

import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_       	=	logging.get_logger(__name__)


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	ASTConfig()

						if "10-10" in model_name:
												pass
						elif "speech-commands" in model_name:
												snake_case_   =	128
						elif "12-12" in model_name:
												snake_case_   =	12
												snake_case_   =	12
						elif "14-14" in model_name:
												snake_case_   =	14
												snake_case_   =	14
						elif "16-16" in model_name:
												snake_case_   =	16
												snake_case_   =	16
						else:
												raise ValueError('''Model not supported'''       )

						snake_case_   =	'''huggingface/label-files'''
						if "speech-commands" in model_name:
												snake_case_   =	35
												snake_case_   =	'''speech-commands-v2-id2label.json'''
						else:
												snake_case_   =	527
												snake_case_   =	'''audioset-id2label.json'''

						snake_case_   =	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		repo_type='''dataset'''       )  ,		'''r'''       )       )
						snake_case_   =	{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
						snake_case_   =	idalabel
						snake_case_   =	{v: k for k, v in idalabel.items()}

						return config


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						if "module.v" in name:
												snake_case_   =	name.replace('''module.v'''  ,		'''audio_spectrogram_transformer'''       )
						if "cls_token" in name:
												snake_case_   =	name.replace('''cls_token'''  ,		'''embeddings.cls_token'''       )
						if "dist_token" in name:
												snake_case_   =	name.replace('''dist_token'''  ,		'''embeddings.distillation_token'''       )
						if "pos_embed" in name:
												snake_case_   =	name.replace('''pos_embed'''  ,		'''embeddings.position_embeddings'''       )
						if "patch_embed.proj" in name:
												snake_case_   =	name.replace('''patch_embed.proj'''  ,		'''embeddings.patch_embeddings.projection'''       )
						# transformer blocks
						if "blocks" in name:
												snake_case_   =	name.replace('''blocks'''  ,		'''encoder.layer'''       )
						if "attn.proj" in name:
												snake_case_   =	name.replace('''attn.proj'''  ,		'''attention.output.dense'''       )
						if "attn" in name:
												snake_case_   =	name.replace('''attn'''  ,		'''attention.self'''       )
						if "norm1" in name:
												snake_case_   =	name.replace('''norm1'''  ,		'''layernorm_before'''       )
						if "norm2" in name:
												snake_case_   =	name.replace('''norm2'''  ,		'''layernorm_after'''       )
						if "mlp.fc1" in name:
												snake_case_   =	name.replace('''mlp.fc1'''  ,		'''intermediate.dense'''       )
						if "mlp.fc2" in name:
												snake_case_   =	name.replace('''mlp.fc2'''  ,		'''output.dense'''       )
						# final layernorm
						if "audio_spectrogram_transformer.norm" in name:
												snake_case_   =	name.replace('''audio_spectrogram_transformer.norm'''  ,		'''audio_spectrogram_transformer.layernorm'''       )
						# classifier head
						if "module.mlp_head.0" in name:
												snake_case_   =	name.replace('''module.mlp_head.0'''  ,		'''classifier.layernorm'''       )
						if "module.mlp_head.1" in name:
												snake_case_   =	name.replace('''module.mlp_head.1'''  ,		'''classifier.dense'''       )

						return name


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						for key in orig_state_dict.copy().keys():
												snake_case_   =	orig_state_dict.pop(SCREAMING_SNAKE_CASE__       )

												if "qkv" in key:
																		snake_case_   =	key.split('''.'''       )
																		snake_case_   =	int(key_split[3]       )
																		snake_case_   =	config.hidden_size
																		if "weight" in key:
																								snake_case_   =	val[:dim, :]
																								snake_case_   =	val[dim : dim * 2, :]
																								snake_case_   =	val[-dim:, :]
																		else:
																								snake_case_   =	val[:dim]
																								snake_case_   =	val[dim : dim * 2]
																								snake_case_   =	val[-dim:]
												else:
																		snake_case_   =	val

						return orig_state_dict


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[
						    '''module.v.head.weight''',
						    '''module.v.head.bias''',
						    '''module.v.head_dist.weight''',
						    '''module.v.head_dist.bias''',
						]
						for k in ignore_keys:
												state_dict.pop(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )


@torch.no_grad()
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__=False       ):
						snake_case_   =	get_audio_spectrogram_transformer_config(SCREAMING_SNAKE_CASE__       )

						snake_case_   =	{
						    '''ast-finetuned-audioset-10-10-0.4593''': (
						        '''https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1'''
						    ),
						    '''ast-finetuned-audioset-10-10-0.450''': (
						        '''https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1'''
						    ),
						    '''ast-finetuned-audioset-10-10-0.448''': (
						        '''https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1'''
						    ),
						    '''ast-finetuned-audioset-10-10-0.448-v2''': (
						        '''https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1'''
						    ),
						    '''ast-finetuned-audioset-12-12-0.447''': (
						        '''https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1'''
						    ),
						    '''ast-finetuned-audioset-14-14-0.443''': (
						        '''https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1'''
						    ),
						    '''ast-finetuned-audioset-16-16-0.442''': (
						        '''https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1'''
						    ),
						    '''ast-finetuned-speech-commands-v2''': (
						        '''https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1'''
						    ),
						}

						# load original state_dict
						snake_case_   =	model_name_to_url[model_name]
						snake_case_   =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__  ,		map_location='''cpu'''       )
						# remove some keys
						remove_keys(SCREAMING_SNAKE_CASE__       )
						# rename some keys
						snake_case_   =	convert_state_dict(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       )

						# load 🤗 model
						snake_case_   =	ASTForAudioClassification(SCREAMING_SNAKE_CASE__       )
						model.eval()

						model.load_state_dict(SCREAMING_SNAKE_CASE__       )

						# verify outputs on dummy input
						# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
						snake_case_   =	-4.267_7393 if '''speech-commands''' not in model_name else -6.84_5978
						snake_case_   =	4.568_9974 if '''speech-commands''' not in model_name else 5.565_4526
						snake_case_   =	1024 if '''speech-commands''' not in model_name else 128
						snake_case_   =	ASTFeatureExtractor(mean=SCREAMING_SNAKE_CASE__  ,		std=SCREAMING_SNAKE_CASE__  ,		max_length=SCREAMING_SNAKE_CASE__       )

						if "speech-commands" in model_name:
												snake_case_   =	load_dataset('''speech_commands'''  ,		'''v0.02'''  ,		split='''validation'''       )
												snake_case_   =	dataset[0]['''audio''']['''array''']
						else:
												snake_case_   =	hf_hub_download(
												    repo_id='''nielsr/audio-spectogram-transformer-checkpoint'''  ,		filename='''sample_audio.flac'''  ,		repo_type='''dataset'''  ,		)

												snake_case_,    snake_case_   =	torchaudio.load(SCREAMING_SNAKE_CASE__       )
												snake_case_   =	waveform.squeeze().numpy()

						snake_case_   =	feature_extractor(SCREAMING_SNAKE_CASE__  ,		sampling_rate=16000  ,		return_tensors='''pt'''       )

						# forward pass
						snake_case_   =	model(**SCREAMING_SNAKE_CASE__       )
						snake_case_   =	outputs.logits

						if model_name == "ast-finetuned-audioset-10-10-0.4593":
												snake_case_   =	torch.tensor([-0.8760, -7.0042, -8.6602]       )
						elif model_name == "ast-finetuned-audioset-10-10-0.450":
												snake_case_   =	torch.tensor([-1.1986, -7.0903, -8.2718]       )
						elif model_name == "ast-finetuned-audioset-10-10-0.448":
												snake_case_   =	torch.tensor([-2.6128, -8.0080, -9.4344]       )
						elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
												snake_case_   =	torch.tensor([-1.5080, -7.4534, -8.8917]       )
						elif model_name == "ast-finetuned-audioset-12-12-0.447":
												snake_case_   =	torch.tensor([-0.5050, -6.5833, -8.0843]       )
						elif model_name == "ast-finetuned-audioset-14-14-0.443":
												snake_case_   =	torch.tensor([-0.3826, -7.0336, -8.2413]       )
						elif model_name == "ast-finetuned-audioset-16-16-0.442":
												snake_case_   =	torch.tensor([-1.2113, -6.9101, -8.3470]       )
						elif model_name == "ast-finetuned-speech-commands-v2":
												snake_case_   =	torch.tensor([6.1589, -8.0566, -8.7984]       )
						else:
												raise ValueError('''Unknown model name'''       )
						if not torch.allclose(logits[0, :3]  ,		SCREAMING_SNAKE_CASE__  ,		atol=1E-4       ):
												raise ValueError('''Logits don\'t match'''       )
						print('''Looks ok!'''       )

						if pytorch_dump_folder_path is not None:
												Path(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
												print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''       )
												model.save_pretrained(SCREAMING_SNAKE_CASE__       )
												print(F'''Saving feature extractor to {pytorch_dump_folder_path}'''       )
												feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__       )

						if push_to_hub:
												print('''Pushing model and feature extractor to the hub...'''       )
												model.push_to_hub(F'''MIT/{model_name}'''       )
												feature_extractor.push_to_hub(F'''MIT/{model_name}'''       )


if __name__ == "__main__":
					lowerCAmelCase_       	=	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--model_name''',
					    default='''ast-finetuned-audioset-10-10-0.4593''',
					    type=str,
					    help='''Name of the Audio Spectrogram Transformer model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
					)

					lowerCAmelCase_       	=	parser.parse_args()
					convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


"""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


_a       =							'CompVis/stable-diffusion-v1-1'
_a       =							'CompVis/stable-diffusion-v1-2'
_a       =							'CompVis/stable-diffusion-v1-3'
_a       =							'CompVis/stable-diffusion-v1-4'




class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







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

    __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__	)
    __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__	)
    __lowercase								=						StableDiffusionPipeline.from_pretrained(UpperCAmelCase__	)
    __lowercase								=						StableDiffusionPipeline(
        vae=UpperCAmelCase__,					text_encoder=UpperCAmelCase__,					tokenizer=UpperCAmelCase__,					unet=UpperCAmelCase__,					scheduler=UpperCAmelCase__,					safety_checker=UpperCAmelCase__,					feature_extractor=UpperCAmelCase__,					requires_safety_checker=UpperCAmelCase__,					)

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


 @property
 def 					_lowercase (			self							:			List[str]	):
    return {k: getattr(self,					UpperCAmelCase__	) for k in self.config.keys() if not k.startswith("_"	)}


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


 def 					_lowercase (			self							:			List[str]	):
    self.enable_attention_slicing(UpperCAmelCase__	)


 @torch.no_grad()
 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			Tuple,					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)


 @torch.no_grad()
 def 					_lowercase (			self							:			Tuple,					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			str,					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)


 @torch.no_grad()
 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			Any,					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)


 @torch.no_grad()
 def 					_lowercase (			self							:			Union[str, Any],					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			Optional[int],					):
    return self.pipea(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)



 @torch.no_grad()
 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Union[str, List[str]],					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_1_2,					UpperCAmelCase__							:			int = 5_0,					UpperCAmelCase__							:			float = 7.5,					UpperCAmelCase__							:			Optional[Union[str, List[str]]] = None,					UpperCAmelCase__							:			Optional[int] = 1,					UpperCAmelCase__							:			float = 0.0,					UpperCAmelCase__							:			Optional[torch.Generator] = None,					UpperCAmelCase__							:			Optional[torch.FloatTensor] = None,					UpperCAmelCase__							:			Optional[str] = "pil",					UpperCAmelCase__							:			bool = True,					UpperCAmelCase__							:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None,					UpperCAmelCase__							:			int = 1,					**UpperCAmelCase__							:			str,					):
    __lowercase								=						"cuda" if torch.cuda.is_available() else "cpu"
    self.to(UpperCAmelCase__	)

    # 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
    __lowercase								=						self.textaimg_sda_a(
        prompt=UpperCAmelCase__,					height=UpperCAmelCase__,					width=UpperCAmelCase__,					num_inference_steps=UpperCAmelCase__,					guidance_scale=UpperCAmelCase__,					negative_prompt=UpperCAmelCase__,					num_images_per_prompt=UpperCAmelCase__,					eta=UpperCAmelCase__,					generator=UpperCAmelCase__,					latents=UpperCAmelCase__,					output_type=UpperCAmelCase__,					return_dict=UpperCAmelCase__,					callback=UpperCAmelCase__,					callback_steps=UpperCAmelCase__,					**UpperCAmelCase__,					)

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

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

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

    # 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]]	)




