





'''simple docstring'''




from typing import Any, Callable, Dict, List, Optional, Union

import PIL.Image
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImgaImgPipeline,
    StableDiffusionInpaintPipelineLegacy,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.configuration_utils import FrozenDict
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import deprecate, logging


__UpperCAmelCase         =   logging.get_logger(__name__)  # pylint: disable=invalid-name




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					["safety_checker", "feature_extractor"]


					def __init__(		self							:	int     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	Tuple = True     ,		):

							'''simple docstring'''



							super().__init__()
							if hasattr(scheduler.config     ,		"""steps_offset"""		) and scheduler.config.steps_offset != 1:
									SCREAMING_SNAKE_CASE				:							Any						=	(
									    f'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
									    f''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
									    """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
									    """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
									    """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
									    """ file"""
									)
									deprecate("""steps_offset!=1"""     ,		"""1.0.0"""     ,		lowercase__     ,		standard_warn=lowercase__		)
									SCREAMING_SNAKE_CASE				:							Dict						=	dict(scheduler.config		)
									SCREAMING_SNAKE_CASE				:							Tuple						=	1
									SCREAMING_SNAKE_CASE				:							List[str]						=	FrozenDict(lowercase__		)

							self.register_modules(
							    vae=lowercase__     ,		text_encoder=lowercase__     ,		tokenizer=lowercase__     ,		unet=lowercase__     ,		scheduler=lowercase__     ,		safety_checker=lowercase__     ,		feature_extractor=lowercase__     ,		)
							self.register_to_config(requires_safety_checker=lowercase__		)


					@property
					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							return {k: getattr(self     ,		lowercase__		) for k in self.config.keys() if not k.startswith("""_"""		)}


					def lowerCamelCase_	(		self							:	str     ,		lowerCamelCase_							:	List[Any] = "auto"		):

							'''simple docstring'''



							if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									SCREAMING_SNAKE_CASE				:							List[str]						=	self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(lowercase__		)


					def lowerCamelCase_	(		self							:	Union[str, Any]		):

							'''simple docstring'''



							self.enable_attention_slicing(lowercase__		)


					@torch.no_grad()
					def lowerCamelCase_	(		self							:	Optional[int]     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	int = 0.8     ,		lowerCamelCase_							:	Tuple = 50     ,		lowerCamelCase_							:	Union[str, Any] = 7.5     ,		lowerCamelCase_							:	Dict = None     ,		lowerCamelCase_							:	Union[str, Any] = 1     ,		lowerCamelCase_							:	List[Any] = 0.0     ,		lowerCamelCase_							:	Optional[int] = None     ,		lowerCamelCase_							:	Dict = "pil"     ,		lowerCamelCase_							:	Any = True     ,		lowerCamelCase_							:	str = None     ,		lowerCamelCase_							:	Dict = 1     ,		):

							'''simple docstring'''



							return StableDiffusionInpaintPipelineLegacy(**self.components		)(
							    prompt=lowercase__     ,		image=lowercase__     ,		mask_image=lowercase__     ,		strength=lowercase__     ,		num_inference_steps=lowercase__     ,		guidance_scale=lowercase__     ,		negative_prompt=lowercase__     ,		num_images_per_prompt=lowercase__     ,		eta=lowercase__     ,		generator=lowercase__     ,		output_type=lowercase__     ,		return_dict=lowercase__     ,		callback=lowercase__     ,		)


					@torch.no_grad()
					def lowerCamelCase_	(		self							:	List[Any]     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	List[str]     ,		lowerCamelCase_							:	Tuple = 0.8     ,		lowerCamelCase_							:	str = 50     ,		lowerCamelCase_							:	List[Any] = 7.5     ,		lowerCamelCase_							:	List[Any] = None     ,		lowerCamelCase_							:	Tuple = 1     ,		lowerCamelCase_							:	Tuple = 0.0     ,		lowerCamelCase_							:	Dict = None     ,		lowerCamelCase_							:	int = "pil"     ,		lowerCamelCase_							:	int = True     ,		lowerCamelCase_							:	str = None     ,		lowerCamelCase_							:	Tuple = 1     ,		**lowerCamelCase_							:	List[str]     ,		):

							'''simple docstring'''



							return StableDiffusionImgaImgPipeline(**self.components		)(
							    prompt=lowercase__     ,		image=lowercase__     ,		strength=lowercase__     ,		num_inference_steps=lowercase__     ,		guidance_scale=lowercase__     ,		negative_prompt=lowercase__     ,		num_images_per_prompt=lowercase__     ,		eta=lowercase__     ,		generator=lowercase__     ,		output_type=lowercase__     ,		return_dict=lowercase__     ,		callback=lowercase__     ,		callback_steps=lowercase__     ,		)



					@torch.no_grad()
					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	List[str] = 5_12     ,		lowerCamelCase_							:	str = 5_12     ,		lowerCamelCase_							:	str = 50     ,		lowerCamelCase_							:	int = 7.5     ,		lowerCamelCase_							:	List[Any] = None     ,		lowerCamelCase_							:	Tuple = 1     ,		lowerCamelCase_							:	Optional[Any] = 0.0     ,		lowerCamelCase_							:	Union[str, Any] = None     ,		lowerCamelCase_							:	List[str] = None     ,		lowerCamelCase_							:	int = "pil"     ,		lowerCamelCase_							:	int = True     ,		lowerCamelCase_							:	Optional[int] = None     ,		lowerCamelCase_							:	List[Any] = 1     ,		):

							'''simple docstring'''



							return StableDiffusionPipeline(**self.components		)(
							    prompt=lowercase__     ,		height=lowercase__     ,		width=lowercase__     ,		num_inference_steps=lowercase__     ,		guidance_scale=lowercase__     ,		negative_prompt=lowercase__     ,		num_images_per_prompt=lowercase__     ,		eta=lowercase__     ,		generator=lowercase__     ,		latents=lowercase__     ,		output_type=lowercase__     ,		return_dict=lowercase__     ,		callback=lowercase__     ,		callback_steps=lowercase__     ,		)










'''simple docstring'''




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	{"""small""": (5_12, 8, 24), """medium""": (10_24, 8, 36), """large""": (15_36, 16, 48)}
		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE				:							Any						=	ImageGPTConfig(n_embd=lowerCamelCase_	,	n_layer=lowerCamelCase_	,	n_head=lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							str						=	ImageGPTForCausalLM(lowerCamelCase_					)

		# Load weights from numpy
		load_tf_weights_in_imagegpt(lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_					)

		# Save pytorch-model
		SCREAMING_SNAKE_CASE				:							Any						=	pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''					)
		torch.save(model.state_dict()	,	lowerCamelCase_					)
		print(f'''Save configuration file to {pytorch_config_dump_path}'''					)
		with open(lowerCamelCase_	,	"""w"""	,	encoding="""utf-8"""					) as f:
				f.write(config.to_json_string()					)


if __name__ == "__main__":
							__UpperCAmelCase         =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--imagegpt_checkpoint_path""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Path to the TensorFlow checkpoint path.""",
							)
							parser.add_argument(
							    """--model_size""",
							    default=None,
							    type=str,
							    required=True,
							    help="""Size of the model (can be either 'small', 'medium' or 'large').""",
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							__UpperCAmelCase         =   parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)



