



"""simple docstring"""





from ...processing_utils import ProcessorMixin
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''SpeechT5FeatureExtractor'''
       UpperCamelCase		=  '''SpeechT5Tokenizer'''



       def __init__(							self    :						Tuple	,       _UpperCAmelCase    :						Any	,       _UpperCAmelCase    :						Optional[Any]					)				->		Dict:


             '''simple docstring'''




             super().__init__(_UpperCAmelCase	,       _UpperCAmelCase					)



       def __call__(							self    :						Any	,       *_UpperCAmelCase    :						int	,       **_UpperCAmelCase    :						Optional[Any]					)				->		Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_    				=							kwargs.pop("audio"	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							kwargs.pop("text"	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							kwargs.pop("text_target"	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							kwargs.pop("audio_target"	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							kwargs.pop("sampling_rate"	,       _UpperCAmelCase					)

             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:
                   UpperCAmelCase_    				=							self.feature_extractor(_UpperCAmelCase	,       *_UpperCAmelCase	,       sampling_rate=_UpperCAmelCase	,       **_UpperCAmelCase					)
             elif text is not None:
                   UpperCAmelCase_    				=							self.tokenizer(_UpperCAmelCase	,       **_UpperCAmelCase					)
             else:
                   UpperCAmelCase_    				=							None

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

             if inputs is None:
                   return targets

             if targets is not None:
                   UpperCAmelCase_    				=							labels

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

             return inputs



       def   lowercase__       (							self    :						int	,       *_UpperCAmelCase    :						Union[str, Any]	,       **_UpperCAmelCase    :						int					)				->		List[str]:


             '''simple docstring'''




             UpperCAmelCase_    				=							kwargs.pop("input_values"	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							kwargs.pop("input_ids"	,       _UpperCAmelCase					)
             UpperCAmelCase_    				=							kwargs.pop("labels"	,       _UpperCAmelCase					)

             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:
                   UpperCAmelCase_    				=							self.feature_extractor.pad(_UpperCAmelCase	,       *_UpperCAmelCase	,       **_UpperCAmelCase					)
             elif input_ids is not None:
                   UpperCAmelCase_    				=							self.tokenizer.pad(_UpperCAmelCase	,       **_UpperCAmelCase					)
             else:
                   UpperCAmelCase_    				=							None

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

             if inputs is None:
                   return targets

             if targets is not None:
                   UpperCAmelCase_    				=							labels

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

             return inputs



       def   lowercase__       (							self    :						Tuple	,       *_UpperCAmelCase    :						str	,       **_UpperCAmelCase    :						Any					)				->		str:


             '''simple docstring'''




             return self.tokenizer.batch_decode(*_UpperCAmelCase	,       **_UpperCAmelCase					)







       def   lowercase__       (							self    :						List[Any]	,       *_UpperCAmelCase    :						List[Any]	,       **_UpperCAmelCase    :						Optional[int]					)				->		List[str]:


             '''simple docstring'''




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









"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase      =		logging.get_logger(__name__)

lowerCamelCase      =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       UpperCamelCase		=  '''convbert'''



       def __init__(							self    :						Any	,       _UpperCAmelCase    :						Optional[int]=30522	,       _UpperCAmelCase    :						Tuple=768	,       _UpperCAmelCase    :						Optional[int]=12	,       _UpperCAmelCase    :						Dict=12	,       _UpperCAmelCase    :						Any=3072	,       _UpperCAmelCase    :						Dict="gelu"	,       _UpperCAmelCase    :						Optional[int]=0.1	,       _UpperCAmelCase    :						str=0.1	,       _UpperCAmelCase    :						Tuple=512	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						Union[str, Any]=0.02	,       _UpperCAmelCase    :						Dict=1e-12	,       _UpperCAmelCase    :						Optional[int]=1	,       _UpperCAmelCase    :						int=0	,       _UpperCAmelCase    :						Optional[Any]=2	,       _UpperCAmelCase    :						List[Any]=768	,       _UpperCAmelCase    :						Optional[int]=2	,       _UpperCAmelCase    :						str=9	,       _UpperCAmelCase    :						List[Any]=1	,       _UpperCAmelCase    :						Optional[Any]=None	,       **_UpperCAmelCase    :						str	,       )				->		List[Any]:


             '''simple docstring'''




             super().__init__(
                 pad_token_id=_UpperCAmelCase	,       bos_token_id=_UpperCAmelCase	,       eos_token_id=_UpperCAmelCase	,       **_UpperCAmelCase	,       )

             UpperCAmelCase_    				=							vocab_size
             UpperCAmelCase_    				=							hidden_size
             UpperCAmelCase_    				=							num_hidden_layers
             UpperCAmelCase_    				=							num_attention_heads
             UpperCAmelCase_    				=							intermediate_size
             UpperCAmelCase_    				=							hidden_act
             UpperCAmelCase_    				=							hidden_dropout_prob
             UpperCAmelCase_    				=							attention_probs_dropout_prob
             UpperCAmelCase_    				=							max_position_embeddings
             UpperCAmelCase_    				=							type_vocab_size
             UpperCAmelCase_    				=							initializer_range
             UpperCAmelCase_    				=							layer_norm_eps
             UpperCAmelCase_    				=							embedding_size
             UpperCAmelCase_    				=							head_ratio
             UpperCAmelCase_    				=							conv_kernel_size
             UpperCAmelCase_    				=							num_groups
             UpperCAmelCase_    				=							classifier_dropout



class    lowercase__     (					SCREAMING_SNAKE_CASE   ):







       '''simple docstring'''


       @property
       def   lowercase__       (							self    :						Dict					)				->		Mapping[str, Mapping[int, str]]:


             '''simple docstring'''




             if self.task == "multiple-choice":
                   UpperCAmelCase_    				=							{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   UpperCAmelCase_    				=							{0: "batch", 1: "sequence"}
             return OrderedDict(
                 [
                     ("input_ids", dynamic_axis),
                     ("attention_mask", dynamic_axis),
                     ("token_type_ids", dynamic_axis),
                 ]					)




