






import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
  import onnxruntime as ort
class 							A_    (						__lowercase ,   unittest.TestCase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			List[str] =					"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"


 def  snake_case__				(	self							,  _A=0) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				np.random.RandomState(_A)
     _UpperCAmelCase				:      Optional[Any]			  =				{
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''generator''': generator,
         '''num_inference_steps''': 2,
         '''guidance_scale''': 7.5,
         '''output_type''': '''numpy''',
     }
     return inputs


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='''CPUExecutionProvider''')
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Union[str, Any]			  =				self.get_dummy_inputs()
     _UpperCAmelCase				:      Optional[int]			  =				pipe(**_A).images
     _UpperCAmelCase				:      int			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     _UpperCAmelCase				:      Tuple			  =				np.array([0.65072, 0.58492, 0.48219, 0.55521, 0.53180, 0.55939, 0.50697, 0.39800, 0.46455])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


 def  snake_case__				(	self) ->		Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='''CPUExecutionProvider''')
     _UpperCAmelCase				:      str			  =				PNDMScheduler.from_config(pipe.scheduler.config							,  skip_prk_steps=_A)
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Optional[Any]			  =				self.get_dummy_inputs()
     _UpperCAmelCase				:      str			  =				pipe(**_A).images
     _UpperCAmelCase				:      str			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     _UpperCAmelCase				:      List[Any]			  =				np.array([0.65863, 0.59425, 0.49326, 0.56313, 0.53875, 0.56627, 0.51065, 0.39777, 0.46330])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


 def  snake_case__				(	self) ->		List[str]:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='''CPUExecutionProvider''')
     _UpperCAmelCase				:      Tuple			  =				LMSDiscreteScheduler.from_config(pipe.scheduler.config)
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Tuple			  =				self.get_dummy_inputs()
     _UpperCAmelCase				:      Union[str, Any]			  =				pipe(**_A).images
     _UpperCAmelCase				:      List[str]			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     _UpperCAmelCase				:      Tuple			  =				np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='''CPUExecutionProvider''')
     _UpperCAmelCase				:      Any			  =				EulerDiscreteScheduler.from_config(pipe.scheduler.config)
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Tuple			  =				self.get_dummy_inputs()
     _UpperCAmelCase				:      Any			  =				pipe(**_A).images
     _UpperCAmelCase				:      Tuple			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     _UpperCAmelCase				:      Tuple			  =				np.array([0.53755, 0.60786, 0.47402, 0.49488, 0.51869, 0.49819, 0.47985, 0.38957, 0.44279])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='''CPUExecutionProvider''')
     _UpperCAmelCase				:      Tuple			  =				EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      List[Any]			  =				self.get_dummy_inputs()
     _UpperCAmelCase				:      List[Any]			  =				pipe(**_A).images
     _UpperCAmelCase				:      Dict			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     _UpperCAmelCase				:      Union[str, Any]			  =				np.array([0.53817, 0.60812, 0.47384, 0.49530, 0.51894, 0.49814, 0.47984, 0.38958, 0.44271])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


 def  snake_case__				(	self) ->		List[Any]:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='''CPUExecutionProvider''')
     _UpperCAmelCase				:      str			  =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Optional[int]			  =				self.get_dummy_inputs()
     _UpperCAmelCase				:      Optional[Any]			  =				pipe(**_A).images
     _UpperCAmelCase				:      Tuple			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 128, 128, 3)
     _UpperCAmelCase				:      str			  =				np.array([0.53895, 0.60808, 0.47933, 0.49608, 0.51886, 0.49950, 0.48053, 0.38957, 0.44200])

     assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='''CPUExecutionProvider''')
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Optional[Any]			  =				self.get_dummy_inputs()
     _UpperCAmelCase				:      Union[str, Any]			  =				3 * [inputs['''prompt''']]

     # forward
     _UpperCAmelCase				:      Any			  =				pipe(**_A)
     _UpperCAmelCase				:      Union[str, Any]			  =				output.images[0, -3:, -3:, -1]

     _UpperCAmelCase				:      int			  =				self.get_dummy_inputs()
     _UpperCAmelCase				:      Union[str, Any]			  =				3 * [inputs.pop('''prompt''')]

     _UpperCAmelCase				:      Union[str, Any]			  =				pipe.tokenizer(
         _A							,  padding='''max_length'''							,  max_length=pipe.tokenizer.model_max_length							,  truncation=_A							,  return_tensors='''np'''							,  )
     _UpperCAmelCase				:      Optional[Any]			  =				text_inputs['''input_ids''']

     _UpperCAmelCase				:      Any			  =				pipe.text_encoder(input_ids=text_inputs.astype(np.intaa))[0]

     _UpperCAmelCase				:      Optional[int]			  =				prompt_embeds

     # forward
     _UpperCAmelCase				:      Optional[int]			  =				pipe(**_A)
     _UpperCAmelCase				:      int			  =				output.images[0, -3:, -3:, -1]

     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4


 def  snake_case__				(	self) ->		Any:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint							,  provider='''CPUExecutionProvider''')
     pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Union[str, Any]			  =				self.get_dummy_inputs()
     _UpperCAmelCase				:      str			  =				3 * ['''this is a negative prompt''']
     _UpperCAmelCase				:      Dict			  =				negative_prompt
     _UpperCAmelCase				:      Tuple			  =				3 * [inputs['''prompt''']]

     # forward
     _UpperCAmelCase				:      List[str]			  =				pipe(**_A)
     _UpperCAmelCase				:      str			  =				output.images[0, -3:, -3:, -1]

     _UpperCAmelCase				:      int			  =				self.get_dummy_inputs()
     _UpperCAmelCase				:      Any			  =				3 * [inputs.pop('''prompt''')]

     _UpperCAmelCase				:      str			  =				[]
     for p in [prompt, negative_prompt]:
         _UpperCAmelCase				:      Optional[Any]			  =				pipe.tokenizer(
             _A							,  padding='''max_length'''							,  max_length=pipe.tokenizer.model_max_length							,  truncation=_A							,  return_tensors='''np'''							,  )
         _UpperCAmelCase				:      Dict			  =				text_inputs['''input_ids''']

         embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa))[0])

     _UpperCAmelCase  , _UpperCAmelCase				:      List[Any]			  =				embeds

     # forward
     _UpperCAmelCase				:      List[Any]			  =				pipe(**_A)
     _UpperCAmelCase				:      Union[str, Any]			  =				output.images[0, -3:, -3:, -1]

     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()).max() < 1e-4



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






 '''simple docstring'''




 @property
 def  snake_case__				(	self) ->		int:

     """simple docstring"""



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


 @property
 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				ort.SessionOptions()
     _UpperCAmelCase				:      Any			  =				False
     return options


 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				OnnxStableDiffusionPipeline.from_pretrained(
         '''CompVis/stable-diffusion-v1-4'''							,  revision='''onnx'''							,  safety_checker=_A							,  feature_extractor=_A							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
     sd_pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Optional[Any]			  =				'''A painting of a squirrel eating a burger'''
     np.random.seed(0)
     _UpperCAmelCase				:      List[str]			  =				sd_pipe([prompt]							,  guidance_scale=6.0							,  num_inference_steps=10							,  output_type='''np''')
     _UpperCAmelCase				:      Dict			  =				output.images

     _UpperCAmelCase				:      Dict			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     _UpperCAmelCase				:      Union[str, Any]			  =				np.array([0.0452, 0.0390, 0.0087, 0.0350, 0.0617, 0.0364, 0.0544, 0.0523, 0.0720])

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


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

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				DDIMScheduler.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''							,  subfolder='''scheduler'''							,  revision='''onnx''')
     _UpperCAmelCase				:      Any			  =				OnnxStableDiffusionPipeline.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							,  )
     sd_pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      Any			  =				'''open neural network exchange'''
     _UpperCAmelCase				:      List[Any]			  =				np.random.RandomState(0)
     _UpperCAmelCase				:      Optional[int]			  =				sd_pipe([prompt]							,  guidance_scale=7.5							,  num_inference_steps=10							,  generator=_A							,  output_type='''np''')
     _UpperCAmelCase				:      int			  =				output.images
     _UpperCAmelCase				:      Dict			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     _UpperCAmelCase				:      Dict			  =				np.array([0.2867, 0.1974, 0.1481, 0.7294, 0.7251, 0.6667, 0.4194, 0.5642, 0.6486])

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


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      List[str]			  =				LMSDiscreteScheduler.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''							,  subfolder='''scheduler'''							,  revision='''onnx''')
     _UpperCAmelCase				:      int			  =				OnnxStableDiffusionPipeline.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							,  )
     sd_pipe.set_progress_bar_config(disable=_A)

     _UpperCAmelCase				:      int			  =				'''open neural network exchange'''
     _UpperCAmelCase				:      List[Any]			  =				np.random.RandomState(0)
     _UpperCAmelCase				:      str			  =				sd_pipe([prompt]							,  guidance_scale=7.5							,  num_inference_steps=10							,  generator=_A							,  output_type='''np''')
     _UpperCAmelCase				:      Optional[Any]			  =				output.images
     _UpperCAmelCase				:      List[Any]			  =				image[0, -3:, -3:, -1]

     assert image.shape == (1, 512, 512, 3)
     _UpperCAmelCase				:      List[Any]			  =				np.array([0.2306, 0.1959, 0.1593, 0.6549, 0.6394, 0.5408, 0.5065, 0.6010, 0.6161])

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


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				0

     def test_callback_fn(_A							,  _A							,  _A) -> None:
         _UpperCAmelCase				:      Optional[Any]			  =				True
         nonlocal number_of_steps
         number_of_steps += 1
         if step == 0:
             assert latents.shape == (1, 4, 64, 64)
             _UpperCAmelCase				:      Union[str, Any]			  =				latents[0, -3:, -3:, -1]
             _UpperCAmelCase				:      List[str]			  =				np.array(
                 [-0.6772, -0.3835, -1.2456, 0.1905, -1.0974, 0.6967, -1.9353, 0.0178, 1.0167])

             assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
         elif step == 5:
             assert latents.shape == (1, 4, 64, 64)
             _UpperCAmelCase				:      Union[str, Any]			  =				latents[0, -3:, -3:, -1]
             _UpperCAmelCase				:      Any			  =				np.array(
                 [-0.3351, 0.2241, -0.1837, -0.2325, -0.6577, 0.3393, -0.0241, 0.5899, 1.3875])

             assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3

     _UpperCAmelCase				:      Any			  =				False

     _UpperCAmelCase				:      Dict			  =				OnnxStableDiffusionPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''							,  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				:      str			  =				'''Andromeda galaxy in a bottle'''

     _UpperCAmelCase				:      Optional[Any]			  =				np.random.RandomState(0)
     pipe(
         prompt=_A							,  num_inference_steps=5							,  guidance_scale=7.5							,  generator=_A							,  callback=_A							,  callback_steps=1							,  )
     assert test_callback_fn.has_been_called
     assert number_of_steps == 6


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				OnnxStableDiffusionPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5'''							,  revision='''onnx'''							,  safety_checker=_A							,  feature_extractor=_A							,  provider=self.gpu_provider							,  sess_options=self.gpu_options							,  )
     assert isinstance(_A							,  _A)
     assert pipe.safety_checker is None

     _UpperCAmelCase				:      List[Any]			  =				pipe('''example prompt'''							,  num_inference_steps=2).images[0]
     assert image is not None

     # check that there's no error when saving a pipeline with one of the models being None
     with tempfile.TemporaryDirectory() as tmpdirname:
         pipe.save_pretrained(_A)
         _UpperCAmelCase				:      Tuple			  =				OnnxStableDiffusionPipeline.from_pretrained(_A)

     # sanity check that the pipeline still works
     assert pipe.safety_checker is None
     _UpperCAmelCase				:      str			  =				pipe('''example prompt'''							,  num_inference_steps=2).images[0]
     assert image is not None












import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()

def      _lowerCamelCase	(						__A    :							int		,		__A    :							Optional[Any]		,		__A    :							int )      ->					int:
    # Initialise PyTorch model
    _UpperCAmelCase				:      Dict			  =				RemBertConfig.from_json_file(__A )
    print('''Building PyTorch model from configuration: {}'''.format(str(__A ) ) )
    _UpperCAmelCase				:      int			  =				RemBertModel(__A )

    # Load weights from tf checkpoint
    load_tf_weights_in_rembert(__A		,		__A		,		__A )

    # Save pytorch-model
    print('''Save PyTorch model to {}'''.format(__A ) )
    torch.save(model.state_dict()		,		__A )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						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(
      '--rembert_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained RemBERT 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.'
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)




