





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


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




def    A_(			A				,      A				,      A   ):
 hf_model.apply_weight_norm()

 UpperCAmelCase_								=     checkpoint["""input_conv.weight_g"""]
 UpperCAmelCase_								=     checkpoint["""input_conv.weight_v"""]
 UpperCAmelCase_								=     checkpoint["""input_conv.bias"""]

 for i in range(len(config.upsample_rates   )   ):
  UpperCAmelCase_								=     checkpoint[f"""upsamples.{i}.1.weight_g"""]
  UpperCAmelCase_								=     checkpoint[f"""upsamples.{i}.1.weight_v"""]
  UpperCAmelCase_								=     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_								=     checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
   UpperCAmelCase_								=     checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
   UpperCAmelCase_								=     checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

   UpperCAmelCase_								=     checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
   UpperCAmelCase_								=     checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
   UpperCAmelCase_								=     checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

 UpperCAmelCase_								=     checkpoint["""output_conv.1.weight_g"""]
 UpperCAmelCase_								=     checkpoint["""output_conv.1.weight_v"""]
 UpperCAmelCase_								=     checkpoint["""output_conv.1.bias"""]

 hf_model.remove_weight_norm()




@torch.no_grad()
def    A_(			A				,      A				,      A				,      A=None				,      A=None				,      ):
 if config_path is not None:
  UpperCAmelCase_								=     SpeechTaHifiGanConfig.from_pretrained(lowerCAmelCase__   )
 else:
  UpperCAmelCase_								=     SpeechTaHifiGanConfig()

 UpperCAmelCase_								=     SpeechTaHifiGan(lowerCAmelCase__   )

 UpperCAmelCase_								=     torch.load(lowerCAmelCase__   )
 load_weights(orig_checkpoint["""model"""]["""generator"""]				,      lowerCAmelCase__				,      lowerCAmelCase__   )

 UpperCAmelCase_								=     np.load(lowerCAmelCase__   )
 UpperCAmelCase_								=     stats[0].reshape(-1   )
 UpperCAmelCase_								=     stats[1].reshape(-1   )
 UpperCAmelCase_								=     torch.from_numpy(lowerCAmelCase__   ).float()
 UpperCAmelCase_								=     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__":
    UpperCamelCase__   : 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."""
    )

    UpperCamelCase__   : 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,
    )









from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)







@dataclass
class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Union[List[PIL.Image.Image], np.ndarray]
					lowerCamelCase     :     Optional[List[bool]]


try:
				if not (is_transformers_available() and is_torch_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
				from .pipeline_cycle_diffusion import CycleDiffusionPipeline
				from .pipeline_stable_diffusion import StableDiffusionPipeline
				from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
				from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
				from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
				from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
				from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
				from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
				from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
				from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
				from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
				from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
				from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
				from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
				from .pipeline_stable_unclip import StableUnCLIPPipeline
				from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
				from .safety_checker import StableDiffusionSafetyChecker
				from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
				if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
				from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
				if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_objects import (
				    StableDiffusionDepthaImgPipeline,
				    StableDiffusionDiffEditPipeline,
				    StableDiffusionPixaPixZeroPipeline,
				)
else:
				from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
				from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
				from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
				if not (
				    is_torch_available()
				    and is_transformers_available()
				    and is_k_diffusion_available()
				    and is_k_diffusion_version(""">=""", """0.0.12""")
				):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
				from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
				if not (is_transformers_available() and is_onnx_available()):
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				from ...utils.dummy_onnx_objects import *  # noqa F403
else:
				from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
				from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
				from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
				from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
				from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
				import flax







				@flax.struct.dataclass
				class   _UpperCamelCase  (       A_  ):







									'''simple docstring'''

									lowerCamelCase     :     np.ndarray
									lowerCamelCase     :     List[bool]


				from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
				from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
				from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
				from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
				from .safety_checker_flax import FlaxStableDiffusionSafetyChecker


