



from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)



class     __snake_case						(					lowercase_						):




							def __init__(	self					:	Optional[Any]    ,    A_					:	AutoencoderKL    ,    A_					:	CLIPTextModel    ,    A_					:	CLIPTokenizer    ,    A_					:	UNetaDConditionModel    ,    A_					:	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,    ):
									super().__init__()

									self.register_modules(vae=__UpperCamelCase    ,    text_encoder=__UpperCamelCase    ,    tokenizer=__UpperCamelCase    ,    unet=__UpperCamelCase    ,    scheduler=__UpperCamelCase)




							def        UpperCAmelCase__      (	self					:	Union[str, Any]    ,    A_					:	List[str]):
									with torch.no_grad():
											lowerCAmelCase_      :					Any     =			self.vae.encode(tfms.ToTensor()(__UpperCamelCase).unsqueeze(0).to(self.device) * 2 - 1)
											lowerCAmelCase_      :					int     =			0.1_8215 * latent.latent_dist.sample()
									return latent




							def        UpperCAmelCase__      (	self					:	List[Any]    ,    A_					:	Dict):
									lowerCAmelCase_      :					Optional[Any]     =			(1 / 0.1_8215) * latent
									with torch.no_grad():
											lowerCAmelCase_      :					Any     =			self.vae.decode(__UpperCamelCase).sample
									lowerCAmelCase_      :					str     =			(img / 2 + 0.5).clamp(0    ,    1)
									lowerCAmelCase_      :					List[Any]     =			img.detach().cpu().permute(0    ,    2    ,    3    ,    1).numpy()
									lowerCAmelCase_      :					List[Any]     =			(img * 2_5_5).round().astype('''uint8''')
									return Image.fromarray(img[0])




							def        UpperCAmelCase__      (	self					:	Tuple    ,    A_					:	List[str]):
									lowerCAmelCase_      :					Union[str, Any]     =			self.tokenizer(
									    __UpperCamelCase    ,    padding='''max_length'''    ,    max_length=self.tokenizer.model_max_length    ,    truncation=__UpperCamelCase    ,    return_tensors='''pt'''    ,    )

									lowerCAmelCase_      :					List[str]     =			self.text_encoder(text_input.input_ids.to(self.device))[0]

									lowerCAmelCase_      :					Tuple     =			self.tokenizer(
									    ''''''    ,    padding='''max_length'''    ,    max_length=self.tokenizer.model_max_length    ,    truncation=__UpperCamelCase    ,    return_tensors='''pt'''    ,    )

									lowerCAmelCase_      :					Any     =			self.text_encoder(uncond_input.input_ids.to(self.device))[0]

									return torch.cat([uncond_embedding, text_embedding])







							def __call__(	self					:	Optional[Any]    ,    A_					:	Image.Image    ,    A_					:	str    ,    A_					:	float = 0.3    ,    A_					:	float = 0.6    ,    A_					:	float = 0.5    ,    A_					:	int = 4_2    ,    A_					:	int = 5_0    ,    A_					:	float = 7.5    ,    ):
									lowerCAmelCase_      :					List[Any]     =			steps - int(kmin * steps)
									lowerCAmelCase_      :					Optional[int]     =			steps - int(kmax * steps)

									lowerCAmelCase_      :					List[str]     =			self.prep_text(__UpperCamelCase)

									self.scheduler.set_timesteps(__UpperCamelCase)

									lowerCAmelCase_ ,  lowerCAmelCase_      :					Any     =			img.size
									lowerCAmelCase_      :					str     =			self.encode(__UpperCamelCase)

									torch.manual_seed(__UpperCamelCase)
									lowerCAmelCase_      :					Any     =			torch.randn(
									    (1, self.unet.config.in_channels, height // 8, width // 8)    ,    ).to(self.device)

									lowerCAmelCase_      :					Any     =			self.scheduler.add_noise(
									    __UpperCamelCase    ,    __UpperCamelCase    ,    timesteps=self.scheduler.timesteps[tmax]    ,    )

									lowerCAmelCase_      :					List[str]     =			torch.cat([latents] * 2)

									lowerCAmelCase_      :					Optional[int]     =			self.scheduler.scale_model_input(__UpperCamelCase    ,    self.scheduler.timesteps[tmax])

									with torch.no_grad():
											lowerCAmelCase_      :					Union[str, Any]     =			self.unet(
											    __UpperCamelCase    ,    self.scheduler.timesteps[tmax]    ,    encoder_hidden_states=__UpperCamelCase    ,    ).sample

									lowerCAmelCase_ ,  lowerCAmelCase_      :					Any     =			pred.chunk(2)
									lowerCAmelCase_      :					Dict     =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

									lowerCAmelCase_      :					Tuple     =			self.scheduler.step(__UpperCamelCase    ,    self.scheduler.timesteps[tmax]    ,    __UpperCamelCase).prev_sample

									for i, t in enumerate(tqdm(self.scheduler.timesteps)):
											if i > tmax:
													if i < tmin:  # layout generation phase
															lowerCAmelCase_      :					str     =			self.scheduler.add_noise(
															    __UpperCamelCase    ,    __UpperCamelCase    ,    timesteps=__UpperCamelCase    ,    )

															lowerCAmelCase_      :					int     =			(mix_factor * latents) + (
															    1 - mix_factor
															) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
															lowerCAmelCase_      :					Optional[int]     =			torch.cat([input] * 2)

													else:  # content generation phase
															lowerCAmelCase_      :					str     =			torch.cat([latents] * 2)

													lowerCAmelCase_      :					str     =			self.scheduler.scale_model_input(__UpperCamelCase    ,    __UpperCamelCase)

													with torch.no_grad():
															lowerCAmelCase_      :					Dict     =			self.unet(
															    __UpperCamelCase    ,    __UpperCamelCase    ,    encoder_hidden_states=__UpperCamelCase    ,    ).sample

													lowerCAmelCase_ ,  lowerCAmelCase_      :					Dict     =			pred.chunk(2)
													lowerCAmelCase_      :					Any     =			pred_uncond + guidance_scale * (pred_text - pred_uncond)

													lowerCAmelCase_      :					Union[str, Any]     =			self.scheduler.step(__UpperCamelCase    ,    __UpperCamelCase    ,    __UpperCamelCase).prev_sample

									return self.decode(__UpperCamelCase)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__  :  Tuple       = logging.get_logger(__name__)

A__  :  Tuple       = {
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}



class     __snake_case						(					UpperCamelCase_						):
							_a      				=			'''opt'''
							_a      				=			['''past_key_values''']




							def __init__(	self					:	Optional[int]    ,    A_					:	Any=5_0_2_7_2    ,    A_					:	Tuple=7_6_8    ,    A_					:	Optional[Any]=1_2    ,    A_					:	str=3_0_7_2    ,    A_					:	Dict=2_0_4_8    ,    A_					:	List[Any]=True    ,    A_					:	Optional[int]=False    ,    A_					:	Optional[int]=None    ,    A_					:	Dict=0.1    ,    A_					:	str=0.0    ,    A_					:	Union[str, Any]=1_2    ,    A_					:	List[str]="relu"    ,    A_					:	Dict=0.0    ,    A_					:	Union[str, Any]=0.02    ,    A_					:	Optional[Any]=True    ,    A_					:	Optional[Any]=1    ,    A_					:	List[Any]=2    ,    A_					:	Dict=2    ,    A_					:	Optional[int]=True    ,    A_					:	Optional[int]=True    ,    **A_					:	List[Any]    ,    ):
									super().__init__(
									    pad_token_id=A_    ,    bos_token_id=A_    ,    eos_token_id=A_    ,    **A_    ,    )
									lowerCAmelCase_      :					List[Any]     =			vocab_size
									lowerCAmelCase_      :					Union[str, Any]     =			max_position_embeddings
									lowerCAmelCase_      :					Optional[int]     =			num_attention_heads
									lowerCAmelCase_      :					int     =			word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
									lowerCAmelCase_      :					List[Any]     =			ffn_dim
									lowerCAmelCase_      :					Any     =			hidden_size
									lowerCAmelCase_      :					Union[str, Any]     =			num_hidden_layers
									lowerCAmelCase_      :					str     =			dropout
									lowerCAmelCase_      :					List[str]     =			attention_dropout
									lowerCAmelCase_      :					Any     =			activation_function
									lowerCAmelCase_      :					Union[str, Any]     =			init_std
									lowerCAmelCase_      :					int     =			layerdrop
									lowerCAmelCase_      :					Optional[int]     =			use_cache
									lowerCAmelCase_      :					int     =			do_layer_norm_before
									# We keep these variables at `True` for backward compatibility.
									lowerCAmelCase_      :					Optional[Any]     =			enable_bias
									lowerCAmelCase_      :					Optional[int]     =			layer_norm_elementwise_affine

									# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
									# with checkpoints that have been fine-tuned before transformers v4.20.1
									# see https://github.com/facebookresearch/metaseq/pull/164
									lowerCAmelCase_      :					Optional[Any]     =			_remove_final_layer_norm





