
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_						=						logging.get_logger(__name__)

class      _SCREAMING_SNAKE_CASE				(		__a				):





				def __init__(		self			:		Any       ,   snake_case_			:		int				):


								"""simple docstring"""

								super().__init__()
								A  :	List[Any]				=					nn.ModuleList(__UpperCamelCase				)





				def 							_UpperCAmelCase    (		self			:		Optional[int]       ,   snake_case_			:		List[Any]       ,   snake_case_			:		Dict       ,   snake_case_			:		List[str]       ,   snake_case_			:		List[Any]       ,   snake_case_			:		int       ,   snake_case_			:		List[str] = None       ,   snake_case_			:		Optional[int] = None       ,   snake_case_			:		List[Any] = None       ,   snake_case_			:		Dict = None       ,   snake_case_			:		List[Any] = False       ,   snake_case_			:		str = True       ,   ):


								"""simple docstring"""

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

												# merge samples
												if i == 0:
																A  :	Optional[Any]				=					down_samples, mid_sample
												else:
																A  :	Any				=					[
																    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 							_UpperCAmelCase    (		self			:		Tuple       ,   snake_case_			:		Optional[int]       ,   snake_case_			:		Optional[int] = True       ,   snake_case_			:		Optional[Any] = None       ,   snake_case_			:		str = False       ,   snake_case_			:		Union[str, Any] = None       ,   ):


								"""simple docstring"""

								A  :	Any				=					0
								A  :	str				=					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
												A  :	List[Any]				=					model_path_to_save + f"""_{idx}"""






				@classmethod
				def 							_UpperCAmelCase    (		cls			:		List[str]       ,   snake_case_			:		Optional[int]       ,   **snake_case_			:		int				):


								"""simple docstring"""

								A  :	Optional[Any]				=					0
								A  :	List[str]				=					[]

								# 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`, ...
								A  :	Optional[Any]				=					pretrained_model_path
								while os.path.isdir(__UpperCamelCase				):
												A  :	Optional[Any]				=					ControlNetModel.from_pretrained(__UpperCamelCase       ,   **__UpperCamelCase				)
												controlnets.append(__UpperCamelCase				)

												idx += 1
												A  :	Optional[int]				=					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 unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin

@require_torch
class        UpperCamelCase						(				unittest.TestCase	,	__a				):





						def        A_							(self						)				->				Tuple:
												UpperCamelCase_		:							Any  =		load_tool("""text-to-speech"""						)
												self.tool.setup()





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Optional[Any]  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Optional[Any]  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)





						def        A_							(self						)				->				Dict:
												# SpeechT5 isn't deterministic
												torch.manual_seed(0						)
												UpperCamelCase_		:							Any  =		self.tool("""hey"""						)
												UpperCamelCase_		:							Tuple  =		result.to_raw()
												self.assertTrue(
												    torch.allclose(
												        resulting_tensor[:3]			,     torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]						)			,     )						)






