






"""simple docstring"""


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


lowercase_								=	logging.get_logger(__name__)

lowercase_								=	{
    'facebook/opt-125m': 'https://huggingface.co/facebook/opt-125m/blob/main/config.json',
    'facebook/opt-350m': 'https://huggingface.co/facebook/opt-350m/blob/main/config.json',
    'facebook/opt-1.3b': 'https://huggingface.co/facebook/opt-1.3b/blob/main/config.json',
    'facebook/opt-2.7b': 'https://huggingface.co/facebook/opt-2.7b/blob/main/config.json',
    'facebook/opt-6.7b': 'https://huggingface.co/facebook/opt-6.7b/blob/main/config.json',
    'facebook/opt-13b': 'https://huggingface.co/facebook/opt-13b/blob/main/config.json',
}


class    snake_case						(				_lowerCAmelCase		):


   '''simple docstring'''

   A_		:      Any							=					"opt"
   A_		:      Union[str, Any]							=					["past_key_values"]


   def __init__(      self					:	int,			_lowerCamelCase					:	str=5_02_72,			_lowerCamelCase					:	Dict=7_68,			_lowerCamelCase					:	List[str]=12,			_lowerCamelCase					:	List[str]=30_72,			_lowerCamelCase					:	Any=20_48,			_lowerCamelCase					:	List[Any]=True,			_lowerCamelCase					:	Tuple=False,			_lowerCamelCase					:	Optional[int]=None,			_lowerCamelCase					:	List[Any]=0.1,			_lowerCamelCase					:	Any=0.0,			_lowerCamelCase					:	Optional[int]=12,			_lowerCamelCase					:	int="relu",			_lowerCamelCase					:	Union[str, Any]=0.0,			_lowerCamelCase					:	int=0.02,			_lowerCamelCase					:	Dict=True,			_lowerCamelCase					:	Optional[int]=1,			_lowerCamelCase					:	List[Any]=2,			_lowerCamelCase					:	Optional[int]=2,			_lowerCamelCase					:	Optional[int]=True,			_lowerCamelCase					:	Any=True,			**_lowerCamelCase					:	List[str],			):

     '''simple docstring'''
     super().__init__(
         pad_token_id=_lowerCamelCase,			bos_token_id=_lowerCamelCase,			eos_token_id=_lowerCamelCase,			**_lowerCamelCase,			)
     __A		     =					vocab_size
     __A		     =					max_position_embeddings
     __A		     =					num_attention_heads
     __A		     =					word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
     __A		     =					ffn_dim
     __A		     =					hidden_size
     __A		     =					num_hidden_layers
     __A		     =					dropout
     __A		     =					attention_dropout
     __A		     =					activation_function
     __A		     =					init_std
     __A		     =					layerdrop
     __A		     =					use_cache
     __A		     =					do_layer_norm_before
     # We keep these variables at `True` for backward compatibility.
     __A		     =					enable_bias
     __A		     =					layer_norm_elementwise_affine

     # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
     # with checkpoints that have been fine-tuned before transformers v4.20.1
     # see https://github.com/facebookresearch/metaseq/pull/164
     __A		     =					_remove_final_layer_norm









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 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       tempfile.mkdtemp()

									_lowerCAmelCase         =       SpeechTaTokenizer(_snake_case						)
									tokenizer.save_pretrained(self.tmpdirname						)

									_lowerCAmelCase         =       {
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 16000,
									    """do_normalize""": False,
									    """num_mel_bins""": 80,
									    """hop_length""": 16,
									    """win_length""": 64,
									    """win_function""": """hann_window""",
									    """fmin""": 80,
									    """fmax""": 7600,
									    """mel_floor""": 1e-10,
									    """reduction_factor""": 2,
									    """return_attention_mask""": True,
									}

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




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									return SpeechTaTokenizer.from_pretrained(self.tmpdirname		,							**_snake_case						)




				def 				snake_case  (				self		,							**_snake_case						):



									"""simple docstring"""





									return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname		,							**_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									shutil.rmtree(self.tmpdirname						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_tokenizer()
									_lowerCAmelCase         =       self.get_feature_extractor()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

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

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

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_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=_snake_case		,							padding_value=1.0						)

									_lowerCAmelCase         =       SpeechTaProcessor.from_pretrained(
									    self.tmpdirname		,							bos_token="""(BOS)"""		,							eos_token="""(EOS)"""		,							do_normalize=_snake_case		,							padding_value=1.0						)

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

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       floats_list((3, 1000)						)

									_lowerCAmelCase         =       feature_extractor(audio=_snake_case		,							return_tensors="""np"""						)
									_lowerCAmelCase         =       processor(audio=_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						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       floats_list((3, 1000)						)

									_lowerCAmelCase         =       feature_extractor(audio_target=_snake_case		,							return_tensors="""np"""						)
									_lowerCAmelCase         =       processor(audio_target=_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						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       """This is a test string"""

									_lowerCAmelCase         =       processor(text=_snake_case						)
									_lowerCAmelCase         =       tokenizer(_snake_case						)

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

									_lowerCAmelCase         =       """This is a test string"""

									_lowerCAmelCase         =       processor(text_target=_snake_case						)
									_lowerCAmelCase         =       tokenizer(_snake_case						)

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




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

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

									_lowerCAmelCase         =       processor.batch_decode(_snake_case						)
									_lowerCAmelCase         =       tokenizer.batch_decode(_snake_case						)

									self.assertListEqual(_snake_case		,							_snake_case						)




				def 				snake_case  (				self						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.get_feature_extractor()
									_lowerCAmelCase         =       self.get_tokenizer()

									_lowerCAmelCase         =       SpeechTaProcessor(tokenizer=_snake_case		,							feature_extractor=_snake_case						)

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



