
from typing import TYPE_CHECKING

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


lowercase_											=	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_											=	[
         """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """RegNetForImageClassification""",
         """RegNetModel""",
         """RegNetPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_											=	[
         """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFRegNetForImageClassification""",
         """TFRegNetModel""",
         """TFRegNetPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowercase_											=	[
         """FlaxRegNetForImageClassification""",
         """FlaxRegNetModel""",
         """FlaxRegNetPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_regnet import (
              REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              RegNetForImageClassification,
              RegNetModel,
              RegNetPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_regnet import (
              TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFRegNetForImageClassification,
              TFRegNetModel,
              TFRegNetPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_regnet import (
              FlaxRegNetForImageClassification,
              FlaxRegNetModel,
              FlaxRegNetPreTrainedModel,
          )


else:
     import sys

     lowercase_											=	_LazyModule(__name__, globals()["""__file__"""], _import_structure)


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   __UpperCamelCase				(    lowerCAmelCase__   ):






     """simple docstring"""







     def __init__(							self    :    Optional[int]      ,		_A    :    AutoencoderKL      ,		_A    :    CLIPTextModel      ,		_A    :    CLIPTokenizer      ,		_A    :    UNetaDConditionModel      ,		_A    :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]      ,		):







       """simple docstring"""




       super().__init__()

       self.register_modules(vae=_A      ,		text_encoder=_A      ,		tokenizer=_A      ,		unet=_A      ,		scheduler=_A				)



     def 				UpperCAmelCase__    (							self    :    Optional[int]      ,		_A    :    Dict				):







       """simple docstring"""




       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Dict			=						self.vae.encode(tfms.ToTensor()(_A				).unsqueeze(0				).to(self.device				) * 2 - 1				)
         __SCREAMING_SNAKE_CASE     :					str			=						0.1_82_15 * latent.latent_dist.sample()
       return latent



     def 				UpperCAmelCase__    (							self    :    Any      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					List[str]			=						(1 / 0.1_82_15) * latent
       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Any			=						self.vae.decode(_A				).sample
       __SCREAMING_SNAKE_CASE     :					Any			=						(img / 2 + 0.5).clamp(0      ,		1				)
       __SCREAMING_SNAKE_CASE     :					List[str]			=						img.detach().cpu().permute(0      ,		2      ,		3      ,		1				).numpy()
       __SCREAMING_SNAKE_CASE     :					List[str]			=						(img * 255).round().astype('''uint8'''				)
       return Image.fromarray(img[0]				)



     def 				UpperCAmelCase__    (							self    :    Dict      ,		_A    :    List[Any]				):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Any			=						self.tokenizer(
           _A      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors='''pt'''      ,		)

       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.text_encoder(text_input.input_ids.to(self.device				)				)[0]

       __SCREAMING_SNAKE_CASE     :					Tuple			=						self.tokenizer(
           ''''''      ,		padding='''max_length'''      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors='''pt'''      ,		)

       __SCREAMING_SNAKE_CASE     :					str			=						self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]

       return torch.cat([uncond_embedding, text_embedding]				)




     def __call__(							self    :    Union[str, Any]      ,		_A    :    Image.Image      ,		_A    :    str      ,		_A    :    float = 0.3      ,		_A    :    float = 0.6      ,		_A    :    float = 0.5      ,		_A    :    int = 42      ,		_A    :    int = 50      ,		_A    :    float = 7.5      ,		):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						steps - int(kmin * steps				)
       __SCREAMING_SNAKE_CASE     :					List[Any]			=						steps - int(kmax * steps				)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						self.prep_text(_A				)

       self.scheduler.set_timesteps(_A				)

       __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Dict			=						img.size
       __SCREAMING_SNAKE_CASE     :					Dict			=						self.encode(_A				)

       torch.manual_seed(_A				)
       __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)      ,		).to(self.device				)

       __SCREAMING_SNAKE_CASE     :					Dict			=						self.scheduler.add_noise(
           _A      ,		_A      ,		timesteps=self.scheduler.timesteps[tmax]      ,		)

       __SCREAMING_SNAKE_CASE     :					List[str]			=						torch.cat([latents] * 2				)

       __SCREAMING_SNAKE_CASE     :					List[Any]			=						self.scheduler.scale_model_input(_A      ,		self.scheduler.timesteps[tmax]				)

       with torch.no_grad():
         __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						self.unet(
             _A      ,		self.scheduler.timesteps[tmax]      ,		encoder_hidden_states=_A      ,		).sample

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

       __SCREAMING_SNAKE_CASE     :					Any			=						self.scheduler.step(_A      ,		self.scheduler.timesteps[tmax]      ,		_A				).prev_sample

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

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

           else:  # content generation phase
             __SCREAMING_SNAKE_CASE     :					List[str]			=						torch.cat([latents] * 2				)

           __SCREAMING_SNAKE_CASE     :					str			=						self.scheduler.scale_model_input(_A      ,		_A				)

           with torch.no_grad():
             __SCREAMING_SNAKE_CASE     :					int			=						self.unet(
                 _A      ,		_A      ,		encoder_hidden_states=_A      ,		).sample

           __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					Optional[int]			=						pred.chunk(2				)
           __SCREAMING_SNAKE_CASE     :					Optional[int]			=						pred_uncond + guidance_scale * (pred_text - pred_uncond)

           __SCREAMING_SNAKE_CASE     :					Any			=						self.scheduler.step(_A      ,		_A      ,		_A				).prev_sample

       return self.decode(_A				)
