


"""simple docstring"""
import functools
import operator

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


__SCREAMING_SNAKE_CASE							:      List[str]						=      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							:      List[Any]						=      {
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}





class        __A       (snake_case__):





							'''simple docstring'''

							__lowercase:						Any							 =      """sew"""



							def __init__(			self							: int						,    UpperCAmelCase_							: List[str]=32						,    UpperCAmelCase_							: Any=768						,    UpperCAmelCase_							: Dict=12						,    UpperCAmelCase_							: Dict=12						,    UpperCAmelCase_							: int=3_072						,    UpperCAmelCase_							: int=2						,    UpperCAmelCase_							: Tuple="gelu"						,    UpperCAmelCase_							: Tuple=0.1						,    UpperCAmelCase_							: Any=0.1						,    UpperCAmelCase_							: Tuple=0.1						,    UpperCAmelCase_							: Tuple=0.0						,    UpperCAmelCase_							: Union[str, Any]=0.1						,    UpperCAmelCase_							: Optional[Any]=0.1						,    UpperCAmelCase_							: Tuple=0.02						,    UpperCAmelCase_							: int=1E-5						,    UpperCAmelCase_							: Union[str, Any]="group"						,    UpperCAmelCase_							: Dict="gelu"						,    UpperCAmelCase_							: Optional[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,    UpperCAmelCase_							: Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,    UpperCAmelCase_							: int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,    UpperCAmelCase_							: List[str]=False						,    UpperCAmelCase_							: Dict=128						,    UpperCAmelCase_							: Tuple=16						,    UpperCAmelCase_							: Dict=True						,    UpperCAmelCase_							: int=0.05						,    UpperCAmelCase_							: Union[str, Any]=10						,    UpperCAmelCase_							: List[str]=2						,    UpperCAmelCase_							: str=0.0						,    UpperCAmelCase_							: List[str]=10						,    UpperCAmelCase_							: Tuple=0						,    UpperCAmelCase_							: int="mean"						,    UpperCAmelCase_							: int=False						,    UpperCAmelCase_							: int=False						,    UpperCAmelCase_							: Any=256						,    UpperCAmelCase_							: Dict=0						,    UpperCAmelCase_							: List[Any]=1						,    UpperCAmelCase_							: Tuple=2						,    **UpperCAmelCase_							: Any						,    )   ->List[Any]:



														"""simple docstring"""



														super().__init__(**UpperCAmelCase_						,    pad_token_id=UpperCAmelCase_						,    bos_token_id=UpperCAmelCase_						,    eos_token_id=UpperCAmelCase_    )
														snake_case_          =						hidden_size
														snake_case_          =						feat_extract_norm
														snake_case_          =						feat_extract_activation
														snake_case_          =						list(UpperCAmelCase_    )
														snake_case_          =						list(UpperCAmelCase_    )
														snake_case_          =						list(UpperCAmelCase_    )
														snake_case_          =						conv_bias
														snake_case_          =						num_conv_pos_embeddings
														snake_case_          =						num_conv_pos_embedding_groups
														snake_case_          =						len(self.conv_dim    )
														snake_case_          =						num_hidden_layers
														snake_case_          =						intermediate_size
														snake_case_          =						squeeze_factor
														snake_case_          =						hidden_act
														snake_case_          =						num_attention_heads
														snake_case_          =						hidden_dropout
														snake_case_          =						attention_dropout
														snake_case_          =						activation_dropout
														snake_case_          =						feat_proj_dropout
														snake_case_          =						final_dropout
														snake_case_          =						layerdrop
														snake_case_          =						layer_norm_eps
														snake_case_          =						initializer_range
														snake_case_          =						vocab_size

														if (
														    (len(self.conv_stride    ) != self.num_feat_extract_layers)
														    or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
														    or (len(self.conv_dim    ) != self.num_feat_extract_layers)
														):
																					raise ValueError(
																					    """Configuration for convolutional layers is incorrect."""
																					    """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
																					    F"""but is `len(config.conv_dim) = {len(self.conv_dim    )}`, `len(config.conv_stride)"""
																					    F"""= {len(self.conv_stride    )}`, `len(config.conv_kernel) = {len(self.conv_kernel    )}`."""    )

														# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
														snake_case_          =						apply_spec_augment
														snake_case_          =						mask_time_prob
														snake_case_          =						mask_time_length
														snake_case_          =						mask_time_min_masks
														snake_case_          =						mask_feature_prob
														snake_case_          =						mask_feature_length
														snake_case_          =						mask_feature_min_masks

														# ctc loss
														snake_case_          =						ctc_loss_reduction
														snake_case_          =						ctc_zero_infinity

														# sequence classification
														snake_case_          =						use_weighted_layer_sum
														snake_case_          =						classifier_proj_size



							@property
							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->str:



														"""simple docstring"""



														return functools.reduce(operator.mul						,    self.conv_stride						,    1    )







"""simple docstring"""
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class        __A       (unittest.TestCase):





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: Dict    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						"""laion/clap-htsat-unfused"""
														snake_case_          =						tempfile.mkdtemp()



							def 						lowerCAmelCase    (			self							: List[str]						,    **UpperCAmelCase_							: Tuple    )   ->str:



														"""simple docstring"""



														return RobertaTokenizer.from_pretrained(self.checkpoint						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    **UpperCAmelCase_							: Any    )   ->Optional[Any]:



														"""simple docstring"""



														return ClapFeatureExtractor.from_pretrained(self.checkpoint						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: str    )   ->Any:



														"""simple docstring"""



														shutil.rmtree(self.tmpdirname    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.get_tokenizer()
														snake_case_          =						self.get_feature_extractor()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														processor.save_pretrained(self.tmpdirname    )
														snake_case_          =						ClapProcessor.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 						lowerCAmelCase    (			self							: int    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()    )
														processor.save_pretrained(self.tmpdirname    )

														snake_case_          =						self.get_tokenizer(bos_token="""(BOS)"""						,    eos_token="""(EOS)"""    )
														snake_case_          =						self.get_feature_extractor(do_normalize=UpperCAmelCase_						,    padding_value=1.0    )

														snake_case_          =						ClapProcessor.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 						lowerCAmelCase    (			self							: str    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						floats_list((3, 1_000)    )

														snake_case_          =						feature_extractor(UpperCAmelCase_						,    return_tensors="""np"""    )
														snake_case_          =						processor(audios=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 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						"""This is a test string"""

														snake_case_          =						processor(text=UpperCAmelCase_    )

														snake_case_          =						tokenizer(UpperCAmelCase_    )

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



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

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

														snake_case_          =						processor.batch_decode(UpperCAmelCase_    )
														snake_case_          =						tokenizer.batch_decode(UpperCAmelCase_    )

														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: List[str]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														self.assertListEqual(
														    processor.model_input_names[2:]						,    feature_extractor.model_input_names						,    msg="""`processor` and `feature_extractor` model input names do not match"""						,    )



