import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "deta"
							UpperCAmelCase				   =     {
							    "hidden_size": "d_model",
							    "num_attention_heads": "encoder_attention_heads",
							}







							def __init__(   self      :     Optional[int] ,			_A      :     Tuple=None ,			_A      :     Dict=900 ,			_A      :     Union[str, Any]=2048 ,			_A      :     Union[str, Any]=6 ,			_A      :     List[str]=2048 ,			_A      :     str=8 ,			_A      :     Optional[int]=6 ,			_A      :     List[str]=1024 ,			_A      :     Optional[int]=8 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     Any="relu" ,			_A      :     Any=256 ,			_A      :     Optional[int]=0.1 ,			_A      :     str=0.0 ,			_A      :     Dict=0.0 ,			_A      :     str=0.02 ,			_A      :     Union[str, Any]=1.0 ,			_A      :     Union[str, Any]=True ,			_A      :     Any=False ,			_A      :     Union[str, Any]="sine" ,			_A      :     int=5 ,			_A      :     Optional[Any]=4 ,			_A      :     Any=4 ,			_A      :     Union[str, Any]=True ,			_A      :     Dict=300 ,			_A      :     List[Any]=True ,			_A      :     Any=True ,			_A      :     Tuple=1 ,			_A      :     Optional[int]=5 ,			_A      :     str=2 ,			_A      :     Tuple=1 ,			_A      :     Tuple=1 ,			_A      :     Any=5 ,			_A      :     Tuple=2 ,			_A      :     str=0.1 ,			_A      :     List[str]=0.25 ,			**_A      :     Dict ,			):
												if backbone_config is None:
																	logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''		)
																	_UpperCamelCase			     =     CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']		)
												else:
																	if isinstance(_A ,			_A		):
																						_UpperCamelCase			     =     backbone_config.pop('''model_type'''		)
																						_UpperCamelCase			     =     CONFIG_MAPPING[backbone_model_type]
																						_UpperCamelCase			     =     config_class.from_dict(_A		)

												_UpperCamelCase			     =     backbone_config
												_UpperCamelCase			     =     num_queries
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     d_model
												_UpperCamelCase			     =     encoder_ffn_dim
												_UpperCamelCase			     =     encoder_layers
												_UpperCamelCase			     =     encoder_attention_heads
												_UpperCamelCase			     =     decoder_ffn_dim
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     decoder_attention_heads
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     activation_dropout
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     init_std
												_UpperCamelCase			     =     init_xavier_std
												_UpperCamelCase			     =     encoder_layerdrop
												_UpperCamelCase			     =     auxiliary_loss
												_UpperCamelCase			     =     position_embedding_type
												# deformable attributes
												_UpperCamelCase			     =     num_feature_levels
												_UpperCamelCase			     =     encoder_n_points
												_UpperCamelCase			     =     decoder_n_points
												_UpperCamelCase			     =     two_stage
												_UpperCamelCase			     =     two_stage_num_proposals
												_UpperCamelCase			     =     with_box_refine
												_UpperCamelCase			     =     assign_first_stage
												if two_stage is True and with_box_refine is False:
																	raise ValueError('''If two_stage is True, with_box_refine must be True.'''		)
												# Hungarian matcher
												_UpperCamelCase			     =     class_cost
												_UpperCamelCase			     =     bbox_cost
												_UpperCamelCase			     =     giou_cost
												# Loss coefficients
												_UpperCamelCase			     =     mask_loss_coefficient
												_UpperCamelCase			     =     dice_loss_coefficient
												_UpperCamelCase			     =     bbox_loss_coefficient
												_UpperCamelCase			     =     giou_loss_coefficient
												_UpperCamelCase			     =     eos_coefficient
												_UpperCamelCase			     =     focal_alpha
												super().__init__(is_encoder_decoder=_A ,			**_A		)







							@property
							def  UpperCamelCase_						(   self      :     Optional[int]		):
												return self.encoder_attention_heads







							@property
							def  UpperCamelCase_						(   self      :     int		):
												return self.d_model







							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     copy.deepcopy(self.__dict__		)
												_UpperCamelCase			     =     self.backbone_config.to_dict()
												_UpperCamelCase			     =     self.__class__.model_type
												return output






'''simple docstring'''



from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


UpperCamelCase_    	=				logging.get_logger(__name__)






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				str        =						['''audio_values''', '''audio_mask''']







       def __init__(      self,							A=2_048,							A=1,							A=[16, 16],							A=128,							A=44_100,							A=86,							A=2_048,							A=0.0,							**A,							):







             '''simple docstring'''







             super().__init__(
                 feature_size=A,							sampling_rate=A,							padding_value=A,							**A,							)

             SCREAMING_SNAKE_CASE   :      str    					=						spectrogram_length
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						num_channels
             SCREAMING_SNAKE_CASE   :      List[str]    					=						patch_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						feature_size // self.patch_size[1]
             SCREAMING_SNAKE_CASE   :      Dict    					=						n_fft
             SCREAMING_SNAKE_CASE   :      Tuple    					=						sampling_rate // hop_length_to_sampling_rate
             SCREAMING_SNAKE_CASE   :      str    					=						sampling_rate
             SCREAMING_SNAKE_CASE   :      int    					=						padding_value
             SCREAMING_SNAKE_CASE   :      Any    					=						mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2,							num_mel_filters=A,							min_frequency=0.0,							max_frequency=2_20_50.0,							sampling_rate=A,							norm='slaney',							mel_scale='slaney',							).T







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						spectrogram(
                 A,							window_function(self.n_fft,							'hann'  ),							frame_length=self.n_fft,							hop_length=self.hop_length,							power=2.0,							mel_filters=self.mel_filters.T,							log_mel='dB',							db_range=80.0,							)
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						log_spec[:, :-1]
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						log_spec - 20.0
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.clip(log_spec / 40.0,							-2.0,							0.0  ) + 1.0
             return log_spec







       def __call__(      self,							A,							A = None,							A = True,							A = None,							A = False,							A = False,							**A,							):







             '''simple docstring'''







             if sampling_rate is not None:
                   if sampling_rate != self.sampling_rate:
                         raise ValueError(
                             'This feature extractor is set to support sampling rate'
                             F" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"
                             F" with {self.sampling_rate} and not {sampling_rate}."  )
             else:
                   logger.warning(
                       'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                       'Failing to do so can result in silent errors that might be hard to debug.'  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						isinstance(A,							np.ndarray  ) and len(raw_speech.shape  ) > 1
             if is_batched_numpy and len(raw_speech.shape  ) > 2:
                   raise ValueError(F"Only mono-channel audio is supported for input to {self}"  )
             SCREAMING_SNAKE_CASE   :      int    					=						is_batched_numpy or (
                 isinstance(A,							(list, tuple)  ) and (isinstance(raw_speech[0],							(np.ndarray, tuple, list)  ))
             )
             if is_batched:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray([speech],							dtype=np.floataa  ).T for speech in raw_speech]
             elif not is_batched and not isinstance(A,							np.ndarray  ):
                   SCREAMING_SNAKE_CASE   :      Any    					=						np.asarray(A,							dtype=np.floataa  )
             elif isinstance(A,							np.ndarray  ) and raw_speech.dtype is np.dtype(np.floataa  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						raw_speech.astype(np.floataa  )
             # always return batch
             if not is_batched:
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray([raw_speech]  ).T]

             # Convert audio signals to log mel spectrograms, truncate by time axis
             SCREAMING_SNAKE_CASE   :      int    					=						[
                 self._np_extract_fbank_features(waveform.squeeze()  ).T[: self.spectrogram_length] for waveform in raw_speech
             ]
             if isinstance(audio_features[0],							A  ):
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[np.asarray(A,							dtype=np.floataa  ) for feature in audio_features]

             # Create audio attention mask
             SCREAMING_SNAKE_CASE   :      Tuple    					=						max(
                 [ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len for feature in audio_features]  )  # The maximum number of audio patches in a batch
             if return_attention_mask:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						[
                       (ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [1]
                       + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]  ) * self.freq_len) * [0]
                       for feature in audio_features
                   ]
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						np.array(A  ).astype(np.floataa  )

             # convert into correct format for padding
             SCREAMING_SNAKE_CASE   :      Tuple    					=						max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						np.ones([len(A  ), 1, max_time_len, self.feature_size]  ).astype(np.floataa  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						padded_audio_features * self.padding_value
             for i in range(len(A  )  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						audio_features[i]
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						feature

             # return as BatchFeature
             if return_attention_mask:
                   SCREAMING_SNAKE_CASE   :      Any    					=						{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
             else:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						{'audio_values': padded_audio_features}

             SCREAMING_SNAKE_CASE   :      str    					=						BatchFeature(data=A,							tensor_type=A  )
             return encoded_inputs


