

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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	if isinstance(_A     ,			torch.Tensor      ):
		return image
	elif isinstance(_A     ,			PIL.Image.Image      ):
		__lowerCamelCase    :    str 	=			[image]

	__lowerCamelCase    :    Dict 	=			[trans(img.convert('RGB'      )      ) for img in image]
	__lowerCamelCase    :    Optional[int] 	=			torch.stack(_A      )
	return image






class 	A_		(	lowerCamelCase__    ):
							def __init__(		self		:      List[Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								super().__init__()

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

								self.register_modules(unet=__snake_case       ,scheduler=__snake_case)
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]):
								if strength < 0 or strength > 1:
									raise ValueError(F"The value of strength should in [0.0, 1.0] but is {strength}")
							def    lowerCAmelCase					(		self		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      List[str]):
								# get the original timestep using init_timestep
								__lowerCamelCase    :    Union[str, Any] 	=			min(int(num_inference_steps * strength)       ,__snake_case)

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

								return timesteps, num_inference_steps - t_start
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Dict=None):
								if not isinstance(__snake_case       ,(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(__snake_case)}")

								__lowerCamelCase    :    Union[str, Any] 	=			image.to(device=__snake_case       ,dtype=__snake_case)

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

								__lowerCamelCase    :    List[Any] 	=			init_latents.shape
								__lowerCamelCase    :    str 	=			randn_tensor(__snake_case       ,generator=__snake_case       ,device=__snake_case       ,dtype=__snake_case)

								# get latents
								print('add noise to latents at timestep'       ,__snake_case)
								__lowerCamelCase    :    List[Any] 	=			self.scheduler.add_noise(__snake_case       ,__snake_case       ,__snake_case)
								__lowerCamelCase    :    Dict 	=			init_latents

								return latents


							@torch.no_grad()
							def __call__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Union[torch.FloatTensor, PIL.Image.Image] = None       ,SCREAMING_SNAKE_CASE__		:      float = 0.8       ,SCREAMING_SNAKE_CASE__		:      int = 1       ,SCREAMING_SNAKE_CASE__		:      Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,SCREAMING_SNAKE_CASE__		:      float = 0.0       ,SCREAMING_SNAKE_CASE__		:      int = 5_0       ,SCREAMING_SNAKE_CASE__		:      Optional[bool] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[str] = "pil"       ,SCREAMING_SNAKE_CASE__		:      bool = True       ,):
								self.check_inputs(__snake_case)

								# 2. Preprocess image
								__lowerCamelCase    :    Tuple 	=			preprocess(__snake_case)

								# 3. set timesteps
								self.scheduler.set_timesteps(__snake_case       ,device=self.device)
								__lowerCamelCase    :    Optional[Any] 	=			self.get_timesteps(__snake_case       ,__snake_case       ,self.device)
								__lowerCamelCase    :    List[str] 	=			timesteps[:1].repeat(__snake_case)

								# 4. Prepare latent variables
								__lowerCamelCase    :    List[str] 	=			self.prepare_latents(__snake_case       ,__snake_case       ,__snake_case       ,self.unet.dtype       ,self.device       ,__snake_case)
								__lowerCamelCase    :    Optional[Any] 	=			latents

								# 5. Denoising loop
								for t in self.progress_bar(__snake_case):
									# 1. predict noise model_output
									__lowerCamelCase    :    List[Any] 	=			self.unet(__snake_case       ,__snake_case).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
									__lowerCamelCase    :    Optional[int] 	=			self.scheduler.step(
									    __snake_case       ,__snake_case       ,__snake_case       ,eta=__snake_case       ,use_clipped_model_output=__snake_case       ,generator=__snake_case       ,).prev_sample

								__lowerCamelCase    :    Optional[Any] 	=			(image / 2 + 0.5).clamp(0       ,1)
								__lowerCamelCase    :    Union[str, Any] 	=			image.cpu().permute(0       ,2       ,3       ,1).numpy()
								if output_type == "pil":
									__lowerCamelCase    :    List[str] 	=			self.numpy_to_pil(__snake_case)

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

								return ImagePipelineOutput(images=__snake_case)







# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


a		=[
    """EAGER""",
    """AOT_EAGER""",
    """INDUCTOR""",
    """NVFUSER""",
    """AOT_NVFUSER""",
    """AOT_CUDAGRAPHS""",
    """OFI""",
    """FX2TRT""",
    """ONNXRT""",
    """IPEX""",
]






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None     ,			lowerCamelCase__=None      )  ->					Optional[int]:
	__lowerCamelCase    :    int 	=			True
	while ask_again:
		__lowerCamelCase    :    Dict 	=			input(lowerCamelCase__      )
		try:
			if default is not None and len(lowerCamelCase__      ) == 0:
				return default
			return convert_value(lowerCamelCase__      ) if convert_value is not None else result
		except Exception:
			if error_message is not None:
				print(lowerCamelCase__      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=[]     ,			lowerCamelCase__=None     ,			lowerCamelCase__=0      )  ->					str:
	__lowerCamelCase    :    Union[str, Any] 	=			BulletMenu(lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase    :    Tuple 	=			menu.run(default_choice=lowerCamelCase__      )
	return convert_value(lowerCamelCase__      ) if convert_value is not None else result






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Dict:
	__lowerCamelCase    :    List[str] 	=			int(lowerCamelCase__      )
	return ComputeEnvironment(['LOCAL_MACHINE', 'AMAZON_SAGEMAKER'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return DistributedType(['NO', 'MULTI_CPU', 'MULTI_XPU', 'MULTI_GPU', 'MULTI_NPU', 'TPU'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Union[str, Any]:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return DynamoBackend(DYNAMO_BACKENDS[value]      ).value






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					int:
	__lowerCamelCase    :    Union[str, Any] 	=			int(lowerCamelCase__      )
	return PrecisionType(['no', 'fp16', 'bf16', 'fp8'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					str:
	__lowerCamelCase    :    Optional[Any] 	=			int(lowerCamelCase__      )
	return SageMakerDistributedType(['NO', 'DATA_PARALLEL', 'MODEL_PARALLEL'][value]      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	return {"yes": True, "no": False}[value.lower()]






class 	A_		(	argparse.RawDescriptionHelpFormatter    ):
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str):
								__lowerCamelCase    :    int 	=			super()._format_usage(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			usage.replace('<command> [<args>] '       ,'')
								return usage




