
import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    List[Any]	):


				'''simple docstring'''


				_snake_case		=			inspect.getfile(accelerate.test_utils	)
				_snake_case		=			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_script.py']	)
				_snake_case		=			os.path.sep.join(
				    mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_distributed_data_loop.py']	)
				_snake_case		=			os.path.sep.join(mod_file.split(os.path.sep	)[:-1] + ['scripts', 'test_ops.py']	)



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


				'''simple docstring'''


				print(f'''Found {torch.cuda.device_count()} devices.'''	)
				_snake_case		=			['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
				with patch_environment(omp_num_threads=1	):
							execute_subprocess_async(lowercase			,  env=os.environ.copy()	)



	@require_multi_gpu
	def  A							(  self	:    Dict	):


				'''simple docstring'''


				print(f'''Found {torch.cuda.device_count()} devices.'''	)
				_snake_case		=			['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
				print(f'''Command: {cmd}'''	)
				with patch_environment(omp_num_threads=1	):
							execute_subprocess_async(lowercase			,  env=os.environ.copy()	)



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


				'''simple docstring'''


				_snake_case		=			['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__	)]
				with patch_environment(omp_num_threads=1	):
							execute_subprocess_async(lowercase			,  env=os.environ.copy()	)





	@require_multi_gpu
	def  A							(  self	:    Optional[int]	):


				'''simple docstring'''


				print(f'''Found {torch.cuda.device_count()} devices, using 2 devices only'''	)
				_snake_case		=			['torchrun', f'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
				with patch_environment(omp_num_threads=1			,  cuda_visible_devices='0,1'	):
							execute_subprocess_async(lowercase			,  env=os.environ.copy()	)


if __name__ == "__main__":
			_lowerCamelCase       :				Any       =			Accelerator()
			_lowerCamelCase       :				Optional[int]       =			(accelerator.state.process_index + 2, 10)
			_lowerCamelCase       :				Tuple       =			torch.randint(0, 10, shape).to(accelerator.device)

			_lowerCamelCase       :				Optional[int]       =			''''''

			_lowerCamelCase       :				List[str]       =			accelerator.pad_across_processes(tensor)
			if tensora.shape[0] != accelerator.state.num_processes + 1:
						error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
			if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
						error_msg += "Tensors have different values."
			if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
						error_msg += "Padding was not done with the right value (0)."

			_lowerCamelCase       :				int       =			accelerator.pad_across_processes(tensor, pad_first=True)
			if tensora.shape[0] != accelerator.state.num_processes + 1:
						error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
			_lowerCamelCase       :				int       =			accelerator.state.num_processes - accelerator.state.process_index - 1
			if not torch.equal(tensora[index:], tensor):
						error_msg += "Tensors have different values."
			if not torch.all(tensora[:index] == 0):
						error_msg += "Padding was not done with the right value (0)."

			# 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)

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


_lowerCamelCase       :				List[str]       =			logging.get_logger(__name__)




class    SCREAMING_SNAKE_CASE__     (	UpperCAmelCase					):





	'''simple docstring'''



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


				'''simple docstring'''


				super().__init__()
				_snake_case		=			nn.ModuleList(lowercase	)



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


				'''simple docstring'''


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

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

				return down_block_res_samples, mid_block_res_sample



	def  A							(  self	:    Dict			,  lowercase	:    Union[str, os.PathLike]			,  lowercase	:    bool = True			,  lowercase	:    Callable = None			,  lowercase	:    bool = False			,  lowercase	:    Optional[str] = None			,  ):


				'''simple docstring'''


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

							idx += 1
							_snake_case		=			model_path_to_save + f'''_{idx}'''





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


				'''simple docstring'''


				_snake_case		=			0
				_snake_case		=			[]

				# 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`, ...
				_snake_case		=			pretrained_model_path
				while os.path.isdir(lowercase	):
							_snake_case		=			ControlNetModel.from_pretrained(lowercase			,  **lowercase	)
							controlnets.append(lowercase	)

							idx += 1
							_snake_case		=			pretrained_model_path + f'''_{idx}'''

				logger.info(f'''{len(lowercase	)} controlnets loaded from {pretrained_model_path}.'''	)

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

				return cls(lowercase	)