

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


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

class 			a					(		UpperCAmelCase			):


						def __init__(    self  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   A_  ,   ):







										'''simple docstring'''


										super().__init__()

										if hasattr(scheduler.config  ,   "steps_offset"		) and scheduler.config.steps_offset != 1:
														_UpperCAmelCase    :						Optional[int]							=      (
														    f'The configuration file of this scheduler: {scheduler} is outdated.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"  ,   A_  ,   standard_warn=A_		)
														_UpperCAmelCase    :						Optional[int]							=      dict(scheduler.config		)
														_UpperCAmelCase    :						Union[str, Any]							=      1
														_UpperCAmelCase    :						str							=      FrozenDict(A_		)

										if hasattr(scheduler.config  ,   "skip_prk_steps"		) and scheduler.config.skip_prk_steps is False:
														_UpperCAmelCase    :						List[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"  ,   A_  ,   standard_warn=A_		)
														_UpperCAmelCase    :						str							=      dict(scheduler.config		)
														_UpperCAmelCase    :						str							=      True
														_UpperCAmelCase    :						str							=      FrozenDict(A_		)

										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=A_  ,   segmentation_processor=A_  ,   vae=A_  ,   text_encoder=A_  ,   tokenizer=A_  ,   unet=A_  ,   scheduler=A_  ,   safety_checker=A_  ,   feature_extractor=A_  ,   )


						def     _UpperCAmelCase			(    self  ,   A_ = "auto"		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										self.enable_attention_slicing(A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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

										_UpperCAmelCase    :						str							=      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(A_  ,   A_		)


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     _UpperCAmelCase			(    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(A_  ,   "_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  ,   A_  ,   A_  ,   A_  ,   A_ = 512  ,   A_ = 512  ,   A_ = 50  ,   A_ = 7.5  ,   A_ = None  ,   A_ = 1  ,   A_ = 0.0  ,   A_ = None  ,   A_ = None  ,   A_ = "pil"  ,   A_ = True  ,   A_ = None  ,   A_ = 1  ,   **A_  ,   ):







										'''simple docstring'''


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

										# Run inpainting pipeline with the generated mask
										_UpperCAmelCase    :						int							=      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=A_  ,   image=A_  ,   mask_image=A_  ,   height=A_  ,   width=A_  ,   num_inference_steps=A_  ,   guidance_scale=A_  ,   negative_prompt=A_  ,   num_images_per_prompt=A_  ,   eta=A_  ,   generator=A_  ,   latents=A_  ,   output_type=A_  ,   return_dict=A_  ,   callback=A_  ,   callback_steps=A_  ,   )





from typing import List, Optional

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "autoformer"
						_lowercase        =     {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						    "num_hidden_layers": "encoder_layers",
						}


						def __init__(    self  ,   A_ = None  ,   A_ = None  ,   A_ = "student_t"  ,   A_ = "nll"  ,   A_ = 1  ,   A_ = [1, 2, 3, 4, 5, 6, 7]  ,   A_ = True  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = None  ,   A_ = None  ,   A_ = 64  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 32  ,   A_ = 32  ,   A_ = "gelu"  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 100  ,   A_ = 0.02  ,   A_ = True  ,   A_=True  ,   A_ = 10  ,   A_ = 25  ,   A_ = 3  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      prediction_length
										_UpperCAmelCase    :						Dict							=      context_length if context_length is not None else prediction_length
										_UpperCAmelCase    :						Tuple							=      distribution_output
										_UpperCAmelCase    :						List[Any]							=      loss
										_UpperCAmelCase    :						Optional[Any]							=      input_size
										_UpperCAmelCase    :						int							=      num_time_features
										_UpperCAmelCase    :						str							=      lags_sequence
										_UpperCAmelCase    :						Union[str, Any]							=      scaling
										_UpperCAmelCase    :						Union[str, Any]							=      num_dynamic_real_features
										_UpperCAmelCase    :						int							=      num_static_real_features
										_UpperCAmelCase    :						int							=      num_static_categorical_features
										if cardinality is not None and num_static_categorical_features > 0:
														if len(A_		) != num_static_categorical_features:
																		raise ValueError(
																		    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
														_UpperCAmelCase    :						Union[str, Any]							=      cardinality
										else:
														_UpperCAmelCase    :						Any							=      [0]
										if embedding_dimension is not None and num_static_categorical_features > 0:
														if len(A_		) != num_static_categorical_features:
																		raise ValueError(
																		    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
														_UpperCAmelCase    :						int							=      embedding_dimension
										else:
														_UpperCAmelCase    :						int							=      [min(50  ,   (cat + 1) // 2		) for cat in self.cardinality]
										_UpperCAmelCase    :						Union[str, Any]							=      num_parallel_samples

										# Transformer architecture configuration
										_UpperCAmelCase    :						Union[str, Any]							=      input_size * len(self.lags_sequence		) + self._number_of_features
										_UpperCAmelCase    :						int							=      d_model
										_UpperCAmelCase    :						Any							=      encoder_attention_heads
										_UpperCAmelCase    :						str							=      decoder_attention_heads
										_UpperCAmelCase    :						Union[str, Any]							=      encoder_ffn_dim
										_UpperCAmelCase    :						Any							=      decoder_ffn_dim
										_UpperCAmelCase    :						Any							=      encoder_layers
										_UpperCAmelCase    :						Optional[int]							=      decoder_layers

										_UpperCAmelCase    :						Optional[Any]							=      dropout
										_UpperCAmelCase    :						Union[str, Any]							=      attention_dropout
										_UpperCAmelCase    :						Dict							=      activation_dropout
										_UpperCAmelCase    :						Dict							=      encoder_layerdrop
										_UpperCAmelCase    :						int							=      decoder_layerdrop

										_UpperCAmelCase    :						Union[str, Any]							=      activation_function
										_UpperCAmelCase    :						str							=      init_std

										_UpperCAmelCase    :						Tuple							=      use_cache

										# Autoformer
										_UpperCAmelCase    :						str							=      label_length
										_UpperCAmelCase    :						Any							=      moving_average
										_UpperCAmelCase    :						Optional[Any]							=      autocorrelation_factor

										super().__init__(is_encoder_decoder=A_  ,   **A_		)





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return (
										    sum(self.embedding_dimension		)
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)


