
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_						: str         =  logging.get_logger(__name__)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def __init__(       self :       List[str]						,	snake_case_ :       Union[List[ControlNetModel], Tuple[ControlNetModel]]      ):
							super().__init__()
							UpperCamelCase_:							int	=						nn.ModuleList(snake_case_      )

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       torch.FloatTensor						,	snake_case_ :       Union[torch.Tensor, float, int]						,	snake_case_ :       torch.Tensor						,	snake_case_ :       List[torch.tensor]						,	snake_case_ :       List[float]						,	snake_case_ :       Optional[torch.Tensor] = None						,	snake_case_ :       Optional[torch.Tensor] = None						,	snake_case_ :       Optional[torch.Tensor] = None						,	snake_case_ :       Optional[Dict[str, Any]] = None						,	snake_case_ :       bool = False						,	snake_case_ :       bool = True						,	):
							for i, (image, scale, controlnet) in enumerate(zip(snake_case_						,	snake_case_						,	self.nets      )      ):
											UpperCamelCase_,   UpperCamelCase_:							Any	=						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_:							List[Any]	=						down_samples, mid_sample
											else:
															UpperCamelCase_:							Optional[int]	=						[
															    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       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       Union[str, os.PathLike]						,	snake_case_ :       bool = True						,	snake_case_ :       Callable = None						,	snake_case_ :       bool = False						,	snake_case_ :       Optional[str] = None						,	):
							UpperCamelCase_:							Any	=						0
							UpperCamelCase_:							Any	=						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_:							List[Any]	=						model_path_to_save + f'''_{idx}'''




			@classmethod
			def       lowerCAmelCase__	(       cls :       List[str]						,	snake_case_ :       Optional[Union[str, os.PathLike]]						,	**snake_case_ :       Any      ):
							UpperCamelCase_:							Tuple	=						0
							UpperCamelCase_:							Optional[int]	=						[]

							# 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_:							Union[str, Any]	=						pretrained_model_path
							while os.path.isdir(snake_case_      ):
											UpperCamelCase_:							List[Any]	=						ControlNetModel.from_pretrained(snake_case_						,	**snake_case_      )
											controlnets.append(snake_case_      )

											idx += 1
											UpperCamelCase_:							List[Any]	=						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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_						: str         =  {
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Union[str, Any]         =  ["""RoFormerTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Any         =  [
							    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RoFormerForCausalLM""",
							    """RoFormerForMaskedLM""",
							    """RoFormerForMultipleChoice""",
							    """RoFormerForQuestionAnswering""",
							    """RoFormerForSequenceClassification""",
							    """RoFormerForTokenClassification""",
							    """RoFormerLayer""",
							    """RoFormerModel""",
							    """RoFormerPreTrainedModel""",
							    """load_tf_weights_in_roformer""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRoFormerForCausalLM""",
							    """TFRoFormerForMaskedLM""",
							    """TFRoFormerForMultipleChoice""",
							    """TFRoFormerForQuestionAnswering""",
							    """TFRoFormerForSequenceClassification""",
							    """TFRoFormerForTokenClassification""",
							    """TFRoFormerLayer""",
							    """TFRoFormerModel""",
							    """TFRoFormerPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[Any]         =  [
							    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """FlaxRoFormerForMaskedLM""",
							    """FlaxRoFormerForMultipleChoice""",
							    """FlaxRoFormerForQuestionAnswering""",
							    """FlaxRoFormerForSequenceClassification""",
							    """FlaxRoFormerForTokenClassification""",
							    """FlaxRoFormerModel""",
							    """FlaxRoFormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
							from .tokenization_roformer import RoFormerTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_roformer_fast import RoFormerTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roformer import (
														    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RoFormerForCausalLM,
														    RoFormerForMaskedLM,
														    RoFormerForMultipleChoice,
														    RoFormerForQuestionAnswering,
														    RoFormerForSequenceClassification,
														    RoFormerForTokenClassification,
														    RoFormerLayer,
														    RoFormerModel,
														    RoFormerPreTrainedModel,
														    load_tf_weights_in_roformer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_roformer import (
														    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRoFormerForCausalLM,
														    TFRoFormerForMaskedLM,
														    TFRoFormerForMultipleChoice,
														    TFRoFormerForQuestionAnswering,
														    TFRoFormerForSequenceClassification,
														    TFRoFormerForTokenClassification,
														    TFRoFormerLayer,
														    TFRoFormerModel,
														    TFRoFormerPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_roformer import (
														    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxRoFormerForMaskedLM,
														    FlaxRoFormerForMultipleChoice,
														    FlaxRoFormerForQuestionAnswering,
														    FlaxRoFormerForSequenceClassification,
														    FlaxRoFormerForTokenClassification,
														    FlaxRoFormerModel,
														    FlaxRoFormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_						: Tuple         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






