



'''simple docstring'''





import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
    import librosa  # noqa: E402

    A__  :					Optional[int]				= True
    A__  :					Union[str, Any]				= ''''''
except Exception as e:
    A__  :					List[str]				= False
    A__  :					Union[str, Any]				= (
        F"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
    )


from PIL import Image  # noqa: E402



class       snake_case__		(  _lowerCAmelCase   ,			_lowerCAmelCase							):
 A__							=      '''mel_config.json'''
 @register_to_config
 def __init__(					self		:				Union[str, Any]	,			__a		:				int = 256	,			__a		:				int = 256	,			__a		:				int = 22050	,			__a		:				int = 2048	,			__a		:				int = 512	,			__a		:				int = 80	,			__a		:				int = 32	,			)	->					Tuple:







       '''simple docstring'''
       __snake_case      :					List[Any]						=  hop_length
       __snake_case      :					str						=  sample_rate
       __snake_case      :					Dict						=  n_fft
       __snake_case      :					Union[str, Any]						=  top_db
       __snake_case      :					List[str]						=  n_iter
       self.set_resolution(lowerCAmelCase__	,			lowerCAmelCase__   )
       __snake_case      :					str						=  None

       if not _librosa_can_be_imported:
             raise ValueError(_import_error   )
 def A_					(					self		:				List[Any]	,			__a		:				int	,			__a		:				int   )	->					List[Any]:







       '''simple docstring'''
       __snake_case      :					Union[str, Any]						=  x_res
       __snake_case      :					str						=  y_res
       __snake_case      :					int						=  self.y_res
       __snake_case      :					Optional[int]						=  self.x_res * self.hop_length - 1
 def A_					(					self		:				List[str]	,			__a		:				str = None	,			__a		:				np.ndarray = None   )	->					Union[str, Any]:







       '''simple docstring'''



       if audio_file is not None:
             __snake_case      :					Dict						=  librosa.load(lowerCAmelCase__	,			mono=lowerCAmelCase__	,			sr=self.sr   )
       else:
             __snake_case      :					Dict						=  raw_audio

       # Pad with silence if necessary.
       if len(self.audio   ) < self.x_res * self.hop_length:
             __snake_case      :					str						=  np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio   ),)   )]   )
 def A_					(					self		:				Union[str, Any]   )	->					int:







       '''simple docstring'''
       return len(self.audio   ) // self.slice_size
 def A_					(					self		:				Dict	,			__a		:				int = 0   )	->					np.ndarray:







       '''simple docstring'''
       return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
 def A_					(					self		:				Union[str, Any]   )	->					int:







       '''simple docstring'''
       return self.sr
 def A_					(					self		:				Tuple	,			__a		:				int   )	->					Image.Image:







       '''simple docstring'''
       __snake_case      :					Optional[Any]						=  librosa.feature.melspectrogram(
           y=self.get_audio_slice(lowerCAmelCase__   )	,			sr=self.sr	,			n_fft=self.n_fft	,			hop_length=self.hop_length	,			n_mels=self.n_mels   )
       __snake_case      :					Tuple						=  librosa.power_to_db(lowerCAmelCase__	,			ref=np.max	,			top_db=self.top_db   )
       __snake_case      :					Tuple						=  (((log_S + self.top_db) * 255 / self.top_db).clip(0	,			255   ) + 0.5).astype(np.uinta   )
       __snake_case      :					Tuple						=  Image.fromarray(lowerCAmelCase__   )
       return image





 def A_					(					self		:				List[Any]	,			__a		:				Image.Image   )	->					np.ndarray:







       '''simple docstring'''
       __snake_case      :					Optional[int]						=  np.frombuffer(image.tobytes()	,			dtype='uint8'   ).reshape((image.height, image.width)   )
       __snake_case      :					Optional[int]						=  bytedata.astype('float'   ) * self.top_db / 255 - self.top_db
       __snake_case      :					Any						=  librosa.db_to_power(lowerCAmelCase__   )
       __snake_case      :					str						=  librosa.feature.inverse.mel_to_audio(
           lowerCAmelCase__	,			sr=self.sr	,			n_fft=self.n_fft	,			hop_length=self.hop_length	,			n_iter=self.n_iter   )
       return audio










"""simple docstring"""




import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.electra.modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						)




class  _a						(      unittest.TestCase				):






				def __init__( self		:    str, lowerCAmelCase__		:    Tuple, lowerCAmelCase__		:    List[str]=1_3, lowerCAmelCase__		:    Optional[int]=7, lowerCAmelCase__		:    Optional[Any]=True, lowerCAmelCase__		:    Optional[int]=True, lowerCAmelCase__		:    Any=True, lowerCAmelCase__		:    str=True, lowerCAmelCase__		:    Any=9_9, lowerCAmelCase__		:    Union[str, Any]=2_4, lowerCAmelCase__		:    Optional[Any]=3_2, lowerCAmelCase__		:    Tuple=5, lowerCAmelCase__		:    int=4, lowerCAmelCase__		:    List[Any]=3_7, lowerCAmelCase__		:    Dict="gelu", lowerCAmelCase__		:    Any=0.1, lowerCAmelCase__		:    Any=0.1, lowerCAmelCase__		:    str=5_1_2, lowerCAmelCase__		:    Tuple=1_6, lowerCAmelCase__		:    int=2, lowerCAmelCase__		:    int=0.02, lowerCAmelCase__		:    Optional[int]=4, )	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    parent
										_UpperCamelCase			:					Tuple     =    batch_size
										_UpperCamelCase			:					str     =    seq_length
										_UpperCamelCase			:					Any     =    is_training
										_UpperCamelCase			:					Union[str, Any]     =    use_attention_mask
										_UpperCamelCase			:					List[Any]     =    use_token_type_ids
										_UpperCamelCase			:					str     =    use_labels
										_UpperCamelCase			:					Union[str, Any]     =    vocab_size
										_UpperCamelCase			:					int     =    hidden_size
										_UpperCamelCase			:					Optional[int]     =    embedding_size
										_UpperCamelCase			:					int     =    num_hidden_layers
										_UpperCamelCase			:					int     =    num_attention_heads
										_UpperCamelCase			:					Tuple     =    intermediate_size
										_UpperCamelCase			:					Optional[int]     =    hidden_act
										_UpperCamelCase			:					Any     =    hidden_dropout_prob
										_UpperCamelCase			:					Union[str, Any]     =    attention_probs_dropout_prob
										_UpperCamelCase			:					Any     =    max_position_embeddings
										_UpperCamelCase			:					List[str]     =    type_vocab_size
										_UpperCamelCase			:					Union[str, Any]     =    type_sequence_label_size
										_UpperCamelCase			:					str     =    initializer_range
										_UpperCamelCase			:					Union[str, Any]     =    num_choices






				def 				snake_case	( self		:    Optional[Any]							)	->      Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    ids_tensor([self.batch_size, self.seq_length], self.vocab_size							)

										_UpperCamelCase			:					Dict     =    None
										if self.use_attention_mask:
																_UpperCamelCase			:					Union[str, Any]     =    random_attention_mask([self.batch_size, self.seq_length]							)

										_UpperCamelCase			:					Optional[Any]     =    None
										if self.use_token_type_ids:
																_UpperCamelCase			:					Union[str, Any]     =    ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size							)

										_UpperCamelCase			:					Optional[Any]     =    ElectraConfig(
										    vocab_size=self.vocab_size, hidden_size=self.hidden_size, embedding_size=self.embedding_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, )

										return config, input_ids, token_type_ids, attention_mask






				def 				snake_case	( self		:    Any							)	->      Optional[int]:




										'''simple docstring'''



										_UpperCamelCase			:					Dict     =    self.prepare_config_and_inputs()
										_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase   ,							_UpperCamelCase			:					Tuple     =    config_and_inputs
										_UpperCamelCase			:					List[Any]     =    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict




@require_flax
class  _a						(      _lowerCAmelCase	,				unittest.TestCase				):
				UpperCamelCase								=    True

				UpperCamelCase								=    (
				    (
				        FlaxElectraModel,
				        FlaxElectraForCausalLM,
				        FlaxElectraForMaskedLM,
				        FlaxElectraForPreTraining,
				        FlaxElectraForTokenClassification,
				        FlaxElectraForQuestionAnswering,
				        FlaxElectraForMultipleChoice,
				        FlaxElectraForSequenceClassification,
				    )
				    if is_flax_available()
				    else ()
				)






				def 				snake_case	( self		:    Union[str, Any]							)	->      Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    FlaxElectraModelTester(self							)






				@slow
				def 				snake_case	( self		:    Optional[Any]							)	->      str:




										'''simple docstring'''



										for model_class_name in self.all_model_classes:
																if model_class_name == FlaxElectraForMaskedLM:
																						_UpperCamelCase			:					int     =    model_class_name.from_pretrained('''google/electra-small-generator'''							)
																else:
																						_UpperCamelCase			:					Any     =    model_class_name.from_pretrained('''google/electra-small-discriminator'''							)
																_UpperCamelCase			:					List[str]     =    model(np.ones((1, 1)							)							)
																self.assertIsNotNone(lowerCAmelCase__							)



