'''simple docstring'''
import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
 import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME




class _lowerCAmelCase			(	snake_case					):







      def __init__(  self     ):
        self.register_to_config()
        super().__init__()







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase     ):
        if dicta == dicta:
          return True
        else:
          lowercase				,      lowercase				:							Any									=   self._remove_meta_keys(lowercase     )
          lowercase				,      lowercase				:							List[str]									=   self._remove_meta_keys(lowercase     )
          if configa == configa:
            print(F"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."""     )
            return True
        return False







      def      lowerCamelCase__      (  self  ,    lowercase     ):
        lowercase				:							Optional[Any]									=   []
        lowercase				:							Optional[Any]									=   config_dict.copy()
        for key in config_dict.keys():
          if key.startswith('''_'''     ):
            temp_dict.pop(lowercase     )
            meta_keys.append(lowercase     )
        return (temp_dict, meta_keys)







      @torch.no_grad()
      def      lowerCamelCase__      (  self  ,    lowercase  ,    **lowercase     ):
        lowercase				:							str									=   kwargs.pop('''cache_dir'''  ,    lowercase     )
        lowercase				:							Optional[int]									=   kwargs.pop('''resume_download'''  ,    lowercase     )
        lowercase				:							Union[str, Any]									=   kwargs.pop('''force_download'''  ,    lowercase     )
        lowercase				:							Optional[Any]									=   kwargs.pop('''proxies'''  ,    lowercase     )
        lowercase				:							Dict									=   kwargs.pop('''local_files_only'''  ,    lowercase     )
        lowercase				:							List[str]									=   kwargs.pop('''use_auth_token'''  ,    lowercase     )
        lowercase				:							Dict									=   kwargs.pop('''revision'''  ,    lowercase     )
        lowercase				:							Union[str, Any]									=   kwargs.pop('''torch_dtype'''  ,    lowercase     )
        lowercase				:							Tuple									=   kwargs.pop('''device_map'''  ,    lowercase     )

        lowercase				:							Optional[int]									=   kwargs.pop('''alpha'''  ,    0.5     )
        lowercase				:							Any									=   kwargs.pop('''interp'''  ,    lowercase     )

        print('''Received list'''  ,    lowercase     )
        print(F"""Combining with alpha={alpha}, interpolation mode={interp}"""     )

        lowercase				:							Union[str, Any]									=   len(lowercase     )
        # Ignore result from model_index_json comparision of the two checkpoints
        lowercase				:							List[Any]									=   kwargs.pop('''force'''  ,    lowercase     )

        # If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
        if checkpoint_count > 3 or checkpoint_count < 2:
          raise ValueError(
              '''Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being'''
              ''' passed.'''     )

        print('''Received the right number of checkpoints'''     )
        # chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
        # chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

        # Validate that the checkpoints can be merged
        # Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
        lowercase				:							Union[str, Any]									=   []
        for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
          lowercase				:							Union[str, Any]									=   DiffusionPipeline.load_config(
              lowercase  ,    cache_dir=lowercase  ,    resume_download=lowercase  ,    force_download=lowercase  ,    proxies=lowercase  ,    local_files_only=lowercase  ,    use_auth_token=lowercase  ,    revision=lowercase  ,    )
          config_dicts.append(lowercase     )

        lowercase				:							Optional[Any]									=   True
        for idx in range(1  ,    len(lowercase     )     ):
          comparison_result &= self._compare_model_configs(config_dicts[idx - 1]  ,    config_dicts[idx]     )
          if not force and comparison_result is False:
            raise ValueError('''Incompatible checkpoints. Please check model_index.json for the models.'''     )
            print(config_dicts[0]  ,    config_dicts[1]     )
        print('''Compatible model_index.json files found'''     )
        # Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
        lowercase				:							str									=   []
        for pretrained_model_name_or_path, config_dict in zip(lowercase  ,    lowercase     ):
          lowercase				:							List[str]									=   [k for k in config_dict.keys() if not k.startswith('''_'''     )]
          lowercase				:							str									=   [os.path.join(lowercase  ,    '''*'''     ) for k in folder_names]
          allow_patterns += [
              WEIGHTS_NAME,
              SCHEDULER_CONFIG_NAME,
              CONFIG_NAME,
              ONNX_WEIGHTS_NAME,
              DiffusionPipeline.config_name,
          ]
          lowercase				:							Tuple									=   config_dict.get('''_class_name'''     )
          lowercase				:							Any									=   {'''diffusers''': __version__, '''pipeline_class''': requested_pipeline_class}

          lowercase				:							List[Any]									=   (
              pretrained_model_name_or_path
              if os.path.isdir(lowercase     )
              else snapshot_download(
                  lowercase  ,    cache_dir=lowercase  ,    resume_download=lowercase  ,    proxies=lowercase  ,    local_files_only=lowercase  ,    use_auth_token=lowercase  ,    revision=lowercase  ,    allow_patterns=lowercase  ,    user_agent=lowercase  ,    )
          )
          print('''Cached Folder'''  ,    lowercase     )
          cached_folders.append(lowercase     )

        # Step 3:-
        # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
        lowercase				:							List[Any]									=   DiffusionPipeline.from_pretrained(
            cached_folders[0]  ,    torch_dtype=lowercase  ,    device_map=lowercase     )
        final_pipe.to(self.device     )

        lowercase				:							str									=   None
        if len(lowercase     ) > 2:
          lowercase				:							Any									=   os.path.join(cached_folders[2]     )

        if interp == "sigmoid":
          lowercase				:							Union[str, Any]									=   CheckpointMergerPipeline.sigmoid
        elif interp == "inv_sigmoid":
          lowercase				:							str									=   CheckpointMergerPipeline.inv_sigmoid
        elif interp == "add_diff":
          lowercase				:							Optional[Any]									=   CheckpointMergerPipeline.add_difference
        else:
          lowercase				:							Union[str, Any]									=   CheckpointMergerPipeline.weighted_sum

        # Find each module's state dict.
        for attr in final_pipe.config.keys():
          if not attr.startswith('''_'''     ):
            lowercase				:							int									=   os.path.join(cached_folders[1]  ,    lowercase     )
            if os.path.exists(lowercase     ):
              lowercase				:							Optional[Any]									=   [
                  *glob.glob(os.path.join(lowercase  ,    '''*.safetensors'''     )     ),
                  *glob.glob(os.path.join(lowercase  ,    '''*.bin'''     )     ),
              ]
              lowercase				:							Tuple									=   files[0] if len(lowercase     ) > 0 else None
            if len(lowercase     ) < 3:
              lowercase				:							Tuple									=   None
            else:
              lowercase				:							int									=   os.path.join(cached_folders[2]  ,    lowercase     )
              if os.path.exists(lowercase     ):
                lowercase				:							int									=   [
                    *glob.glob(os.path.join(lowercase  ,    '''*.safetensors'''     )     ),
                    *glob.glob(os.path.join(lowercase  ,    '''*.bin'''     )     ),
                ]
                lowercase				:							int									=   files[0] if len(lowercase     ) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
            if checkpoint_path_a is None and checkpoint_path_a is None:
              print(F"""Skipping {attr}: not present in 2nd or 3d model"""     )
              continue
            try:
              lowercase				:							Tuple									=   getattr(lowercase  ,    lowercase     )
              if isinstance(lowercase  ,    lowercase     ):  # ignore requires_safety_checker boolean
                continue
              lowercase				:							List[Any]									=   getattr(lowercase  ,    '''state_dict'''     )
              lowercase				:							Optional[int]									=   theta_a()

              lowercase				:							str									=   getattr(lowercase  ,    '''load_state_dict'''     )
              lowercase				:							List[Any]									=   (
                  safetensors.torch.load_file(lowercase     )
                  if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''     ))
                  else torch.load(lowercase  ,    map_location='''cpu'''     )
              )
              lowercase				:							str									=   None
              if checkpoint_path_a:
                lowercase				:							Tuple									=   (
                    safetensors.torch.load_file(lowercase     )
                    if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''     ))
                    else torch.load(lowercase  ,    map_location='''cpu'''     )
                )

              if not theta_a.keys() == theta_a.keys():
                print(F"""Skipping {attr}: key mismatch"""     )
                continue
              if theta_a and not theta_a.keys() == theta_a.keys():
                print(F"""Skipping {attr}:y mismatch"""     )
            except Exception as e:
              print(F"""Skipping {attr} do to an unexpected error: {str(lowercase     )}"""     )
              continue
            print(F"""MERGING {attr}"""     )

            for key in theta_a.keys():
              if theta_a:
                lowercase				:							Any									=   theta_func(theta_a[key]  ,    theta_a[key]  ,    theta_a[key]  ,    lowercase     )
              else:
                lowercase				:							Optional[int]									=   theta_func(theta_a[key]  ,    theta_a[key]  ,    lowercase  ,    lowercase     )

            del theta_a
            del theta_a
            update_theta_a(lowercase     )

            del theta_a
        return final_pipe







      @staticmethod
      def      lowerCamelCase__      (  lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ):
        return ((1 - alpha) * thetaa) + (alpha * thetaa)







      @staticmethod
      def      lowerCamelCase__      (  lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Union[str, Any]									=   alpha * alpha * (3 - (2 * alpha))
        return thetaa + ((thetaa - thetaa) * alpha)







      @staticmethod
      def      lowerCamelCase__      (  lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ):
        import math

        lowercase				:							Any									=   0.5 - math.sin(math.asin(1.0 - 2.0 * alpha     ) / 3.0     )
        return thetaa + ((thetaa - thetaa) * alpha)







      @staticmethod
      def      lowerCamelCase__      (  lowercase  ,    lowercase  ,    lowercase  ,    lowercase     ):
        return thetaa + (thetaa - thetaa) * (1.0 - alpha)
'''simple docstring'''
import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin







def 				__UpperCamelCase (					__UpperCAmelCase  :					List[Any]=2 ,   __UpperCAmelCase  :					Dict=True ,   __UpperCAmelCase  :					str=False			)							->	List[Any]:
  lowercase				:							Tuple									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[int]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							List[Any]									=   torch.optim.lr_scheduler.OneCycleLR(__UpperCAmelCase ,   max_lr=0.01 ,   steps_per_epoch=2 ,   epochs=1			)
  lowercase				,      lowercase				,      lowercase				:							Optional[Any]									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  scheduler.step()
  if step_scheduler_with_optimizer or (num_processes == 1):
    assert (
        scheduler.scheduler.last_epoch == num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})"""
  else:
    assert (
        scheduler.scheduler.last_epoch != num_processes
    ), f"""Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})"""







def 				__UpperCamelCase (					__UpperCAmelCase  :					str=2 ,   __UpperCAmelCase  :					str=True ,   __UpperCAmelCase  :					Tuple=False			)							->	str:
  lowercase				:							List[Any]									=   Accelerator(step_scheduler_with_optimizer=__UpperCAmelCase ,   split_batches=__UpperCAmelCase			)
  lowercase				:							Optional[Any]									=   torch.nn.Linear(2 ,   4			)
  lowercase				:							List[str]									=   torch.optim.AdamW(model.parameters() ,   lr=1.0			)
  lowercase				:							Dict									=   torch.optim.lr_scheduler.LambdaLR(__UpperCAmelCase ,   lr_lambda=lambda __UpperCAmelCase			: 1 - n / 10			)
  lowercase				,      lowercase				,      lowercase				:							int									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

  # Optimizer has stepped
  lowercase				:							Any									=   False
  scheduler.step()
  lowercase				:							Optional[Any]									=   1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""

  # Optimizer has not stepped
  lowercase				:							List[Any]									=   True
  scheduler.step()
  if not step_scheduler_with_optimizer:
    lowercase				:							List[Any]									=   1 - 2 / 10
  assert (
      scheduler.get_last_lr()[0] == expected_lr
  ), f"""Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}"""







def 				__UpperCamelCase (					__UpperCAmelCase  :					int = 2			)							->	Dict:
  from transformers import get_linear_schedule_with_warmup

  lowercase				:							Optional[Any]									=   [1, 2, 4]
  for num_steps in steps:
    lowercase				:							Dict									=   GradientAccumulationPlugin(num_steps=__UpperCAmelCase ,   adjust_scheduler=num_steps > 1			)
    lowercase				:							Tuple									=   Accelerator(gradient_accumulation_plugin=__UpperCAmelCase			)
    lowercase				:							str									=   torch.nn.Linear(2 ,   4			)
    lowercase				:							Dict									=   torch.optim.AdamW(model.parameters() ,   lr=10.0			)
    lowercase				:							List[str]									=   get_linear_schedule_with_warmup(optimizer=__UpperCAmelCase ,   num_warmup_steps=0 ,   num_training_steps=20			)

    lowercase				,      lowercase				,      lowercase				:							Dict									=   accelerator.prepare(__UpperCAmelCase ,   __UpperCAmelCase ,   __UpperCAmelCase			)

    for i in range(10 * num_steps			):
      with accelerator.accumulate(__UpperCAmelCase			):
        optimizer.step()
        scheduler.step()

      if i == (10 * num_steps - 2):
        assert (
            scheduler.get_last_lr()[0] != 0
        ), f"""Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}"""
    assert (
        scheduler.get_last_lr()[0] == 0
    ), f"""Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}"""
    GradientState._reset_state()






@require_cpu
class _lowerCAmelCase			(	unittest.TestCase					):







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1     )  ,    num_processes=1     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    step_scheduler_with_optimizer=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1  ,    split_batches=lowercase     )  ,    num_processes=1     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    step_scheduler_with_optimizer=lowercase     )     )







      @require_huggingface_suite
      def      lowerCamelCase__      (  self     ):
        AcceleratorState._reset_state(lowercase     )
        debug_launcher(partial(lowercase  ,    num_processes=1     )     )
        debug_launcher(lowercase     )