






'''simple docstring'''



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


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)
class UpperCAmelCase_       (     __A					):




      """simple docstring"""




      def __init__(			self      :		Tuple  ,   UpperCAmelCase      :		Union[List[ControlNetModel], Tuple[ControlNetModel]]		)	->	Optional[int]:


       '''simple docstring'''

       super().__init__()
       lowercase       : Dict						=nn.ModuleList(UpperCAmelCase		)






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


       '''simple docstring'''

       for i, (image, scale, controlnet) in enumerate(zip(UpperCAmelCase  ,   UpperCAmelCase  ,   self.nets		)		):
        lowercase	,		lowercase       : List[Any]						=controlnet(
            UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase  ,   )

        # merge samples
        if i == 0:
         lowercase	,		lowercase       : Union[str, Any]						=down_samples, mid_sample
        else:
         lowercase       : Union[str, 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 					A__   (			self      :		Dict  ,   UpperCAmelCase      :		Union[str, os.PathLike]  ,   UpperCAmelCase      :		bool = True  ,   UpperCAmelCase      :		Callable = None  ,   UpperCAmelCase      :		bool = False  ,   UpperCAmelCase      :		Optional[str] = None  ,   )	->	List[Any]:


       '''simple docstring'''

       lowercase       : Optional[int]						=0
       lowercase       : Dict						=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
        lowercase       : str						=model_path_to_save + f'_{idx}'






      @classmethod
      def 					A__   (			cls      :		str  ,   UpperCAmelCase      :		Optional[Union[str, os.PathLike]]  ,   **UpperCAmelCase      :		Dict		)	->	Any:


       '''simple docstring'''

       lowercase       : Optional[int]						=0
       lowercase       : 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`, ...
       lowercase       : List[str]						=pretrained_model_path
       while os.path.isdir(UpperCAmelCase		):
        lowercase       : Optional[Any]						=ControlNetModel.from_pretrained(UpperCAmelCase  ,   **UpperCAmelCase		)
        controlnets.append(UpperCAmelCase		)

        idx += 1
        lowercase       : Dict						=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		)











'''simple docstring'''



from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
      from .deepspeed import (
          DeepSpeedEngineWrapper,
          DeepSpeedOptimizerWrapper,
          DeepSpeedSchedulerWrapper,
          DummyOptim,
          DummyScheduler,
          HfDeepSpeedConfig,
      )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers



