





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


A_      =      transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def        __UpperCAmelCase    (       UpperCAmelCase       )->       Dict:



			"""simple docstring"""







			if isinstance(UpperCAmelCase,		torch.Tensor       ):
						return image
			elif isinstance(UpperCAmelCase,		PIL.Image.Image       ):
						lowercase							=		[image]

			lowercase							=		[trans(img.convert('''RGB'''       )       ) for img in image]
			lowercase							=		torch.stack(UpperCAmelCase       )
			return image






class      __lowercase					(    _lowerCAmelCase    ):

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[Any]   )							->		Dict:


									'''simple docstring'''

									super().__init__()

									# make sure scheduler can always be converted to DDIM
									lowercase							=		DDIMScheduler.from_config(scheduler.config   )

									self.register_modules(unet=_lowerCAmelCase							,							scheduler=_lowerCAmelCase   )

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      List[Any]   )							->		List[str]:


									'''simple docstring'''

									if strength < 0 or strength > 1:
												raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}'   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Optional[Any]   )							->		str:


									'''simple docstring'''

									lowercase							=		min(int(num_inference_steps * strength   )							,							_lowerCAmelCase   )

									lowercase							=		max(num_inference_steps - init_timestep							,							0   )
									lowercase							=		self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start

						def        __a		(	self				:      int							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      List[Any]=None   )							->		Optional[int]:


									'''simple docstring'''

									if not isinstance(_lowerCAmelCase							,							(torch.Tensor, PIL.Image.Image, list)   ):
												raise ValueError(
												    f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCAmelCase   )}'   )

									lowercase							=		image.to(device=_lowerCAmelCase							,							dtype=_lowerCAmelCase   )

									if isinstance(_lowerCAmelCase							,							_lowerCAmelCase   ) and len(_lowerCAmelCase   ) != batch_size:
												raise ValueError(
												    f'You have passed a list of generators of length {len(_lowerCAmelCase   )}, but requested an effective batch'
												    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

									lowercase							=		init_latents.shape
									lowercase							=		randn_tensor(_lowerCAmelCase							,							generator=_lowerCAmelCase							,							device=_lowerCAmelCase							,							dtype=_lowerCAmelCase   )

									# get latents
									print('''add noise to latents at timestep'''							,							_lowerCAmelCase   )
									lowercase							=		self.scheduler.add_noise(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase   )
									lowercase							=		init_latents

									return latents



						@torch.no_grad()
						def __call__(	self				:      Any							,							__lowerCamelCase				:      Union[torch.FloatTensor, PIL.Image.Image] = None							,							__lowerCamelCase				:      float = 0.8							,							__lowerCamelCase				:      int = 1							,							__lowerCamelCase				:      Optional[Union[torch.Generator, List[torch.Generator]]] = None							,							__lowerCamelCase				:      float = 0.0							,							__lowerCamelCase				:      int = 50							,							__lowerCamelCase				:      Optional[bool] = None							,							__lowerCamelCase				:      Optional[str] = "pil"							,							__lowerCamelCase				:      bool = True							,							)							->		Union[ImagePipelineOutput, Tuple]:


									'''simple docstring'''

									self.check_inputs(_lowerCAmelCase   )

									# 2. Preprocess image
									lowercase							=		preprocess(_lowerCAmelCase   )

									# 3. set timesteps
									self.scheduler.set_timesteps(_lowerCAmelCase							,							device=self.device   )
									lowercase		,lowercase							=		self.get_timesteps(_lowerCAmelCase							,							_lowerCAmelCase							,							self.device   )
									lowercase							=		timesteps[:1].repeat(_lowerCAmelCase   )

									# 4. Prepare latent variables
									lowercase							=		self.prepare_latents(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase							,							self.unet.dtype							,							self.device							,							_lowerCAmelCase   )
									lowercase							=		latents

									# 5. Denoising loop
									for t in self.progress_bar(_lowerCAmelCase   ):
												# 1. predict noise model_output
												lowercase							=		self.unet(_lowerCAmelCase							,							_lowerCAmelCase   ).sample

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												lowercase							=		self.scheduler.step(
												    _lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase							,							eta=_lowerCAmelCase							,							use_clipped_model_output=_lowerCAmelCase							,							generator=_lowerCAmelCase							,							).prev_sample

									lowercase							=		(image / 2 + 0.5).clamp(0							,							1   )
									lowercase							=		image.cpu().permute(0							,							2							,							3							,							1   ).numpy()
									if output_type == "pil":
												lowercase							=		self.numpy_to_pil(_lowerCAmelCase   )

									if not return_dict:
												return (image, latent_timestep.item())

									return ImagePipelineOutput(images=_lowerCAmelCase   )






from typing import TYPE_CHECKING

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


A_      =      {
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_      =      [
						    "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "GPTBigCodeForSequenceClassification",
						    "GPTBigCodeForTokenClassification",
						    "GPTBigCodeForCausalLM",
						    "GPTBigCodeModel",
						    "GPTBigCodePreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_gpt_bigcode import (
												    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    GPTBigCodeForCausalLM,
												    GPTBigCodeForSequenceClassification,
												    GPTBigCodeForTokenClassification,
												    GPTBigCodeModel,
												    GPTBigCodePreTrainedModel,
												)


else:
						import sys

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





