

'''simple docstring'''


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


lowerCAmelCase__						:       str										=						transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def 	_a       (				__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		if isinstance(__lowerCAmelCase   ,							torch.Tensor     ):
				return image
		elif isinstance(__lowerCAmelCase   ,							PIL.Image.Image     ):
				snake_case__						:       Any       						=  [image]

		snake_case__						:       Any       						=  [trans(img.convert('''RGB'''     )     ) for img in image]
		snake_case__						:       Optional[Any]       						=  torch.stack(__lowerCAmelCase     )
		return image





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def __init__(							self					:      str			,			snake_case_					:      List[Any]			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case__						:       Union[str, Any]       						=  DDIMScheduler.from_config(scheduler.config  )

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







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      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       __magic_name__							(							self					:      List[str]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Tuple			,			snake_case_					:      Optional[int]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  min(int(num_inference_steps * strength  )			,			snake_case_  )

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

								return timesteps, num_inference_steps - t_start







						def       __magic_name__							(							self					:      str			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Optional[Any]			,			snake_case_					:      Dict			,			snake_case_					:      List[str]			,			snake_case_					:      Dict			,			snake_case_					:      Union[str, Any]=None  ):



								'''simple docstring'''



								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_  )}"""  )

								snake_case__						:       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."""  )

								snake_case__						:       List[str]       						=  init_latents.shape
								snake_case__						:       Optional[Any]       						=  randn_tensor(snake_case_			,			generator=snake_case_			,			device=snake_case_			,			dtype=snake_case_  )

								# get latents
								print('''add noise to latents at timestep'''			,			snake_case_  )
								snake_case__						:       str       						=  self.scheduler.add_noise(snake_case_			,			snake_case_			,			snake_case_  )
								snake_case__						:       Tuple       						=  init_latents

								return latents







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



								'''simple docstring'''



								self.check_inputs(snake_case_  )

								# 2. Preprocess image
								snake_case__						:       Dict       						=  preprocess(snake_case_  )

								# 3. set timesteps
								self.scheduler.set_timesteps(snake_case_			,			device=self.device  )
								snake_case__						,       snake_case__						:       List[Any]       						=  self.get_timesteps(snake_case_			,			snake_case_			,			self.device  )
								snake_case__						:       Any       						=  timesteps[:1].repeat(snake_case_  )

								# 4. Prepare latent variables
								snake_case__						:       int       						=  self.prepare_latents(snake_case_			,			snake_case_			,			snake_case_			,			self.unet.dtype			,			self.device			,			snake_case_  )
								snake_case__						:       Optional[Any]       						=  latents

								# 5. Denoising loop
								for t in self.progress_bar(snake_case_  ):
										# 1. predict noise model_output
										snake_case__						:       Dict       						=  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
										snake_case__						:       Tuple       						=  self.scheduler.step(
										    snake_case_			,			snake_case_			,			snake_case_			,			eta=snake_case_			,			use_clipped_model_output=snake_case_			,			generator=snake_case_			,			).prev_sample

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

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

								return ImagePipelineOutput(images=snake_case_  )






'''simple docstring'''


# Copyright 2022 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
import os
import subprocess

from packaging.version import Version, parse

from accelerate.commands.config.config_args import default_config_file, load_config_from_file


lowerCAmelCase__						:       List[Any]										=						"""Run commands across TPU VMs for initial setup before running `accelerate launch`."""




def 	_a       (				__lowerCAmelCase		:      Any=None     ):

		"""simple docstring"""

		if subparsers is not None:
				snake_case__						:       Tuple       						=  subparsers.add_parser('''tpu-config'''   ,							description=_description     )
		else:
				snake_case__						:       str       						=  argparse.ArgumentParser('''Accelerate tpu-config command'''   ,							description=_description     )
		# Core arguments
		snake_case__						:       Any       						=  parser.add_argument_group(
		    '''Config Arguments'''   ,							'''Arguments that can be configured through `accelerate config`.'''     )
		config_args.add_argument(
		    '''--config_file'''   ,							type=__lowerCAmelCase   ,							default=__lowerCAmelCase   ,							help='''Path to the config file to use for accelerate.'''   ,							)
		config_args.add_argument(
		    '''--tpu_name'''   ,							default=__lowerCAmelCase   ,							help='''The name of the TPU to use. If not specified, will use the TPU specified in the config file.'''   ,							)
		config_args.add_argument(
		    '''--tpu_zone'''   ,							default=__lowerCAmelCase   ,							help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''   ,							)
		snake_case__						:       Optional[Any]       						=  parser.add_argument_group('''TPU Arguments'''   ,							'''Arguments for options ran inside the TPU.'''     )
		pod_args.add_argument(
		    '''--use_alpha'''   ,							action='''store_true'''   ,							help='''Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.'''   ,							)
		pod_args.add_argument(
		    '''--command_file'''   ,							default=__lowerCAmelCase   ,							help='''The path to the file containing the commands to run on the pod on startup.'''   ,							)
		pod_args.add_argument(
		    '''--command'''   ,							action='''append'''   ,							nargs='''+'''   ,							help='''A command to run on the pod. Can be passed multiple times.'''   ,							)
		pod_args.add_argument(
		    '''--install_accelerate'''   ,							action='''store_true'''   ,							help='''Whether to install accelerate on the pod. Defaults to False.'''   ,							)
		pod_args.add_argument(
		    '''--accelerate_version'''   ,							default='''latest'''   ,							help='''The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify \'dev\' to install from GitHub.'''   ,							)
		pod_args.add_argument(
		    '''--debug'''   ,							action='''store_true'''   ,							help='''If set, will print the command that would be run instead of running it.'''     )

		if subparsers is not None:
				parser.set_defaults(func=__lowerCAmelCase     )
		return parser




def 	_a       (				__lowerCAmelCase		:      Optional[Any]     ):

		"""simple docstring"""

		snake_case__						:       Optional[int]       						=  None

		# Get the default from the config file if it exists.
		if args.config_file is not None or os.path.isfile(__lowerCAmelCase     ):
				snake_case__						:       Tuple       						=  load_config_from_file(args.config_file     )
				if not args.command_file and defaults.command_file is not None and not args.command:
						snake_case__						:       Optional[int]       						=  defaults.command_file
				if not args.command and defaults.commands is not None:
						snake_case__						:       Union[str, Any]       						=  defaults.commands
				if not args.tpu_name:
						snake_case__						:       Union[str, Any]       						=  defaults.tpu_name
				if not args.tpu_zone:
						snake_case__						:       Optional[Any]       						=  defaults.tpu_zone
		if args.accelerate_version == "dev":
				snake_case__						:       Tuple       						=  '''git+https://github.com/huggingface/accelerate.git'''
		elif args.accelerate_version == "latest":
				snake_case__						:       Union[str, Any]       						=  '''accelerate -U'''
		elif isinstance(parse(args.accelerate_version     )   ,							__lowerCAmelCase     ):
				snake_case__						:       Any       						=  F"""accelerate=={args.accelerate_version}"""

		if not args.command_file and not args.command:
				raise ValueError('''You must specify either a command file or a command to run on the pod.'''     )

		if args.command_file:
				with open(args.command_file   ,							'''r'''     ) as f:
						snake_case__						:       str       						=  [f.read().splitlines()]

    # To turn list of lists into list of strings
		if isinstance(args.command[0]   ,							__lowerCAmelCase     ):
				snake_case__						:       List[Any]       						=  [line for cmd in args.command for line in cmd]
		# Default to the shared folder and install accelerate
		snake_case__						:       Optional[int]       						=  ['''cd /usr/share''']
		if args.install_accelerate:
				new_cmd += [F"""pip install {args.accelerate_version}"""]
		new_cmd += args.command
		snake_case__						:       Tuple       						=  '''; '''.join(__lowerCAmelCase     )

		# Then send it to gcloud
		# Eventually try to use google-api-core to do this instead of subprocess
		snake_case__						:       str       						=  ['''gcloud''']
		if args.use_alpha:
				cmd += ["alpha"]
		cmd += [
		    "compute",
		    "tpus",
		    "tpu-vm",
		    "ssh",
		    args.tpu_name,
		    "--zone",
		    args.tpu_zone,
		    "--command",
		    args.command,
		    "--worker",
		    "all",
		]
		if args.debug:
				print(F"""Running {' '.join(__lowerCAmelCase     )}"""     )
				return
		subprocess.run(__lowerCAmelCase     )
		print('''Successfully setup pod.'''     )





def 	_a       (				):

		"""simple docstring"""

		snake_case__						:       Dict       						=  tpu_command_parser()
		snake_case__						:       Dict       						=  parser.parse_args()

		tpu_command_launcher(__lowerCAmelCase     )



