




__A        =    {
    "Pillow": "Pillow<10.0.0",
    "accelerate": "accelerate>=0.20.3",
    "av": "av==9.2.0",
    "beautifulsoup4": "beautifulsoup4",
    "black": "black~=23.1",
    "codecarbon": "codecarbon==1.2.0",
    "cookiecutter": "cookiecutter==1.7.3",
    "dataclasses": "dataclasses",
    "datasets": "datasets!=2.5.0",
    "decord": "decord==0.6.0",
    "deepspeed": "deepspeed>=0.9.3",
    "diffusers": "diffusers",
    "dill": "dill<0.3.5",
    "evaluate": "evaluate>=0.2.0",
    "fairscale": "fairscale>0.3",
    "faiss-cpu": "faiss-cpu",
    "fastapi": "fastapi",
    "filelock": "filelock",
    "flax": "flax>=0.4.1,<=0.7.0",
    "ftfy": "ftfy",
    "fugashi": "fugashi>=1.0",
    "GitPython": "GitPython<3.1.19",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.14.1,<1.0",
    "importlib_metadata": "importlib_metadata",
    "ipadic": "ipadic>=1.0.0,<2.0",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2,<=0.4.13",
    "jaxlib": "jaxlib>=0.1.65,<=0.4.13",
    "jieba": "jieba",
    "kenlm": "kenlm",
    "keras-nlp": "keras-nlp>=0.3.1",
    "librosa": "librosa",
    "nltk": "nltk",
    "natten": "natten>=0.14.6",
    "numpy": "numpy>=1.17",
    "onnxconverter-common": "onnxconverter-common",
    "onnxruntime-tools": "onnxruntime-tools>=1.4.2",
    "onnxruntime": "onnxruntime>=1.4.0",
    "opencv-python": "opencv-python",
    "optuna": "optuna",
    "optax": "optax>=0.0.8,<=0.1.4",
    "packaging": "packaging>=20.0",
    "parameterized": "parameterized",
    "phonemizer": "phonemizer",
    "protobuf": "protobuf",
    "psutil": "psutil",
    "pyyaml": "pyyaml>=5.1",
    "pydantic": "pydantic<2",
    "pytest": "pytest>=7.2.0",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "python": "python>=3.8.0",
    "ray[tune]": "ray[tune]",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
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    "rouge-score": "rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1",
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    "sacremoses": "sacremoses",
    "safetensors": "safetensors>=0.3.1",
    "sagemaker": "sagemaker>=2.31.0",
    "scikit-learn": "scikit-learn",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "sigopt": "sigopt",
    "starlette": "starlette",
    "sudachipy": "sudachipy>=0.6.6",
    "sudachidict_core": "sudachidict_core>=20220729",
    "tensorflow-cpu": "tensorflow-cpu>=2.6,<2.14",
    "tensorflow": "tensorflow>=2.6,<2.14",
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    "tf2onnx": "tf2onnx",
    "timeout-decorator": "timeout-decorator",
    "timm": "timm",
    "tokenizers": "tokenizers>=0.11.1,!=0.11.3,<0.14",
    "torch": "torch>=1.9,!=1.12.0",
    "torchaudio": "torchaudio",
    "torchvision": "torchvision",
    "pyctcdecode": "pyctcdecode>=0.4.0",
    "tqdm": "tqdm>=4.27",
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    "urllib3": "urllib3<2.0.0",
    "uvicorn": "uvicorn",
}







"""simple docstring"""

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        _UpperCAmelCase       (			lowerCAmelCase__):

 def __init__(     self		:       Any  ,     lowercase_		:       TransformeraDModel  ,     lowercase_		:       AutoencoderKL  ,     lowercase_		:       KarrasDiffusionSchedulers  ,     lowercase_		:       Optional[Dict[int, str]] = None  ,     ):
     super().__init__()
     self.register_modules(transformer=lowercase_  ,     vae=lowercase_  ,     scheduler=lowercase_				)

     # create a imagenet -> id dictionary for easier use
     snake_case_ :     Tuple				=	{}
     if idalabel is not None:
         for key, value in idalabel.items():
             for label in value.split(''','''				):
                 snake_case_ :     str				=	int(lowercase_				)
         snake_case_ :     Any				=	dict(sorted(self.labels.items()				)				)

 def 						_snake_case					(     self		:       List[Any]  ,     lowercase_		:       Union[str, List[str]]				):

     if not isinstance(lowercase_  ,     lowercase_				):
         snake_case_ :     Tuple				=	list(lowercase_				)

     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[int]  ,     lowercase_		:       List[int]  ,     lowercase_		:       float = 4.0  ,     lowercase_		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     lowercase_		:       int = 50  ,     lowercase_		:       Optional[str] = "pil"  ,     lowercase_		:       bool = True  ,     ):
     snake_case_ :     Any				=	len(lowercase_				)
     snake_case_ :     List[str]				=	self.transformer.config.sample_size
     snake_case_ :     Union[str, Any]				=	self.transformer.config.in_channels

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

     snake_case_ :     Optional[int]				=	torch.tensor(lowercase_  ,     device=self.device				).reshape(-1				)
     snake_case_ :     Dict				=	torch.tensor([1000] * batch_size  ,     device=self.device				)
     snake_case_ :     Tuple				=	torch.cat([class_labels, class_null]  ,     0				) if guidance_scale > 1 else class_labels

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

     for t in self.progress_bar(self.scheduler.timesteps				):
         if guidance_scale > 1:
             snake_case_ :     List[Any]				=	latent_model_input[: len(lowercase_				) // 2]
             snake_case_ :     Union[str, Any]				=	torch.cat([half, half]  ,     dim=0				)
         snake_case_ :     Optional[Any]				=	self.scheduler.scale_model_input(lowercase_  ,     lowercase_				)

         snake_case_ :     int				=	t
         if not torch.is_tensor(lowercase_				):
             # 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+)
             snake_case_ :     Tuple				=	latent_model_input.device.type == '''mps'''
             if isinstance(lowercase_  ,     lowercase_				):
                 snake_case_ :     List[str]				=	torch.floataa if is_mps else torch.floataa
             else:
                 snake_case_ :     Optional[int]				=	torch.intaa if is_mps else torch.intaa
             snake_case_ :     List[Any]				=	torch.tensor([timesteps]  ,     dtype=lowercase_  ,     device=latent_model_input.device				)
         elif len(timesteps.shape				) == 0:
             snake_case_ :     str				=	timesteps[None].to(latent_model_input.device				)
         # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
         snake_case_ :     Tuple				=	timesteps.expand(latent_model_input.shape[0]				)
         # predict noise model_output
         snake_case_ :     List[Any]				=	self.transformer(
             lowercase_  ,     timestep=lowercase_  ,     class_labels=lowercase_				).sample

         # perform guidance
         if guidance_scale > 1:
             snake_case_,	snake_case_ :     Dict				=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
             snake_case_,	snake_case_ :     Any				=	torch.split(lowercase_  ,     len(lowercase_				) // 2  ,     dim=0				)

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

             snake_case_ :     List[Any]				=	torch.cat([eps, rest]  ,     dim=1				)

         # learned sigma
         if self.transformer.config.out_channels // 2 == latent_channels:
             snake_case_,	snake_case_ :     Optional[Any]				=	torch.split(lowercase_  ,     lowercase_  ,     dim=1				)
         else:
             snake_case_ :     List[str]				=	noise_pred

         # compute previous image: x_t -> x_t-1
         snake_case_ :     int				=	self.scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_				).prev_sample

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

     snake_case_ :     Union[str, Any]				=	1 / self.vae.config.scaling_factor * latents
     snake_case_ :     Tuple				=	self.vae.decode(lowercase_				).sample

     snake_case_ :     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
     snake_case_ :     Union[str, Any]				=	samples.cpu().permute(0  ,     2  ,     3  ,     1				).float().numpy()

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

     if not return_dict:
         return (samples,)

     return ImagePipelineOutput(images=lowercase_				)





