

'''simple docstring'''


from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy




class      __UpperCamelCase       (_UpperCamelCase			):
	__A			  =					"EncodecFeatureExtractor"
	__A			  =					("T5Tokenizer", "T5TokenizerFast")







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Union[str, Any]:
							'''simple docstring'''






							super().__init__(__a ,				__a						)
							lowercase        =							self.feature_extractor
							lowercase        =							False







	def   _a   (    self ,				_lowerCAmelCase=None ,				_lowerCAmelCase=None ,				_lowerCAmelCase=True						)    ->    Dict:
							'''simple docstring'''






							return self.tokenizer.get_decoder_prompt_ids(task=__a ,				language=__a ,				no_timestamps=__a						)







	def __call__(    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Any:
							'''simple docstring'''






							if self._in_target_context_manager:
													return self.current_processor(*__a ,				**__a						)

							lowercase        =							kwargs.pop("""audio""" ,				__a						)
							lowercase        =							kwargs.pop("""sampling_rate""" ,				__a						)
							lowercase        =							kwargs.pop("""text""" ,				__a						)
							if len(__a						) > 0:
													lowercase        =							args[0]
													lowercase        =							args[1:]

							if audio is None and text is None:
													raise ValueError("""You need to specify either an `audio` or `text` input to process."""						)

							if text is not None:
													lowercase        =							self.tokenizer(__a ,				**__a						)

							if audio is not None:
													lowercase        =							self.feature_extractor(__a ,				*__a ,				sampling_rate=__a ,				**__a						)

							if audio is None:
													return inputs

							elif text is None:
													return audio_inputs

							else:
													lowercase        =							audio_inputs["input_values"]
													if "padding_mask" in audio_inputs:
																			lowercase        =							audio_inputs["padding_mask"]
													return inputs







	def   _a   (    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							kwargs.pop("""audio""" ,				__a						)
							lowercase        =							kwargs.pop("""padding_mask""" ,				__a						)

							if len(__a						) > 0:
													lowercase        =							args[0]
													lowercase        =							args[1:]

							if audio_values is not None:
													return self._decode_audio(__a ,				padding_mask=__a						)
							else:
													return self.tokenizer.batch_decode(*__a ,				**__a						)







	def   _a   (    self ,				*_lowerCAmelCase ,				**_lowerCAmelCase						)    ->    Union[str, Any]:
							'''simple docstring'''






							return self.tokenizer.decode(*__a ,				**__a						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase = None						)    ->    List[np.ndarray]:
							'''simple docstring'''






							lowercase        =							to_numpy(__a						)
							lowercase        =							audio_values.shape

							if padding_mask is None:
													return list(__a						)

							lowercase        =							to_numpy(__a						)

							# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
							# token (so that the generated audio values are **not** treated as padded tokens)
							lowercase        =							seq_len - padding_mask.shape[-1]
							lowercase        =							1 - self.feature_extractor.padding_value
							lowercase        =							np.pad(__a ,				((0, 0), (0, difference)) ,				"""constant""" ,				constant_values=__a						)

							lowercase        =							audio_values.tolist()
							for i in range(__a						):
													lowercase        =							np.asarray(audio_values[i]						)[
													    padding_mask[i][None, :] != self.feature_extractor.padding_value
													]
													lowercase        =							sliced_audio.reshape(__a ,				-1						)

							return audio_values







'''simple docstring'''


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


lowercase_      :				Any   					=    logging.get_logger(__name__)

lowercase_      :				str   					=    {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class      __UpperCamelCase       (_UpperCAmelCase			):
	__A			  =					'''vit_msn'''







	def __init__(    self ,				_lowerCAmelCase=768 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=12 ,				_lowerCAmelCase=3072 ,				_lowerCAmelCase="gelu" ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.0 ,				_lowerCAmelCase=0.02 ,				_lowerCAmelCase=1E-06 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=16 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				**_lowerCAmelCase ,				)    ->    List[Any]:
							'''simple docstring'''






							super().__init__(**_lowerCAmelCase						)

							lowercase        =							hidden_size
							lowercase        =							num_hidden_layers
							lowercase        =							num_attention_heads
							lowercase        =							intermediate_size
							lowercase        =							hidden_act
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							initializer_range
							lowercase        =							layer_norm_eps
							lowercase        =							image_size
							lowercase        =							patch_size
							lowercase        =							num_channels
							lowercase        =							qkv_bias


