


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        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case     ):

						'''simple docstring'''


						super().__init__()
						UpperCamelCase__					=       nn.ModuleList(snake_case     )




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = None      ,						snake_case = False      ,						snake_case = True      ,						):

						'''simple docstring'''


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

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

						return down_block_res_samples, mid_block_res_sample




			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = True      ,						snake_case = None      ,						snake_case = False      ,						snake_case = None      ,						):

						'''simple docstring'''


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

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







			@classmethod
			def 				snake_case__				(							cls      ,						snake_case      ,						**snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       0
						UpperCamelCase__					=       []

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

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

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

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

						return cls(snake_case     )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__UpperCamelCase    		=       {
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TrOCRForCausalLM',
						    'TrOCRPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

						__UpperCamelCase    		=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


