
'''simple docstring'''







from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase   ):
	def __init__(				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    ):



					'''simple docstring'''






					super().__init__()

					self.register_modules(vae=__UpperCamelCase  ,    text_encoder=__UpperCamelCase  ,    tokenizer=__UpperCamelCase  ,    unet=__UpperCamelCase  ,    scheduler=__UpperCamelCase			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					with torch.no_grad():
									__a       :     List[Any]								=	self.vae.encode(tfms.ToTensor()(__UpperCamelCase			).unsqueeze(0			).to(self.device			) * 2 - 1			)
									__a       :     str								=	0.1_8_2_1_5 * latent.latent_dist.sample()
					return latent
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     Optional[int]								=	(1 / 0.1_8_2_1_5) * latent
					with torch.no_grad():
									__a       :     Dict								=	self.vae.decode(__UpperCamelCase			).sample
					__a       :     Dict								=	(img / 2 + 0.5).clamp(0  ,    1			)
					__a       :     Tuple								=	img.detach().cpu().permute(0  ,    2  ,    3  ,    1			).numpy()
					__a       :     Optional[int]								=	(img * 255).round().astype("""uint8"""			)
					return Image.fromarray(img[0]			)
	def __lowerCamelCase    (				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     List[str]								=	self.tokenizer(
					    __UpperCamelCase  ,    padding="""max_length"""  ,    max_length=self.tokenizer.model_max_length  ,    truncation=__UpperCamelCase  ,    return_tensors="""pt"""  ,    )

					__a       :     Optional[int]								=	self.text_encoder(text_input.input_ids.to(self.device			)			)[0]

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

					__a       :     List[Any]								=	self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

					return torch.cat([uncond_embedding, text_embedding]			)

	def __call__(				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase = 0.3  ,    __UpperCamelCase = 0.6  ,    __UpperCamelCase = 0.5  ,    __UpperCamelCase = 42  ,    __UpperCamelCase = 50  ,    __UpperCamelCase = 7.5  ,    ):



					'''simple docstring'''






					__a       :     Dict								=	steps - int(kmin * steps			)
					__a       :     List[Any]								=	steps - int(kmax * steps			)

					__a       :     List[str]								=	self.prep_text(__UpperCamelCase			)

					self.scheduler.set_timesteps(__UpperCamelCase			)

					__a							,	__a       :     str								=	img.size
					__a       :     List[str]								=	self.encode(__UpperCamelCase			)

					torch.manual_seed(__UpperCamelCase			)
					__a       :     List[str]								=	torch.randn(
					    (1, self.unet.config.in_channels, height // 8, width // 8)  ,    ).to(self.device			)

					__a       :     List[str]								=	self.scheduler.add_noise(
					    __UpperCamelCase  ,    __UpperCamelCase  ,    timesteps=self.scheduler.timesteps[tmax]  ,    )

					__a       :     Dict								=	torch.cat([latents] * 2			)

					__a       :     List[Any]								=	self.scheduler.scale_model_input(__UpperCamelCase  ,    self.scheduler.timesteps[tmax]			)

					with torch.no_grad():
									__a       :     Tuple								=	self.unet(
									    __UpperCamelCase  ,    self.scheduler.timesteps[tmax]  ,    encoder_hidden_states=__UpperCamelCase  ,    ).sample

					__a							,	__a       :     List[str]								=	pred.chunk(2			)
					__a       :     Optional[Any]								=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

					__a       :     Tuple								=	self.scheduler.step(__UpperCamelCase  ,    self.scheduler.timesteps[tmax]  ,    __UpperCamelCase			).prev_sample

					for i, t in enumerate(tqdm(self.scheduler.timesteps			)			):
									if i > tmax:
													if i < tmin:  # layout generation phase
																	__a       :     List[str]								=	self.scheduler.add_noise(
																	    __UpperCamelCase  ,    __UpperCamelCase  ,    timesteps=__UpperCamelCase  ,    )

																	__a       :     Optional[int]								=	(mix_factor * latents) + (
																	    1 - mix_factor
																	) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																	__a       :     List[str]								=	torch.cat([input] * 2			)

													else:  # content generation phase
																	__a       :     Dict								=	torch.cat([latents] * 2			)

													__a       :     List[Any]								=	self.scheduler.scale_model_input(__UpperCamelCase  ,    __UpperCamelCase			)

													with torch.no_grad():
																	__a       :     Optional[int]								=	self.unet(
																	    __UpperCamelCase  ,    __UpperCamelCase  ,    encoder_hidden_states=__UpperCamelCase  ,    ).sample

													__a							,	__a       :     List[Any]								=	pred.chunk(2			)
													__a       :     Dict								=	pred_uncond + guidance_scale * (pred_text - pred_uncond)

													__a       :     Tuple								=	self.scheduler.step(__UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			).prev_sample

					return self.decode(__UpperCamelCase			)

'''simple docstring'''







from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE		:  Tuple        =      {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  Dict        =      ['SegformerFeatureExtractor']
					__SCREAMING_SNAKE_CASE		:  str        =      ['SegformerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SegformerDecodeHead',
					    'SegformerForImageClassification',
					    'SegformerForSemanticSegmentation',
					    'SegformerLayer',
					    'SegformerModel',
					    'SegformerPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__SCREAMING_SNAKE_CASE		:  List[str]        =      [
					    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFSegformerDecodeHead',
					    'TFSegformerForImageClassification',
					    'TFSegformerForSemanticSegmentation',
					    'TFSegformerModel',
					    'TFSegformerPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_segformer import SegformerFeatureExtractor
										from .image_processing_segformer import SegformerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_segformer import (
										    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SegformerDecodeHead,
										    SegformerForImageClassification,
										    SegformerForSemanticSegmentation,
										    SegformerLayer,
										    SegformerModel,
										    SegformerPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_segformer import (
										    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSegformerDecodeHead,
										    TFSegformerForImageClassification,
										    TFSegformerForSemanticSegmentation,
										    TFSegformerModel,
										    TFSegformerPreTrainedModel,
										)

else:
					import sys

					__SCREAMING_SNAKE_CASE		:  Optional[Any]        =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)