






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


__UpperCamelCase   :   Optional[int]      =    logging.get_logger(__name__)


class 							__UpperCamelCase     (  _lowerCAmelCase ):







	def __init__(	self   :				int							,					_lowerCAmelCase   :				Union[List[ControlNetModel], Tuple[ControlNetModel]]      )		->			List[str]:
		"""simple docstring"""







		super().__init__()
		__lowercase   							=	nn.ModuleList(_lowerCAmelCase      )







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				torch.FloatTensor							,					_lowerCAmelCase   :				Union[torch.Tensor, float, int]							,					_lowerCAmelCase   :				torch.Tensor							,					_lowerCAmelCase   :				List[torch.tensor]							,					_lowerCAmelCase   :				List[float]							,					_lowerCAmelCase   :				Optional[torch.Tensor] = None							,					_lowerCAmelCase   :				Optional[torch.Tensor] = None							,					_lowerCAmelCase   :				Optional[torch.Tensor] = None							,					_lowerCAmelCase   :				Optional[Dict[str, Any]] = None							,					_lowerCAmelCase   :				bool = False							,					_lowerCAmelCase   :				bool = True							,					)		->			Union[ControlNetOutput, Tuple]:
		"""simple docstring"""







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

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

		return down_block_res_samples, mid_block_res_sample







	def 			_a (	self   :				List[Any]							,					_lowerCAmelCase   :				Union[str, os.PathLike]							,					_lowerCAmelCase   :				bool = True							,					_lowerCAmelCase   :				Callable = None							,					_lowerCAmelCase   :				bool = False							,					_lowerCAmelCase   :				Optional[str] = None							,					)		->			str:
		"""simple docstring"""







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

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







	@classmethod
	def 			_a (	cls   :				List[Any]							,					_lowerCAmelCase   :				Optional[Union[str, os.PathLike]]							,					**_lowerCAmelCase   :				Optional[int]      )		->			Optional[int]:
		"""simple docstring"""







		__lowercase   							=	0
		__lowercase   							=	[]

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

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

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

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

		return cls(_lowerCAmelCase      )



import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory

def 		UpperCamelCase						(   )		->						List[Any]:


  '''simple docstring'''






  raise RuntimeError("""CUDA out of memory.""" )



class    _snake_case (						nn.Module				):


 def __init__(       self):


   '''simple docstring'''




   super().__init__()
   lowercase__	: Optional[Any]     		=   nn.Linear(3       ,	4)
   lowercase__	: Union[str, Any]     		=   nn.BatchNormad(4)
   lowercase__	: str     		=   nn.Linear(4       ,	5)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.lineara(self.batchnorm(self.lineara(SCREAMING_SNAKE_CASE_)))



class    _snake_case (						unittest.TestCase				):


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()

   mock_training_loop_function()
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: int     		=   []

   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     nonlocal batch_sizes
     batch_sizes.append(SCREAMING_SNAKE_CASE_)
     if batch_size != 8:
       raise_fake_out_of_memory()
     return batch_size, arga

   lowercase__		,     lowercase__	: int     		=   mock_training_loop_function("""hello""")
   self.assertListEqual(SCREAMING_SNAKE_CASE_       ,	[1_28, 64, 32, 16, 8])
   self.assertListEqual([bs, arga]       ,	[8, """hello"""])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=0)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     pass

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""No executable batch size found, reached zero."""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     if batch_size > 0:
       raise_fake_out_of_memory()
     pass

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""No executable batch size found, reached zero."""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=1_28)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):
     if batch_size != 8:
       raise raise_fake_out_of_memory()

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function(1_28       ,	"""hello"""       ,	"""world""")
     self.assertIn("""Batch size was passed into `f`"""       ,	cm.exception.args[0])
     self.assertIn("""`f(arg1='hello', arg2='world')"""       ,	cm.exception.args[0])


 def   lowercase__  (       self):


   '''simple docstring'''




   @find_executable_batch_size(starting_batch_size=16)
   def mock_training_loop_function(SCREAMING_SNAKE_CASE_):
     raise ValueError("""Oops, we had an error!""")

   with self.assertRaises(SCREAMING_SNAKE_CASE_) as cm:
     mock_training_loop_function()
     self.assertIn("""Oops, we had an error!"""       ,	cm.exception.args[0])



 @require_cuda
 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   torch.cuda.memory_allocated()
   lowercase__	: str     		=   ModelForTest()
   model.cuda()
   self.assertGreater(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Tuple     		=   release_memory(SCREAMING_SNAKE_CASE_)
   self.assertEqual(torch.cuda.memory_allocated()       ,	SCREAMING_SNAKE_CASE_)






