
from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


lowerCamelCase__     :		Optional[int]        = logging.get_logger(__name__)  # pylint: disable=invalid-name



class    _snake_case (						UpperCAmelCase_				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__()

   if hasattr(scheduler.config       ,	"""steps_offset""") and scheduler.config.steps_offset != 1:
     lowercase__	: Any     		=   (
         f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
         f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
         """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
         """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
         """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
         """ file"""
     )
     deprecate("""steps_offset!=1"""       ,	"""1.0.0"""       ,	SCREAMING_SNAKE_CASE_       ,	standard_warn=SCREAMING_SNAKE_CASE_)
     lowercase__	: List[Any]     		=   dict(scheduler.config)
     lowercase__	: Optional[int]     		=   1
     lowercase__	: Tuple     		=   FrozenDict(SCREAMING_SNAKE_CASE_)

   if hasattr(scheduler.config       ,	"""skip_prk_steps""") and scheduler.config.skip_prk_steps is False:
     lowercase__	: Any     		=   (
         f'The configuration file of this scheduler: {scheduler} has not set the configuration'
         """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
         """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
         """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
         """ Hub, it would be very nice if you could open a Pull request for the"""
         """ `scheduler/scheduler_config.json` file"""
     )
     deprecate("""skip_prk_steps not set"""       ,	"""1.0.0"""       ,	SCREAMING_SNAKE_CASE_       ,	standard_warn=SCREAMING_SNAKE_CASE_)
     lowercase__	: Tuple     		=   dict(scheduler.config)
     lowercase__	: int     		=   True
     lowercase__	: List[Any]     		=   FrozenDict(SCREAMING_SNAKE_CASE_)

   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(
       segmentation_model=SCREAMING_SNAKE_CASE_       ,	segmentation_processor=SCREAMING_SNAKE_CASE_       ,	vae=SCREAMING_SNAKE_CASE_       ,	text_encoder=SCREAMING_SNAKE_CASE_       ,	tokenizer=SCREAMING_SNAKE_CASE_       ,	unet=SCREAMING_SNAKE_CASE_       ,	scheduler=SCREAMING_SNAKE_CASE_       ,	safety_checker=SCREAMING_SNAKE_CASE_       ,	feature_extractor=SCREAMING_SNAKE_CASE_       ,	)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_ = "auto"):


   '''simple docstring'''




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


 def   lowercase__  (       self):


   '''simple docstring'''




   self.enable_attention_slicing(SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   if is_accelerate_available():
     from accelerate import cpu_offload
   else:
     raise ImportError("""Please install accelerate via `pip install accelerate`""")

   lowercase__	: Union[str, Any]     		=   torch.device("""cuda""")

   for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
     if cpu_offloaded_model is not None:
       cpu_offload(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def   lowercase__  (       self):


   '''simple docstring'''




   if self.device != torch.device("""meta""") or not hasattr(self.unet       ,	"""_hf_hook"""):
     return self.device
   for module in self.unet.modules():
     if (
         hasattr(SCREAMING_SNAKE_CASE_       ,	"""_hf_hook""")
         and hasattr(module._hf_hook       ,	"""execution_device""")
         and module._hf_hook.execution_device is not None
     ):
       return torch.device(module._hf_hook.execution_device)
   return self.device



 @torch.no_grad()
 def __call__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = 5_12       ,	SCREAMING_SNAKE_CASE_ = 5_12       ,	SCREAMING_SNAKE_CASE_ = 50       ,	SCREAMING_SNAKE_CASE_ = 7.5       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = 1       ,	SCREAMING_SNAKE_CASE_ = 0.0       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = "pil"       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = 1       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.segmentation_processor(
       text=[text]       ,	images=[image]       ,	padding="""max_length"""       ,	return_tensors="""pt""").to(self.device)
   lowercase__	: Dict     		=   self.segmentation_model(**SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   torch.sigmoid(outputs.logits).cpu().detach().unsqueeze(-1).numpy()
   lowercase__	: Optional[Any]     		=   self.numpy_to_pil(SCREAMING_SNAKE_CASE_)[0].resize(image.size)

   # Run inpainting pipeline with the generated mask
   lowercase__	: Dict     		=   StableDiffusionInpaintPipeline(
       vae=self.vae       ,	text_encoder=self.text_encoder       ,	tokenizer=self.tokenizer       ,	unet=self.unet       ,	scheduler=self.scheduler       ,	safety_checker=self.safety_checker       ,	feature_extractor=self.feature_extractor       ,	)
   return inpainting_pipeline(
       prompt=SCREAMING_SNAKE_CASE_       ,	image=SCREAMING_SNAKE_CASE_       ,	mask_image=SCREAMING_SNAKE_CASE_       ,	height=SCREAMING_SNAKE_CASE_       ,	width=SCREAMING_SNAKE_CASE_       ,	num_inference_steps=SCREAMING_SNAKE_CASE_       ,	guidance_scale=SCREAMING_SNAKE_CASE_       ,	negative_prompt=SCREAMING_SNAKE_CASE_       ,	num_images_per_prompt=SCREAMING_SNAKE_CASE_       ,	eta=SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_       ,	latents=SCREAMING_SNAKE_CASE_       ,	output_type=SCREAMING_SNAKE_CASE_       ,	return_dict=SCREAMING_SNAKE_CASE_       ,	callback=SCREAMING_SNAKE_CASE_       ,	callback_steps=SCREAMING_SNAKE_CASE_       ,	)











"""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     lowerCAmelCase__						(	_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		Dict  						= [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']





	@register_to_config
	def __init__(      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       int = 50_257     ,       _SCREAMING_SNAKE_CASE				:       int = 1_024     ,       _SCREAMING_SNAKE_CASE				:       int = 768     ,       _SCREAMING_SNAKE_CASE				:       int = 12     ,       _SCREAMING_SNAKE_CASE				:       int = 12     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       _SCREAMING_SNAKE_CASE				:       str = "gelu_new"     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 0.1     ,       _SCREAMING_SNAKE_CASE				:       float = 1E-5     ,       _SCREAMING_SNAKE_CASE				:       float = 0.0_2     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = True     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       _SCREAMING_SNAKE_CASE				:       bool = False     ,       )						->	Tuple:





					"""simple docstring"""


					super().__init__()

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     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."""    )

					SCREAMING_SNAKE_CASE						:							int							=     prefix_inner_dim
					SCREAMING_SNAKE_CASE						:							Dict							=     prefix_hidden_dim

					SCREAMING_SNAKE_CASE						:							int							=     (
					    nn.Linear(self.prefix_inner_dim     ,       self.prefix_hidden_dim    )
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					SCREAMING_SNAKE_CASE						:							List[Any]							=     (
					    nn.Linear(self.prefix_hidden_dim     ,       _SCREAMING_SNAKE_CASE    ) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     GPTaConfig(
					    vocab_size=_SCREAMING_SNAKE_CASE     ,       n_positions=_SCREAMING_SNAKE_CASE     ,       n_embd=_SCREAMING_SNAKE_CASE     ,       n_layer=_SCREAMING_SNAKE_CASE     ,       n_head=_SCREAMING_SNAKE_CASE     ,       n_inner=_SCREAMING_SNAKE_CASE     ,       activation_function=_SCREAMING_SNAKE_CASE     ,       resid_pdrop=_SCREAMING_SNAKE_CASE     ,       embd_pdrop=_SCREAMING_SNAKE_CASE     ,       attn_pdrop=_SCREAMING_SNAKE_CASE     ,       layer_norm_epsilon=_SCREAMING_SNAKE_CASE     ,       initializer_range=_SCREAMING_SNAKE_CASE     ,       scale_attn_weights=_SCREAMING_SNAKE_CASE     ,       use_cache=_SCREAMING_SNAKE_CASE     ,       scale_attn_by_inverse_layer_idx=_SCREAMING_SNAKE_CASE     ,       reorder_and_upcast_attn=_SCREAMING_SNAKE_CASE     ,       )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     GPTaLMHeadModel(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       torch.Tensor     ,       _SCREAMING_SNAKE_CASE				:       torch.Tensor     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       )						->	Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     self.encode_prefix(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     self.decode_prefix(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     torch.cat((prefix_embeds, embedding_text)     ,       dim=1    )

					if labels is not None:
									SCREAMING_SNAKE_CASE						:							Dict							=     self.get_dummy_token(input_ids.shape[0]     ,       input_ids.device    )
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     torch.cat((dummy_token, input_ids)     ,       dim=1    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE     ,       labels=_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE    )
					if self.prefix_hidden_dim is not None:
									return out, hidden
					else:
									return out





	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       int     ,       _SCREAMING_SNAKE_CASE				:       torch.device    )						->	torch.Tensor:





					"""simple docstring"""


					return torch.zeros(_SCREAMING_SNAKE_CASE     ,       self.prefix_length     ,       dtype=torch.intaa     ,       device=_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]    )						->	str:





					"""simple docstring"""


					return self.encode_prefix(_SCREAMING_SNAKE_CASE    )





	@torch.no_grad()
	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       List[str]    )						->	Dict:





					"""simple docstring"""


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







	@torch.no_grad()
	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       int=None     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       int = 5     ,       _SCREAMING_SNAKE_CASE				:       int = 67     ,       _SCREAMING_SNAKE_CASE				:       float = 1.0     ,       _SCREAMING_SNAKE_CASE				:       Optional[int] = None     ,       )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     eos_token_id
					SCREAMING_SNAKE_CASE						:							Tuple							=     None
					SCREAMING_SNAKE_CASE						:							int							=     None
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.ones(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.int    )
					SCREAMING_SNAKE_CASE						:							Dict							=     torch.zeros(_SCREAMING_SNAKE_CASE     ,       device=_SCREAMING_SNAKE_CASE     ,       dtype=torch.bool    )

					if input_embeds is not None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     input_embeds
					else:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.transformer.transformer.wte(_SCREAMING_SNAKE_CASE    )

					for i in range(_SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							Tuple							=     self.transformer(inputs_embeds=_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							int							=     outputs.logits
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     logits.softmax(-1    ).log()

									if scores is None:
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Tuple							=     logits.topk(_SCREAMING_SNAKE_CASE     ,       -1    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     generated.expand(_SCREAMING_SNAKE_CASE     ,       *generated.shape[1:]    )
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     next_tokens.permute(1     ,       0    ), scores.squeeze(0    )
													if tokens is None:
																	SCREAMING_SNAKE_CASE						:							Optional[Any]							=     next_tokens
													else:
																	SCREAMING_SNAKE_CASE						:							List[Any]							=     tokens.expand(_SCREAMING_SNAKE_CASE     ,       *tokens.shape[1:]    )
																	SCREAMING_SNAKE_CASE						:							List[str]							=     torch.cat((tokens, next_tokens)     ,       dim=1    )
									else:
													SCREAMING_SNAKE_CASE						:							List[str]							=     -float(np.inf    )
													SCREAMING_SNAKE_CASE						:							int							=     0
													SCREAMING_SNAKE_CASE						:							Tuple							=     scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													SCREAMING_SNAKE_CASE						:							Any							=     scores_sum / seq_lengths[:, None]
													SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Dict							=     scores_sum_average.view(-1    ).topk(_SCREAMING_SNAKE_CASE     ,       -1    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     next_tokens // scores_sum.shape[1]
													SCREAMING_SNAKE_CASE						:							Dict							=     seq_lengths[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							List[str]							=     next_tokens % scores_sum.shape[1]
													SCREAMING_SNAKE_CASE						:							int							=     next_tokens.unsqueeze(1    )
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     tokens[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.cat((tokens, next_tokens)     ,       dim=1    )
													SCREAMING_SNAKE_CASE						:							Any							=     generated[next_tokens_source]
													SCREAMING_SNAKE_CASE						:							Any							=     scores_sum_average * seq_lengths
													SCREAMING_SNAKE_CASE						:							Tuple							=     is_stopped[next_tokens_source]

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

					SCREAMING_SNAKE_CASE						:							Tuple							=     scores / seq_lengths
					SCREAMING_SNAKE_CASE						:							Tuple							=     scores.argsort(descending=_SCREAMING_SNAKE_CASE    )
					# tokens tensors are already padded to max_seq_length
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     [tokens[i] for i in order]
					SCREAMING_SNAKE_CASE						:							List[str]							=     torch.stack(_SCREAMING_SNAKE_CASE     ,       dim=0    )
					SCREAMING_SNAKE_CASE						:							int							=     torch.tensor([seq_lengths[i] for i in order]     ,       dtype=seq_lengths.dtype    )
					return output_texts, seq_lengths





