





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     SCREAMING_SNAKE_CASE       (UpperCAmelCase      ,     unittest.TestCase				):
   _UpperCamelCase   :		Tuple			 =	'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'


   def 	SCREAMING_SNAKE_CASE_							(   self      :    int      ,					a      :    Optional[Any]=0	)->       Dict:





     """simple docstring"""







     lowercase__           =  floats_tensor((1, 3, 128, 128)      ,					rng=random.Random(a	)	)
     lowercase__           =  np.random.RandomState(a	)
     lowercase__           =  {
         'prompt': 'A painting of a squirrel eating a burger',
         'image': image,
         'generator': generator,
         'num_inference_steps': 3,
         'strength': 0.75,
         'guidance_scale': 7.5,
         'output_type': 'numpy',
     }
     return inputs


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       int:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     pipe.set_progress_bar_config(disable=a	)

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

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]	)
     assert np.abs(image_slice - expected_slice	).max() < 1E-1


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Union[str, Any]	)->       Dict:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     lowercase__           =  PNDMScheduler.from_config(pipe.scheduler.config      ,					skip_prk_steps=a	)
     pipe.set_progress_bar_config(disable=a	)

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

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[Any]	)->       Optional[int]:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     lowercase__           =  LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
     pipe.set_progress_bar_config(disable=a	)

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

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

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Dict	)->       int:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     lowercase__           =  EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
     pipe.set_progress_bar_config(disable=a	)

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

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Any:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     lowercase__           =  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
     pipe.set_progress_bar_config(disable=a	)

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

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]	)

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       List[Any]:





     """simple docstring"""







     lowercase__           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint      ,					provider='CPUExecutionProvider'	)
     lowercase__           =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
     pipe.set_progress_bar_config(disable=a	)

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

     assert image.shape == (1, 128, 128, 3)
     lowercase__           =  np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]	)

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






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


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    Tuple	)->       Any:





     """simple docstring"""







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


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  ort.SessionOptions()
     lowercase__           =  False
     return options


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       Optional[Any]:





     """simple docstring"""







     lowercase__           =  load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/img2img/sketch-mountains-input.jpg'	)
     lowercase__           =  init_image.resize((768, 512)	)
     # using the PNDM scheduler by default
     lowercase__           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         'CompVis/stable-diffusion-v1-4'      ,					revision='onnx'      ,					safety_checker=a      ,					feature_extractor=a      ,					provider=self.gpu_provider      ,					sess_options=self.gpu_options      ,					)
     pipe.set_progress_bar_config(disable=a	)

     lowercase__           =  'A fantasy landscape, trending on artstation'

     lowercase__           =  np.random.RandomState(0	)
     lowercase__           =  pipe(
         prompt=a      ,					image=a      ,					strength=0.75      ,					guidance_scale=7.5      ,					num_inference_steps=10      ,					generator=a      ,					output_type='np'      ,					)
     lowercase__           =  output.images
     lowercase__           =  images[0, 255:258, 383:386, -1]

     assert images.shape == (1, 512, 768, 3)
     lowercase__           =  np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]	)
     # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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


   def 	SCREAMING_SNAKE_CASE_							(   self      :    str	)->       int:





     """simple docstring"""







     lowercase__           =  load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/img2img/sketch-mountains-input.jpg'	)
     lowercase__           =  init_image.resize((768, 512)	)
     lowercase__           =  LMSDiscreteScheduler.from_pretrained(
         'runwayml/stable-diffusion-v1-5'      ,					subfolder='scheduler'      ,					revision='onnx'	)
     lowercase__           =  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
         'runwayml/stable-diffusion-v1-5'      ,					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	)

     lowercase__           =  'A fantasy landscape, trending on artstation'

     lowercase__           =  np.random.RandomState(0	)
     lowercase__           =  pipe(
         prompt=a      ,					image=a      ,					strength=0.75      ,					guidance_scale=7.5      ,					num_inference_steps=20      ,					generator=a      ,					output_type='np'      ,					)
     lowercase__           =  output.images
     lowercase__           =  images[0, 255:258, 383:386, -1]

     assert images.shape == (1, 512, 768, 3)
     lowercase__           =  np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]	)
     # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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









import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_          =		logging.get_logger(__name__)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  lowercase__           =  MobileNetVaConfig(layer_norm_eps=0.0_0_1		)

  if "_quant" in model_name:
    raise ValueError('Quantized models are not supported.'		)

  lowercase__           =  re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'		,						_SCREAMING_SNAKE_CASE		)
  if matches:
    lowercase__           =  float(matches[1]		)
    lowercase__           =  int(matches[2]		)

  # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
  # the usual 1000. The first class (index 0) is "background".
  lowercase__           =  1001
  lowercase__           =  'imagenet-1k-id2label.json'
  lowercase__           =  'huggingface/label-files'
  lowercase__           =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						repo_type='dataset'		)		,						'r'		)		)
  lowercase__           =  {int(_SCREAMING_SNAKE_CASE		) + 1: v for k, v in idalabel.items()}
  lowercase__           =  'background'
  lowercase__           =  idalabel
  lowercase__           =  {v: k for k, v in idalabel.items()}

  return config




def       __UpperCamelCase  ()    ->       int:
  lowercase__           =  'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase__           =  Image.open(requests.get(_SCREAMING_SNAKE_CASE		,						stream=_SCREAMING_SNAKE_CASE		).raw		)
  return im




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=False		)    ->       Union[str, Any]:
  lowercase__           =  get_mobilenet_va_config(_SCREAMING_SNAKE_CASE		)

  # Load 🤗 model
  lowercase__           =  MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE		).eval()

  # Load weights from TensorFlow checkpoint
  load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

  # Check outputs on an image, prepared by MobileNetV1ImageProcessor
  lowercase__           =  MobileNetVaImageProcessor(
      crop_size={'width': config.image_size, 'height': config.image_size}		,						size={'shortest_edge': config.image_size + 32}		,						)
  lowercase__           =  image_processor(images=prepare_img()		,						return_tensors='pt'		)
  lowercase__           =  model(**_SCREAMING_SNAKE_CASE		)
  lowercase__           =  outputs.logits

  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v1_1.0_224":
    lowercase__           =  torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]		)
  elif model_name == "mobilenet_v1_0.75_192":
    lowercase__           =  torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]		)
  else:
    lowercase__           =  None

  if expected_logits is not None:
    assert torch.allclose(logits[0, :3]		,						_SCREAMING_SNAKE_CASE		,						atol=1E-4		)

  Path(_SCREAMING_SNAKE_CASE		).mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
  print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""		)
  model.save_pretrained(_SCREAMING_SNAKE_CASE		)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""		)
  image_processor.save_pretrained(_SCREAMING_SNAKE_CASE		)

  if push_to_hub:
    print('Pushing to the hub...'		)
    lowercase__           =  'google/' + model_name
    image_processor.push_to_hub(_SCREAMING_SNAKE_CASE		)
    model.push_to_hub(_SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""mobilenet_v1_1.0_224""",
          type=str,
          help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
      )
      parser.add_argument(
          """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      lowercase_          =		parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )


