"""simple docstring"""






from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCAmelCase__						:						Optional[Any]		  =			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def 						a_		(      lowerCamelCase ):


				if isinstance(lowerCamelCase      ,    torch.Tensor ):
								return image
				elif isinstance(lowerCamelCase      ,    PIL.Image.Image ):
								UpperCAmelCase__    =							[image]

				UpperCAmelCase__    =							[trans(img.convert('RGB' ) ) for img in image]
				UpperCAmelCase__    =							torch.stack(lowerCamelCase )
				return image







class        snake_case (				__UpperCAmelCase       ):




					"""simple docstring"""

					def __init__(       self     :			List[str]					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			List[str]    ):
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									UpperCAmelCase__    =							DDIMScheduler.from_config(scheduler.config    )

									self.register_modules(unet=lowerCamelCase__					,scheduler=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			List[str]    ):
									if strength < 0 or strength > 1:
													raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			str    ):
									# get the original timestep using init_timestep
									UpperCAmelCase__    =							min(int(num_inference_steps * strength    )					,lowerCamelCase__    )

									UpperCAmelCase__    =							max(num_inference_steps - init_timestep					,0    )
									UpperCAmelCase__    =							self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start







					def 	__lowerCAmelCase		(       self     :			List[str]					,lowerCamelCase__     :			Dict					,lowerCamelCase__     :			Optional[Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			List[str]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Optional[int]=None    ):
									if not isinstance(lowerCamelCase__					,(torch.Tensor, PIL.Image.Image, list)    ):
													raise ValueError(
													    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase__    )}'''    )

									UpperCAmelCase__    =							image.to(device=lowerCamelCase__					,dtype=lowerCamelCase__    )

									if isinstance(lowerCamelCase__					,lowerCamelCase__    ) and len(lowerCamelCase__    ) != batch_size:
													raise ValueError(
													    f'''You have passed a list of generators of length {len(lowerCamelCase__    )}, but requested an effective batch'''
													    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''    )

									UpperCAmelCase__    =							init_latents.shape
									UpperCAmelCase__    =							randn_tensor(lowerCamelCase__					,generator=lowerCamelCase__					,device=lowerCamelCase__					,dtype=lowerCamelCase__    )

									# get latents
									print('add noise to latents at timestep'					,lowerCamelCase__    )
									UpperCAmelCase__    =							self.scheduler.add_noise(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__    )
									UpperCAmelCase__    =							init_latents

									return latents







					@torch.no_grad()
					def __call__(       self     :			Optional[Any]					,lowerCamelCase__     :			Tuple = None					,lowerCamelCase__     :			Any = 0.8					,lowerCamelCase__     :			int = 1					,lowerCamelCase__     :			List[str] = None					,lowerCamelCase__     :			int = 0.0					,lowerCamelCase__     :			Dict = 50					,lowerCamelCase__     :			int = None					,lowerCamelCase__     :			Optional[int] = "pil"					,lowerCamelCase__     :			Optional[int] = True					,):
									self.check_inputs(lowerCamelCase__    )

									# 2. Preprocess image
									UpperCAmelCase__    =							preprocess(lowerCamelCase__    )

									# 3. set timesteps
									self.scheduler.set_timesteps(lowerCamelCase__					,device=self.device    )
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.get_timesteps(lowerCamelCase__					,lowerCamelCase__					,self.device    )
									UpperCAmelCase__    =							timesteps[:1].repeat(lowerCamelCase__    )

									# 4. Prepare latent variables
									UpperCAmelCase__    =							self.prepare_latents(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,self.unet.dtype					,self.device					,lowerCamelCase__    )
									UpperCAmelCase__    =							latents

									# 5. Denoising loop
									for t in self.progress_bar(lowerCamelCase__    ):
													# 1. predict noise model_output
													UpperCAmelCase__    =							self.unet(lowerCamelCase__					,lowerCamelCase__    ).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													UpperCAmelCase__    =							self.scheduler.step(
													    lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					,eta=lowerCamelCase__					,use_clipped_model_output=lowerCamelCase__					,generator=lowerCamelCase__					,).prev_sample

									UpperCAmelCase__    =							(image / 2 + 0.5).clamp(0					,1    )
									UpperCAmelCase__    =							image.cpu().permute(0					,2					,3					,1    ).numpy()
									if output_type == "pil":
													UpperCAmelCase__    =							self.numpy_to_pil(lowerCamelCase__    )

									if not return_dict:
													return (image, latent_timestep.item())

									return ImagePipelineOutput(images=lowerCamelCase__    )




"""simple docstring"""






from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					__UpperCAmelCase       ):




					"""simple docstring"""

					snake_case__       					=				[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]







					@register_to_config
					def __init__(       self     :			str					,lowerCamelCase__     :			int					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			int = 50_257					,lowerCamelCase__     :			int = 1_024					,lowerCamelCase__     :			int = 768					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			int = 12					,lowerCamelCase__     :			Optional[int] = None					,lowerCamelCase__     :			str = "gelu_new"					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 0.1					,lowerCamelCase__     :			float = 1e-5					,lowerCamelCase__     :			float = 0.0_2					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = True					,lowerCamelCase__     :			bool = False					,lowerCamelCase__     :			bool = False					,):
									super().__init__()

									UpperCAmelCase__    =							prefix_length

									if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
													raise ValueError(
													    f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
													    f''' `n_embd`: {n_embd} are not equal.'''    )

									UpperCAmelCase__    =							prefix_inner_dim
									UpperCAmelCase__    =							prefix_hidden_dim

									UpperCAmelCase__    =							(
									    nn.Linear(self.prefix_inner_dim					,self.prefix_hidden_dim    )
									    if self.prefix_hidden_dim is not None
									    else nn.Identity()
									)
									UpperCAmelCase__    =							(
									    nn.Linear(self.prefix_hidden_dim					,lowerCamelCase__    ) if self.prefix_hidden_dim is not None else nn.Identity()
									)

									UpperCAmelCase__    =							GPTaConfig(
									    vocab_size=lowerCamelCase__					,n_positions=lowerCamelCase__					,n_embd=lowerCamelCase__					,n_layer=lowerCamelCase__					,n_head=lowerCamelCase__					,n_inner=lowerCamelCase__					,activation_function=lowerCamelCase__					,resid_pdrop=lowerCamelCase__					,embd_pdrop=lowerCamelCase__					,attn_pdrop=lowerCamelCase__					,layer_norm_epsilon=lowerCamelCase__					,initializer_range=lowerCamelCase__					,scale_attn_weights=lowerCamelCase__					,use_cache=lowerCamelCase__					,scale_attn_by_inverse_layer_idx=lowerCamelCase__					,reorder_and_upcast_attn=lowerCamelCase__					,)
									UpperCAmelCase__    =							GPTaLMHeadModel(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			torch.Tensor					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,lowerCamelCase__     :			Optional[torch.Tensor] = None					,):
									UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )
									UpperCAmelCase__    =							self.encode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							self.decode_prefix(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.cat((prefix_embeds, embedding_text)					,dim=1    )

									if labels is not None:
													UpperCAmelCase__    =							self.get_dummy_token(input_ids.shape[0]					,input_ids.device    )
													UpperCAmelCase__    =							torch.cat((dummy_token, input_ids)					,dim=1    )
									UpperCAmelCase__    =							self.transformer(inputs_embeds=lowerCamelCase__					,labels=lowerCamelCase__					,attention_mask=lowerCamelCase__    )
									if self.prefix_hidden_dim is not None:
													return out, hidden
									else:
													return out







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			torch.device    ):
									return torch.zeros(lowerCamelCase__					,self.prefix_length					,dtype=torch.intaa					,device=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,lowerCamelCase__     :			List[str]    ):
									return self.encode_prefix(lowerCamelCase__    )







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			Optional[int]    ):
									UpperCAmelCase__    =							torch.split(lowerCamelCase__					,1					,dim=0    )
									UpperCAmelCase__    =							[]
									UpperCAmelCase__    =							[]
									for feature in features:
													UpperCAmelCase__    =							self.decode_prefix(feature.to(lowerCamelCase__    )    )  # back to the clip feature
													# Only support beam search for now
													UpperCAmelCase__   ,			UpperCAmelCase__    =							self.generate_beam(
													    input_embeds=lowerCamelCase__					,device=lowerCamelCase__					,eos_token_id=lowerCamelCase__    )
													generated_tokens.append(output_tokens[0]    )
													generated_seq_lengths.append(seq_lengths[0]    )
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__    )
									return generated_tokens, generated_seq_lengths







					@torch.no_grad()
					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			str=None					,lowerCamelCase__     :			List[Any]=None					,lowerCamelCase__     :			Tuple=None					,lowerCamelCase__     :			int = 5					,lowerCamelCase__     :			int = 67					,lowerCamelCase__     :			float = 1.0					,lowerCamelCase__     :			Optional[int] = None					,):
									UpperCAmelCase__    =							eos_token_id
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							None
									UpperCAmelCase__    =							torch.ones(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.int    )
									UpperCAmelCase__    =							torch.zeros(lowerCamelCase__					,device=lowerCamelCase__					,dtype=torch.bool    )

									if input_embeds is not None:
													UpperCAmelCase__    =							input_embeds
									else:
													UpperCAmelCase__    =							self.transformer.transformer.wte(lowerCamelCase__    )

									for i in range(lowerCamelCase__    ):
													UpperCAmelCase__    =							self.transformer(inputs_embeds=lowerCamelCase__    )
													UpperCAmelCase__    =							outputs.logits
													UpperCAmelCase__    =							logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
													UpperCAmelCase__    =							logits.softmax(-1    ).log()

													if scores is None:
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							logits.topk(lowerCamelCase__					,-1    )
																	UpperCAmelCase__    =							generated.expand(lowerCamelCase__					,*generated.shape[1:]    )
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							next_tokens.permute(1					,0    ), scores.squeeze(0    )
																	if tokens is None:
																					UpperCAmelCase__    =							next_tokens
																	else:
																					UpperCAmelCase__    =							tokens.expand(lowerCamelCase__					,*tokens.shape[1:]    )
																					UpperCAmelCase__    =							torch.cat((tokens, next_tokens)					,dim=1    )
													else:
																	UpperCAmelCase__    =							-float(np.inf    )
																	UpperCAmelCase__    =							0
																	UpperCAmelCase__    =							scores[:, None] + logits
																	seq_lengths[~is_stopped] += 1
																	UpperCAmelCase__    =							scores_sum / seq_lengths[:, None]
																	UpperCAmelCase__   ,			UpperCAmelCase__    =							scores_sum_average.view(-1    ).topk(lowerCamelCase__					,-1    )
																	UpperCAmelCase__    =							next_tokens // scores_sum.shape[1]
																	UpperCAmelCase__    =							seq_lengths[next_tokens_source]
																	UpperCAmelCase__    =							next_tokens % scores_sum.shape[1]
																	UpperCAmelCase__    =							next_tokens.unsqueeze(1    )
																	UpperCAmelCase__    =							tokens[next_tokens_source]
																	UpperCAmelCase__    =							torch.cat((tokens, next_tokens)					,dim=1    )
																	UpperCAmelCase__    =							generated[next_tokens_source]
																	UpperCAmelCase__    =							scores_sum_average * seq_lengths
																	UpperCAmelCase__    =							is_stopped[next_tokens_source]

													UpperCAmelCase__    =							self.transformer.transformer.wte(next_tokens.squeeze()    ).view(generated.shape[0]					,1					,-1    )
													UpperCAmelCase__    =							torch.cat((generated, next_token_embed)					,dim=1    )
													UpperCAmelCase__    =							is_stopped + next_tokens.eq(lowerCamelCase__    ).squeeze()
													if is_stopped.all():
																	break

									UpperCAmelCase__    =							scores / seq_lengths
									UpperCAmelCase__    =							scores.argsort(descending=lowerCamelCase__    )
									# tokens tensors are already padded to max_seq_length
									UpperCAmelCase__    =							[tokens[i] for i in order]
									UpperCAmelCase__    =							torch.stack(lowerCamelCase__					,dim=0    )
									UpperCAmelCase__    =							torch.tensor([seq_lengths[i] for i in order]					,dtype=seq_lengths.dtype    )
									return output_texts, seq_lengths



