
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        _UpperCamelCase				(					__UpperCAmelCase     ,       unittest.TestCase  ):






			'''simple docstring'''

			pass




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






			'''simple docstring'''

			@property
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							return (
							    "CUDAExecutionProvider",
							    {
							        "gpu_mem_limit": "15000000000",  # 15GB
							        "arena_extend_strategy": "kSameAsRequested",
							    },
							)

			@property
			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							Any	=						ort.SessionOptions()
							UpperCamelCase_:							Union[str, Any]	=						False
							return options

			def       lowerCAmelCase__	(       self :       int      ):
							UpperCamelCase_:							Tuple	=						load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo.png"""      )
							UpperCamelCase_:							str	=						load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""      )
							UpperCamelCase_:							int	=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    """runwayml/stable-diffusion-inpainting"""						,	revision="""onnx"""						,	safety_checker=_lowerCamelCase						,	feature_extractor=_lowerCamelCase						,	provider=self.gpu_provider						,	sess_options=self.gpu_options						,	)
							pipe.set_progress_bar_config(disable=_lowerCamelCase      )

							UpperCamelCase_:							Optional[Any]	=						"""A red cat sitting on a park bench"""

							UpperCamelCase_:							List[Any]	=						np.random.RandomState(0      )
							UpperCamelCase_:							Optional[int]	=						pipe(
							    prompt=_lowerCamelCase						,	image=_lowerCamelCase						,	mask_image=_lowerCamelCase						,	guidance_scale=7.5						,	num_inference_steps=10						,	generator=_lowerCamelCase						,	output_type="""np"""						,	)
							UpperCamelCase_:							Optional[int]	=						output.images
							UpperCamelCase_:							Tuple	=						images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							UpperCamelCase_:							Union[str, Any]	=						np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464]      )

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




			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Optional[Any]	=						load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo.png"""      )
							UpperCamelCase_:							Any	=						load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/in_paint/overture-creations-5sI6fQgYIuo_mask.png"""      )
							UpperCamelCase_:							int	=						LMSDiscreteScheduler.from_pretrained(
							    """runwayml/stable-diffusion-inpainting"""						,	subfolder="""scheduler"""						,	revision="""onnx"""      )
							UpperCamelCase_:							List[str]	=						OnnxStableDiffusionInpaintPipeline.from_pretrained(
							    """runwayml/stable-diffusion-inpainting"""						,	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      )

							UpperCamelCase_:							List[Any]	=						"""A red cat sitting on a park bench"""

							UpperCamelCase_:							Optional[int]	=						np.random.RandomState(0      )
							UpperCamelCase_:							Union[str, Any]	=						pipe(
							    prompt=_lowerCamelCase						,	image=_lowerCamelCase						,	mask_image=_lowerCamelCase						,	guidance_scale=7.5						,	num_inference_steps=20						,	generator=_lowerCamelCase						,	output_type="""np"""						,	)
							UpperCamelCase_:							Union[str, Any]	=						output.images
							UpperCamelCase_:							List[Any]	=						images[0, 255:258, 255:258, -1]

							assert images.shape == (1, 512, 512, 3)
							UpperCamelCase_:							Any	=						np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125]      )

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








import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
lowerCamelCase_						: Optional[int]         =  logging.get_logger("""transformers.models.speecht5""")







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Optional[Any]:
				hf_model.apply_weight_norm()

				UpperCamelCase_:							Union[str, Any]	=						checkpoint["""input_conv.weight_g"""]
				UpperCamelCase_:							Optional[int]	=						checkpoint["""input_conv.weight_v"""]
				UpperCamelCase_:							List[Any]	=						checkpoint["""input_conv.bias"""]

				for i in range(len(config.upsample_rates						)						):
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.weight_g''']
								UpperCamelCase_:							Dict	=						checkpoint[F'''upsamples.{i}.1.weight_v''']
								UpperCamelCase_:							List[str]	=						checkpoint[F'''upsamples.{i}.1.bias''']

				for i in range(len(config.upsample_rates						) * len(config.resblock_kernel_sizes						)						):
								for j in range(len(config.resblock_dilation_sizes						)						):
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.weight_v''']
												UpperCamelCase_:							Tuple	=						checkpoint[F'''blocks.{i}.convs1.{j}.1.bias''']

												UpperCamelCase_:							Union[str, Any]	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_g''']
												UpperCamelCase_:							Any	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.weight_v''']
												UpperCamelCase_:							int	=						checkpoint[F'''blocks.{i}.convs2.{j}.1.bias''']

				UpperCamelCase_:							int	=						checkpoint["""output_conv.1.weight_g"""]
				UpperCamelCase_:							Tuple	=						checkpoint["""output_conv.1.weight_v"""]
				UpperCamelCase_:							List[str]	=						checkpoint["""output_conv.1.bias"""]

				hf_model.remove_weight_norm()







@torch.no_grad()
def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase=None     ,    lowerCamelCase=None     ,    )  ->	Optional[int]:
				if config_path is not None:
								UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase						)
				else:
								UpperCamelCase_:							str	=						SpeechTaHifiGanConfig()

				UpperCamelCase_:							Union[str, Any]	=						SpeechTaHifiGan(lowerCamelCase						)

				UpperCamelCase_:							str	=						torch.load(lowerCamelCase						)
				load_weights(orig_checkpoint["""model"""]["""generator"""]     ,    lowerCamelCase     ,    lowerCamelCase						)

				UpperCamelCase_:							Union[str, Any]	=						np.load(lowerCamelCase						)
				UpperCamelCase_:							int	=						stats[0].reshape(-1						)
				UpperCamelCase_:							Union[str, Any]	=						stats[1].reshape(-1						)
				UpperCamelCase_:							Dict	=						torch.from_numpy(lowerCamelCase						).float()
				UpperCamelCase_:							Optional[Any]	=						torch.from_numpy(lowerCamelCase						).float()

				model.save_pretrained(lowerCamelCase						)

				if repo_id:
								print("""Pushing to the hub..."""						)
								model.push_to_hub(lowerCamelCase						)


if __name__ == "__main__":
							lowerCamelCase_						: Union[str, Any]         =  argparse.ArgumentParser()
							parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
							parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
							)

							lowerCamelCase_						: Optional[int]         =  parser.parse_args()
							convert_hifigan_checkpoint(
							    args.checkpoint_path,
							    args.stats_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.push_to_hub,
							)






