


# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      42
     _lowerCamelCase   =      None







def  a   (lowerCAmelCase__		,				lowerCAmelCase__=0.9_9_9		,				lowerCAmelCase__="cosine"		,				):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(lowerCAmelCase__   ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(lowerCAmelCase__   ):
         return math.exp(t * -1_2.0   )

   else:
      raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''   )

   __a					=  []
   for i in range(lowerCAmelCase__   ):
      __a					=  i / num_diffusion_timesteps
      __a					=  (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(lowerCAmelCase__   ) / alpha_bar_fn(lowerCAmelCase__   )		,				lowerCAmelCase__   )   )
   return torch.tensor(lowerCAmelCase__		,				dtype=torch.floataa   )


class     __UpperCAmelCase							(       __A ,					__A							):


     """simple docstring"""
     _lowerCamelCase   =      1


     @register_to_config
     def __init__(	self  ,       __A = 1000  ,       __A = 0.0001  ,       __A = 0.02  ,       __A = "linear"  ,       __A = None  ,       __A = True  ,       __A = True  ,       __A = 0  ,       __A = "epsilon"  ,       __A = 1.0  ,       **__A  ,       ):
        if kwargs.get("""set_alpha_to_one"""  ,       __A    ) is not None:
           __a					=  (
               """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
           )
           deprecate("""set_alpha_to_one"""  ,       """1.0.0"""  ,       __A  ,       standard_warn=__A    )
           __a					=  kwargs["""set_alpha_to_one"""]
        if trained_betas is not None:
           __a					=  torch.tensor(__A  ,       dtype=torch.floataa    )
        elif beta_schedule == "linear":
           __a					=  torch.linspace(__A  ,       __A  ,       __A  ,       dtype=torch.floataa    )
        elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           __a					=  (
               torch.linspace(beta_start**0.5  ,       beta_end**0.5  ,       __A  ,       dtype=torch.floataa    ) ** 2
           )
        elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           __a					=  betas_for_alpha_bar(__A    )
        else:
           raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''    )

        __a					=  1.0 - self.betas
        __a					=  torch.cumprod(self.alphas  ,       dim=0    )

        # At every step in inverted ddim, we are looking into the next alphas_cumprod
        # For the final step, there is no next alphas_cumprod, and the index is out of bounds
        # `set_alpha_to_zero` decides whether we set this parameter simply to zero
        # in this case, self.step() just output the predicted noise
        # or whether we use the final alpha of the "non-previous" one.
        __a					=  torch.tensor(0.0    ) if set_alpha_to_zero else self.alphas_cumprod[-1]

        # standard deviation of the initial noise distribution
        __a					=  1.0

        # setable values
        __a					=  None
        __a					=  torch.from_numpy(np.arange(0  ,       __A    ).copy().astype(np.intaa    )    )


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):
        return sample


     def 	snake_case_  (	self  ,       __A  ,       __A = None    ):

        if num_inference_steps > self.config.num_train_timesteps:
           raise ValueError(
               f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
               f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
               f''' maximal {self.config.num_train_timesteps} timesteps.'''    )

        __a					=  num_inference_steps
        __a					=  self.config.num_train_timesteps // self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        __a					=  (np.arange(0  ,       __A    ) * step_ratio).round().copy().astype(np.intaa    )
        __a					=  torch.from_numpy(__A    ).to(__A    )
        self.timesteps += self.config.steps_offset


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A = 0.0  ,       __A = False  ,       __A = None  ,       __A = True  ,       ):
        # 1. get previous step value (=t+1)
        __a					=  timestep + self.config.num_train_timesteps // self.num_inference_steps

        # 2. compute alphas, betas
        # change original implementation to exactly match noise levels for analogous forward process
        __a					=  self.alphas_cumprod[timestep]
        __a					=  (
            self.alphas_cumprod[prev_timestep]
            if prev_timestep < self.config.num_train_timesteps
            else self.final_alpha_cumprod
        )

        __a					=  1 - alpha_prod_t

        # 3. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        if self.config.prediction_type == "epsilon":
           __a					=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           __a					=  model_output
        elif self.config.prediction_type == "sample":
           __a					=  model_output
           __a					=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
        elif self.config.prediction_type == "v_prediction":
           __a					=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
           __a					=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
        else:
           raise ValueError(
               f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
               """ `v_prediction`"""    )

        # 4. Clip or threshold "predicted x_0"
        if self.config.clip_sample:
           __a					=  pred_original_sample.clamp(
               -self.config.clip_sample_range  ,       self.config.clip_sample_range    )

        # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        __a					=  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

        # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        __a					=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

        if not return_dict:
           return (prev_sample, pred_original_sample)
        return DDIMSchedulerOutput(prev_sample=__A  ,       pred_original_sample=__A    )


     def __len__(	self    ):
        return self.config.num_train_timesteps










# 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


__A							:			Optional[int]         =			"Run commands across TPU VMs for initial setup before running `accelerate launch`."





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE=None				)							-> str:




    """simple docstring"""




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





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> int:




    """simple docstring"""




    _A     =							None

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

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

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





def 				__lowerCAmelCase(       )							-> Tuple:




    """simple docstring"""




    _A     =							tpu_command_parser()
    _A     =							parser.parse_args()

    tpu_command_launcher(_SCREAMING_SNAKE_CASE				)





