






'''simple docstring'''







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__    (lowercase_			):




		'''simple docstring'''





		_UpperCamelCase				  =					42
		_UpperCamelCase				  =					42


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__    (lowercase_			):




									'''simple docstring'''





									_UpperCamelCase				  =					42
									_UpperCamelCase				  =					42



							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









'''simple docstring'''







import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase    :      Optional[Any]  			=		['small', 'medium', 'large']

UpperCamelCase    :      Dict  			=		'lm_head.decoder.weight'
UpperCamelCase    :      int  			=		'lm_head.weight'
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase						)
						lowerCamelCase__         =  d.pop(__lowerCAmelCase						)
						os.makedirs(__lowerCAmelCase   ,    exist_ok=__lowerCAmelCase						)
						torch.save(__lowerCAmelCase   ,    os.path.join(__lowerCAmelCase   ,    __lowerCAmelCase						)						)


if __name__ == "__main__":
							UpperCamelCase    :      Any  			=		argparse.ArgumentParser()
							parser.add_argument('--dialogpt_path', default='.', type=str)
							UpperCamelCase    :      Dict  			=		parser.parse_args()
							for MODEL in DIALOGPT_MODELS:
														UpperCamelCase    :      Any  			=		os.path.join(args.dialogpt_path, F'{MODEL}_ft.pkl')
														UpperCamelCase    :      str  			=		F'./DialoGPT-{MODEL}'
														convert_dialogpt_checkpoint(
														    checkpoint_path,
														    pytorch_dump_folder_path,
														)

