





import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipImageProcessor,
    InstructBlipConfig,
    InstructBlipForConditionalGeneration,
    InstructBlipProcessor,
    InstructBlipQFormerConfig,
    InstructBlipVisionConfig,
    LlamaConfig,
    LlamaTokenizerFast,
    TaConfig,
    TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD







def        __lowerCamelCase (							)		->    Tuple:






		"""simple docstring"""

		A__  				=       """https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"""
		A__  				=       Image.open(requests.get(__a   ,					stream=__a   ).raw   ).convert("""RGB"""   )

		return image







def        __lowerCamelCase (							__a       :str   )		->    List[Any]:






		"""simple docstring"""

		A__  				=       []
		# fmt: off

		# vision encoder
		rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""")   )
		rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""")   )
		rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""")   )
		rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""")   )
		rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""")   )
		rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""")   )

		for i in range(config.vision_config.num_hidden_layers   ):
				rename_keys.append((F'visual_encoder.blocks.{i}.norm1.weight', F'vision_model.encoder.layers.{i}.layer_norm1.weight')   )
				rename_keys.append((F'visual_encoder.blocks.{i}.norm1.bias', F'vision_model.encoder.layers.{i}.layer_norm1.bias')   )
				rename_keys.append((F'visual_encoder.blocks.{i}.norm2.weight', F'vision_model.encoder.layers.{i}.layer_norm2.weight')   )
				rename_keys.append((F'visual_encoder.blocks.{i}.norm2.bias', F'vision_model.encoder.layers.{i}.layer_norm2.bias')   )
				rename_keys.append((F'visual_encoder.blocks.{i}.attn.qkv.weight', F'vision_model.encoder.layers.{i}.self_attn.qkv.weight')   )
				rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.weight', F'vision_model.encoder.layers.{i}.self_attn.projection.weight',)   )
				rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.bias', F'vision_model.encoder.layers.{i}.self_attn.projection.bias')   )
				rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.weight', F'vision_model.encoder.layers.{i}.mlp.fc1.weight')   )
				rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.bias', F'vision_model.encoder.layers.{i}.mlp.fc1.bias')   )
				rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.weight', F'vision_model.encoder.layers.{i}.mlp.fc2.weight')   )
				rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.bias', F'vision_model.encoder.layers.{i}.mlp.fc2.bias')   )

		# QFormer
		rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.embeddings.layernorm.weight""")   )
		rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.embeddings.layernorm.bias""")   )

		# fmt: on
		return rename_keys







def        __lowerCamelCase (							__a       :str   ,					__a       :int   ,					__a       :int   )		->    int:






		"""simple docstring"""

		A__  				=       dct.pop(__a   )
		A__  				=       val







def        __lowerCamelCase (							__a       :Optional[int]   ,					__a       :List[Any]   )		->    List[str]:






		"""simple docstring"""

		for i in range(config.vision_config.num_hidden_layers   ):
				# read in original q and v biases
				A__  				=       state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias'   )
				A__  				=       state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias'   )

				# next, set bias in the state dict
				A__  				=       torch.cat((q_bias, torch.zeros_like(__a   ,					requires_grad=__a   ), v_bias)   )
				A__  				=       qkv_bias







def        __lowerCamelCase (							__a       :Optional[int]   )		->    Union[str, Any]:






		"""simple docstring"""

		A__  				=       3_6_4 if """coco""" in model_name else 2_2_4
		A__  				=       InstructBlipVisionConfig(image_size=__a   ).to_dict()

		# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
		# seems like flan-T5 models don't have bos_token_id properly set?
		if "t5-xl" in model_name:
				A__  				=       TaConfig.from_pretrained("""google/flan-t5-xl"""   ,					dense_act_fn="""gelu"""   ,					bos_token_id=1   ).to_dict()
		elif "t5-xxl" in model_name:
				A__  				=       TaConfig.from_pretrained("""google/flan-t5-xxl"""   ,					dense_act_fn="""gelu"""   ,					bos_token_id=1   ).to_dict()
		elif "vicuna-7b" in model_name:
				A__  				=       LlamaConfig.from_pretrained("""decapoda-research/llama-7b-hf"""   ,					vocab_size=3_2_0_0_1   ).to_dict()
		elif "vicuna-13b" in model_name:
				A__  				=       LlamaConfig.from_pretrained("""decapoda-research/llama-13b-hf"""   ,					vocab_size=3_2_0_0_1   ).to_dict()
		else:
				raise ValueError("""Model name not supported"""   )

		# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
		A__  				=       InstructBlipQFormerConfig(vocab_size=3_0_5_2_3   ).to_dict()
		A__  				=       InstructBlipConfig(vision_config=__a   ,					text_config=__a   ,					qformer_config=__a   )

		return config, image_size







@torch.no_grad()
def        __lowerCamelCase (							__a       :Dict   ,					__a       :str=None   ,					__a       :Optional[Any]=False   )		->    Any:






		"""simple docstring"""

		A__  				=       AutoTokenizer.from_pretrained("""bert-base-uncased"""   ,					truncation_side="""left"""   )
		qformer_tokenizer.add_special_tokens({"""bos_token""": """[DEC]"""}   )

		if "t5" in model_name:
				A__  				=       TaTokenizerFast.from_pretrained("""google/flan-t5-xl"""   ,					truncation_side="""left"""   )
		elif "vicuna" in model_name:
				# the following was used in the original implementation:
				# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
				# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
				# tokenizer.add_special_tokens({"bos_token": "</s>"})
				# tokenizer.add_special_tokens({"eos_token": "</s>"})
				# tokenizer.add_special_tokens({"unk_token": "</s>"})
				A__  				=       LlamaTokenizerFast.from_pretrained(
				    """huggyllama/llama-7b"""   ,					truncation_side="""left"""   ,					bos_token="""</s>"""   ,					unk_token="""</s>"""   )
				tokenizer.add_special_tokens({"""pad_token""": """[PAD]"""}   )

		A__				,      A__  				=       get_blipa_config(__a   )
		A__  				=       InstructBlipForConditionalGeneration(__a   ).eval()

		A__  				=       {
		    """instructblip-vicuna-7b""": ("""blip2_vicuna_instruct""", """vicuna7b"""),
		    """instructblip-vicuna-13b""": ("""blip2_vicuna_instruct""", """vicuna13b"""),
		    """instructblip-flan-t5-xl""": ("""blip2_t5_instruct""", """flant5xl"""),
		    """instructblip-flan-t5-xxl""": ("""blip2_t5_instruct""", """flant5xxl"""),
		}

		A__				,      A__  				=       model_name_to_original[model_name]

		# load original model
		print("""Loading original model..."""   )
		A__  				=       """cuda:1""" if torch.cuda.is_available() else """cpu"""
		A__  				=       """cuda:2""" if torch.cuda.is_available() else """cpu"""
		A__				,      A__				,      A__  				=       load_model_and_preprocess(
		    name=__a   ,					model_type=__a   ,					is_eval=__a   ,					device=__a   )
		original_model.eval()
		print("""Done!"""   )

		# update state dict keys
		A__  				=       original_model.state_dict()
		A__  				=       create_rename_keys(__a   )
		for src, dest in rename_keys:
				rename_key(__a   ,					__a   ,					__a   )

		# some keys can be renamed efficiently
		for key, val in state_dict.copy().items():
				A__  				=       state_dict.pop(__a   )
				if key.startswith("""Qformer.bert"""   ):
						A__  				=       key.replace("""Qformer.bert"""   ,					"""qformer"""   )
				if "attention.self" in key:
						A__  				=       key.replace("""self"""   ,					"""attention"""   )
				if "llm_proj" in key:
						A__  				=       key.replace("""llm_proj"""   ,					"""language_projection"""   )
				if "t5_proj" in key:
						A__  				=       key.replace("""t5_proj"""   ,					"""language_projection"""   )
				if key.startswith("""llm_model"""   ):
						A__  				=       key.replace("""llm_model"""   ,					"""language_model"""   )
				if key.startswith("""t5"""   ):
						A__  				=       key.replace("""t5"""   ,					"""language"""   )
				A__  				=       val

		# read in qv biases
		read_in_q_v_bias(__a   ,					__a   )

		# note: weights get loaded in torch.float32 by default
		hf_model.load_state_dict(__a   ,					strict=__a   )

		A__  				=       load_demo_image()
		A__  				=       """What is unusual about this image?"""

		# create processor
		A__  				=       BlipImageProcessor(
		    size={"""height""": image_size, """width""": image_size}   ,					image_mean=__a   ,					image_std=__a   )
		A__  				=       InstructBlipProcessor(
		    image_processor=__a   ,					tokenizer=__a   ,					qformer_tokenizer=__a   ,					)
		A__  				=       processor(images=__a   ,					text=__a   ,					return_tensors="""pt"""   ).to(__a   )

		# make sure processor creates exact same pixel values
		A__  				=       vis_processors["""eval"""](__a   ).unsqueeze(0   ).to(__a   )
		A__  				=       inputs.pixel_values
		assert torch.allclose(original_pixel_values.to(pixel_values.device   )   ,					__a   )

		original_model.to(__a   )
		hf_model.to(__a   )
		with torch.no_grad():
				if "vicuna" in model_name:
						A__  				=       original_model({"""image""": original_pixel_values, """text_input""": [prompt]}   ).logits
						A__  				=       hf_model(**__a   ).logits
				else:
						A__  				=       original_model(
						    {"""image""": original_pixel_values, """text_input""": [prompt], """text_output""": ["""\n"""]}   ).logits
						A__  				=       tokenizer("""\n"""   ,					return_tensors="""pt"""   ).input_ids.to(__a   )
						A__  				=       label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id   ,					-1_0_0   )
						A__  				=       hf_model(**__a   ,					labels=__a   ).logits

		print("""First values of original logits:"""   ,					original_logits[0, :3, :3]   )
		print("""First values of HF logits:"""   ,					logits[0, :3, :3]   )

		# assert values
		assert original_logits.shape == logits.shape
		A__  				=       1E-4 if """vicuna""" in model_name else 1E-5
		assert torch.allclose(original_logits.to(logits.device   )   ,					__a   ,					atol=__a   )
		print("""Looks ok!"""   )

		print("""Generating with original model..."""   )
		A__  				=       original_model.generate({"""image""": original_pixel_values, """prompt""": prompt}   ,					num_beams=5   )

		# important: we need to cast the weights of the HF model to the appropriate type
		print("""Generating with HF model..."""   )
		A__  				=       hf_model.generate(
		    **__a   ,					do_sample=__a   ,					num_beams=5   ,					max_length=2_5_6   ,					min_length=1   ,					top_p=0.9   ,					repetition_penalty=1.5   ,					length_penalty=1.0   ,					temperature=1   ,					)
		if "vicuna" in model_name:
				# convert output id 0 to 2 (eos_token_id)
				# TODO add this in the generate method?
				A__  				=       2
		print("""Original generation:"""   ,					__a   )
		A__  				=       processor.batch_decode(__a   ,					skip_special_tokens=__a   )
		A__  				=       [text.strip() for text in output_text]
		print("""HF generation:"""   ,					__a   )

		if pytorch_dump_folder_path is not None:
				processor.save_pretrained(__a   )
				hf_model.save_pretrained(__a   )

		if push_to_hub:
				processor.push_to_hub(F'Salesforce/{model_name}'   )
				hf_model.push_to_hub(F'Salesforce/{model_name}'   )


if __name__ == "__main__":
					A   : Tuple			=				argparse.ArgumentParser()
					A   : Tuple			=				[
					    '''instructblip-vicuna-7b''',
					    '''instructblip-vicuna-13b''',
					    '''instructblip-flan-t5-xl''',
					    '''instructblip-flan-t5-xxl''',
					]
					parser.add_argument(
					    '''--model_name''',
					    default='''instructblip-flan-t5-xl''',
					    choices=choices,
					    type=str,
					    help='''Path to hf config.json of model to convert''',
					)
					parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
					parser.add_argument(
					    '''--push_to_hub''',
					    action='''store_true''',
					    help='''Whether to push the model and processor to the hub after converting''',
					)

					A   : Tuple			=				parser.parse_args()

					convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

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


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


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			def __init__(      self       :   Optional[Any]							,     __lowerCAmelCase       :   WhisperForConditionalGeneration							,     __lowerCAmelCase       :   WhisperProcessor							,     __lowerCAmelCase       :   AutoencoderKL							,     __lowerCAmelCase       :   CLIPTextModel							,     __lowerCAmelCase       :   CLIPTokenizer							,     __lowerCAmelCase       :   UNetaDConditionModel							,     __lowerCAmelCase       :   Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,     __lowerCAmelCase       :   StableDiffusionSafetyChecker							,     __lowerCAmelCase       :   CLIPImageProcessor							,     )     ->     List[str]:







					"""simple docstring"""






					super().__init__()

					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(
					    speech_model=__lowerCAmelCase							,     speech_processor=__lowerCAmelCase							,     vae=__lowerCAmelCase							,     text_encoder=__lowerCAmelCase							,     tokenizer=__lowerCAmelCase							,     unet=__lowerCAmelCase							,     scheduler=__lowerCAmelCase							,     feature_extractor=__lowerCAmelCase							,     )






			def        a_	(      self       :   Tuple							,     __lowerCAmelCase       :   Optional[Union[str, int]] = "auto"       )     ->     Union[str, Any]:







					"""simple docstring"""






					if slice_size == "auto":
							A__  				=       self.unet.config.attention_head_dim // 2
					self.unet.set_attention_slice(__lowerCAmelCase       )






			def        a_	(      self       :   Any       )     ->     str:







					"""simple docstring"""






					self.enable_attention_slicing(__lowerCAmelCase       )







			@torch.no_grad()
			def __call__(      self       :   Optional[Any]							,     __lowerCAmelCase       :   Dict							,     __lowerCAmelCase       :   int=1_60_00							,     __lowerCAmelCase       :   int = 5_12							,     __lowerCAmelCase       :   int = 5_12							,     __lowerCAmelCase       :   int = 50							,     __lowerCAmelCase       :   float = 7.5							,     __lowerCAmelCase       :   Optional[Union[str, List[str]]] = None							,     __lowerCAmelCase       :   Optional[int] = 1							,     __lowerCAmelCase       :   float = 0.0							,     __lowerCAmelCase       :   Optional[torch.Generator] = None							,     __lowerCAmelCase       :   Optional[torch.FloatTensor] = None							,     __lowerCAmelCase       :   Optional[str] = "pil"							,     __lowerCAmelCase       :   bool = True							,     __lowerCAmelCase       :   Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,     __lowerCAmelCase       :   int = 1							,     **__lowerCAmelCase       :   Optional[Any]							,     )     ->     Any:







					"""simple docstring"""






					A__  				=       self.speech_processor.feature_extractor(
					    __lowerCAmelCase							,     return_tensors="""pt"""							,     sampling_rate=__lowerCAmelCase       ).input_features.to(self.device       )
					A__  				=       self.speech_model.generate(__lowerCAmelCase							,     max_length=48_00_00       )

					A__  				=       self.speech_processor.tokenizer.batch_decode(__lowerCAmelCase							,     skip_special_tokens=__lowerCAmelCase							,     normalize=__lowerCAmelCase       )[
					    0
					]

					if isinstance(__lowerCAmelCase							,     __lowerCAmelCase       ):
							A__  				=       1
					elif isinstance(__lowerCAmelCase							,     __lowerCAmelCase       ):
							A__  				=       len(__lowerCAmelCase       )
					else:
							raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__lowerCAmelCase       )}'       )

					if height % 8 != 0 or width % 8 != 0:
							raise ValueError(f'`height` and `width` have to be divisible by 8 but are {height} and {width}.'       )

					if (callback_steps is None) or (
					    callback_steps is not None and (not isinstance(__lowerCAmelCase							,     __lowerCAmelCase       ) or callback_steps <= 0)
					):
							raise ValueError(
							    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
							    f' {type(__lowerCAmelCase       )}.'       )

					# get prompt text embeddings
					A__  				=       self.tokenizer(
					    __lowerCAmelCase							,     padding="""max_length"""							,     max_length=self.tokenizer.model_max_length							,     return_tensors="""pt"""							,     )
					A__  				=       text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							A__  				=       self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]       )
							logger.warning(
							    """The following part of your input was truncated because CLIP can only handle sequences up to"""
							    f' {self.tokenizer.model_max_length} tokens: {removed_text}'       )
							A__  				=       text_input_ids[:, : self.tokenizer.model_max_length]
					A__  				=       self.text_encoder(text_input_ids.to(self.device       )       )[0]

					# duplicate text embeddings for each generation per prompt, using mps friendly method
					A__				,      A__				,      A__  				=       text_embeddings.shape
					A__  				=       text_embeddings.repeat(1							,     __lowerCAmelCase							,     1       )
					A__  				=       text_embeddings.view(bs_embed * num_images_per_prompt							,     __lowerCAmelCase							,     -1       )

					# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
					# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
					# corresponds to doing no classifier free guidance.
					A__  				=       guidance_scale > 1.0
					# get unconditional embeddings for classifier free guidance
					if do_classifier_free_guidance:
							A__  				=       42
							if negative_prompt is None:
									A__  				=       [""""""] * batch_size
							elif type(__lowerCAmelCase       ) is not type(__lowerCAmelCase       ):
									raise TypeError(
									    f'`negative_prompt` should be the same type to `prompt`, but got {type(__lowerCAmelCase       )} !='
									    f' {type(__lowerCAmelCase       )}.'       )
							elif isinstance(__lowerCAmelCase							,     __lowerCAmelCase       ):
									A__  				=       [negative_prompt]
							elif batch_size != len(__lowerCAmelCase       ):
									raise ValueError(
									    f'`negative_prompt`: {negative_prompt} has batch size {len(__lowerCAmelCase       )}, but `prompt`:'
									    f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
									    """ the batch size of `prompt`."""       )
							else:
									A__  				=       negative_prompt

							A__  				=       text_input_ids.shape[-1]
							A__  				=       self.tokenizer(
							    __lowerCAmelCase							,     padding="""max_length"""							,     max_length=__lowerCAmelCase							,     truncation=__lowerCAmelCase							,     return_tensors="""pt"""							,     )
							A__  				=       self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]

							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
							A__  				=       uncond_embeddings.shape[1]
							A__  				=       uncond_embeddings.repeat(1							,     __lowerCAmelCase							,     1       )
							A__  				=       uncond_embeddings.view(batch_size * num_images_per_prompt							,     __lowerCAmelCase							,     -1       )

							# For classifier free guidance, we need to do two forward passes.
							# Here we concatenate the unconditional and text embeddings into a single batch
							# to avoid doing two forward passes
							A__  				=       torch.cat([uncond_embeddings, text_embeddings]       )

					# get the initial random noise unless the user supplied it

					# Unlike in other pipelines, latents need to be generated in the target device
					# for 1-to-1 results reproducibility with the CompVis implementation.
					# However this currently doesn't work in `mps`.
					A__  				=       (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
					A__  				=       text_embeddings.dtype
					if latents is None:
							if self.device.type == "mps":
									# randn does not exist on mps
									A__  				=       torch.randn(__lowerCAmelCase							,     generator=__lowerCAmelCase							,     device="""cpu"""							,     dtype=__lowerCAmelCase       ).to(
									    self.device       )
							else:
									A__  				=       torch.randn(__lowerCAmelCase							,     generator=__lowerCAmelCase							,     device=self.device							,     dtype=__lowerCAmelCase       )
					else:
							if latents.shape != latents_shape:
									raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'       )
							A__  				=       latents.to(self.device       )

					# set timesteps
					self.scheduler.set_timesteps(__lowerCAmelCase       )

					# Some schedulers like PNDM have timesteps as arrays
					# It's more optimized to move all timesteps to correct device beforehand
					A__  				=       self.scheduler.timesteps.to(self.device       )

					# scale the initial noise by the standard deviation required by the scheduler
					A__  				=       latents * self.scheduler.init_noise_sigma

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
					# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
					# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
					# and should be between [0, 1]
					A__  				=       """eta""" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )
					A__  				=       {}
					if accepts_eta:
							A__  				=       eta

					for i, t in enumerate(self.progress_bar(__lowerCAmelCase       )       ):
							# expand the latents if we are doing classifier free guidance
							A__  				=       torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents
							A__  				=       self.scheduler.scale_model_input(__lowerCAmelCase							,     __lowerCAmelCase       )

							# predict the noise residual
							A__  				=       self.unet(__lowerCAmelCase							,     __lowerCAmelCase							,     encoder_hidden_states=__lowerCAmelCase       ).sample

							# perform guidance
							if do_classifier_free_guidance:
									A__				,      A__  				=       noise_pred.chunk(2       )
									A__  				=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

							# compute the previous noisy sample x_t -> x_t-1
							A__  				=       self.scheduler.step(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase							,     **__lowerCAmelCase       ).prev_sample

							# call the callback, if provided
							if callback is not None and i % callback_steps == 0:
									callback(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )

					A__  				=       1 / 0.1_8_2_1_5 * latents
					A__  				=       self.vae.decode(__lowerCAmelCase       ).sample

					A__  				=       (image / 2 + 0.5).clamp(0							,     1       )

					# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
					A__  				=       image.cpu().permute(0							,     2							,     3							,     1       ).float().numpy()

					if output_type == "pil":
							A__  				=       self.numpy_to_pil(__lowerCAmelCase       )

					if not return_dict:
							return image

					return StableDiffusionPipelineOutput(images=__lowerCAmelCase							,     nsfw_content_detected=__lowerCAmelCase       )



