



"""simple docstring"""


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


snake_case_    					=	trt.Logger(trt.Logger.WARNING)
snake_case_    					=	absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

snake_case_    					=	logging.getLogger(__name__)

snake_case_    					=	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=384,
    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=128,
    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""",
)

snake_case_    					=	parser.parse_args()

if args.tokenizer_name:
				snake_case_    					=	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)

snake_case_    					=	args.per_device_eval_batch_size

snake_case_    					=	(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
snake_case_    					=	True

snake_case_    					=	"""temp_engine/bert-fp32.engine"""
if args.fpaa:
				snake_case_    					=	"""temp_engine/bert-fp16.engine"""
if args.inta:
				snake_case_    					=	"""temp_engine/bert-int8.engine"""

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

snake_case_    					=	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
				snake_case_    					=	[network.get_input(i) for i in range(network.num_inputs)]
				snake_case_    					=	[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

				with builder.create_builder_config() as config:
								snake_case_    					=	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)
								snake_case_    					=	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)
								snake_case_    					=	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 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_		,     lowercase_						):
						UpperCAmelCase					=			np.asarray(inputs['input_ids']		,     dtype=np.intaa						)
						UpperCAmelCase					=			np.asarray(inputs['attention_mask']		,     dtype=np.intaa						)
						UpperCAmelCase					=			np.asarray(inputs['token_type_ids']		,     dtype=np.intaa						)

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

snake_case_    					=	raw_datasets["""validation"""].column_names

snake_case_    					=	"""question""" if """question""" in column_names else column_names[0]
snake_case_    					=	"""context""" if """context""" in column_names else column_names[1]
snake_case_    					=	"""answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
snake_case_    					=	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}.'''
				)

snake_case_    					=	min(args.max_seq_length, tokenizer.model_max_length)

def 			_lowerCAmelCase				(       lowercase_						):
						# Some of the questions have lots of whitespace on the left, which is not useful and will make the
						# truncation of the context fail (the tokenized question will take a lots of space). So we remove that
						# left whitespace
						UpperCAmelCase					=			[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.
						UpperCAmelCase					=			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=a_		,     stride=args.doc_stride		,     return_overflowing_tokens=a_		,     return_offsets_mapping=a_		,     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.
						UpperCAmelCase					=			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.
						UpperCAmelCase					=			[]

						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).
												UpperCAmelCase					=			tokenized_examples.sequence_ids(a_						)
												UpperCAmelCase					=			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.
												UpperCAmelCase					=			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.
												UpperCAmelCase					=			[
												    (o if sequence_ids[k] == context_index else None)
												    for k, o in enumerate(tokenized_examples['offset_mapping'][i]						)
												]

						return tokenized_examples


snake_case_    					=	raw_datasets["""validation"""]
# Validation Feature Creation
snake_case_    					=	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""",
)

snake_case_    					=	default_data_collator

snake_case_    					=	eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
snake_case_    					=	DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_		,     lowercase_		,     lowercase_="eval"						):
						# Post-processing: we match the start logits and end logits to answers in the original context.
						UpperCAmelCase					=			postprocess_qa_predictions(
						    examples=a_		,     features=a_		,     predictions=a_		,     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=a_		,     )
						# Format the result to the format the metric expects.
						if args.version_2_with_negative:
												UpperCAmelCase					=			[
												    {'id': k, 'prediction_text': v, 'no_answer_probability': 0.0} for k, v in predictions.items()
												]
						else:
												UpperCAmelCase					=			[{'id': k, 'prediction_text': v} for k, v in predictions.items()]

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


snake_case_    					=	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 			_lowerCAmelCase				(       lowercase_						):
										return trt.volume(engine.get_binding_shape(a_						)						) * engine.get_binding_dtype(a_						).itemsize

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

				# Allocate output buffer
				snake_case_    					=	cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
				snake_case_    					=	cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
				snake_case_    					=	cuda.mem_alloc(h_outputa.nbytes)
				snake_case_    					=	cuda.mem_alloc(h_outputa.nbytes)

				# Create a stream in which to copy inputs/outputs and run inference.
				snake_case_    					=	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}''')

				snake_case_    					=	0.0
				snake_case_    					=	0
				snake_case_    					=	timeit.default_timer()

				snake_case_    					=	None
				for step, batch in enumerate(eval_dataloader):
								snake_case_					,       snake_case_    					=	model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
								total_time += infer_time
								niter += 1

								snake_case_					,       snake_case_    					=	outputs
								snake_case_    					=	torch.tensor(start_logits)
								snake_case_    					=	torch.tensor(end_logits)

								# necessary to pad predictions and labels for being gathered
								snake_case_    					=	accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
								snake_case_    					=	accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

								snake_case_    					=	(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
								snake_case_    					=	logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

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

				snake_case_    					=	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 * 1000 / niter))
				logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1000))






				logger.info("""Total Number of Inference =  %d""", niter)

snake_case_    					=	post_processing_function(eval_examples, eval_dataset, all_preds)
snake_case_    					=	metric.compute(predictions=prediction.predictions, references=prediction.label_ids)






logger.info(f'''Evaluation metrics: {eval_metric}''')










"""simple docstring"""


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


snake_case_    					=	logging.get_logger(__name__)  # pylint: disable=invalid-name







class 				A_      (					SCREAMING_SNAKE_CASE_    ):



						"""simple docstring"""






						def __init__( self			:Optional[Any]       ,  lowercase_			:CLIPSegForImageSegmentation       ,  lowercase_			:CLIPSegProcessor       ,  lowercase_			:AutoencoderKL       ,  lowercase_			:CLIPTextModel       ,  lowercase_			:CLIPTokenizer       ,  lowercase_			:UNetaDConditionModel       ,  lowercase_			:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,  lowercase_			:StableDiffusionSafetyChecker       ,  lowercase_			:CLIPImageProcessor       ,  )    ->      List[str]:
												super().__init__()

												if hasattr(scheduler.config       ,  'steps_offset' ) and scheduler.config.steps_offset != 1:
																		UpperCAmelCase					=			(
																		    f"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
																		    f""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
																		    'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
																		    ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
																		    ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
																		    ' file'
																		)
																		deprecate('steps_offset!=1'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		UpperCAmelCase					=			dict(scheduler.config )
																		UpperCAmelCase					=			1
																		UpperCAmelCase					=			FrozenDict(lowercase_ )

												if hasattr(scheduler.config       ,  'skip_prk_steps' ) and scheduler.config.skip_prk_steps is False:
																		UpperCAmelCase					=			(
																		    f"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
																		    ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
																		    ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
																		    ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
																		    ' Hub, it would be very nice if you could open a Pull request for the'
																		    ' `scheduler/scheduler_config.json` file'
																		)
																		deprecate('skip_prk_steps not set'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		UpperCAmelCase					=			dict(scheduler.config )
																		UpperCAmelCase					=			True
																		UpperCAmelCase					=			FrozenDict(lowercase_ )

												if safety_checker is None:
																		logger.warning(
																		    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
																		    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
																		    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
																		    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
																		    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
																		    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .' )

												self.register_modules(
												    segmentation_model=lowercase_       ,  segmentation_processor=lowercase_       ,  vae=lowercase_       ,  text_encoder=lowercase_       ,  tokenizer=lowercase_       ,  unet=lowercase_       ,  scheduler=lowercase_       ,  safety_checker=lowercase_       ,  feature_extractor=lowercase_       ,  )






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Optional[Union[str, int]] = "auto" )    ->      int:
												if slice_size == "auto":
																		# half the attention head size is usually a good trade-off between
																		# speed and memory
																		UpperCAmelCase					=			self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int] )    ->      List[str]:
												self.enable_attention_slicing(lowercase_ )






						def      UpperCAmelCase__					( self			:int )    ->      int:
												if is_accelerate_available():
																		from accelerate import cpu_offload
												else:
																		raise ImportError('Please install accelerate via `pip install accelerate`' )

												UpperCAmelCase					=			torch.device('cuda' )

												for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
																		if cpu_offloaded_model is not None:
																								cpu_offload(lowercase_       ,  lowercase_ )






						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def      UpperCAmelCase__					( self			:Optional[Any] )    ->      List[str]:
												if self.device != torch.device('meta' ) or not hasattr(self.unet       ,  '_hf_hook' ):
																		return self.device
												for module in self.unet.modules():
																		if (
																		    hasattr(lowercase_       ,  '_hf_hook' )
																		    and hasattr(module._hf_hook       ,  'execution_device' )
																		    and module._hf_hook.execution_device is not None
																		):
																								return torch.device(module._hf_hook.execution_device )
												return self.device






						@torch.no_grad()
						def __call__( self			:Optional[Any]       ,  lowercase_			:Union[str, List[str]]       ,  lowercase_			:Union[torch.FloatTensor, PIL.Image.Image]       ,  lowercase_			:str       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 5_12       ,  lowercase_			:int = 50       ,  lowercase_			:float = 7.5       ,  lowercase_			:Optional[Union[str, List[str]]] = None       ,  lowercase_			:Optional[int] = 1       ,  lowercase_			:float = 0.0       ,  lowercase_			:Optional[torch.Generator] = None       ,  lowercase_			:Optional[torch.FloatTensor] = None       ,  lowercase_			:Optional[str] = "pil"       ,  lowercase_			:bool = True       ,  lowercase_			:Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,  lowercase_			:int = 1       ,  **lowercase_			:int       ,  )    ->      int:
												UpperCAmelCase					=			self.segmentation_processor(
												    text=[text]       ,  images=[image]       ,  padding='max_length'       ,  return_tensors='pt' ).to(self.device )
												UpperCAmelCase					=			self.segmentation_model(**lowercase_ )
												UpperCAmelCase					=			torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
												UpperCAmelCase					=			self.numpy_to_pil(lowercase_ )[0].resize(image.size )

												# Run inpainting pipeline with the generated mask
												UpperCAmelCase					=			StableDiffusionInpaintPipeline(
												    vae=self.vae       ,  text_encoder=self.text_encoder       ,  tokenizer=self.tokenizer       ,  unet=self.unet       ,  scheduler=self.scheduler       ,  safety_checker=self.safety_checker       ,  feature_extractor=self.feature_extractor       ,  )
												return inpainting_pipeline(
												    prompt=lowercase_       ,  image=lowercase_       ,  mask_image=lowercase_       ,  height=lowercase_       ,  width=lowercase_       ,  num_inference_steps=lowercase_       ,  guidance_scale=lowercase_       ,  negative_prompt=lowercase_       ,  num_images_per_prompt=lowercase_       ,  eta=lowercase_       ,  generator=lowercase_       ,  latents=lowercase_       ,  output_type=lowercase_       ,  return_dict=lowercase_       ,  callback=lowercase_       ,  callback_steps=lowercase_       ,  )





