
from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
    import torch




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""image_processor""", """tokenizer"""]
  a__   	=		"""OneFormerImageProcessor"""
  a__   	=		("""CLIPTokenizer""", """CLIPTokenizerFast""")
  def __init__(			self      :			Optional[int]      ,							UpperCamelCase__      :			Optional[Any]=None      ,							UpperCamelCase__      :			int=None      ,							UpperCamelCase__      :			int = 77      ,							UpperCamelCase__      :			int = 77      ,							**UpperCamelCase__      :			str   )      ->     Tuple:







    """simple docstring"""






    if image_processor is None:
      raise ValueError("""You need to specify an `image_processor`."""   )
    if tokenizer is None:
      raise ValueError("""You need to specify a `tokenizer`."""   )

    __magic_name__    					=	max_seq_length
    __magic_name__    					=	task_seq_length

    super().__init__(UpperCamelCase__      ,							UpperCamelCase__   )
  def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			List[str]=None      ,							UpperCamelCase__      :			Any=77   )      ->     List[str]:







    """simple docstring"""






    if text_list is None:
      raise ValueError("""tokens cannot be None."""   )

    __magic_name__    					=	self.tokenizer(UpperCamelCase__      ,							padding="""max_length"""      ,							max_length=UpperCamelCase__      ,							truncation=UpperCamelCase__   )

    __magic_name__					,   __magic_name__    					=	tokens["""attention_mask"""], tokens["""input_ids"""]

    __magic_name__    					=	[]
    for attn_mask, input_id in zip(UpperCamelCase__      ,							UpperCamelCase__   ):
      __magic_name__    					=	torch.tensor(UpperCamelCase__   ) * torch.tensor(UpperCamelCase__   )
      token_inputs.append(token.unsqueeze(0   )   )

    __magic_name__    					=	torch.cat(UpperCamelCase__      ,							dim=0   )
    return token_inputs
  def __call__(			self      :			Any      ,							UpperCamelCase__      :			Tuple=None      ,							UpperCamelCase__      :			List[str]=None      ,							UpperCamelCase__      :			Union[str, Any]=None      ,							**UpperCamelCase__      :			Dict   )      ->     int:







    """simple docstring"""






    if task_inputs is None:
      raise ValueError("""You have to specify the task_input. Found None."""   )
    elif images is None:
      raise ValueError("""You have to specify the image. Found None."""   )

    if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs   ):
      raise ValueError("""task_inputs must be semantic, instance, or panoptic."""   )

    __magic_name__    					=	self.image_processor(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__   )

    if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
      __magic_name__    					=	[task_inputs]

    if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ) and all(isinstance(UpperCamelCase__      ,							UpperCamelCase__   ) for task_input in task_inputs   ):
      __magic_name__    					=	[]
      for task in task_inputs:
        __magic_name__    					=	F'''the task is {task}'''
        task_token_inputs.append(UpperCamelCase__   )
      __magic_name__    					=	self._preprocess_text(UpperCamelCase__      ,							max_length=self.task_seq_length   )
    else:
      raise TypeError("""Task Inputs should be a string or a list of strings."""   )

    if hasattr(UpperCamelCase__      ,							"""text_inputs"""   ):
      __magic_name__    					=	encoded_inputs.text_inputs

      __magic_name__    					=	[]
      for texts in texts_list:
        __magic_name__    					=	self._preprocess_text(UpperCamelCase__      ,							max_length=self.max_seq_length   )
        text_inputs.append(text_input_list.unsqueeze(0   )   )

      __magic_name__    					=	torch.cat(UpperCamelCase__      ,							dim=0   )

    return encoded_inputs
  def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			int=None      ,							UpperCamelCase__      :			List[Any]=None      ,							UpperCamelCase__      :			Dict=None      ,							**UpperCamelCase__      :			Any   )      ->     Optional[int]:







    """simple docstring"""






    if task_inputs is None:
      raise ValueError("""You have to specify the task_input. Found None."""   )
    elif images is None:
      raise ValueError("""You have to specify the image. Found None."""   )

    if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs   ):
      raise ValueError("""task_inputs must be semantic, instance, or panoptic."""   )

    __magic_name__    					=	self.image_processor.encode_inputs(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__   )

    if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
      __magic_name__    					=	[task_inputs]

    if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ) and all(isinstance(UpperCamelCase__      ,							UpperCamelCase__   ) for task_input in task_inputs   ):
      __magic_name__    					=	[]
      for task in task_inputs:
        __magic_name__    					=	F'''the task is {task}'''
        task_token_inputs.append(UpperCamelCase__   )
      __magic_name__    					=	self._preprocess_text(UpperCamelCase__      ,							max_length=self.task_seq_length   )
    else:
      raise TypeError("""Task Inputs should be a string or a list of strings."""   )

    if hasattr(UpperCamelCase__      ,							"""text_inputs"""   ):
      __magic_name__    					=	encoded_inputs.text_inputs

      __magic_name__    					=	[]
      for texts in texts_list:
        __magic_name__    					=	self._preprocess_text(UpperCamelCase__      ,							max_length=self.max_seq_length   )
        text_inputs.append(text_input_list.unsqueeze(0   )   )

      __magic_name__    					=	torch.cat(UpperCamelCase__      ,							dim=0   )

    return encoded_inputs
  def 							_lowercase							(			self      :			Union[str, Any]      ,							*UpperCamelCase__      :			Dict      ,							**UpperCamelCase__      :			Union[str, Any]   )      ->     Tuple:







    """simple docstring"""






    return self.image_processor.post_process_semantic_segmentation(*UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Any      ,							*UpperCamelCase__      :			str      ,							**UpperCamelCase__      :			int   )      ->     List[Any]:







    """simple docstring"""






    return self.image_processor.post_process_instance_segmentation(*UpperCamelCase__      ,							**UpperCamelCase__   )





  def 							_lowercase							(			self      :			Tuple      ,							*UpperCamelCase__      :			List[str]      ,							**UpperCamelCase__      :			str   )      ->     Tuple:







    """simple docstring"""






    return self.image_processor.post_process_panoptic_segmentation(*UpperCamelCase__      ,							**UpperCamelCase__   )



import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
    import transformer_engine.pytorch as te

def a__   (	A_,					A_=True,					A_=True,					A_=True ):






  '''simple docstring'''





  if not is_fpa_available():
    raise ImportError("""Using `convert_model` requires transformer_engine to be installed.""" )
  for name, module in model.named_children():
    if isinstance(A_,					nn.Linear ) and to_transformer_engine and _convert_linear:
      # Return early if the linear layer weights are not multiples of 16
      if any(p % 16 != 0 for p in module.weight.shape ):
        return
      __magic_name__    					=	module.bias is not None
      __magic_name__    					=	te.Linear(
          module.in_features,					module.out_features,					bias=A_,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      if has_bias:
        __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					nn.LayerNorm ) and to_transformer_engine and _convert_ln:
      __magic_name__    					=	te.LayerNorm(module.normalized_shape[0],					eps=module.eps,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					te.Linear ) and not to_transformer_engine and _convert_linear:
      __magic_name__    					=	module.bias is not None
      __magic_name__    					=	nn.Linear(
          module.in_features,					module.out_features,					bias=A_,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      if has_bias:
        __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    elif isinstance(A_,					te.LayerNorm ) and not to_transformer_engine and _convert_ln:
      __magic_name__    					=	nn.LayerNorm(module.normalized_shape[0],					eps=module.eps,					params_dtype=module.weight.dtype )
      __magic_name__    					=	module.weight.data.clone()
      __magic_name__    					=	module.bias.data.clone()

      setattr(A_,					A_,					A_ )
    else:
      convert_model(
          A_,					to_transformer_engine=A_,					_convert_linear=_convert_linear,					_convert_ln=_convert_ln,					)





def a__   (	A_ ):






  '''simple docstring'''





  if not is_fpa_available():
    raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed.""" )
  for m in model.modules():
    if isinstance(A_,					(te.LayerNorm, te.Linear) ):
      return True
  return False

