

'''simple docstring'''

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 lowerCamelCase   (    __lowerCAmelCase				):

				def __init__(       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						)    ->							Tuple:
					super().__init__()

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

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							List[Any]:
					with torch.no_grad():
						snake_case							=					self.vae.encode(tfms.ToTensor()(lowercase_ ).unsqueeze(0 ).to(self.device ) * 2 - 1 )
						snake_case							=					0.18_215 * latent.latent_dist.sample()
					return latent

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							List[Any]:
					snake_case							=					(1 / 0.18_215) * latent
					with torch.no_grad():
						snake_case							=					self.vae.decode(lowercase_ ).sample
					snake_case							=					(img / 2 + 0.5).clamp(0,						1 )
					snake_case							=					img.detach().cpu().permute(0,						2,						3,						1 ).numpy()
					snake_case							=					(img * 255).round().astype('uint8' )
					return Image.fromarray(img[0] )

				def 					_lowerCamelCase    (       self,						lowercase_ )    ->							Dict:
					snake_case							=					self.tokenizer(
					    lowercase_,						padding='max_length',						max_length=self.tokenizer.model_max_length,						truncation=lowercase_,						return_tensors='pt',						)

					snake_case							=					self.text_encoder(text_input.input_ids.to(self.device ) )[0]

					snake_case							=					self.tokenizer(
					    '',						padding='max_length',						max_length=self.tokenizer.model_max_length,						truncation=lowercase_,						return_tensors='pt',						)

					snake_case							=					self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]

					return torch.cat([uncond_embedding, text_embedding] )






				def __call__(       self,						lowercase_,						lowercase_,						lowercase_ = 0.3,						lowercase_ = 0.6,						lowercase_ = 0.5,						lowercase_ = 42,						lowercase_ = 50,						lowercase_ = 7.5,						)    ->							Image.Image:
					snake_case							=					steps - int(kmin * steps )
					snake_case							=					steps - int(kmax * steps )

					snake_case							=					self.prep_text(lowercase_ )

					self.scheduler.set_timesteps(lowercase_ )

					snake_case			,			snake_case							=					img.size
					snake_case							=					self.encode(lowercase_ )

					torch.manual_seed(lowercase_ )
					snake_case							=					torch.randn(
					    (1, self.unet.config.in_channels, height // 8, width // 8),						).to(self.device )

					snake_case							=					self.scheduler.add_noise(
					    lowercase_,						lowercase_,						timesteps=self.scheduler.timesteps[tmax],						)

					snake_case							=					torch.cat([latents] * 2 )

					snake_case							=					self.scheduler.scale_model_input(lowercase_,						self.scheduler.timesteps[tmax] )

					with torch.no_grad():
						snake_case							=					self.unet(
						    lowercase_,						self.scheduler.timesteps[tmax],						encoder_hidden_states=lowercase_,						).sample

					snake_case			,			snake_case							=					pred.chunk(2 )
					snake_case							=					pred_uncond + guidance_scale * (pred_text - pred_uncond)

					snake_case							=					self.scheduler.step(lowercase_,						self.scheduler.timesteps[tmax],						lowercase_ ).prev_sample

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

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

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

							snake_case							=					self.scheduler.scale_model_input(lowercase_,						lowercase_ )

							with torch.no_grad():
								snake_case							=					self.unet(
								    lowercase_,						lowercase_,						encoder_hidden_states=lowercase_,						).sample

							snake_case			,			snake_case							=					pred.chunk(2 )
							snake_case							=					pred_uncond + guidance_scale * (pred_text - pred_uncond)

							snake_case							=					self.scheduler.step(lowercase_,						lowercase_,						lowercase_ ).prev_sample

					return self.decode(lowercase_ )








'''simple docstring'''

from typing import List, Union

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


lowerCAmelCase_     =      logging.get_logger(__name__)

lowerCAmelCase_     =      {
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    # See all LED models at https://huggingface.co/models?filter=led
}







class lowerCamelCase   (    __lowerCAmelCase				):
				snake_case_							     = '''led'''
				snake_case_							     = {
				    '''num_attention_heads''': '''encoder_attention_heads''',
				    '''hidden_size''': '''d_model''',
				    '''attention_probs_dropout_prob''': '''attention_dropout''',
				    '''initializer_range''': '''init_std''',
				}

				def __init__(       self,						lowercase_=50265,						lowercase_=16384,						lowercase_=1024,						lowercase_=12,						lowercase_=4096,						lowercase_=16,						lowercase_=12,						lowercase_=4096,						lowercase_=16,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=True,						lowercase_=True,						lowercase_="gelu",						lowercase_=1024,						lowercase_=0.1,						lowercase_=0.0,						lowercase_=0.0,						lowercase_=0.02,						lowercase_=2,						lowercase_=0.0,						lowercase_=1,						lowercase_=0,						lowercase_=2,						lowercase_ = 512,						**lowercase_,						)    ->							Union[str, Any]:
					snake_case							=					vocab_size
					snake_case							=					max_encoder_position_embeddings
					snake_case							=					max_decoder_position_embeddings
					snake_case							=					d_model
					snake_case							=					encoder_ffn_dim
					snake_case							=					encoder_layers
					snake_case							=					encoder_attention_heads
					snake_case							=					decoder_ffn_dim
					snake_case							=					decoder_layers
					snake_case							=					decoder_attention_heads
					snake_case							=					dropout
					snake_case							=					attention_dropout
					snake_case							=					activation_dropout
					snake_case							=					activation_function
					snake_case							=					init_std
					snake_case							=					encoder_layerdrop
					snake_case							=					decoder_layerdrop
					snake_case							=					classifier_dropout
					snake_case							=					use_cache
					snake_case							=					encoder_layers
					snake_case							=					attention_window

					super().__init__(
					    pad_token_id=lowercase_,						bos_token_id=lowercase_,						eos_token_id=lowercase_,						is_encoder_decoder=lowercase_,						decoder_start_token_id=lowercase_,						**lowercase_,						)





