




import os

import numpy
import onnx







def      _a				(						SCREAMING_SNAKE_CASE_							:    Tuple				,				SCREAMING_SNAKE_CASE_							:    Optional[int]					):
     __lowerCAmelCase           =	a.name
     __lowerCAmelCase           =	b.name

     __lowerCAmelCase           =	""
     __lowerCAmelCase           =	""

     __lowerCAmelCase           =	a == b

     __lowerCAmelCase           =	name_a
     __lowerCAmelCase           =	name_b

     return res







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    List[Any]					):
     for i, input_name in enumerate(node_proto.input					):
          if input_name == name:
               node_proto.input.insert(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
               node_proto.input.pop(i + 1					)

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







def      _a				(						SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    List[Any]					):
     for n in graph_proto.node:
          _node_replace_input_with(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    str					):
     __lowerCAmelCase           =	list(model.graph.initializer					)
     __lowerCAmelCase           =	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           =	inits[i].name
          __lowerCAmelCase           =	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				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[int]					):
     __lowerCAmelCase           =	os.path.dirname(SCREAMING_SNAKE_CASE_					)
     __lowerCAmelCase           =	os.path.basename(SCREAMING_SNAKE_CASE_					)

     __lowerCAmelCase           =	onnx.load(os.path.join(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)					)

     __lowerCAmelCase           =	list(model.graph.initializer					)

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

     __lowerCAmelCase           =	0

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

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

                    __lowerCAmelCase           =	inits[j].data_type
                    __lowerCAmelCase           =	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: "				,				SCREAMING_SNAKE_CASE_					)
                    total_reduced_size += mem_size

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

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

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

     __lowerCAmelCase           =	sorted(SCREAMING_SNAKE_CASE_					)
     _remove_dup_initializers_from_model(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

     __lowerCAmelCase           =	"optimized_" + model_file_name
     __lowerCAmelCase           =	os.path.join(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
     onnx.save(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

     return new_model










import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image
else:
					class       a__   :






											@staticmethod
											def     __SCREAMING_SNAKE_CASE(       *_A		,      **_A      ):




																"""simple docstring"""



																pass







def      _a				(						SCREAMING_SNAKE_CASE_							:    Image					):
					__lowerCAmelCase           =	hashlib.mda(image.tobytes()					)
					return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       a__   ( unittest.TestCase				):
						_a							:   Tuple =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	DepthEstimationPipeline(model=_A		,      image_processor=_A      )
											return depth_estimator, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
											self.assertEqual({"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )}		,      _A      )
											import datasets

											__lowerCAmelCase           =	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"		,      "image"		,      split="test"      )
											__lowerCAmelCase           =	depth_estimator(
											    [
											        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      ),
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											        # RGBA
											        dataset[0]["file"],
											        # LA
											        dataset[1]["file"],
											        # L
											        dataset[2]["file"],
											    ]      )
											self.assertEqual(
											    [
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											        {"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )},
											    ]		,      _A		,      )






						@require_tf
						@unittest.skip("Depth estimation is not implemented in TF"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@slow
						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"Intel/dpt-large"
											__lowerCAmelCase           =	pipeline("depth-estimation"		,      model=_A      )
											__lowerCAmelCase           =	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"      )
											__lowerCAmelCase           =	hashimage(outputs["depth"]      )

											# This seems flaky.
											# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
											self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()      )		,      29.3_04      )
											self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()      )		,      2.6_62      )






						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"      )




