



"""simple docstring"""







from ...processing_utils import ProcessorMixin






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Tuple							  =     "SpeechT5FeatureExtractor"
			A__	:					List[Any]							  =     "SpeechT5Tokenizer"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[Any]:
				super().__init__(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def __call__(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	str:
				A__											=					kwargs.pop("audio"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("text"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("text_target"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("audio_target"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("sampling_rate"						,	SCREAMING_SNAKE_CASE__		)

				if audio is not None and text is not None:
					raise ValueError(
					    "Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?"		)
				if audio_target is not None and text_target is not None:
					raise ValueError(
					    "Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?"		)
				if audio is None and audio_target is None and text is None and text_target is None:
					raise ValueError(
					    "You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process."		)

				if audio is not None:
					A__											=					self.feature_extractor(SCREAMING_SNAKE_CASE__						,	*SCREAMING_SNAKE_CASE__						,	sampling_rate=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				elif text is not None:
					A__											=					self.tokenizer(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				else:
					A__											=					None

				if audio_target is not None:
					A__											=					self.feature_extractor(audio_target=SCREAMING_SNAKE_CASE__						,	*SCREAMING_SNAKE_CASE__						,	sampling_rate=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
					A__											=					targets["input_values"]
				elif text_target is not None:
					A__											=					self.tokenizer(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
					A__											=					targets["input_ids"]
				else:
					A__											=					None

				if inputs is None:
					return targets

				if targets is not None:
					A__											=					labels

					A__											=					targets.get("attention_mask"		)
					if decoder_attention_mask is not None:
						A__											=					decoder_attention_mask

				return inputs


			def   snake_case__	(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					kwargs.pop("input_values"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("input_ids"						,	SCREAMING_SNAKE_CASE__		)
				A__											=					kwargs.pop("labels"						,	SCREAMING_SNAKE_CASE__		)

				if input_values is not None and input_ids is not None:
					raise ValueError("Cannot process both `input_values` and `input_ids` inputs."		)
				if input_values is None and input_ids is None and labels is None:
					raise ValueError(
					    "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."		)

				if input_values is not None:
					A__											=					self.feature_extractor.pad(SCREAMING_SNAKE_CASE__						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				elif input_ids is not None:
					A__											=					self.tokenizer.pad(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
				else:
					A__											=					None

				if labels is not None:
					if "input_ids" in labels or (isinstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		) and "input_ids" in labels[0]):
						A__											=					self.tokenizer.pad(SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
						A__											=					targets["input_ids"]
					else:
						A__											=					self.feature_extractor.feature_size
						A__											=					self.feature_extractor.num_mel_bins
						A__											=					self.feature_extractor.pad(SCREAMING_SNAKE_CASE__						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)
						A__											=					feature_size_hack
						A__											=					targets["input_values"]
				else:
					A__											=					None

				if inputs is None:
					return targets

				if targets is not None:
					A__											=					labels

					A__											=					targets.get("attention_mask"		)
					if decoder_attention_mask is not None:
						A__											=					decoder_attention_mask

				return inputs


			def   snake_case__	(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	Dict:
				return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self						,	*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	int:
				return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)









"""simple docstring"""







import math

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


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[str]							  =     "data2vec-audio"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 512, 512, 512)						,	SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2)						,	SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2)						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=19						,	SCREAMING_SNAKE_CASE__=5						,	SCREAMING_SNAKE_CASE__=0.0_5						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__="sum"						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 1500)						,	SCREAMING_SNAKE_CASE__=(5, 3, 3, 1, 1)						,	SCREAMING_SNAKE_CASE__=(1, 2, 3, 1, 1)						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Dict:
				super().__init__(**SCREAMING_SNAKE_CASE__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__		)
				A__											=					hidden_size
				A__											=					feat_extract_activation
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					conv_bias
				A__											=					num_conv_pos_embeddings
				A__											=					num_conv_pos_embedding_groups
				A__											=					conv_pos_kernel_size
				A__											=					len(self.conv_dim		)
				A__											=					num_hidden_layers
				A__											=					intermediate_size
				A__											=					hidden_act
				A__											=					num_attention_heads
				A__											=					hidden_dropout
				A__											=					attention_dropout
				A__											=					activation_dropout
				A__											=					feat_proj_dropout
				A__											=					final_dropout
				A__											=					layerdrop
				A__											=					layer_norm_eps
				A__											=					initializer_range
				A__											=					vocab_size
				A__											=					use_weighted_layer_sum

				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)`, but is `len(config.conv_dim) ="
					    f""" {len(self.conv_dim		)}`, `len(config.conv_stride) = {len(self.conv_stride		)}`,"""
					    f""" `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

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

				# ctc loss
				A__											=					ctc_loss_reduction
				A__											=					ctc_zero_infinity

				# adapter
				A__											=					add_adapter
				A__											=					adapter_kernel_size
				A__											=					adapter_stride
				A__											=					num_adapter_layers
				A__											=					output_hidden_size or hidden_size

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				A__											=					classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					xvector_output_dim







			@property
			def   snake_case__	(      self		)      ->	List[str]:
				return math.prod(self.conv_stride		)




