from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class    snake_case       (   UpperCamelCase_    ):




  def __init__(   self  :						Optional[int]  ,			a_  :						TransformeraDModel  ,			a_  :						AutoencoderKL  ,			a_  :						KarrasDiffusionSchedulers  ,			a_  :						Optional[Dict[int, str]] = None  ,			)->							Union[str, Any]:




       """simple docstring"""
       super().__init__()
       self.register_modules(transformer=a_  ,			vae=a_  ,			scheduler=a_					)

       # create a imagenet -> id dictionary for easier use
       SCREAMING_SNAKE_CASE__					:					Dict      =		{}
       if idalabel is not None:
            for key, value in idalabel.items():
                 for label in value.split(','					):
                      SCREAMING_SNAKE_CASE__					:					List[Any]      =		int(a_					)
            SCREAMING_SNAKE_CASE__					:					Optional[int]      =		dict(sorted(self.labels.items()					)					)




  def 						__lowercase(   self  :						Dict  ,			a_  :						Union[str, List[str]]					)->							List[int]:




       """simple docstring"""

       if not isinstance(a_  ,			a_					):
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		list(a_					)

       for l in label:
            if l not in self.labels:
                 raise ValueError(
                     F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''					)

       return [self.labels[l] for l in label]




  @torch.no_grad()
  def __call__(   self  :						Optional[Any]  ,			a_  :						List[int]  ,			a_  :						float = 4.0  ,			a_  :						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,			a_  :						int = 50  ,			a_  :						Optional[str] = "pil"  ,			a_  :						bool = True  ,			)->							Union[ImagePipelineOutput, Tuple]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		len(a_					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.transformer.config.sample_size
       SCREAMING_SNAKE_CASE__					:					int      =		self.transformer.config.in_channels

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		randn_tensor(
           shape=(batch_size, latent_channels, latent_size, latent_size)  ,			generator=a_  ,			device=self.device  ,			dtype=self.transformer.dtype  ,			)
       SCREAMING_SNAKE_CASE__					:					str      =		torch.cat([latents] * 2					) if guidance_scale > 1 else latents

       SCREAMING_SNAKE_CASE__					:					int      =		torch.tensor(a_  ,			device=self.device					).reshape(-1					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.tensor([1000] * batch_size  ,			device=self.device					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		torch.cat([class_labels, class_null]  ,			0					) if guidance_scale > 1 else class_labels

       # set step values
       self.scheduler.set_timesteps(a_					)

       for t in self.progress_bar(self.scheduler.timesteps					):
            if guidance_scale > 1:
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		latent_model_input[: len(a_					) // 2]
                 SCREAMING_SNAKE_CASE__					:					Dict      =		torch.cat([half, half]  ,			dim=0					)
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.scheduler.scale_model_input(a_  ,			a_					)

            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		t
            if not torch.is_tensor(a_					):
                 # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
                 # This would be a good case for the `match` statement (Python 3.10+)
                 SCREAMING_SNAKE_CASE__					:					Optional[int]      =		latent_model_input.device.type == 'mps'
                 if isinstance(a_  ,			a_					):
                      SCREAMING_SNAKE_CASE__					:					Optional[int]      =		torch.floataa if is_mps else torch.floataa
                 else:
                      SCREAMING_SNAKE_CASE__					:					int      =		torch.intaa if is_mps else torch.intaa
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.tensor([timesteps]  ,			dtype=a_  ,			device=latent_model_input.device					)
            elif len(timesteps.shape					) == 0:
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		timesteps[None].to(latent_model_input.device					)
            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            SCREAMING_SNAKE_CASE__					:					List[str]      =		timesteps.expand(latent_model_input.shape[0]					)
            # predict noise model_output
            SCREAMING_SNAKE_CASE__					:					Tuple      =		self.transformer(
                a_  ,			timestep=a_  ,			class_labels=a_					).sample

            # perform guidance
            if guidance_scale > 1:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[str]      =		noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.split(a_  ,			len(a_					) // 2  ,			dim=0					)

                 SCREAMING_SNAKE_CASE__					:					str      =		uncond_eps + guidance_scale * (cond_eps - uncond_eps)
                 SCREAMING_SNAKE_CASE__					:					Any      =		torch.cat([half_eps, half_eps]  ,			dim=0					)

                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.cat([eps, rest]  ,			dim=1					)

            # learned sigma
            if self.transformer.config.out_channels // 2 == latent_channels:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					str      =		torch.split(a_  ,			a_  ,			dim=1					)
            else:
                 SCREAMING_SNAKE_CASE__					:					int      =		noise_pred

            # compute previous image: x_t -> x_t-1
            SCREAMING_SNAKE_CASE__					:					Tuple      =		self.scheduler.step(a_  ,			a_  ,			a_					).prev_sample

       if guidance_scale > 1:
            SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					int      =		latent_model_input.chunk(2  ,			dim=0					)
       else:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		latent_model_input

       SCREAMING_SNAKE_CASE__					:					List[str]      =		1 / self.vae.config.scaling_factor * latents
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.vae.decode(a_					).sample

       SCREAMING_SNAKE_CASE__					:					List[str]      =		(samples / 2 + 0.5).clamp(0  ,			1					)

       # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
       SCREAMING_SNAKE_CASE__					:					Dict      =		samples.cpu().permute(0  ,			2  ,			3  ,			1					).float().numpy()

       if output_type == "pil":
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		self.numpy_to_pil(a_					)

       if not return_dict:
            return (samples,)

       return ImagePipelineOutput(images=a_					)






def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						list:



    """simple docstring"""






    if bit_count < 0:
        raise ValueError("The given input must be positive"			)

    # get the generated string sequence
    UpperCamelCase_			  =						gray_code_sequence_string(SCREAMING_SNAKE_CASE_			)
    #
    # convert them to integers
    for i in range(len(SCREAMING_SNAKE_CASE_			)			):
        UpperCamelCase_			  =						int(sequence[i]			,						2			)

    return sequence




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						list:



    """simple docstring"""






    if bit_count == 0:
        return ["0"]

    if bit_count == 1:
        return ["0", "1"]

    UpperCamelCase_			  =						1 << bit_count  # defines the length of the sequence
    # 1<< n is equivalent to 2^n

    # recursive answer will generate answer for n-1 bits
    UpperCamelCase_			  =						gray_code_sequence_string(bit_count - 1			)

    UpperCamelCase_			  =						[]

    # append 0 to first half of the smaller sequence generated
    for i in range(seq_len // 2			):
        UpperCamelCase_			  =						"0" + smaller_sequence[i]
        sequence.append(SCREAMING_SNAKE_CASE_			)

    # append 1 to second half ... start from the end of the list
    for i in reversed(range(seq_len // 2			)			):
        UpperCamelCase_			  =						"1" + smaller_sequence[i]
        sequence.append(SCREAMING_SNAKE_CASE_			)

    return sequence


if __name__ == "__main__":
 import doctest

 doctest.testmod()
