





# 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      							=   'Run commands across TPU VMs for initial setup before running `accelerate launch`.'




def  __UpperCAmelCase(   lowercase_=None   ):
       if subparsers is not None:
              _lowerCamelCase				:  int						=		subparsers.add_parser('''tpu-config'''			,     description=_description   )
       else:
              _lowerCamelCase				:  Optional[int]						=		argparse.ArgumentParser('''Accelerate tpu-config command'''			,     description=_description   )
       # Core arguments
       _lowerCamelCase				:  List[str]						=		parser.add_argument_group(
           '''Config Arguments'''			,     '''Arguments that can be configured through `accelerate config`.'''   )
       config_args.add_argument(
           '''--config_file'''			,     type=lowercase_			,     default=lowercase_			,     help='''Path to the config file to use for accelerate.'''			,     )
       config_args.add_argument(
           '''--tpu_name'''			,     default=lowercase_			,     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=lowercase_			,     help='''The zone of the TPU to use. If not specified, will use the zone specified in the config file.'''			,     )
       _lowerCamelCase				:  Tuple						=		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=lowercase_			,     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=lowercase_   )
       return parser




def  __UpperCAmelCase(   lowercase_   ):
       _lowerCamelCase				:  Dict						=		None

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

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

       # Then send it to gcloud
       # Eventually try to use google-api-core to do this instead of subprocess
       _lowerCamelCase				:  int						=		['''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(lowercase_   )}"""   )
              return
       subprocess.run(lowercase_   )
       print('''Successfully setup pod.'''   )




def  __UpperCAmelCase(   ):
       _lowerCamelCase				:  str						=		tpu_command_parser()
       _lowerCamelCase				:  Any						=		parser.parse_args()

       tpu_command_launcher(lowercase_   )








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




def  __UpperCAmelCase(   lowercase_   ):
       if isinstance(lowercase_			,     torch.Tensor   ):
              return image
       elif isinstance(lowercase_			,     PIL.Image.Image   ):
              _lowerCamelCase				:  List[str]						=		[image]

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







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()

             # make sure scheduler can always be converted to DDIM
             _lowerCamelCase				:  Optional[Any]						=		DDIMScheduler.from_config(scheduler.config)

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


      def 					__snake_case      (		self   ,				a__):



             """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 					__snake_case      (		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		min(int(num_inference_steps * strength)   ,				a__)

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

             return timesteps, num_inference_steps - t_start


      def 					__snake_case      (		self   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__   ,				a__=None):



             """simple docstring"""





             if not isinstance(a__   ,				(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(a__)}""")

             _lowerCamelCase				:  Any						=		image.to(device=a__   ,				dtype=a__)

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

             _lowerCamelCase				:  Any						=		init_latents.shape
             _lowerCamelCase				:  Tuple						=		randn_tensor(a__   ,				generator=a__   ,				device=a__   ,				dtype=a__)

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

             return latents



      @torch.no_grad()
      def __call__(		self   ,				a__ = None   ,				a__ = 0.8   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = None   ,				a__ = "pil"   ,				a__ = True   ,				):



             """simple docstring"""





             self.check_inputs(a__)

             # 2. Preprocess image
             _lowerCamelCase				:  Union[str, Any]						=		preprocess(a__)

             # 3. set timesteps
             self.scheduler.set_timesteps(a__   ,				device=self.device)
             _lowerCamelCase,	_lowerCamelCase				:  Any						=		self.get_timesteps(a__   ,				a__   ,				self.device)
             _lowerCamelCase				:  Union[str, Any]						=		timesteps[:1].repeat(a__)

             # 4. Prepare latent variables
             _lowerCamelCase				:  int						=		self.prepare_latents(a__   ,				a__   ,				a__   ,				self.unet.dtype   ,				self.device   ,				a__)
             _lowerCamelCase				:  int						=		latents

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

             _lowerCamelCase				:  str						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  Dict						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  Any						=		self.numpy_to_pil(a__)

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

             return ImagePipelineOutput(images=a__)

