"""simple docstring"""




import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

__lowerCAmelCase     :				Any    				=    logging.getLogger(__name__)

@dataclass
class    a_   :
					UpperCamelCase_			:						Optional[int]      =     field(
					    default=128			,		metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }			,		)
					UpperCamelCase_			:						bool      =     field(
					    default=__UpperCamelCase			,		metadata={"help": "Overwrite the cached preprocessed datasets or not."}      )
					UpperCamelCase_			:						bool      =     field(
					    default=__UpperCamelCase			,		metadata={
					        "help": (
					            "Whether to pad all samples to `max_seq_length`. "
					            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
					        )
					    }			,		)
					UpperCamelCase_			:						Optional[int]      =     field(
					    default=__UpperCamelCase			,		metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of training examples to this "
					            "value if set."
					        )
					    }			,		)
					UpperCamelCase_			:						Optional[int]      =     field(
					    default=__UpperCamelCase			,		metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
					            "value if set."
					        )
					    }			,		)
					UpperCamelCase_			:						Optional[int]      =     field(
					    default=__UpperCamelCase			,		metadata={
					        "help": (
					            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
					            "value if set."
					        )
					    }			,		)

@dataclass
class    a_   :
					UpperCamelCase_			:						str      =     field(
					    default=__UpperCamelCase			,		metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}      )
					UpperCamelCase_			:						str      =     field(
					    default=__UpperCamelCase			,		metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}      )
					UpperCamelCase_			:						Optional[str]      =     field(
					    default=__UpperCamelCase			,		metadata={"help": "Train language if it is different from the evaluation language."}      )
					UpperCamelCase_			:						Optional[str]      =     field(
					    default=__UpperCamelCase			,		metadata={"help": "Pretrained config name or path if not the same as model_name"}      )
					UpperCamelCase_			:						Optional[str]      =     field(
					    default=__UpperCamelCase			,		metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}      )
					UpperCamelCase_			:						Optional[str]      =     field(
					    default=__UpperCamelCase			,		metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}			,		)
					UpperCamelCase_			:						Optional[bool]      =     field(
					    default=__UpperCamelCase			,		metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}			,		)
					UpperCamelCase_			:						bool      =     field(
					    default=__UpperCamelCase			,		metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}			,		)
					UpperCamelCase_			:						str      =     field(
					    default="main"			,		metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}			,		)
					UpperCamelCase_			:						bool      =     field(
					    default=__UpperCamelCase			,		metadata={
					        "help": (
					            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
					            "with private models)."
					        )
					    }			,		)
					UpperCamelCase_			:						bool      =     field(
					    default=__UpperCamelCase			,		metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}			,		)







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		lowerCAmelCase__   = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
		lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = parser.parse_args_into_dataclasses()

		# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
		# information sent is the one passed as arguments along with your Python/PyTorch versions.
		send_example_telemetry("""run_xnli"""      ,	lowerCamelCase__  )

		# Setup logging
		logging.basicConfig(
		    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,	datefmt="""%m/%d/%Y %H:%M:%S"""      ,	handlers=[logging.StreamHandler(sys.stdout  )]      ,	)

		if training_args.should_log:
				# The default of training_args.log_level is passive, so we set log level at info here to have that default.
				transformers.utils.logging.set_verbosity_info()

		lowerCAmelCase__   = training_args.get_process_log_level()
		logger.setLevel(lowerCamelCase__  )
		datasets.utils.logging.set_verbosity(lowerCamelCase__  )
		transformers.utils.logging.set_verbosity(lowerCamelCase__  )
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()

		# Log on each process the small summary:
		logger.warning(
		    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
		    + f"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
		logger.info(f"""Training/evaluation parameters {training_args}"""  )

		# Detecting last checkpoint.
		lowerCAmelCase__   = None
		if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
				lowerCAmelCase__   = get_last_checkpoint(training_args.output_dir  )
				if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
						raise ValueError(
						    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
						    """Use --overwrite_output_dir to overcome."""  )
				elif last_checkpoint is not None:
						logger.info(
						    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
						    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""  )

    # Set seed before initializing model.
		set_seed(training_args.seed  )

		# In distributed training, the load_dataset function guarantees that only one local process can concurrently
		# download the dataset.
		# Downloading and loading xnli dataset from the hub.
		if training_args.do_train:
				if model_args.train_language is None:
						lowerCAmelCase__   = load_dataset(
						    """xnli"""      ,	model_args.language      ,	split="""train"""      ,	cache_dir=model_args.cache_dir      ,	use_auth_token=True if model_args.use_auth_token else None      ,	)
				else:
						lowerCAmelCase__   = load_dataset(
						    """xnli"""      ,	model_args.train_language      ,	split="""train"""      ,	cache_dir=model_args.cache_dir      ,	use_auth_token=True if model_args.use_auth_token else None      ,	)
				lowerCAmelCase__   = train_dataset.features["""label"""].names

		if training_args.do_eval:
				lowerCAmelCase__   = load_dataset(
				    """xnli"""      ,	model_args.language      ,	split="""validation"""      ,	cache_dir=model_args.cache_dir      ,	use_auth_token=True if model_args.use_auth_token else None      ,	)
				lowerCAmelCase__   = eval_dataset.features["""label"""].names

		if training_args.do_predict:
				lowerCAmelCase__   = load_dataset(
				    """xnli"""      ,	model_args.language      ,	split="""test"""      ,	cache_dir=model_args.cache_dir      ,	use_auth_token=True if model_args.use_auth_token else None      ,	)
				lowerCAmelCase__   = predict_dataset.features["""label"""].names

		# Labels
		lowerCAmelCase__   = len(lowerCamelCase__  )

		# Load pretrained model and tokenizer
		# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.
		lowerCAmelCase__   = AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,	num_labels=lowerCamelCase__      ,	idalabel={str(lowerCamelCase__  ): label for i, label in enumerate(lowerCamelCase__  )}      ,	labelaid={label: i for i, label in enumerate(lowerCamelCase__  )}      ,	finetuning_task="""xnli"""      ,	cache_dir=model_args.cache_dir      ,	revision=model_args.model_revision      ,	use_auth_token=True if model_args.use_auth_token else None      ,	)
		lowerCAmelCase__   = AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,	do_lower_case=model_args.do_lower_case      ,	cache_dir=model_args.cache_dir      ,	use_fast=model_args.use_fast_tokenizer      ,	revision=model_args.model_revision      ,	use_auth_token=True if model_args.use_auth_token else None      ,	)
		lowerCAmelCase__   = AutoModelForSequenceClassification.from_pretrained(
		    model_args.model_name_or_path      ,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )      ,	config=lowerCamelCase__      ,	cache_dir=model_args.cache_dir      ,	revision=model_args.model_revision      ,	use_auth_token=True if model_args.use_auth_token else None      ,	ignore_mismatched_sizes=model_args.ignore_mismatched_sizes      ,	)

		# Preprocessing the datasets
		# Padding strategy
		if data_args.pad_to_max_length:
				lowerCAmelCase__   = """max_length"""
		else:
				# We will pad later, dynamically at batch creation, to the max sequence length in each batch
				lowerCAmelCase__   = False

		def preprocess_function(lowerCamelCase__  ):
				# Tokenize the texts
				return tokenizer(
				    examples["""premise"""]      ,	examples["""hypothesis"""]      ,	padding=lowerCamelCase__      ,	max_length=data_args.max_seq_length      ,	truncation=lowerCamelCase__      ,	)

		if training_args.do_train:
				if data_args.max_train_samples is not None:
						lowerCAmelCase__   = min(len(lowerCamelCase__  )      ,	data_args.max_train_samples  )
						lowerCAmelCase__   = train_dataset.select(range(lowerCamelCase__  )  )
				with training_args.main_process_first(desc="""train dataset map pre-processing"""  ):
						lowerCAmelCase__   = train_dataset.map(
						    lowerCamelCase__      ,	batched=lowerCamelCase__      ,	load_from_cache_file=not data_args.overwrite_cache      ,	desc="""Running tokenizer on train dataset"""      ,	)
				# Log a few random samples from the training set:
				for index in random.sample(range(len(lowerCamelCase__  )  )      ,	3  ):
						logger.info(f"""Sample {index} of the training set: {train_dataset[index]}."""  )

		if training_args.do_eval:
				if data_args.max_eval_samples is not None:
						lowerCAmelCase__   = min(len(lowerCamelCase__  )      ,	data_args.max_eval_samples  )
						lowerCAmelCase__   = eval_dataset.select(range(lowerCamelCase__  )  )
				with training_args.main_process_first(desc="""validation dataset map pre-processing"""  ):
						lowerCAmelCase__   = eval_dataset.map(
						    lowerCamelCase__      ,	batched=lowerCamelCase__      ,	load_from_cache_file=not data_args.overwrite_cache      ,	desc="""Running tokenizer on validation dataset"""      ,	)

		if training_args.do_predict:
				if data_args.max_predict_samples is not None:
						lowerCAmelCase__   = min(len(lowerCamelCase__  )      ,	data_args.max_predict_samples  )
						lowerCAmelCase__   = predict_dataset.select(range(lowerCamelCase__  )  )
				with training_args.main_process_first(desc="""prediction dataset map pre-processing"""  ):
						lowerCAmelCase__   = predict_dataset.map(
						    lowerCamelCase__      ,	batched=lowerCamelCase__      ,	load_from_cache_file=not data_args.overwrite_cache      ,	desc="""Running tokenizer on prediction dataset"""      ,	)

    # Get the metric function
		lowerCAmelCase__   = evaluate.load("""xnli"""  )

		# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
		# predictions and label_ids field) and has to return a dictionary string to float.
		def compute_metrics(lowerCamelCase__  ):
				lowerCAmelCase__   = p.predictions[0] if isinstance(p.predictions      ,	lowerCamelCase__  ) else p.predictions
				lowerCAmelCase__   = np.argmax(lowerCamelCase__      ,	axis=1  )
				return metric.compute(predictions=lowerCamelCase__      ,	references=p.label_ids  )

		# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
		if data_args.pad_to_max_length:
				lowerCAmelCase__   = default_data_collator
		elif training_args.fpaa:
				lowerCAmelCase__   = DataCollatorWithPadding(lowerCamelCase__      ,	pad_to_multiple_of=8  )
		else:
				lowerCAmelCase__   = None

		# Initialize our Trainer
		lowerCAmelCase__   = Trainer(
		    model=lowerCamelCase__      ,	args=lowerCamelCase__      ,	train_dataset=train_dataset if training_args.do_train else None      ,	eval_dataset=eval_dataset if training_args.do_eval else None      ,	compute_metrics=lowerCamelCase__      ,	tokenizer=lowerCamelCase__      ,	data_collator=lowerCamelCase__      ,	)

		# Training
		if training_args.do_train:
				lowerCAmelCase__   = None
				if training_args.resume_from_checkpoint is not None:
						lowerCAmelCase__   = training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						lowerCAmelCase__   = last_checkpoint
				lowerCAmelCase__   = trainer.train(resume_from_checkpoint=lowerCamelCase__  )
				lowerCAmelCase__   = train_result.metrics
				lowerCAmelCase__   = (
				    data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase__  )
				)
				lowerCAmelCase__   = min(lowerCamelCase__      ,	len(lowerCamelCase__  )  )

				trainer.save_model()  # Saves the tokenizer too for easy upload

				trainer.log_metrics("""train"""      ,	lowerCamelCase__  )
				trainer.save_metrics("""train"""      ,	lowerCamelCase__  )
				trainer.save_state()

		# Evaluation
		if training_args.do_eval:
				logger.info("""*** Evaluate ***"""  )
				lowerCAmelCase__   = trainer.evaluate(eval_dataset=lowerCamelCase__  )

				lowerCAmelCase__   = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase__  )
				lowerCAmelCase__   = min(lowerCamelCase__      ,	len(lowerCamelCase__  )  )

				trainer.log_metrics("""eval"""      ,	lowerCamelCase__  )
				trainer.save_metrics("""eval"""      ,	lowerCamelCase__  )

		# Prediction
		if training_args.do_predict:
				logger.info("""*** Predict ***"""  )
				lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = trainer.predict(lowerCamelCase__      ,	metric_key_prefix="""predict"""  )

				lowerCAmelCase__   = (
				    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowerCamelCase__  )
				)
				lowerCAmelCase__   = min(lowerCamelCase__      ,	len(lowerCamelCase__  )  )

				trainer.log_metrics("""predict"""      ,	lowerCamelCase__  )
				trainer.save_metrics("""predict"""      ,	lowerCamelCase__  )

				lowerCAmelCase__   = np.argmax(lowerCamelCase__      ,	axis=1  )
				lowerCAmelCase__   = os.path.join(training_args.output_dir      ,	"""predictions.txt"""  )
				if trainer.is_world_process_zero():
						with open(lowerCamelCase__      ,	"""w"""  ) as writer:
								writer.write("""index\tprediction\n"""  )
								for index, item in enumerate(lowerCamelCase__  ):
										lowerCAmelCase__   = label_list[item]
										writer.write(f"""{index}\t{item}\n"""  )


if __name__ == "__main__":
			main()





"""simple docstring"""




from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


__lowerCAmelCase     :				Dict    				=    "CompVis/stable-diffusion-v1-1"
__lowerCAmelCase     :				int    				=    "CompVis/stable-diffusion-v1-2"
__lowerCAmelCase     :				int    				=    "CompVis/stable-diffusion-v1-3"
__lowerCAmelCase     :				Union[str, Any]    				=    "CompVis/stable-diffusion-v1-4"

class    a_   (  __UpperCamelCase      ):




					def __init__(						self    :  List[Any]	,    snake_case__    :  AutoencoderKL	,    snake_case__    :  CLIPTextModel	,    snake_case__    :  CLIPTokenizer	,    snake_case__    :  UNetaDConditionModel	,    snake_case__    :  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]	,    snake_case__    :  StableDiffusionSafetyChecker	,    snake_case__    :  CLIPImageProcessor	,    snake_case__    :  bool = True	,    ):
							super()._init_()

							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(snake_case__  )
							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(snake_case__  )
							lowerCAmelCase__   = StableDiffusionPipeline.from_pretrained(snake_case__  )
							lowerCAmelCase__   = StableDiffusionPipeline(
							    vae=snake_case__	,    text_encoder=snake_case__	,    tokenizer=snake_case__	,    unet=snake_case__	,    scheduler=snake_case__	,    safety_checker=snake_case__	,    feature_extractor=snake_case__	,    requires_safety_checker=snake_case__	,    )

							self.register_modules(pipelinea=self.pipea	,    pipelinea=self.pipea	,    pipelinea=self.pipea	,    pipelinea=self.pipea  )




					@property
					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple  ):
							return {k: getattr(self	,    snake_case__  ) for k in self.config.keys() if not k.startswith("""_"""  )}




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




					def     _SCREAMING_SNAKE_CASE    (						self    :  int  ):
							self.enable_attention_slicing(snake_case__  )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  List[Any]	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  Tuple	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  Tuple	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  Optional[int]	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  Optional[Any]	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  List[str]	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  str	,    ):
							return self.pipea(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )




					@torch.no_grad()
					def     _SCREAMING_SNAKE_CASE    (						self    :  Union[str, Any]	,    snake_case__    :  Union[str, List[str]]	,    snake_case__    :  int = 512	,    snake_case__    :  int = 512	,    snake_case__    :  int = 50	,    snake_case__    :  float = 7.5	,    snake_case__    :  Optional[Union[str, List[str]]] = None	,    snake_case__    :  Optional[int] = 1	,    snake_case__    :  float = 0.0	,    snake_case__    :  Optional[torch.Generator] = None	,    snake_case__    :  Optional[torch.FloatTensor] = None	,    snake_case__    :  Optional[str] = "pil"	,    snake_case__    :  bool = True	,    snake_case__    :  Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,    snake_case__    :  int = 1	,    **snake_case__    :  Optional[Any]	,    ):
							lowerCAmelCase__   = """cuda""" if torch.cuda.is_available() else """cpu"""
							self.to(snake_case__  )

							# Checks if the height and width are divisible by 8 or not
							if height % 8 != 0 or width % 8 != 0:
									raise ValueError(F"""`height` and `width` must be divisible by 8 but are {height} and {width}."""  )

							# Get first result from Stable Diffusion Checkpoint v1.1
							lowerCAmelCase__   = self.textaimg_sda_a(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )

							# Get first result from Stable Diffusion Checkpoint v1.2
							lowerCAmelCase__   = self.textaimg_sda_a(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )

							# Get first result from Stable Diffusion Checkpoint v1.3
							lowerCAmelCase__   = self.textaimg_sda_a(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )

							# Get first result from Stable Diffusion Checkpoint v1.4
							lowerCAmelCase__   = self.textaimg_sda_a(
							    prompt=snake_case__	,    height=snake_case__	,    width=snake_case__	,    num_inference_steps=snake_case__	,    guidance_scale=snake_case__	,    negative_prompt=snake_case__	,    num_images_per_prompt=snake_case__	,    eta=snake_case__	,    generator=snake_case__	,    latents=snake_case__	,    output_type=snake_case__	,    return_dict=snake_case__	,    callback=snake_case__	,    callback_steps=snake_case__	,    **snake_case__	,    )

							# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
							return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]]  )




