
import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
    from PIL import Image




class 	UpperCAmelCase_      (			unittest.TestCase  ,	_A					):


  '''simple docstring'''







  def 							_lowercase							(			self      :			List[Any]   )      ->     Optional[Any]:







    """simple docstring"""






    __magic_name__    					=	load_tool("""image-segmentation"""   )
    self.tool.setup()
    __magic_name__    					=	load_tool("""image-segmentation"""      ,							remote=__A   )
  def 							_lowercase							(			self      :			Dict   )      ->     int:







    """simple docstring"""






    __magic_name__    					=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""   )   ) / """000000039769.png"""   )
    __magic_name__    					=	self.tool(__A      ,							"""cat"""   )
    self.assertTrue(isinstance(__A      ,							Image.Image   )   )
  def 							_lowercase							(			self      :			Optional[int]   )      ->     Optional[Any]:







    """simple docstring"""






    __magic_name__    					=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""   )   ) / """000000039769.png"""   )
    __magic_name__    					=	self.remote_tool(__A      ,							"""cat"""   )
    self.assertTrue(isinstance(__A      ,							Image.Image   )   )
  def 							_lowercase							(			self      :			Tuple   )      ->     Dict:







    """simple docstring"""






    __magic_name__    					=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""   )   ) / """000000039769.png"""   )
    __magic_name__    					=	self.tool(image=__A      ,							label="""cat"""   )
    self.assertTrue(isinstance(__A      ,							Image.Image   )   )





  def 							_lowercase							(			self      :			Any   )      ->     Tuple:







    """simple docstring"""






    __magic_name__    					=	Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""   )   ) / """000000039769.png"""   )
    __magic_name__    					=	self.remote_tool(image=__A      ,							label="""cat"""   )
    self.assertTrue(isinstance(__A      ,							Image.Image   )   )



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

