





"""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 					_lowerCAmelCase   (     a  ,   unittest.TestCase       ):
       """simple docstring"""




       pass






@nightly
@require_onnxruntime
@require_torch_gpu
class 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

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






       @property
       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							ort.SessionOptions()
         lowerCAmelCase__			:Union[str, Any]											=							False
         return options






       def      snake_case						(    self      ):



         '''simple docstring'''

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

         lowerCAmelCase__			:Optional[int]											=							'A red cat sitting on a park bench'

         lowerCAmelCase__			:List[str]											=							np.random.RandomState(0      )
         lowerCAmelCase__			:str											=							pipe(
             prompt=__UpperCAmelCase    , image=__UpperCAmelCase    , mask_image=__UpperCAmelCase    , guidance_scale=7.5    , num_inference_steps=1_0    , generator=__UpperCAmelCase    , output_type='np'    , )
         lowerCAmelCase__			:Tuple											=							output.images
         lowerCAmelCase__			:Any											=							images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         lowerCAmelCase__			:Tuple											=							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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo.png'      )
         lowerCAmelCase__			:Tuple											=							load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'      )
         lowerCAmelCase__			:Union[str, Any]											=							LMSDiscreteScheduler.from_pretrained(
             'runwayml/stable-diffusion-inpainting'    , subfolder='scheduler'    , revision='onnx'      )
         lowerCAmelCase__			:int											=							OnnxStableDiffusionInpaintPipeline.from_pretrained(
             'runwayml/stable-diffusion-inpainting'    , revision='onnx'    , scheduler=__UpperCAmelCase    , safety_checker=__UpperCAmelCase    , feature_extractor=__UpperCAmelCase    , provider=self.gpu_provider    , sess_options=self.gpu_options    , )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase      )

         lowerCAmelCase__			:int											=							'A red cat sitting on a park bench'

         lowerCAmelCase__			:List[str]											=							np.random.RandomState(0      )
         lowerCAmelCase__			:List[Any]											=							pipe(
             prompt=__UpperCAmelCase    , image=__UpperCAmelCase    , mask_image=__UpperCAmelCase    , guidance_scale=7.5    , num_inference_steps=2_0    , generator=__UpperCAmelCase    , output_type='np'    , )
         lowerCAmelCase__			:Any											=							output.images
         lowerCAmelCase__			:Optional[Any]											=							images[0, 2_5_5:2_5_8, 2_5_5:2_5_8, -1]

         assert images.shape == (1, 5_1_2, 5_1_2, 3)
         lowerCAmelCase__			:List[Any]											=							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 argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->Dict:





  """simple docstring"""


  lowerCAmelCase__			:str											=							BertConfig.from_json_file(_SCREAMING_SNAKE_CASE			)
  print(F"Building PyTorch model from configuration: {config}"			)
  lowerCAmelCase__			:int											=							BertForPreTraining(_SCREAMING_SNAKE_CASE			)

  # Load weights from tf checkpoint
  load_tf_weights_in_bert(_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()     ,   _SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
  __A											=		argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--bert_config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained BERT model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __A											=		parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
