


'''simple docstring'''







import os

import numpy
import onnx






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							a.name
       _lowerCAmelCase     :				Dict       =							b.name

       _lowerCAmelCase     :				Tuple       =							''
       _lowerCAmelCase     :				Dict       =							''

       _lowerCAmelCase     :				Tuple       =							a == b

       _lowerCAmelCase     :				Dict       =							name_a
       _lowerCAmelCase     :				List[str]       =							name_b

       return res






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







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

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






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







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






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Any       =							list(model.graph.initializer       )
       _lowerCAmelCase     :				str       =							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

              _lowerCAmelCase     :				Dict       =							inits[i].name
              _lowerCAmelCase     :				Optional[int]       =							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 ,    _snake_case ,    _snake_case       )






def       lowercase					(_A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Tuple       =							os.path.dirname(_snake_case       )
       _lowerCAmelCase     :				Optional[int]       =							os.path.basename(_snake_case       )

       _lowerCAmelCase     :				Dict       =							onnx.load(os.path.join(_snake_case ,    _snake_case       )       )

       _lowerCAmelCase     :				Union[str, Any]       =							list(model.graph.initializer       )

       _lowerCAmelCase     :				List[Any]       =							set()
       _lowerCAmelCase     :				List[str]       =							{}
       _lowerCAmelCase     :				Union[str, Any]       =							[]

       _lowerCAmelCase     :				List[str]       =							0

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

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

                            _lowerCAmelCase     :				Union[str, Any]       =							inits[j].data_type
                            _lowerCAmelCase     :				Any       =							numpy.prod(inits[j].dims       )
                            if dtype == 1:
                                   mem_size *= 4
                            elif dtype == 6:
                                   mem_size *= 4
                            elif dtype == 7 or dtype == 1_1:
                                   mem_size *= 8
                            else:
                                   print('unexpected data type: ' ,    _snake_case       )
                            total_reduced_size += mem_size

                            _lowerCAmelCase     :				List[str]       =							inits[i].name
                            _lowerCAmelCase     :				Optional[Any]       =							inits[j].name

                            if name_i in dup_map:
                                   dup_map[name_i].append(_snake_case       )
                            else:
                                   _lowerCAmelCase     :				List[Any]       =							[name_j]
                            ind_to_replace.append((j, i)       )

       print('total reduced size: ' ,    total_reduced_size / 1_0_2_4 / 1_0_2_4 / 1_0_2_4 ,    'GB'       )

       _lowerCAmelCase     :				str       =							sorted(_snake_case       )
       _remove_dup_initializers_from_model(_snake_case ,    _snake_case ,    _snake_case       )

       _lowerCAmelCase     :				int       =							'optimized_' + model_file_name
       _lowerCAmelCase     :				List[str]       =							os.path.join(_snake_case ,    _snake_case       )
       onnx.save(_snake_case ,    _snake_case       )

       return new_model









import unittest

import numpy as np
import requests

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    _SCREAMING_SNAKE_CASE   =  False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor






class 					SCREAMING_SNAKE_CASE_  (       unittest.TestCase ):
       """simple docstring"""


       def __init__(				self	:str,      snake_case	:int,      snake_case	:Dict=7,      snake_case	:List[Any]=3,      snake_case	:int=18,      snake_case	:List[str]=30,      snake_case	:str=400,      snake_case	:int=None,      snake_case	:List[Any]=True,      snake_case	:Optional[int]=True,      snake_case	:Union[str, Any]=None,      ):



          """simple docstring"""

          _lowercase              =size if size is not None else {'height': 20, 'width': 20}
          _lowercase              =parent
          _lowercase              =batch_size
          _lowercase              =num_channels
          _lowercase              =image_size
          _lowercase              =min_resolution
          _lowercase              =max_resolution
          _lowercase              =size
          _lowercase              =do_normalize
          _lowercase              =do_convert_rgb
          _lowercase              =[512, 1024, 2048, 4096]
          _lowercase              =patch_size if patch_size is not None else {'height': 16, 'width': 16}





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

          _lowercase              ='https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
          _lowercase              =Image.open(requests.get(snake_case,      stream=snake_case).raw).convert('RGB')
          return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,						reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,						)
@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Tuple					=PixaStructImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =PixaStructImageProcessingTester(self)





       @property
       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          _lowercase              =self.image_processor_tester.prepare_dummy_image()

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          _lowercase              =2048

          _lowercase              =image_processor(snake_case,      return_tensors='pt',      max_patches=snake_case)
          self.assertTrue(torch.allclose(inputs.flattened_patches.mean(),      torch.tensor(0.0_6_0_6),      atol=1e-3,      rtol=1e-3))





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

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

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

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

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          _lowercase              =True

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             with self.assertRaises(snake_case):
                _lowercase              =image_processor(
                    image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches

             _lowercase              ='Hello'

             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case,      header_text=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case,      header_text=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:int):



          """simple docstring"""

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

          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )





       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

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

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * self.image_processor_tester.num_channels
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11  ,						reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''  ,						)
@require_torch
@require_vision
class 					SCREAMING_SNAKE_CASE_  (       _a  ,						unittest.TestCase ):
       """simple docstring"""


       __lowerCAmelCase    :		Optional[Any]					=PixaStructImageProcessor if is_vision_available() else None





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =PixaStructImageProcessingTester(self,      num_channels=4)
          _lowercase              =3





       @property
       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          return self.image_processor_tester.prepare_image_processor_dict()





       def 				UpperCamelCase__						(				self	:Optional[int]):



          """simple docstring"""

          _lowercase              =self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(snake_case,      'do_normalize'))
          self.assertTrue(hasattr(snake_case,      'do_convert_rgb'))





       def 				UpperCamelCase__						(				self	:Optional[Any]):



          """simple docstring"""

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

          # Test not batched input
          _lowercase              =(
              (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
              * (self.image_processor_tester.num_channels - 1)
          ) + 2

          for max_patch in self.image_processor_tester.max_patches:
             # Test not batched input
             _lowercase              =image_processor(
                 image_inputs[0],      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (1, max_patch, expected_hidden_dim),      )

             # Test batched
             _lowercase              =image_processor(
                 snake_case,      return_tensors='pt',      max_patches=snake_case).flattened_patches
             self.assertEqual(
                 encoded_images.shape,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),      )


