






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_				(      _lowerCAmelCase    ):



	'''simple docstring'''




	def __init__(	self	,      a	,      a	,      a	,      a	,      a	,      a = 0.93	,      a = True	,      )	->   List[Any]:
								snake_case_           =							mixing_coeff
								snake_case_           =							leapfrog_steps

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

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




	def 							_UpperCamelCase      (	self	,      a	,      a = None	,      a = False )	->   Union[str, Any]:
								snake_case_           =							self.tokenizer(
								    _lowerCamelCase	,      padding='max_length'	,      max_length=self.tokenizer.model_max_length	,      truncation=_lowerCamelCase	,      return_tensors='pt'	,      )

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

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

								if do_classifier_free_guidance:
															snake_case_           =							'' if negative_prompt is None else negative_prompt

															snake_case_           =							self.tokenizer(
															    _lowerCamelCase	,      padding='max_length'	,      max_length=self.tokenizer.model_max_length	,      truncation=_lowerCamelCase	,      return_tensors='pt'	,      )

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

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

								return prompt_embeds




	def 							_UpperCamelCase      (	self	,      a	,      a )	->   Optional[Any]:
								snake_case_           =							self.mixing_coeff * x + (1 - self.mixing_coeff) * y
								snake_case_           =							self.mixing_coeff * y + (1 - self.mixing_coeff) * x

								return [x, y]




	def 							_UpperCamelCase      (	self	,      a	,      a )	->   int:
								snake_case_           =							(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
								snake_case_           =							(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

								return [x, y]




	def 							_UpperCamelCase      (	self	,      a )	->   Optional[int]:
								snake_case_           =							int(_lowerCamelCase )

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

								return alpha_prod, 1 - alpha_prod




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a	,      )	->   List[Any]:
								snake_case_           =							timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								snake_case_      ,		snake_case_           =							self._get_alpha_and_beta(_lowerCamelCase )
								snake_case_      ,		snake_case_           =							self._get_alpha_and_beta(_lowerCamelCase )

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

								snake_case_           =							(base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype )




	def 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a	,      )	->   Dict:
								snake_case_           =							timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

								snake_case_      ,		snake_case_           =							self._get_alpha_and_beta(_lowerCamelCase )
								snake_case_      ,		snake_case_           =							self._get_alpha_and_beta(_lowerCamelCase )

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

								return model_input, next_model_input.to(base.dtype )




	@torch.no_grad()
	def 							_UpperCamelCase      (	self	,      a )	->   str:
								snake_case_           =							1 / self.vae.config.scaling_factor * latents
								snake_case_           =							self.vae.decode(_lowerCamelCase ).sample
								snake_case_           =							(image / 2 + 0.5).clamp(0	,      1 )
								return image




	@torch.no_grad()
	def 							_UpperCamelCase      (	self	,      a	,      a	,      a	,      a	,      a = None	,      )	->   List[str]:
								snake_case_           =							guidance_scale > 1.0

								snake_case_           =							image.to(device=self.device	,      dtype=text_embeds.dtype )
								snake_case_           =							self.vae.encode(_lowerCamelCase ).latent_dist.sample(_lowerCamelCase )

								snake_case_           =							self.vae.config.scaling_factor * latent

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

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

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

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

																						snake_case_           =							coupled_latents[j]
																						snake_case_           =							coupled_latents[k]

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

																						snake_case_           =							self.unet(_lowerCamelCase	,      _lowerCamelCase	,      encoder_hidden_states=_lowerCamelCase ).sample

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

																						snake_case_      ,		snake_case_           =							self.noise_step(
																						    base=_lowerCamelCase	,      model_input=_lowerCamelCase	,      model_output=_lowerCamelCase	,      timestep=_lowerCamelCase	,      )

																						snake_case_           =							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"	,      )	->   Union[str, Any]:
								snake_case_           =							guidance_scale > 1.0

								snake_case_           =							self.image_processor.preprocess(_lowerCamelCase )

								snake_case_           =							self._encode_prompt(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )
								snake_case_           =							self._encode_prompt(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )

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

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

								snake_case_           =							self.prepare_latents(_lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase	,      _lowerCamelCase )

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

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

																						snake_case_           =							coupled_latents[j]
																						snake_case_           =							coupled_latents[k]

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

																						snake_case_           =							self.unet(_lowerCamelCase	,      _lowerCamelCase	,      encoder_hidden_states=_lowerCamelCase ).sample

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

																						snake_case_      ,		snake_case_           =							self.denoise_step(
																						    base=_lowerCamelCase	,      model_input=_lowerCamelCase	,      model_output=_lowerCamelCase	,      timestep=_lowerCamelCase	,      )

																						snake_case_           =							model_input

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

								# either one is fine
								snake_case_           =							coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
															snake_case_           =							(
															    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'	,      _lowerCamelCase	,      standard_warn=_lowerCamelCase )
															snake_case_           =							'np'

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

								return image













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase  							= {
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= ["DeformableDetrFeatureExtractor"]
	lowercase  							= ["DeformableDetrImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= [
	    "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DeformableDetrForObjectDetection",
	    "DeformableDetrModel",
	    "DeformableDetrPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

	lowercase  							= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





