




import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


SCREAMING_SNAKE_CASE__     :     List[str]		    =       trt.Logger(trt.Logger.WARNING)
SCREAMING_SNAKE_CASE__     :     Tuple		    =       absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

SCREAMING_SNAKE_CASE__     :     List[str]		    =       logging.getLogger(__name__)

SCREAMING_SNAKE_CASE__     :     Any		    =       argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    "--onnx_model_path",
    default=None,
    type=str,
    required=True,
    help="Path to ONNX model: ",
)

parser.add_argument(
    "--output_dir",
    default=None,
    type=str,
    required=True,
    help="The output directory where the model checkpoints and predictions will be written.",
)

# Other parameters

parser.add_argument(
    "--tokenizer_name",
    default="",
    type=str,
    required=True,
    help="Pretrained tokenizer name or path if not the same as model_name",
)

parser.add_argument(
    "--version_2_with_negative",
    action="store_true",
    help="If true, the SQuAD examples contain some that do not have an answer.",
)
parser.add_argument(
    "--null_score_diff_threshold",
    type=float,
    default=0.0,
    help="If null_score - best_non_null is greater than the threshold predict null.",
)

parser.add_argument(
    "--max_seq_length",
    default=3_84,
    type=int,
    help=(
        "The maximum total input sequence length after WordPiece tokenization. Sequences "
        "longer than this will be truncated, and sequences shorter than this will be padded."
    ),
)
parser.add_argument(
    "--doc_stride",
    default=1_28,
    type=int,
    help="When splitting up a long document into chunks, how much stride to take between chunks.",
)

parser.add_argument("--per_device_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.")

parser.add_argument(
    "--n_best_size",
    default=20,
    type=int,
    help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
)
parser.add_argument(
    "--max_answer_length",
    default=30,
    type=int,
    help=(
        "The maximum length of an answer that can be generated. This is needed because the start "
        "and end predictions are not conditioned on one another."
    ),
)

parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")

parser.add_argument(
    "--dataset_name",
    type=str,
    default=None,
    required=True,
    help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--dataset_config_name",
    type=str,
    default=None,
    help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
)
parser.add_argument("--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument(
    "--fp16",
    action="store_true",
    help="Whether to use 16-bit (mixed) precision instead of 32-bit",
)
parser.add_argument(
    "--int8",
    action="store_true",
    help="Whether to use INT8",
)

SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       parser.parse_args()

if args.tokenizer_name:
				SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
				raise ValueError(
				    "You are instantiating a new tokenizer from scratch. This is not supported by this script."
				    "You can do it from another script, save it, and load it from here, using --tokenizer_name."
				)

logger.info("Training/evaluation parameters %s", args)

SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       args.per_device_eval_batch_size

SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
SCREAMING_SNAKE_CASE__     :     List[Any]		    =       True

SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       "temp_engine/bert-fp32.engine"
if args.fpaa:
				SCREAMING_SNAKE_CASE__     :     int		    =       "temp_engine/bert-fp16.engine"
if args.inta:
				SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       "temp_engine/bert-int8.engine"

# import ONNX file
if not os.path.exists("temp_engine"):
				os.makedirs("temp_engine")

SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
				with open(args.onnx_model_path, "rb") as model:
								if not parser.parse(model.read()):
												for error in range(parser.num_errors):
																print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
				SCREAMING_SNAKE_CASE__     :     Tuple		    =       [network.get_input(i) for i in range(network.num_inputs)]
				SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

				with builder.create_builder_config() as config:
								SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       1 << 50
								if STRICT_TYPES:
												config.set_flag(trt.BuilderFlag.STRICT_TYPES)
								if args.fpaa:
												config.set_flag(trt.BuilderFlag.FPaa)
								if args.inta:
												config.set_flag(trt.BuilderFlag.INTa)
								SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       builder.create_optimization_profile()
								config.add_optimization_profile(profile)
								for i in range(len(input_names)):
												profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
								SCREAMING_SNAKE_CASE__     :     List[str]		    =       builder.build_engine(network, config)

								# serialize_engine and store in file (can be directly loaded and deserialized):
								with open(engine_name, "wb") as f:
												f.write(engine.serialize())

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							List[str]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						np.asarray(inputs['input_ids']       ,    dtype=np.intaa )
			SCREAMING_SNAKE_CASE_								=						np.asarray(inputs['attention_mask']       ,    dtype=np.intaa )
			SCREAMING_SNAKE_CASE_								=						np.asarray(inputs['token_type_ids']       ,    dtype=np.intaa )

			# Copy inputs
			cuda.memcpy_htod_async(d_inputs[0]       ,    input_ids.ravel()       ,    SCREAMING_SNAKE_CASE )
			cuda.memcpy_htod_async(d_inputs[1]       ,    attention_mask.ravel()       ,    SCREAMING_SNAKE_CASE )
			cuda.memcpy_htod_async(d_inputs[2]       ,    token_type_ids.ravel()       ,    SCREAMING_SNAKE_CASE )
			# start time
			SCREAMING_SNAKE_CASE_								=						time.time()
			# Run inference
			context.execute_async(
			    bindings=[int(SCREAMING_SNAKE_CASE ) for d_inp in d_inputs] + [int(SCREAMING_SNAKE_CASE ), int(SCREAMING_SNAKE_CASE )]       ,    stream_handle=stream.handle )
			# Transfer predictions back from GPU
			cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )
			# Synchronize the stream and take time
			stream.synchronize()
			# end time
			SCREAMING_SNAKE_CASE_								=						time.time()
			SCREAMING_SNAKE_CASE_								=						end_time - start_time
			SCREAMING_SNAKE_CASE_								=						(h_outputa, h_outputa)
			# print(outputs)
			return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
				datasets.utils.logging.set_verbosity_warning()
				transformers.utils.logging.set_verbosity_info()
else:
				datasets.utils.logging.set_verbosity_error()
				transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
				set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
				# Downloading and loading a dataset from the hub.
				SCREAMING_SNAKE_CASE__     :     int		    =       load_dataset(args.dataset_name, args.dataset_config_name)
else:
				raise ValueError("Evaluation requires a dataset name")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

SCREAMING_SNAKE_CASE__     :     int		    =       raw_datasets["validation"].column_names

SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       "question" if "question" in column_names else column_names[0]
SCREAMING_SNAKE_CASE__     :     int		    =       "context" if "context" in column_names else column_names[1]
SCREAMING_SNAKE_CASE__     :     List[str]		    =       "answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       tokenizer.padding_side == "right"

if args.max_seq_length > tokenizer.model_max_length:
				logger.warning(
				    f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
				    f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
				)

SCREAMING_SNAKE_CASE__     :     int		    =       min(args.max_seq_length, tokenizer.model_max_length)

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Dict:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						[q.lstrip() for q in examples[question_column_name]]

			# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
			# in one example possible giving several features when a context is long, each of those features having a
			# context that overlaps a bit the context of the previous feature.
			SCREAMING_SNAKE_CASE_								=						tokenizer(
			    examples[question_column_name if pad_on_right else context_column_name]       ,    examples[context_column_name if pad_on_right else question_column_name]       ,    truncation='only_second' if pad_on_right else 'only_first'       ,    max_length=SCREAMING_SNAKE_CASE       ,    stride=args.doc_stride       ,    return_overflowing_tokens=SCREAMING_SNAKE_CASE       ,    return_offsets_mapping=SCREAMING_SNAKE_CASE       ,    padding='max_length'       ,    )

			# Since one example might give us several features if it has a long context, we need a map from a feature to
			# its corresponding example. This key gives us just that.
			SCREAMING_SNAKE_CASE_								=						tokenized_examples.pop('overflow_to_sample_mapping' )

			# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
			# corresponding example_id and we will store the offset mappings.
			SCREAMING_SNAKE_CASE_								=						[]

			for i in range(len(tokenized_examples['input_ids'] ) ):
						# Grab the sequence corresponding to that example (to know what is the context and what is the question).
						SCREAMING_SNAKE_CASE_								=						tokenized_examples.sequence_ids(SCREAMING_SNAKE_CASE )
						SCREAMING_SNAKE_CASE_								=						1 if pad_on_right else 0

						# One example can give several spans, this is the index of the example containing this span of text.
						SCREAMING_SNAKE_CASE_								=						sample_mapping[i]
						tokenized_examples["example_id"].append(examples['id'][sample_index] )

						# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
						# position is part of the context or not.
						SCREAMING_SNAKE_CASE_								=						[
						    (o if sequence_ids[k] == context_index else None)
						    for k, o in enumerate(tokenized_examples['offset_mapping'][i] )
						]

			return tokenized_examples


SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       raw_datasets["validation"]
# Validation Feature Creation
SCREAMING_SNAKE_CASE__     :     Tuple		    =       eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="Running tokenizer on validation dataset",
)

SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       default_data_collator

SCREAMING_SNAKE_CASE__     :     List[Any]		    =       eval_dataset.remove_columns(["example_id", "offset_mapping"])
SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE="eval" )			->							Optional[Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						postprocess_qa_predictions(
			    examples=SCREAMING_SNAKE_CASE       ,    features=SCREAMING_SNAKE_CASE       ,    predictions=SCREAMING_SNAKE_CASE       ,    version_2_with_negative=args.version_2_with_negative       ,    n_best_size=args.n_best_size       ,    max_answer_length=args.max_answer_length       ,    null_score_diff_threshold=args.null_score_diff_threshold       ,    output_dir=args.output_dir       ,    prefix=SCREAMING_SNAKE_CASE       ,    )
			# Format the result to the format the metric expects.
			if args.version_2_with_negative:
						SCREAMING_SNAKE_CASE_								=						[
						    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
						]
			else:
						SCREAMING_SNAKE_CASE_								=						[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

			SCREAMING_SNAKE_CASE_								=						[{'id': ex['id'], 'answers': ex[answer_column_name]} for ex in examples]
			return EvalPrediction(predictions=SCREAMING_SNAKE_CASE       ,    label_ids=SCREAMING_SNAKE_CASE )


SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       load_metric("squad_v2" if args.version_2_with_negative else "squad")

# Evaluation!
logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
				# setup for TRT inferrence
				for i in range(len(input_names)):
								context.set_binding_shape(i, INPUT_SHAPE)
				assert context.all_binding_shapes_specified

				def lowercase    (  SCREAMING_SNAKE_CASE )			->							Optional[int]:


							'''simple docstring'''







							return trt.volume(engine.get_binding_shape(SCREAMING_SNAKE_CASE ) ) * engine.get_binding_dtype(SCREAMING_SNAKE_CASE ).itemsize

				# Allocate device memory for inputs and outputs.
				SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

				# Allocate output buffer
				SCREAMING_SNAKE_CASE__     :     Dict		    =       cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
				SCREAMING_SNAKE_CASE__     :     List[str]		    =       cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
				SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       cuda.mem_alloc(h_outputa.nbytes)
				SCREAMING_SNAKE_CASE__     :     Any		    =       cuda.mem_alloc(h_outputa.nbytes)

				# Create a stream in which to copy inputs/outputs and run inference.
				SCREAMING_SNAKE_CASE__     :     List[str]		    =       cuda.Stream()

				# Evaluation
				logger.info("***** Running Evaluation *****")
				logger.info(f"""  Num examples = {len(eval_dataset)}""")
				logger.info(f"""  Batch size = {args.per_device_eval_batch_size}""")

				SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       0.0
				SCREAMING_SNAKE_CASE__     :     int		    =       0
				SCREAMING_SNAKE_CASE__     :     int		    =       timeit.default_timer()

				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       None
				for step, batch in enumerate(eval_dataloader):
								SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__     :     Any		    =       model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
								total_time += infer_time
								niter += 1

								SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__     :     int		    =       outputs
								SCREAMING_SNAKE_CASE__     :     int		    =       torch.tensor(start_logits)
								SCREAMING_SNAKE_CASE__     :     List[str]		    =       torch.tensor(end_logits)

								# necessary to pad predictions and labels for being gathered
								SCREAMING_SNAKE_CASE__     :     List[Any]		    =       accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
								SCREAMING_SNAKE_CASE__     :     Dict		    =       accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

								SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
								SCREAMING_SNAKE_CASE__     :     str		    =       logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

				if all_preds is not None:
								SCREAMING_SNAKE_CASE__     :     Tuple		    =       nested_truncate(all_preds, len(eval_dataset))

				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       timeit.default_timer() - start_time
				logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
				# Inference time from TRT
				logger.info("Average Inference Time = {:.3f} ms".format(total_time * 10_00 / niter))
				logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 10_00))
				logger.info("Total Number of Inference =  %d", niter)

SCREAMING_SNAKE_CASE__     :     Tuple		    =       post_processing_function(eval_examples, eval_dataset, all_preds)
SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"""Evaluation metrics: {eval_metric}""")






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__     :     Union[str, Any]		    =       logging.get_logger(__name__)



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Union[str, Any]:




				"""simple docstring"""



				super().__init__()
				SCREAMING_SNAKE_CASE_								=						nn.ModuleList(SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = True      ,     )  ->					Union[ControlNetOutput, Tuple]:




				"""simple docstring"""



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

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

				return down_block_res_samples, mid_block_res_sample





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = True      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE = False      ,     SCREAMING_SNAKE_CASE = None      ,     )  ->					int:




				"""simple docstring"""



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

							idx += 1
							SCREAMING_SNAKE_CASE_								=						model_path_to_save + f'_{idx}'





	@classmethod
	def 						A_(							cls      ,     SCREAMING_SNAKE_CASE      ,     **SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				SCREAMING_SNAKE_CASE_								=						[]

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

							idx += 1
							SCREAMING_SNAKE_CASE_								=						pretrained_model_path + f'_{idx}'

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

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

				return cls(SCREAMING_SNAKE_CASE      )
