



"""simple docstring"""






import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
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     UpperCAmelCase    (	__SCREAMING_SNAKE_CASE,unittest.TestCase  ):
   A__		:	Optional[int]           =  '''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''



   def    __UpperCAmelCase    (       self    :	Tuple							,       __lowerCamelCase    :	Dict=0				):



     """simple docstring"""
     _snake_case   	=      floats_tensor((1, 3, 1_2_8, 1_2_8)							,       rng=random.Random(__lowerCamelCase				)				)
     _snake_case   	=      np.random.RandomState(__lowerCamelCase				)
     _snake_case   	=      {
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''generator''': generator,
         '''num_inference_steps''': 3,
         '''strength''': 0.7_5,
         '''guidance_scale''': 7.5,
         '''output_type''': '''numpy''',
     }
     return inputs



   def    __UpperCAmelCase    (       self    :	str				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

     _snake_case   	=      self.get_dummy_inputs()
     _snake_case   	=      pipe(**__lowerCamelCase				).images
     _snake_case   	=      image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]				)
     assert np.abs(image_slice - expected_slice				).max() < 1E-1



   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     _snake_case   	=      PNDMScheduler.from_config(pipe.scheduler.config							,       skip_prk_steps=__lowerCamelCase				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

     _snake_case   	=      self.get_dummy_inputs()
     _snake_case   	=      pipe(**__lowerCamelCase				).images
     _snake_case   	=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]				)

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



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     _snake_case   	=      LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

     # warmup pass to apply optimizations
     _snake_case   	=      pipe(**self.get_dummy_inputs()				)

     _snake_case   	=      self.get_dummy_inputs()
     _snake_case   	=      pipe(**__lowerCamelCase				).images
     _snake_case   	=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]				)

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



   def    __UpperCAmelCase    (       self    :	int				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     _snake_case   	=      EulerDiscreteScheduler.from_config(pipe.scheduler.config				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

     _snake_case   	=      self.get_dummy_inputs()
     _snake_case   	=      pipe(**__lowerCamelCase				).images
     _snake_case   	=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]				)

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



   def    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     _snake_case   	=      EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

     _snake_case   	=      self.get_dummy_inputs()
     _snake_case   	=      pipe(**__lowerCamelCase				).images
     _snake_case   	=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]				)

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





   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint							,       provider='''CPUExecutionProvider'''				)
     _snake_case   	=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

     _snake_case   	=      self.get_dummy_inputs()
     _snake_case   	=      pipe(**__lowerCamelCase				).images
     _snake_case   	=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 1_2_8, 1_2_8, 3)
     _snake_case   	=      np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]				)

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



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



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



     """simple docstring"""
     return (
         "CUDAExecutionProvider",
         {
             "gpu_mem_limit": "15000000000",  # 15GB
             "arena_extend_strategy": "kSameAsRequested",
         },
     )



   @property
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     _snake_case   	=      ort.SessionOptions()
     _snake_case   	=      False
     return options



   def    __UpperCAmelCase    (       self    :	Optional[Any]				):



     """simple docstring"""
     _snake_case   	=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''				)
     _snake_case   	=      init_image.resize((7_6_8, 5_1_2)				)
     # using the PNDM scheduler by default
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4'''							,       revision='''onnx'''							,       safety_checker=__lowerCamelCase							,       feature_extractor=__lowerCamelCase							,       provider=self.gpu_provider							,       sess_options=self.gpu_options							,       )
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

     _snake_case   	=      '''A fantasy landscape, trending on artstation'''

     _snake_case   	=      np.random.RandomState(0				)
     _snake_case   	=      pipe(
         prompt=__lowerCamelCase							,       image=__lowerCamelCase							,       strength=0.7_5							,       guidance_scale=7.5							,       num_inference_steps=1_0							,       generator=__lowerCamelCase							,       output_type='''np'''							,       )
     _snake_case   	=      output.images
     _snake_case   	=      images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

     assert images.shape == (1, 5_1_2, 7_6_8, 3)
     _snake_case   	=      np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]				)
     # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

     assert np.abs(image_slice.flatten() - expected_slice				).max() < 2E-2





   def    __UpperCAmelCase    (       self    :	Dict				):



     """simple docstring"""
     _snake_case   	=      load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/img2img/sketch-mountains-input.jpg'''				)
     _snake_case   	=      init_image.resize((7_6_8, 5_1_2)				)
     _snake_case   	=      LMSDiscreteScheduler.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''							,       subfolder='''scheduler'''							,       revision='''onnx'''				)
     _snake_case   	=      OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''							,       revision='''onnx'''							,       scheduler=__lowerCamelCase							,       safety_checker=__lowerCamelCase							,       feature_extractor=__lowerCamelCase							,       provider=self.gpu_provider							,       sess_options=self.gpu_options							,       )
     pipe.set_progress_bar_config(disable=__lowerCamelCase				)

     _snake_case   	=      '''A fantasy landscape, trending on artstation'''

     _snake_case   	=      np.random.RandomState(0				)
     _snake_case   	=      pipe(
         prompt=__lowerCamelCase							,       image=__lowerCamelCase							,       strength=0.7_5							,       guidance_scale=7.5							,       num_inference_steps=2_0							,       generator=__lowerCamelCase							,       output_type='''np'''							,       )
     _snake_case   	=      output.images
     _snake_case   	=      images[0, 2_5_5:2_5_8, 3_8_3:3_8_6, -1]

     assert images.shape == (1, 5_1_2, 7_6_8, 3)
     _snake_case   	=      np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]				)
     # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

     assert np.abs(image_slice.flatten() - expected_slice				).max() < 2E-2












'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      if "cls_token" in name:
            _lowerCAmelCase					=				name.replace("""cls_token"""				,      """vit.embeddings.cls_token"""    )
      if "mask_token" in name:
            _lowerCAmelCase					=				name.replace("""mask_token"""				,      """decoder.mask_token"""    )
      if "decoder_pos_embed" in name:
            _lowerCAmelCase					=				name.replace("""decoder_pos_embed"""				,      """decoder.decoder_pos_embed"""    )
      if "pos_embed" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""pos_embed"""				,      """vit.embeddings.position_embeddings"""    )
      if "patch_embed.proj" in name:
            _lowerCAmelCase					=				name.replace("""patch_embed.proj"""				,      """vit.embeddings.patch_embeddings.projection"""    )
      if "patch_embed.norm" in name:
            _lowerCAmelCase					=				name.replace("""patch_embed.norm"""				,      """vit.embeddings.norm"""    )
      if "decoder_blocks" in name:
            _lowerCAmelCase					=				name.replace("""decoder_blocks"""				,      """decoder.decoder_layers"""    )
      if "blocks" in name:
            _lowerCAmelCase					=				name.replace("""blocks"""				,      """vit.encoder.layer"""    )
      if "attn.proj" in name:
            _lowerCAmelCase					=				name.replace("""attn.proj"""				,      """attention.output.dense"""    )
      if "attn" in name:
            _lowerCAmelCase					=				name.replace("""attn"""				,      """attention.self"""    )
      if "norm1" in name:
            _lowerCAmelCase					=				name.replace("""norm1"""				,      """layernorm_before"""    )
      if "norm2" in name:
            _lowerCAmelCase					=				name.replace("""norm2"""				,      """layernorm_after"""    )
      if "mlp.fc1" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc1"""				,      """intermediate.dense"""    )
      if "mlp.fc2" in name:
            _lowerCAmelCase					=				name.replace("""mlp.fc2"""				,      """output.dense"""    )
      if "decoder_embed" in name:
            _lowerCAmelCase					=				name.replace("""decoder_embed"""				,      """decoder.decoder_embed"""    )
      if "decoder_norm" in name:
            _lowerCAmelCase					=				name.replace("""decoder_norm"""				,      """decoder.decoder_norm"""    )
      if "decoder_pred" in name:
            _lowerCAmelCase					=				name.replace("""decoder_pred"""				,      """decoder.decoder_pred"""    )
      if "norm.weight" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""norm.weight"""				,      """vit.layernorm.weight"""    )
      if "norm.bias" in name and "decoder" not in name:
            _lowerCAmelCase					=				name.replace("""norm.bias"""				,      """vit.layernorm.bias"""    )

      return name
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      for key in orig_state_dict.copy().keys():
            _lowerCAmelCase					=				orig_state_dict.pop(lowerCAmelCase    )

            if "qkv" in key:
                  _lowerCAmelCase					=				key.split("""."""    )
                  _lowerCAmelCase					=				int(key_split[1]    )
                  if "decoder_blocks" in key:
                        _lowerCAmelCase					=				config.decoder_hidden_size
                        _lowerCAmelCase					=				"""decoder.decoder_layers."""
                        if "weight" in key:
                              _lowerCAmelCase					=				val[:dim, :]
                              _lowerCAmelCase					=				val[dim : dim * 2, :]
                              _lowerCAmelCase					=				val[-dim:, :]
                        elif "bias" in key:
                              _lowerCAmelCase					=				val[:dim]
                              _lowerCAmelCase					=				val[dim : dim * 2]
                              _lowerCAmelCase					=				val[-dim:]
                  else:
                        _lowerCAmelCase					=				config.hidden_size
                        _lowerCAmelCase					=				"""vit.encoder.layer."""
                        if "weight" in key:
                              _lowerCAmelCase					=				val[:dim, :]
                              _lowerCAmelCase					=				val[dim : dim * 2, :]
                              _lowerCAmelCase					=				val[-dim:, :]
                        elif "bias" in key:
                              _lowerCAmelCase					=				val[:dim]
                              _lowerCAmelCase					=				val[dim : dim * 2]
                              _lowerCAmelCase					=				val[-dim:]

            else:
                  _lowerCAmelCase					=				val

      return orig_state_dict
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				ViTMAEConfig()
      if "large" in checkpoint_url:
            _lowerCAmelCase					=				10_24
            _lowerCAmelCase					=				40_96
            _lowerCAmelCase					=				24
            _lowerCAmelCase					=				16
      elif "huge" in checkpoint_url:
            _lowerCAmelCase					=				14
            _lowerCAmelCase					=				12_80
            _lowerCAmelCase					=				51_20
            _lowerCAmelCase					=				32
            _lowerCAmelCase					=				16

      _lowerCAmelCase					=				ViTMAEForPreTraining(lowerCAmelCase    )

      _lowerCAmelCase					=				torch.hub.load_state_dict_from_url(lowerCAmelCase				,      map_location="""cpu"""    )["""model"""]

      _lowerCAmelCase					=				ViTMAEImageProcessor(size=config.image_size    )

      _lowerCAmelCase					=				convert_state_dict(lowerCAmelCase				,      lowerCAmelCase    )

      model.load_state_dict(lowerCAmelCase    )
      model.eval()

      _lowerCAmelCase					=				"""https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""

      _lowerCAmelCase					=				Image.open(requests.get(lowerCAmelCase				,      stream=lowerCAmelCase    ).raw    )
      _lowerCAmelCase					=				ViTMAEImageProcessor(size=config.image_size    )
      _lowerCAmelCase					=				image_processor(images=lowerCAmelCase				,      return_tensors="""pt"""    )

      # forward pass
      torch.manual_seed(2    )
      _lowerCAmelCase					=				model(**lowerCAmelCase    )
      _lowerCAmelCase					=				outputs.logits

      if "large" in checkpoint_url:
            _lowerCAmelCase					=				torch.tensor(
                [[-0.7_309, -0.7_128, -1.0_169], [-1.0_161, -0.9_058, -1.1_878], [-1.0_478, -0.9_411, -1.1_911]]    )
      elif "huge" in checkpoint_url:
            _lowerCAmelCase					=				torch.tensor(
                [[-1.1_599, -0.9_199, -1.2_221], [-1.1_952, -0.9_269, -1.2_307], [-1.2_143, -0.9_337, -1.2_262]]    )
      else:
            _lowerCAmelCase					=				torch.tensor(
                [[-0.9_192, -0.8_481, -1.1_259], [-1.1_349, -1.0_034, -1.2_599], [-1.1_757, -1.0_429, -1.2_726]]    )

      # verify logits
      assert torch.allclose(logits[0, :3, :3]				,      lowerCAmelCase				,      atol=1e-4    )

      print(f"Saving model to {pytorch_dump_folder_path}"    )
      model.save_pretrained(lowerCAmelCase    )

      print(f"Saving image processor to {pytorch_dump_folder_path}"    )
      image_processor.save_pretrained(lowerCAmelCase    )


if __name__ == "__main__":
      A__							:  Dict    =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--checkpoint_url''',
          default='''https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth''',
          type=str,
          help='''URL of the checkpoint you\'d like to convert.''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
      )

      A__							:  List[Any]    =parser.parse_args()
      convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






