


"""simple docstring"""



from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Optional[Any]        =   "EncodecFeatureExtractor"
       __A			:      Optional[int]        =   ("T5Tokenizer", "T5TokenizerFast")






       def __init__(   self  :    List[Any]  ,     snake_case__  :    Any  ,     snake_case__  :    Tuple				):

              '''simple docstring'''






              super().__init__(snake_case__  ,     snake_case__				)
              lowercase		:Any       							=				self.feature_extractor
              lowercase		:List[Any]       							=				False






       def  __snake_case						(   self  :    Any  ,     snake_case__  :    List[str]=None  ,     snake_case__  :    Any=None  ,     snake_case__  :    List[str]=True				):

              '''simple docstring'''






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






       def __call__(   self  :    str  ,     *snake_case__  :    List[Any]  ,     **snake_case__  :    List[Any]				):

              '''simple docstring'''






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

              lowercase		:Optional[int]       							=				kwargs.pop('''audio'''  ,     snake_case__				)
              lowercase		:Dict       							=				kwargs.pop('''sampling_rate'''  ,     snake_case__				)
              lowercase		:Dict       							=				kwargs.pop('''text'''  ,     snake_case__				)
              if len(snake_case__				) > 0:
                     lowercase		:int       							=				args[0]
                     lowercase		:List[Any]       							=				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		:Optional[Any]       							=				self.tokenizer(snake_case__  ,     **snake_case__				)

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

              if audio is None:
                     return inputs

              elif text is None:
                     return audio_inputs

              else:
                     lowercase		:List[str]       							=				audio_inputs['''input_values''']
                     if "padding_mask" in audio_inputs:
                            lowercase		:List[Any]       							=				audio_inputs['''padding_mask''']
                     return inputs






       def  __snake_case						(   self  :    Any  ,     *snake_case__  :    Tuple  ,     **snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:Tuple       							=				kwargs.pop('''audio'''  ,     snake_case__				)
              lowercase		:Tuple       							=				kwargs.pop('''padding_mask'''  ,     snake_case__				)

              if len(snake_case__				) > 0:
                     lowercase		:Optional[Any]       							=				args[0]
                     lowercase		:int       							=				args[1:]

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






       def  __snake_case						(   self  :    Dict  ,     *snake_case__  :    List[str]  ,     **snake_case__  :    Optional[int]				):

              '''simple docstring'''






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







       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    Optional = None				):

              '''simple docstring'''






              lowercase		:Tuple       							=				to_numpy(snake_case__				)
              lowercase		:int       							=				audio_values.shape

              if padding_mask is None:
                     return list(snake_case__				)

              lowercase		:List[str]       							=				to_numpy(snake_case__				)

              # 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		:List[str]       							=				seq_len - padding_mask.shape[-1]
              lowercase		:Dict       							=				1 - self.feature_extractor.padding_value
              lowercase		:Union[str, Any]       							=				np.pad(snake_case__  ,     ((0, 0), (0, difference))  ,     '''constant'''  ,     constant_values=snake_case__				)

              lowercase		:int       							=				audio_values.tolist()
              for i in range(snake_case__				):
                     lowercase		:List[str]       							=				np.asarray(audio_values[i]				)[
                         padding_mask[i][None, :] != self.feature_extractor.padding_value
                     ]
                     lowercase		:Optional[Any]       							=				sliced_audio.reshape(snake_case__  ,     -1				)

              return audio_values





"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase      		=			{
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''LayoutLMv3TokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LayoutLMv3ForQuestionAnswering''',
     '''LayoutLMv3ForSequenceClassification''',
     '''LayoutLMv3ForTokenClassification''',
     '''LayoutLMv3Model''',
     '''LayoutLMv3PreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			[
     '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFLayoutLMv3ForQuestionAnswering''',
     '''TFLayoutLMv3ForSequenceClassification''',
     '''TFLayoutLMv3ForTokenClassification''',
     '''TFLayoutLMv3Model''',
     '''TFLayoutLMv3PreTrainedModel''',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase      		=			['''LayoutLMv3FeatureExtractor''']
 UpperCAmelCase      		=			['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
 from .configuration_layoutlmva import (
     LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LayoutLMvaConfig,
     LayoutLMvaOnnxConfig,
 )
 from .processing_layoutlmva import LayoutLMvaProcessor
 from .tokenization_layoutlmva import LayoutLMvaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlmva import (
      LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMvaForQuestionAnswering,
      LayoutLMvaForSequenceClassification,
      LayoutLMvaForTokenClassification,
      LayoutLMvaModel,
      LayoutLMvaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlmva import (
      TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMvaForQuestionAnswering,
      TFLayoutLMvaForSequenceClassification,
      TFLayoutLMvaForTokenClassification,
      TFLayoutLMvaModel,
      TFLayoutLMvaPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
  from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
 import sys

 UpperCAmelCase      		=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

