import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       DiTPipeline
      _snake_case    		=       CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
      _snake_case    		=       PipelineTesterMixin.required_optional_params - {
          'latents',
          'num_images_per_prompt',
          'callback',
          'callback_steps',
      }
      _snake_case    		=       CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
      _snake_case    		=       False



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[str]:


          """simple docstring"""


          torch.manual_seed(0)
          _lowerCamelCase     :		Union[str, Any] 						=						TransformeraDModel(
              sample_size=16    ,  num_layers=2    ,  patch_size=4    ,  attention_head_dim=8    ,  num_attention_heads=2    ,  in_channels=4    ,  out_channels=8    ,  attention_bias=_UpperCamelCase    ,  activation_fn="""gelu-approximate"""    ,  num_embeds_ada_norm=1000    ,  norm_type="""ada_norm_zero"""    ,  norm_elementwise_affine=_UpperCamelCase    ,  )
          _lowerCamelCase     :		Dict 						=						AutoencoderKL()
          _lowerCamelCase     :		Tuple 						=						DDIMScheduler()
          _lowerCamelCase     :		Any 						=						{"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
          return components



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


          """simple docstring"""


          if str(_UpperCamelCase).startswith("""mps"""):
              _lowerCamelCase     :		Any 						=						torch.manual_seed(_UpperCamelCase)
          else:
              _lowerCamelCase     :		Any 						=						torch.Generator(device=_UpperCamelCase).manual_seed(_UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						{
              """class_labels""": [1],
              """generator""": generator,
              """num_inference_steps""": 2,
              """output_type""": """numpy""",
          }
          return inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						"""cpu"""

          _lowerCamelCase     :		List[Any] 						=						self.get_dummy_components()
          _lowerCamelCase     :		List[str] 						=						self.pipeline_class(**_UpperCamelCase)
          pipe.to(_UpperCamelCase)
          pipe.set_progress_bar_config(disable=_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						self.get_dummy_inputs(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						pipe(**_UpperCamelCase).images
          _lowerCamelCase     :		str 						=						image[0, -3:, -3:, -1]

          self.assertEqual(image.shape    ,  (1, 16, 16, 3))
          _lowerCamelCase     :		List[Any] 						=						np.array([0.2_9_4_6, 0.6_6_0_1, 0.4_3_2_9, 0.3_2_9_6, 0.4_1_4_4, 0.5_3_1_9, 0.7_2_7_3, 0.5_0_1_3, 0.4_4_5_7])
          _lowerCamelCase     :		Optional[int] 						=						np.abs(image_slice.flatten() - expected_slice).max()
          self.assertLessEqual(_UpperCamelCase    ,  1E-3)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Tuple:


          """simple docstring"""


          self._test_inference_batch_single_identical(relax_max_difference=_UpperCamelCase    ,  expected_max_diff=1E-3)




      @unittest.skipIf(
          torch_device != """cuda""" or not is_xformers_available()    ,  reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,  )
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Optional[int]:


          """simple docstring"""


          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)





@require_torch_gpu
@slow
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->int:


          """simple docstring"""


          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Dict 						=						DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""")
          pipe.to("""cuda""")

          _lowerCamelCase     :		List[str] 						=						["""vase""", """umbrella""", """white shark""", """white wolf"""]
          _lowerCamelCase     :		Any 						=						pipe.get_label_ids(_UpperCamelCase)

          _lowerCamelCase     :		List[Any] 						=						pipe(_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=40    ,  output_type="""np""").images

          for word, image in zip(_UpperCamelCase    ,  _UpperCamelCase):
              _lowerCamelCase     :		List[str] 						=						load_numpy(
                  F"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""")
              assert np.abs((expected_image - image).max()) < 1E-2




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


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""")
          _lowerCamelCase     :		List[str] 						=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
          pipe.to("""cuda""")

          _lowerCamelCase     :		Tuple 						=						["""vase""", """umbrella"""]
          _lowerCamelCase     :		Dict 						=						pipe.get_label_ids(_UpperCamelCase)

          _lowerCamelCase     :		List[Any] 						=						torch.manual_seed(0)
          _lowerCamelCase     :		List[Any] 						=						pipe(_UpperCamelCase    ,  generator=_UpperCamelCase    ,  num_inference_steps=25    ,  output_type="""np""").images

          for word, image in zip(_UpperCamelCase    ,  _UpperCamelCase):
              _lowerCamelCase     :		str 						=						load_numpy(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  F"""/dit/{word}_512.npy""")

              assert np.abs((expected_image - image).max()) < 1E-1





import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowerCAmelCase			:							Tuple     =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def 						A__  (					__A   , __A   , __A   , __A   , __A   , __A   , __A   , __A=False   , ):







    '''simple docstring'''
    output_path.parent.mkdir(parents=__A   , exist_ok=__A	)
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , use_external_data_format=__A   , enable_onnx_checker=__A   , opset_version=__A   , )
    else:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , opset_version=__A   , )







@torch.no_grad()
def 						A__  (					__A   , __A   , __A   , __A = False	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        _lowerCamelCase     :		str 						=						"""cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""	)
    else:
        _lowerCamelCase     :		List[str] 						=						"""cpu"""
    _lowerCamelCase     :		Dict 						=						Path(__A	)

    # VAE DECODER
    _lowerCamelCase     :		Optional[Any] 						=						AutoencoderKL.from_pretrained(model_path + """/vae"""	)
    _lowerCamelCase     :		List[str] 						=						vae_decoder.config.latent_channels
    # forward only through the decoder part
    _lowerCamelCase     :		Tuple 						=						vae_decoder.decode
    onnx_export(
        __A   , model_args=(
            torch.randn(1   , __A   , 25   , 25	).to(device=__A   , dtype=__A	),
            False,
        )   , output_path=output_path / """vae_decoder""" / """model.onnx"""   , ordered_input_names=["""latent_sample""", """return_dict"""]   , output_names=["""sample"""]   , dynamic_axes={
            """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
        }   , opset=__A   , )
    del vae_decoder


if __name__ == "__main__":
    lowerCAmelCase			:							Optional[int]     =argparse.ArgumentParser()

    parser.add_argument(
        "--model_path",
        type=str,
        required=True,
        help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
    )

    parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
    parser.add_argument(
        "--opset",
        default=14,
        type=int,
        help="The version of the ONNX operator set to use.",
    )
    parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

    lowerCAmelCase			:							Optional[Any]     =parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print("SD: Done: ONNX")




