
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase					:			Optional[Any]			      =		logging.get_logger("transformers.models.speecht5")


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	) ->						int:
    hf_model.apply_weight_norm()

    __lowercase		:			Optional[Any]	    =       checkpoint['''input_conv.weight_g''']
    __lowercase		:			Union[str, Any]	    =       checkpoint['''input_conv.weight_v''']
    __lowercase		:			Tuple	    =       checkpoint['''input_conv.bias''']

    for i in range(len(config.upsample_rates	)	):
        __lowercase		:			Optional[int]	    =       checkpoint[F'upsamples.{i}.1.weight_g']
        __lowercase		:			str	    =       checkpoint[F'upsamples.{i}.1.weight_v']
        __lowercase		:			List[Any]	    =       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	)	):
            __lowercase		:			Any	    =       checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
            __lowercase		:			Any	    =       checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
            __lowercase		:			str	    =       checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

            __lowercase		:			Any	    =       checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
            __lowercase		:			Tuple	    =       checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
            __lowercase		:			List[str]	    =       checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

    __lowercase		:			Dict	    =       checkpoint['''output_conv.1.weight_g''']
    __lowercase		:			Union[str, Any]	    =       checkpoint['''output_conv.1.weight_v''']
    __lowercase		:			Union[str, Any]	    =       checkpoint['''output_conv.1.bias''']

    hf_model.remove_weight_norm()


@torch.no_grad()
def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase=None	,					__lowerCAmelCase=None	,					) ->						List[str]:
    if config_path is not None:
        __lowercase		:			Optional[Any]	    =       SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase__	)
    else:
        __lowercase		:			Tuple	    =       SpeechTaHifiGanConfig()

    __lowercase		:			Union[str, Any]	    =       SpeechTaHifiGan(lowerCAmelCase__	)

    __lowercase		:			Tuple	    =       torch.load(lowerCAmelCase__	)
    load_weights(orig_checkpoint['''model''']['''generator''']	,					lowerCAmelCase__	,					lowerCAmelCase__	)

    __lowercase		:			int	    =       np.load(lowerCAmelCase__	)
    __lowercase		:			Any	    =       stats[0].reshape(-1	)
    __lowercase		:			List[str]	    =       stats[1].reshape(-1	)
    __lowercase		:			Optional[Any]	    =       torch.from_numpy(lowerCAmelCase__	).float()
    __lowercase		:			Tuple	    =       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					:			str			      =		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,
 )




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort


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



      """simple docstring"""







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




      @property
      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Tuple	    =       ort.SessionOptions()
          __lowercase		:			List[Any]	    =       False
          return options




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
          __lowercase		:			Optional[Any]	    =       load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'''				)

          # using the PNDM scheduler by default
          __lowercase		:			Union[str, Any]	    =       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''				,       revision='''onnx'''				,       safety_checker=_snake_case				,       feature_extractor=_snake_case				,       provider=self.gpu_provider				,       sess_options=self.gpu_options				,       )
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Tuple	    =       '''A red cat sitting on a park bench'''

          __lowercase		:			int	    =       np.random.RandomState(0				)
          __lowercase		:			int	    =       pipe(
              prompt=_snake_case				,       image=_snake_case				,       mask_image=_snake_case				,       strength=0.75				,       guidance_scale=7.5				,       num_inference_steps=15				,       generator=_snake_case				,       output_type='''np'''				,       )

          __lowercase		:			Dict	    =       output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image				).max() < 1E-2


