'''simple docstring'''





import unittest

import numpy as np

from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
    import onnxruntime as ort




class 	__A				(			_SCREAMING_SNAKE_CASE		,     unittest.TestCase     ):
    # FIXME: add fast tests
    pass




@nightly
@require_onnxruntime
@require_torch_gpu
class 	__A				(			unittest.TestCase     ):



    @property
    def  _lowercase						(self   :   List[str]			):
         return (
             "CUDAExecutionProvider",
             {
                 "gpu_mem_limit": "15000000000",  # 15GB
                 "arena_extend_strategy": "kSameAsRequested",
             },
         )



    @property
    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       ort.SessionOptions()
         UpperCAmelCase_	   =       False
         return options



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo.png"			)
         UpperCAmelCase_	   =       load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"			)
         UpperCAmelCase_	   =       OnnxStableDiffusionInpaintPipeline.from_pretrained(
             "runwayml/stable-diffusion-inpainting"       ,      revision="onnx"       ,      safety_checker=__a       ,      feature_extractor=__a       ,      provider=self.gpu_provider       ,      sess_options=self.gpu_options       ,      )
         pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       "A red cat sitting on a park bench"

         UpperCAmelCase_	   =       np.random.RandomState(0			)
         UpperCAmelCase_	   =       pipe(
             prompt=__a       ,      image=__a       ,      mask_image=__a       ,      guidance_scale=7.5       ,      num_inference_steps=10       ,      generator=__a       ,      output_type="np"       ,      )
         UpperCAmelCase_	   =       output.images
         UpperCAmelCase_	   =       images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         UpperCAmelCase_	   =       np.array([0.25_14, 0.30_07, 0.35_17, 0.17_90, 0.23_82, 0.31_67, 0.19_44, 0.22_73, 0.24_64]			)

         assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3






    def  _lowercase						(self   :   Optional[int]			):
         UpperCAmelCase_	   =       load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo.png"			)
         UpperCAmelCase_	   =       load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/in_paint/overture-creations-5sI6fQgYIuo_mask.png"			)
         UpperCAmelCase_	   =       LMSDiscreteScheduler.from_pretrained(
             "runwayml/stable-diffusion-inpainting"       ,      subfolder="scheduler"       ,      revision="onnx"			)
         UpperCAmelCase_	   =       OnnxStableDiffusionInpaintPipeline.from_pretrained(
             "runwayml/stable-diffusion-inpainting"       ,      revision="onnx"       ,      scheduler=__a       ,      safety_checker=__a       ,      feature_extractor=__a       ,      provider=self.gpu_provider       ,      sess_options=self.gpu_options       ,      )
         pipe.set_progress_bar_config(disable=__a			)

         UpperCAmelCase_	   =       "A red cat sitting on a park bench"

         UpperCAmelCase_	   =       np.random.RandomState(0			)
         UpperCAmelCase_	   =       pipe(
             prompt=__a       ,      image=__a       ,      mask_image=__a       ,      guidance_scale=7.5       ,      num_inference_steps=20       ,      generator=__a       ,      output_type="np"       ,      )
         UpperCAmelCase_	   =       output.images
         UpperCAmelCase_	   =       images[0, 255:258, 255:258, -1]

         assert images.shape == (1, 512, 512, 3)
         UpperCAmelCase_	   =       np.array([0.00_86, 0.00_77, 0.00_83, 0.00_93, 0.01_07, 0.01_39, 0.00_94, 0.00_97, 0.01_25]			)

         assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-3





'''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




