





'''simple docstring'''




from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)




class       UpperCamelCase__			(			_a	):




					"""simple docstring"""


					def __init__(		self							:	Optional[Any]     ,		lowerCamelCase_							:	List[Any]     ,		lowerCamelCase_							:	Dict     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	Any     ,		lowerCamelCase_							:	List[str] = 0.93     ,		lowerCamelCase_							:	List[str] = True     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	mixing_coeff
							SCREAMING_SNAKE_CASE				:							int						=	leapfrog_steps

							super().__init__()
							self.register_modules(
							    vae=snake_case_     ,		text_encoder=snake_case_     ,		tokenizer=snake_case_     ,		unet=snake_case_     ,		scheduler=snake_case_     ,		)

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	2 ** (len(self.vae.config.block_out_channels		) - 1)
							SCREAMING_SNAKE_CASE				:							str						=	VaeImageProcessor(vae_scale_factor=self.vae_scale_factor		)


					def lowerCamelCase_	(		self							:	Tuple     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	Optional[int] = None     ,		lowerCamelCase_							:	Union[str, Any] = False		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	self.tokenizer(
							    snake_case_     ,		padding="""max_length"""     ,		max_length=self.tokenizer.model_max_length     ,		truncation=snake_case_     ,		return_tensors="""pt"""     ,		)

							SCREAMING_SNAKE_CASE				:							int						=	self.text_encoder(text_inputs.input_ids.to(self.device		)		).last_hidden_state

							SCREAMING_SNAKE_CASE				:							Tuple						=	prompt_embeds.to(dtype=self.text_encoder.dtype     ,		device=self.device		)

							if do_classifier_free_guidance:
									SCREAMING_SNAKE_CASE				:							Any						=	"""""" if negative_prompt is None else negative_prompt

									SCREAMING_SNAKE_CASE				:							Any						=	self.tokenizer(
									    snake_case_     ,		padding="""max_length"""     ,		max_length=self.tokenizer.model_max_length     ,		truncation=snake_case_     ,		return_tensors="""pt"""     ,		)

									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.text_encoder(uncond_input.input_ids.to(self.device		)		).last_hidden_state

									SCREAMING_SNAKE_CASE				:							List[str]						=	torch.cat([negative_prompt_embeds, prompt_embeds]		)

							return prompt_embeds


					def lowerCamelCase_	(		self							:	List[Any]     ,		lowerCamelCase_							:	Dict     ,		lowerCamelCase_							:	str		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							str						=	self.mixing_coeff * x + (1 - self.mixing_coeff) * y
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.mixing_coeff * y + (1 - self.mixing_coeff) * x

							return [x, y]


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Tuple						=	(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

							return [x, y]


					def lowerCamelCase_	(		self							:	List[str]     ,		lowerCamelCase_							:	Tuple		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							int						=	int(snake_case_		)

							SCREAMING_SNAKE_CASE				:							Any						=	self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

							return alpha_prod, 1 - alpha_prod


					def lowerCamelCase_	(		self							:	Tuple     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	List[Any]     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self._get_alpha_and_beta(snake_case_		)
							SCREAMING_SNAKE_CASE				:							Dict						=	self._get_alpha_and_beta(snake_case_		)

							SCREAMING_SNAKE_CASE				:							Tuple						=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

							SCREAMING_SNAKE_CASE				:							Optional[int]						=	(base - b_t * model_output) / a_t

							return model_input, next_model_input.to(base.dtype		)


					def lowerCamelCase_	(		self							:	Tuple     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	Optional[Any]     ,		lowerCamelCase_							:	Optional[int]     ,		lowerCamelCase_							:	int     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[str]						=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

							SCREAMING_SNAKE_CASE				:							List[Any]						=	self._get_alpha_and_beta(snake_case_		)
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self._get_alpha_and_beta(snake_case_		)

							SCREAMING_SNAKE_CASE				:							Dict						=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
							SCREAMING_SNAKE_CASE				:							Any						=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
							SCREAMING_SNAKE_CASE				:							Dict						=	a_t * base + b_t * model_output

							return model_input, next_model_input.to(base.dtype		)


					@torch.no_grad()
					def lowerCamelCase_	(		self							:	Any     ,		lowerCamelCase_							:	List[str]		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	1 / self.vae.config.scaling_factor * latents
							SCREAMING_SNAKE_CASE				:							List[Any]						=	self.vae.decode(snake_case_		).sample
							SCREAMING_SNAKE_CASE				:							Any						=	(image / 2 + 0.5).clamp(0     ,		1		)
							return image


					@torch.no_grad()
					def lowerCamelCase_	(		self							:	Dict     ,		lowerCamelCase_							:	List[Any]     ,		lowerCamelCase_							:	List[Any]     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	List[Any]     ,		lowerCamelCase_							:	int = None     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	guidance_scale > 1.0

							SCREAMING_SNAKE_CASE				:							Any						=	image.to(device=self.device     ,		dtype=text_embeds.dtype		)
							SCREAMING_SNAKE_CASE				:							Dict						=	self.vae.encode(snake_case_		).latent_dist.sample(snake_case_		)

							SCREAMING_SNAKE_CASE				:							Any						=	self.vae.config.scaling_factor * latent

							SCREAMING_SNAKE_CASE				:							str						=	[latent.clone(), latent.clone()]

							for i, t in tqdm(enumerate(snake_case_		)     ,		total=len(snake_case_		)		):
									SCREAMING_SNAKE_CASE				:							Dict						=	self.noise_mixing_layer(x=coupled_latents[0]     ,		y=coupled_latents[1]		)

									# j - model_input index, k - base index
									for j in range(2		):
											SCREAMING_SNAKE_CASE				:							int						=	j ^ 1

											if self.leapfrog_steps:
													if i % 2 == 0:
															SCREAMING_SNAKE_CASE				:							Tuple						=	j, k

											SCREAMING_SNAKE_CASE				:							Any						=	coupled_latents[j]
											SCREAMING_SNAKE_CASE				:							Optional[Any]						=	coupled_latents[k]

											SCREAMING_SNAKE_CASE				:							List[Any]						=	torch.cat([model_input] * 2		) if do_classifier_free_guidance else model_input

											SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	self.unet(snake_case_     ,		snake_case_     ,		encoder_hidden_states=snake_case_		).sample

											if do_classifier_free_guidance:
													SCREAMING_SNAKE_CASE				:							List[Any]						=	noise_pred.chunk(2		)
													SCREAMING_SNAKE_CASE				:							Optional[int]						=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											SCREAMING_SNAKE_CASE				:							str						=	self.noise_step(
											    base=snake_case_     ,		model_input=snake_case_     ,		model_output=snake_case_     ,		timestep=snake_case_     ,		)

											SCREAMING_SNAKE_CASE				:							Any						=	model_input

							return coupled_latents



					@torch.no_grad()
					def __call__(		self							:	str     ,		lowerCamelCase_							:	Dict     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	int     ,		lowerCamelCase_							:	List[str] = 3.0     ,		lowerCamelCase_							:	List[Any] = 50     ,		lowerCamelCase_							:	Any = 0.8     ,		lowerCamelCase_							:	str = None     ,		lowerCamelCase_							:	Optional[int] = None     ,		lowerCamelCase_							:	Union[str, Any] = "pil"     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Any						=	guidance_scale > 1.0

							SCREAMING_SNAKE_CASE				:							int						=	self.image_processor.preprocess(snake_case_		)

							SCREAMING_SNAKE_CASE				:							List[str]						=	self._encode_prompt(snake_case_     ,		snake_case_     ,		snake_case_		)
							SCREAMING_SNAKE_CASE				:							Dict						=	self._encode_prompt(snake_case_     ,		snake_case_     ,		snake_case_		)

							self.scheduler.set_timesteps(snake_case_     ,		self.device		)

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	num_inference_steps - int(num_inference_steps * strength		)
							SCREAMING_SNAKE_CASE				:							Any						=	self.scheduler.timesteps[t_limit:]
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	fwd_timesteps.flip(0		)

							SCREAMING_SNAKE_CASE				:							Any						=	self.prepare_latents(snake_case_     ,		snake_case_     ,		snake_case_     ,		snake_case_     ,		snake_case_		)

							for i, t in tqdm(enumerate(snake_case_		)     ,		total=len(snake_case_		)		):
									# j - model_input index, k - base index
									for k in range(2		):
											SCREAMING_SNAKE_CASE				:							Any						=	k ^ 1

											if self.leapfrog_steps:
													if i % 2 == 1:
															SCREAMING_SNAKE_CASE				:							Dict						=	j, k

											SCREAMING_SNAKE_CASE				:							Any						=	coupled_latents[j]
											SCREAMING_SNAKE_CASE				:							Optional[Any]						=	coupled_latents[k]

											SCREAMING_SNAKE_CASE				:							Optional[Any]						=	torch.cat([model_input] * 2		) if do_classifier_free_guidance else model_input

											SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.unet(snake_case_     ,		snake_case_     ,		encoder_hidden_states=snake_case_		).sample

											if do_classifier_free_guidance:
													SCREAMING_SNAKE_CASE				:							Optional[int]						=	noise_pred.chunk(2		)
													SCREAMING_SNAKE_CASE				:							Dict						=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											SCREAMING_SNAKE_CASE				:							str						=	self.denoise_step(
											    base=snake_case_     ,		model_input=snake_case_     ,		model_output=snake_case_     ,		timestep=snake_case_     ,		)

											SCREAMING_SNAKE_CASE				:							Optional[int]						=	model_input

									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.denoise_mixing_layer(x=coupled_latents[0]     ,		y=coupled_latents[1]		)

							# either one is fine
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	coupled_latents[0]

							if output_type not in ["latent", "pt", "np", "pil"]:
									SCREAMING_SNAKE_CASE				:							Dict						=	(
									    f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
									    """`pil`, `np`, `pt`, `latent`"""
									)
									deprecate("""Unsupported output_type"""     ,		"""1.0.0"""     ,		snake_case_     ,		standard_warn=snake_case_		)
									SCREAMING_SNAKE_CASE				:							Dict						=	"""np"""

							if output_type == "latent":
									SCREAMING_SNAKE_CASE				:							List[Any]						=	final_latent
							else:
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.decode_latents(snake_case_		)
									SCREAMING_SNAKE_CASE				:							int						=	self.image_processor.postprocess(snake_case_     ,		output_type=snake_case_		)

							return image










'''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
							)



