

import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


A__						=							logging.get_logger(__name__)

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	def __init__(   self: Dict  , __UpperCamelCase: Union[List[ControlNetModel], Tuple[ControlNetModel]]      ):

						'''simple docstring'''





						super().__init__()
						__magic_name__							=    nn.ModuleList(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: torch.FloatTensor  , __UpperCamelCase: Union[torch.Tensor, float, int]  , __UpperCamelCase: torch.Tensor  , __UpperCamelCase: List[torch.tensor]  , __UpperCamelCase: List[float]  , __UpperCamelCase: Optional[torch.Tensor] = None  , __UpperCamelCase: Optional[torch.Tensor] = None  , __UpperCamelCase: Optional[torch.Tensor] = None  , __UpperCamelCase: Optional[Dict[str, Any]] = None  , __UpperCamelCase: bool = False  , __UpperCamelCase: bool = True  , ):

						'''simple docstring'''





						for i, (image, scale, controlnet) in enumerate(zip(__UpperCamelCase  , __UpperCamelCase  , self.nets      )      ):
											__magic_name__,	__magic_name__							=    controlnet(
											    __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , )

											# merge samples
											if i == 0:
																__magic_name__,	__magic_name__							=    down_samples, mid_sample
											else:
																__magic_name__							=    [
																    samples_prev + samples_curr
																    for samples_prev, samples_curr in zip(__UpperCamelCase  , __UpperCamelCase      )
																]
																mid_block_res_sample += mid_sample

						return down_block_res_samples, mid_block_res_sample







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Union[str, os.PathLike]  , __UpperCamelCase: bool = True  , __UpperCamelCase: Callable = None  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional[str] = None  , ):

						'''simple docstring'''





						__magic_name__							=    0
						__magic_name__							=    save_directory
						for controlnet in self.nets:
											controlnet.save_pretrained(
											    __UpperCamelCase  , is_main_process=__UpperCamelCase  , save_function=__UpperCamelCase  , safe_serialization=__UpperCamelCase  , variant=__UpperCamelCase  , )

											idx += 1
											__magic_name__							=    model_path_to_save + F'_{idx}'







	@classmethod
	def 	_SCREAMING_SNAKE_CASE							(   cls: List[str]  , __UpperCamelCase: Optional[Union[str, os.PathLike]]  , **__UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    0
						__magic_name__							=    []

						# load controlnet and append to list until no controlnet directory exists anymore
						# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
						# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
						__magic_name__							=    pretrained_model_path
						while os.path.isdir(__UpperCamelCase      ):
											__magic_name__							=    ControlNetModel.from_pretrained(__UpperCamelCase  , **__UpperCamelCase      )
											controlnets.append(__UpperCamelCase      )

											idx += 1
											__magic_name__							=    pretrained_model_path + F'_{idx}'

						logger.info(F'{len(__UpperCamelCase      )} controlnets loaded from {pretrained_model_path}.'      )

						if len(__UpperCamelCase      ) == 0:
											raise ValueError(
											    F'No ControlNets found under {os.path.dirname(__UpperCamelCase      )}. Expected at least {pretrained_model_path + "_0"}.'      )

						return cls(__UpperCamelCase      )







import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler

@dataclass
class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   int       				=  0
	_lowercase  :   bool       				=  False
	_lowercase  :   float       				=  3.0




class 				__UpperCamelCase				(					unittest.TestCase			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						self.assertDictEqual(MockClass().to_kwargs()  , {}      )
						self.assertDictEqual(MockClass(a=2      ).to_kwargs()  , {'a': 2}      )
						self.assertDictEqual(MockClass(a=2  , b=__UpperCamelCase      ).to_kwargs()  , {'a': 2, 'b': True}      )
						self.assertDictEqual(MockClass(a=2  , c=2.25      ).to_kwargs()  , {'a': 2, 'c': 2.25}      )







	@require_cuda
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    GradScalerKwargs(init_scale=10_24  , growth_factor=2      )
						AcceleratorState._reset_state()
						__magic_name__							=    Accelerator(mixed_precision='fp16'  , kwargs_handlers=[scaler_handler]      )
						print(accelerator.use_fpaa      )
						__magic_name__							=    accelerator.scaler

						# Check the kwargs have been applied
						self.assertEqual(scaler._init_scale  , 1024.0      )
						self.assertEqual(scaler._growth_factor  , 2.0      )

						# Check the other values are at the default
						self.assertEqual(scaler._backoff_factor  , 0.5      )
						self.assertEqual(scaler._growth_interval  , 20_00      )
						self.assertEqual(scaler._enabled  , __UpperCamelCase      )







	@require_multi_gpu
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    ['torchrun', F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__      )]
						execute_subprocess_async(__UpperCamelCase  , env=os.environ.copy()      )


if __name__ == "__main__":
						A__						=							DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						A__						=							Accelerator(kwargs_handlers=[ddp_scaler])
						A__						=							torch.nn.Linear(100, 200)
						A__						=							accelerator.prepare(model)

						# Check the values changed in kwargs
						A__						=							""
						A__						=							model.bucket_bytes_cap // (1024 * 1024)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)




