



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 			__snake_case   ( SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_	,	a_	,	a_	,	a_	,	a_ = 0.93	,	a_ = True	,	):
											a__					   =		mixing_coeff
											a__					   =		leapfrog_steps

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

											a__					   =		2 ** (len(self.vae.config.block_out_channels		) - 1)
											a__					   =		VaeImageProcessor(vae_scale_factor=self.vae_scale_factor		)


							def _a   (				self	,	a_	,	a_ = None	,	a_ = False		):
											a__					   =		self.tokenizer(
											    a_	,	padding="""max_length"""	,	max_length=self.tokenizer.model_max_length	,	truncation=a_	,	return_tensors="""pt"""	,	)

											a__					   =		self.text_encoder(text_inputs.input_ids.to(self.device		)		).last_hidden_state

											a__					   =		prompt_embeds.to(dtype=self.text_encoder.dtype	,	device=self.device		)

											if do_classifier_free_guidance:
															a__					   =		"""""" if negative_prompt is None else negative_prompt

															a__					   =		self.tokenizer(
															    a_	,	padding="""max_length"""	,	max_length=self.tokenizer.model_max_length	,	truncation=a_	,	return_tensors="""pt"""	,	)

															a__					   =		self.text_encoder(uncond_input.input_ids.to(self.device		)		).last_hidden_state

															a__					   =		torch.cat([negative_prompt_embeds, prompt_embeds]		)

											return prompt_embeds


							def _a   (				self	,	a_	,	a_		):
											a__					   =		self.mixing_coeff * x + (1 - self.mixing_coeff) * y
											a__					   =		self.mixing_coeff * y + (1 - self.mixing_coeff) * x

											return [x, y]


							def _a   (				self	,	a_	,	a_		):
											a__					   =		(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
											a__					   =		(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

											return [x, y]


							def _a   (				self	,	a_		):
											# as self.alphas_cumprod is always in cpu
											a__					   =		int(a_		)

											a__					   =		self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

											return alpha_prod, 1 - alpha_prod


							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	):
											a__					   =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)
											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)

											a__					   =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
											a__					   =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

											a__					   =		(base - b_t * model_output) / a_t

											return model_input, next_model_input.to(base.dtype		)


							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	):
											a__					   =		timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)
											a__   ,      a__					   =		self._get_alpha_and_beta(a_		)

											a__					   =		(alpha_prod_t_prev / alpha_prod_t) ** 0.5
											a__					   =		-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
											a__					   =		a_t * base + b_t * model_output

											return model_input, next_model_input.to(base.dtype		)


							@torch.no_grad()
							def _a   (				self	,	a_		):
											a__					   =		1 / self.vae.config.scaling_factor * latents
											a__					   =		self.vae.decode(a_		).sample
											a__					   =		(image / 2 + 0.5).clamp(0	,	1		)
											return image


							@torch.no_grad()
							def _a   (				self	,	a_	,	a_	,	a_	,	a_	,	a_ = None	,	):
											a__					   =		guidance_scale > 1.0

											a__					   =		image.to(device=self.device	,	dtype=text_embeds.dtype		)
											a__					   =		self.vae.encode(a_		).latent_dist.sample(a_		)

											a__					   =		self.vae.config.scaling_factor * latent

											a__					   =		[latent.clone(), latent.clone()]

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

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

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

																			a__					   =		coupled_latents[j]
																			a__					   =		coupled_latents[k]

																			a__					   =		torch.cat([model_input] * 2		) if do_classifier_free_guidance else model_input

																			a__					   =		self.unet(a_	,	a_	,	encoder_hidden_states=a_		).sample

																			if do_classifier_free_guidance:
																							a__   ,      a__					   =		noise_pred.chunk(2		)
																							a__					   =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																			a__   ,      a__					   =		self.noise_step(
																			    base=a_	,	model_input=a_	,	model_output=a_	,	timestep=a_	,	)

																			a__					   =		model_input

											return coupled_latents







							@torch.no_grad()
							def __call__(				self	,	a_	,	a_	,	a_	,	a_ = 3.0	,	a_ = 50	,	a_ = 0.8	,	a_ = None	,	a_ = None	,	a_ = "pil"	,	):
											a__					   =		guidance_scale > 1.0

											a__					   =		self.image_processor.preprocess(a_		)

											a__					   =		self._encode_prompt(a_	,	a_	,	a_		)
											a__					   =		self._encode_prompt(a_	,	a_	,	a_		)

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

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

											a__					   =		self.prepare_latents(a_	,	a_	,	a_	,	a_	,	a_		)

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

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

																			a__					   =		coupled_latents[j]
																			a__					   =		coupled_latents[k]

																			a__					   =		torch.cat([model_input] * 2		) if do_classifier_free_guidance else model_input

																			a__					   =		self.unet(a_	,	a_	,	encoder_hidden_states=a_		).sample

																			if do_classifier_free_guidance:
																							a__   ,      a__					   =		noise_pred.chunk(2		)
																							a__					   =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																			a__   ,      a__					   =		self.denoise_step(
																			    base=a_	,	model_input=a_	,	model_output=a_	,	timestep=a_	,	)

																			a__					   =		model_input

															a__					   =		self.denoise_mixing_layer(x=coupled_latents[0]	,	y=coupled_latents[1]		)

											# either one is fine
											a__					   =		coupled_latents[0]

											if output_type not in ["latent", "pt", "np", "pil"]:
															a__					   =		(
															    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"""	,	a_	,	standard_warn=a_		)
															a__					   =		"""np"""

											if output_type == "latent":
															a__					   =		final_latent
											else:
															a__					   =		self.decode_latents(a_		)
															a__					   =		self.image_processor.postprocess(a_	,	output_type=a_		)

											return image







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


a__         =     {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTJForCausalLM''',
		    '''GPTJForQuestionAnswering''',
		    '''GPTJForSequenceClassification''',
		    '''GPTJModel''',
		    '''GPTJPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''TFGPTJForCausalLM''',
		    '''TFGPTJForQuestionAnswering''',
		    '''TFGPTJForSequenceClassification''',
		    '''TFGPTJModel''',
		    '''TFGPTJPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__         =     [
		    '''FlaxGPTJForCausalLM''',
		    '''FlaxGPTJModel''',
		    '''FlaxGPTJPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gptj import (
				    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTJForCausalLM,
				    GPTJForQuestionAnswering,
				    GPTJForSequenceClassification,
				    GPTJModel,
				    GPTJPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_gptj import (
				    TFGPTJForCausalLM,
				    TFGPTJForQuestionAnswering,
				    TFGPTJForSequenceClassification,
				    TFGPTJModel,
				    TFGPTJPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
		import sys

		a__         =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)