



'''simple docstring'''





import os

import numpy
import onnx




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     _a           =    a.name
     _a           =    b.name

     _a           =    ''''''
     _a           =    ''''''

     _a           =    a == b

     _a           =    name_a
     _a           =    name_b

     return res




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



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

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




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



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




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    list(model.graph.initializer)
     _a           =    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

          _a           =    inits[i].name
          _a           =    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    ,      __A    ,      __A)




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    os.path.dirname(__A)
     _a           =    os.path.basename(__A)

     _a           =    onnx.load(os.path.join(__A    ,      __A))

     _a           =    list(model.graph.initializer)

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

     _a           =    0

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

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

                    _a           =    inits[j].data_type
                    _a           =    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: '''    ,      __A)
                    total_reduced_size += mem_size

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

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

     print('''total reduced size: '''    ,      total_reduced_size / 1_024 / 1_024 / 1_024    ,      '''GB''')

     _a           =    sorted(__A)
     _remove_dup_initializers_from_model(__A    ,      __A    ,      __A)

     _a           =    '''optimized_''' + model_file_name
     _a           =    os.path.join(__A    ,      __A)
     onnx.save(__A    ,      __A)

     return new_model








'''simple docstring'''





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor






class __A     (    unittest.TestCase		):



 '''simple docstring'''







 def __init__(self					,  A					,  A=7					,  A=3					,  A=18					,  A=30					,  A=400					,  A=True					,  A=None					,  A=True					,  A=[0.5, 0.5, 0.5]					,  A=[0.5, 0.5, 0.5]					,  )       ->	str:


      """simple docstring"""



      _a           =    size if size is not None else {'''height''': 18, '''width''': 18}
      _a           =    parent
      _a           =    batch_size
      _a           =    num_channels
      _a           =    image_size
      _a           =    min_resolution
      _a           =    max_resolution
      _a           =    do_resize
      _a           =    size
      _a           =    do_normalize
      _a           =    image_mean
      _a           =    image_std



 def    a__    (self			)       ->	Union[str, Any]:


      """simple docstring"""



      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }






@require_torch
@require_vision
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			DPTImageProcessor if is_vision_available() else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    DPTImageProcessingTester(self			)



 @property
 def    a__    (self			)       ->	int:


      """simple docstring"""



      return self.image_processor_tester.prepare_image_processor_dict()



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      self.assertTrue(hasattr(A					,  '''image_mean'''			)			)
      self.assertTrue(hasattr(A					,  '''image_std'''			)			)
      self.assertTrue(hasattr(A					,  '''do_normalize'''			)			)
      self.assertTrue(hasattr(A					,  '''do_resize'''			)			)
      self.assertTrue(hasattr(A					,  '''size'''			)			)



 def    a__    (self			)       ->	Any:


      """simple docstring"""



      _a           =    self.image_processing_class.from_dict(self.image_processor_dict			)
      self.assertEqual(image_processor.size					,  {'''height''': 18, '''width''': 18}			)

      _a           =    self.image_processing_class.from_dict(self.image_processor_dict					,  size=42			)
      self.assertEqual(image_processor.size					,  {'''height''': 42, '''width''': 42}			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PIL images
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  Image.Image			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  numpify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  np.ndarray			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _a           =    prepare_image_inputs(self.image_processor_tester					,  equal_resolution=A					,  torchify=A			)
      for image in image_inputs:
           self.assertIsInstance(A					,  torch.Tensor			)

      # Test not batched input
      _a           =    image_processing(image_inputs[0]					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )

      # Test batched
      _a           =    image_processing(A					,  return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_images.shape					,  (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['''height'''],
              self.image_processor_tester.size['''width'''],
          )					,  )



