'''simple docstring'''





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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

if is_vision_available():
    from PIL import Image

    from transformers import DetaImageProcessor




class 	__A				(			unittest.TestCase     ):



    def __init__(self   :   int       ,      __a   :   Any       ,      __a   :   List[Any]=7       ,      __a   :   Any=3       ,      __a   :   Any=30       ,      __a   :   List[str]=400       ,      __a   :   List[Any]=True       ,      __a   :   str=None       ,      __a   :   str=True       ,      __a   :   List[str]=[0.5, 0.5, 0.5]       ,      __a   :   str=[0.5, 0.5, 0.5]       ,      __a   :   Dict=True       ,      __a   :   Tuple=1 / 255       ,      __a   :   int=True       ,      ):
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         UpperCAmelCase_	   =       size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
         UpperCAmelCase_	   =       parent
         UpperCAmelCase_	   =       batch_size
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       min_resolution
         UpperCAmelCase_	   =       max_resolution
         UpperCAmelCase_	   =       do_resize
         UpperCAmelCase_	   =       size
         UpperCAmelCase_	   =       do_normalize
         UpperCAmelCase_	   =       image_mean
         UpperCAmelCase_	   =       image_std
         UpperCAmelCase_	   =       do_rescale
         UpperCAmelCase_	   =       rescale_factor
         UpperCAmelCase_	   =       do_pad



    def  _lowercase						(self   :   List[Any]			):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }






    def  _lowercase						(self   :   List[Any]       ,      __a   :   str       ,      __a   :   List[str]=False			):
         if not batched:
              UpperCAmelCase_	   =       image_inputs[0]
              if isinstance(__a       ,      Image.Image			):
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       image.size
              else:
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       image.shape[1], image.shape[2]
              if w < h:
                   UpperCAmelCase_	   =       int(self.size["shortest_edge"] * h / w			)
                   UpperCAmelCase_	   =       self.size["shortest_edge"]
              elif w > h:
                   UpperCAmelCase_	   =       self.size["shortest_edge"]
                   UpperCAmelCase_	   =       int(self.size["shortest_edge"] * w / h			)
              else:
                   UpperCAmelCase_	   =       self.size["shortest_edge"]
                   UpperCAmelCase_	   =       self.size["shortest_edge"]

         else:
              UpperCAmelCase_	   =       []
              for image in image_inputs:
                   UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.get_expected_values([image]			)
                   expected_values.append((expected_height, expected_width)			)
              UpperCAmelCase_	   =       max(__a       ,      key=lambda __a			: item[0]			)[0]
              UpperCAmelCase_	   =       max(__a       ,      key=lambda __a			: item[1]			)[1]

         return expected_height, expected_width




@require_torch
@require_vision
class 	__A				(			UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						Union[str, Any]	       =  DetaImageProcessor if is_vision_available() else None



    def  _lowercase						(self   :   Tuple			):
         UpperCAmelCase_	   =       DetaImageProcessingTester(self			)



    @property
    def  _lowercase						(self   :   Tuple			):
         return self.image_processor_tester.prepare_image_processor_dict()



    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       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       ,      "do_rescale"			)			)
         self.assertTrue(hasattr(__a       ,      "do_pad"			)			)
         self.assertTrue(hasattr(__a       ,      "size"			)			)



    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       self.image_processing_class.from_dict(self.image_processor_dict			)
         self.assertEqual(image_processor.size       ,      {"shortest_edge": 18, "longest_edge": 1333}			)
         self.assertEqual(image_processor.do_pad       ,      __a			)



    def  _lowercase						(self   :   Any			):
         pass



    def  _lowercase						(self   :   Union[str, Any]			):
         # Initialize image_processing
         UpperCAmelCase_	   =       self.image_processing_class(**self.image_processor_dict			)
         # create random PIL images
         UpperCAmelCase_	   =       prepare_image_inputs(self.image_processor_tester       ,      equal_resolution=__a			)
         for image in image_inputs:
              self.assertIsInstance(__a       ,      Image.Image			)

         # Test not batched input
         UpperCAmelCase_	   =       image_processing(image_inputs[0]       ,      return_tensors="pt"			).pixel_values

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a			)

         self.assertEqual(
             encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

         # Test batched
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a       ,      batched=__a			)

         UpperCAmelCase_	   =       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,
                 expected_height,
                 expected_width,
             )       ,      )



    def  _lowercase						(self   :   Any			):
         # Initialize image_processing
         UpperCAmelCase_	   =       self.image_processing_class(**self.image_processor_dict			)
         # create random numpy tensors
         UpperCAmelCase_	   =       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
         UpperCAmelCase_	   =       image_processing(image_inputs[0]       ,      return_tensors="pt"			).pixel_values

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a			)

         self.assertEqual(
             encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

         # Test batched
         UpperCAmelCase_	   =       image_processing(__a       ,      return_tensors="pt"			).pixel_values

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a       ,      batched=__a			)

         self.assertEqual(
             encoded_images.shape       ,      (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,      )



    def  _lowercase						(self   :   str			):
         # Initialize image_processing
         UpperCAmelCase_	   =       self.image_processing_class(**self.image_processor_dict			)
         # create random PyTorch tensors
         UpperCAmelCase_	   =       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
         UpperCAmelCase_	   =       image_processing(image_inputs[0]       ,      return_tensors="pt"			).pixel_values

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a			)

         self.assertEqual(
             encoded_images.shape       ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,      )

         # Test batched
         UpperCAmelCase_	   =       image_processing(__a       ,      return_tensors="pt"			).pixel_values

         UpperCAmelCase_   ,			UpperCAmelCase_	   =       self.image_processor_tester.get_expected_values(__a       ,      batched=__a			)

         self.assertEqual(
             encoded_images.shape       ,      (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,      )



    @slow
    def  _lowercase						(self   :   Any			):
         # prepare image and target
         UpperCAmelCase_	   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
         with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"       ,      "r"			) as f:
              UpperCAmelCase_	   =       json.loads(f.read()			)

         UpperCAmelCase_	   =       {"image_id": 39769, "annotations": target}

         # encode them
         UpperCAmelCase_	   =       DetaImageProcessor()
         UpperCAmelCase_	   =       image_processing(images=__a       ,      annotations=__a       ,      return_tensors="pt"			)

         # verify pixel values
         UpperCAmelCase_	   =       torch.Size([1, 3, 800, 1066]			)
         self.assertEqual(encoding["pixel_values"].shape       ,      __a			)

         UpperCAmelCase_	   =       torch.tensor([0.27_96, 0.31_38, 0.34_81]			)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,      __a       ,      atol=1E-4			)			)

         # verify area
         UpperCAmelCase_	   =       torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,      __a			)			)
         # verify boxes
         UpperCAmelCase_	   =       torch.Size([6, 4]			)
         self.assertEqual(encoding["labels"][0]["boxes"].shape       ,      __a			)
         UpperCAmelCase_	   =       torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,      __a       ,      atol=1E-3			)			)
         # verify image_id
         UpperCAmelCase_	   =       torch.tensor([39769]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,      __a			)			)
         # verify is_crowd
         UpperCAmelCase_	   =       torch.tensor([0, 0, 0, 0, 0, 0]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,      __a			)			)
         # verify class_labels
         UpperCAmelCase_	   =       torch.tensor([75, 75, 63, 65, 17, 17]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,      __a			)			)
         # verify orig_size
         UpperCAmelCase_	   =       torch.tensor([480, 640]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,      __a			)			)
         # verify size
         UpperCAmelCase_	   =       torch.tensor([800, 1066]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,      __a			)			)






    @slow
    def  _lowercase						(self   :   Any			):
         # prepare image, target and masks_path
         UpperCAmelCase_	   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"			)
         with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"       ,      "r"			) as f:
              UpperCAmelCase_	   =       json.loads(f.read()			)

         UpperCAmelCase_	   =       {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

         UpperCAmelCase_	   =       pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"			)

         # encode them
         UpperCAmelCase_	   =       DetaImageProcessor(format="coco_panoptic"			)
         UpperCAmelCase_	   =       image_processing(images=__a       ,      annotations=__a       ,      masks_path=__a       ,      return_tensors="pt"			)

         # verify pixel values
         UpperCAmelCase_	   =       torch.Size([1, 3, 800, 1066]			)
         self.assertEqual(encoding["pixel_values"].shape       ,      __a			)

         UpperCAmelCase_	   =       torch.tensor([0.27_96, 0.31_38, 0.34_81]			)
         self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]       ,      __a       ,      atol=1E-4			)			)

         # verify area
         UpperCAmelCase_	   =       torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,      __a			)			)
         # verify boxes
         UpperCAmelCase_	   =       torch.Size([6, 4]			)
         self.assertEqual(encoding["labels"][0]["boxes"].shape       ,      __a			)
         UpperCAmelCase_	   =       torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,      __a       ,      atol=1E-3			)			)
         # verify image_id
         UpperCAmelCase_	   =       torch.tensor([39769]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,      __a			)			)
         # verify is_crowd
         UpperCAmelCase_	   =       torch.tensor([0, 0, 0, 0, 0, 0]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,      __a			)			)
         # verify class_labels
         UpperCAmelCase_	   =       torch.tensor([17, 17, 63, 75, 75, 93]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,      __a			)			)
         # verify masks
         UpperCAmelCase_	   =       822873
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,      __a			)
         # verify orig_size
         UpperCAmelCase_	   =       torch.tensor([480, 640]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,      __a			)			)
         # verify size
         UpperCAmelCase_	   =       torch.tensor([800, 1066]			)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,      __a			)			)





'''simple docstring'''





import os

import numpy
import onnx





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[int]      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       a.name
     UpperCAmelCase_	   =       b.name

     UpperCAmelCase_	   =       ""
     UpperCAmelCase_	   =       ""

     UpperCAmelCase_	   =       a == b

     UpperCAmelCase_	   =       name_a
     UpperCAmelCase_	   =       name_b

     return res





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any]      )   ->				Any:


     '''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      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			List[str] ,   snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Any ,   snake_case_	:			Optional[Any]      )   ->				Optional[int]:


     '''simple docstring'''




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

          UpperCAmelCase_	   =       inits[i].name
          UpperCAmelCase_	   =       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      lowerCAmelCase_    ( snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       os.path.dirname(snake_case_      )
     UpperCAmelCase_	   =       os.path.basename(snake_case_      )

     UpperCAmelCase_	   =       onnx.load(os.path.join(snake_case_ ,   snake_case_      )      )

     UpperCAmelCase_	   =       list(model.graph.initializer      )

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

     UpperCAmelCase_	   =       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_      )

                    UpperCAmelCase_	   =       inits[j].data_type
                    UpperCAmelCase_	   =       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: " ,   snake_case_      )
                    total_reduced_size += mem_size

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

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

     print("total reduced size: " ,   total_reduced_size / 10_24 / 10_24 / 10_24 ,   "GB"      )

     UpperCAmelCase_	   =       sorted(snake_case_      )
     _remove_dup_initializers_from_model(snake_case_ ,   snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       "optimized_" + model_file_name
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   snake_case_      )
     onnx.save(snake_case_ ,   snake_case_      )

     return new_model




