
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


snake_case :		Tuple							      =  logging.get_logger(__name__)





class       _snake_case					(     snake_case				):







			def __init__(				self						,   _a		):
				super().__init__()
				__magic_name__		:		str	   =     nn.ModuleList(_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a = None						,   _a = None						,   _a = None						,   _a = None						,   _a = False						,   _a = True						,   ):
				for i, (image, scale, controlnet) in enumerate(zip(_a						,   _a						,   self.nets		)		):
					__magic_name__   ,    __magic_name__		:		Optional[Any]	   =     controlnet(
					    _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   )

					# merge samples
					if i == 0:
						__magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     down_samples, mid_sample
					else:
						__magic_name__		:		Dict	   =     [
						    samples_prev + samples_curr
						    for samples_prev, samples_curr in zip(_a						,   _a		)
						]
						mid_block_res_sample += mid_sample

				return down_block_res_samples, mid_block_res_sample







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = True						,   _a = None						,   _a = False						,   _a = None						,   ):
				__magic_name__		:		str	   =     0
				__magic_name__		:		Tuple	   =     save_directory
				for controlnet in self.nets:
					controlnet.save_pretrained(
					    _a						,   is_main_process=_a						,   save_function=_a						,   safe_serialization=_a						,   variant=_a						,   )

					idx += 1
					__magic_name__		:		List[str]	   =     model_path_to_save + f'''_{idx}'''







			@classmethod
			def 			SCREAMING_SNAKE_CASE    (				cls						,   _a						,   **_a		):
				__magic_name__		:		int	   =     0
				__magic_name__		:		Union[str, Any]	   =     []

				# 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`, ...
				__magic_name__		:		Dict	   =     pretrained_model_path
				while os.path.isdir(_a		):
					__magic_name__		:		Tuple	   =     ControlNetModel.from_pretrained(_a						,   **_a		)
					controlnets.append(_a		)

					idx += 1
					__magic_name__		:		Dict	   =     pretrained_model_path + f'''_{idx}'''

				logger.info(f'''{len(_a		)} controlnets loaded from {pretrained_model_path}.'''		)

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

				return cls(_a		)


from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)
