096       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0.0_2       ,	UpperCAmelCase=1e-1_2       ,	UpperCAmelCase="absolute"       ,	UpperCAmelCase=True       ,	UpperCAmelCase=9       ,	UpperCAmelCase=64       ,	UpperCAmelCase=None       ,	UpperCAmelCase=True       ,	UpperCAmelCase=True       ,	UpperCAmelCase=1       ,	UpperCAmelCase=0       ,	UpperCAmelCase=2       ,	**UpperCAmelCase       ,	):
							super().__init__(pad_token_id=UpperCAmelCase       ,	bos_token_id=UpperCAmelCase       ,	eos_token_id=UpperCAmelCase       ,	**UpperCAmelCase      )

							lowerCamelCase_ 		=     vocab_size
							lowerCamelCase_ 		=     max_position_embeddings
							lowerCamelCase_ 		=     hidden_size
							lowerCamelCase_ 		=     num_hidden_layers
							lowerCamelCase_ 		=     num_attention_heads
							lowerCamelCase_ 		=     intermediate_size
							lowerCamelCase_ 		=     hidden_act
							lowerCamelCase_ 		=     hidden_dropout_prob
							lowerCamelCase_ 		=     attention_probs_dropout_prob
							lowerCamelCase_ 		=     initializer_range
							lowerCamelCase_ 		=     type_vocab_size
							lowerCamelCase_ 		=     layer_norm_eps
							lowerCamelCase_ 		=     position_embedding_type
							lowerCamelCase_ 		=     use_expectation
							lowerCamelCase_ 		=     hash_code_len
							lowerCamelCase_ 		=     num_hash
							lowerCamelCase_ 		=     conv_window
							lowerCamelCase_ 		=     use_fast_hash
							lowerCamelCase_ 		=     lsh_backward



