


"""simple docstring"""

from ...processing_utils import ProcessorMixin





class    lowerCamelCase__						(		UpperCamelCase_    ):
      a :     int											=		"""SpeechT5FeatureExtractor"""
      a :     Tuple											=		"""SpeechT5Tokenizer"""
      def __init__(		self	:	Optional[int]      ,       A_	:	Any      ,       A_	:	Any							):






       '''simple docstring'''


       super().__init__(__A      ,       __A							)
      def __call__(		self	:	Any      ,       *A_	:	Tuple      ,       **A_	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	kwargs.pop("""audio"""      ,       __A							)
       __lowercase									=	kwargs.pop("""text"""      ,       __A							)
       __lowercase									=	kwargs.pop("""text_target"""      ,       __A							)
       __lowercase									=	kwargs.pop("""audio_target"""      ,       __A							)
       __lowercase									=	kwargs.pop("""sampling_rate"""      ,       __A							)

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

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

       if inputs is None:
        return targets

       if targets is not None:
        __lowercase									=	labels

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

       return inputs
      def SCREAMING_SNAKE_CASE_	(		self	:	Optional[int]      ,       *A_	:	Any      ,       **A_	:	List[Any]							):






       '''simple docstring'''


       __lowercase									=	kwargs.pop("""input_values"""      ,       __A							)
       __lowercase									=	kwargs.pop("""input_ids"""      ,       __A							)
       __lowercase									=	kwargs.pop("""labels"""      ,       __A							)

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

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

       if inputs is None:
        return targets

       if targets is not None:
        __lowercase									=	labels

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

       return inputs
      def SCREAMING_SNAKE_CASE_	(		self	:	int      ,       *A_	:	Optional[Any]      ,       **A_	:	Tuple							):






       '''simple docstring'''


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






      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       *A_	:	Dict      ,       **A_	:	List[Any]							):






       '''simple docstring'''


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




"""simple docstring"""

import os

import numpy
import onnx


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: List[str] ):






 """simple docstring"""







 __lowercase									=	a.name
 __lowercase									=	b.name

 __lowercase									=	""""""
 __lowercase									=	""""""

 __lowercase									=	a == b

 __lowercase									=	name_a
 __lowercase									=	name_b

 return res


def lowerCAmelCase_      (				UpperCamelCase__	: Union[str, Any]				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Dict ):






 """simple docstring"""







 for i, input_name in enumerate(node_proto.input ):
  if input_name == name:
   node_proto.input.insert(UpperCamelCase__				,   UpperCamelCase__ )
   node_proto.input.pop(i + 1 )

 if node_proto.op_type == "If":
  _graph_replace_input_with(node_proto.attribute[0].g				,   UpperCamelCase__				,   UpperCamelCase__ )
  _graph_replace_input_with(node_proto.attribute[1].g				,   UpperCamelCase__				,   UpperCamelCase__ )
 if node_proto.op_type == "Loop":
  _graph_replace_input_with(node_proto.attribute[0].g				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: Optional[Any]				,   UpperCamelCase__	: Optional[int]				,   UpperCamelCase__	: str ):






 """simple docstring"""







 for n in graph_proto.node:
  _node_replace_input_with(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: Tuple				,   UpperCamelCase__	: str				,   UpperCamelCase__	: Any ):






 """simple docstring"""







 __lowercase									=	list(model.graph.initializer )
 __lowercase									=	list(model_without_ext.graph.initializer )
 for i, ref_i in ind_to_replace:
  assert inits_with_data[i].name == inits[i].name
  assert inits_with_data[ref_i].name == inits[ref_i].name
  assert i > ref_i

  __lowercase									=	inits[i].name
  __lowercase									=	inits[ref_i].name

  model_without_ext.graph.initializer.remove(inits[i] )

  # for n in model.graph.node:
  _graph_replace_input_with(model_without_ext.graph				,   UpperCamelCase__				,   UpperCamelCase__ )


def lowerCAmelCase_      (				UpperCamelCase__	: List[Any] ):






 """simple docstring"""







 __lowercase									=	os.path.dirname(UpperCamelCase__ )
 __lowercase									=	os.path.basename(UpperCamelCase__ )

 __lowercase									=	onnx.load(os.path.join(UpperCamelCase__				,   UpperCamelCase__ ) )

 __lowercase									=	list(model.graph.initializer )

 __lowercase									=	set()
 __lowercase									=	{}
 __lowercase									=	[]

 __lowercase									=	0

 for i in range(len(UpperCamelCase__ ) ):
  if i in dup_set:
   continue

  for j in range(i + 1				,   len(UpperCamelCase__ ) ):
   if j in dup_set:
    continue
   if _is_equal_tensor_proto(inits[i]				,   inits[j] ):
    dup_set.add(UpperCamelCase__ )
    dup_set.add(UpperCamelCase__ )

    __lowercase									=	inits[j].data_type
    __lowercase									=	numpy.prod(inits[j].dims )
    if dtype == 1:
     mem_size *= 4
    elif dtype == 6:
     mem_size *= 4
    elif dtype == 7 or dtype == 11:
     mem_size *= 8
    else:
     print("""unexpected data type: """				,   UpperCamelCase__ )
    total_reduced_size += mem_size

    __lowercase									=	inits[i].name
    __lowercase									=	inits[j].name

    if name_i in dup_map:
     dup_map[name_i].append(UpperCamelCase__ )
    else:
     __lowercase									=	[name_j]
    ind_to_replace.append((j, i) )

 print("""total reduced size: """				,   total_reduced_size / 1024 / 1024 / 1024				,   """GB""" )

 __lowercase									=	sorted(UpperCamelCase__ )
 _remove_dup_initializers_from_model(UpperCamelCase__				,   UpperCamelCase__				,   UpperCamelCase__ )

 __lowercase									=	"""optimized_""" + model_file_name
 __lowercase									=	os.path.join(UpperCamelCase__				,   UpperCamelCase__ )
 onnx.save(UpperCamelCase__				,   UpperCamelCase__ )

 return new_model
