"""simple docstring"""





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_UpperCamelCase       :						Optional[int]     =   logging.get_logger(__name__)

_UpperCamelCase       :						Any     =   {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}


class 					UpperCAmelCase_     (   _a):
      lowerCamelCase__		:   Optional[Any]			  =  "oneformer"
      lowerCamelCase__		:   Union[str, Any]			  =  {"hidden_size": "hidden_dim"}


      def __init__(       self		,      a = None		,      a = 2_5_5		,      a = 1_5_0		,      a = 0.1		,      a = 2.0		,      a = 5.0		,      a = 5.0		,      a = 0.5		,      a = 0.07		,      a = 1_2_5_4_4		,      a = 3.0		,      a = 0.75		,      a = 0.02		,      a = 1.0		,      a = 1e-05		,      a = False		,      a = True		,      a = True		,      a = [4, 8, 1_6, 3_2]		,      a = 7_7		,      a = 2_5_6		,      a = 7_7		,      a = 6		,      a = 4_9_4_0_8		,      a = 2		,      a = 1_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 2_5_6		,      a = 1_0_2_4		,      a = "GN"		,      a = 6		,      a = 1_0		,      a = True		,      a = 8		,      a = 0.1		,      a = 2_0_4_8		,      a = False		,      a = False		,      a = 2		,      a = 4		,      **a		,      )	-> List[Any]:
             if backbone_config is None:
                    logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'							)
                    lowercase__      :		List[str]          =     CONFIG_MAPPING['swin'](
                        image_size=2_2_4		,      in_channels=3		,      patch_size=4		,      embed_dim=9_6		,      depths=[2, 2, 6, 2]		,      num_heads=[3, 6, 1_2, 2_4]		,      window_size=7		,      drop_path_rate=0.3		,      use_absolute_embeddings=a		,      out_features=['stage1', 'stage2', 'stage3', 'stage4']		,      )
             elif isinstance(a		,      a							):
                    lowercase__      :		Union[str, Any]          =     backbone_config.get('model_type'							)
                    lowercase__      :		Optional[int]          =     CONFIG_MAPPING[backbone_model_type]
                    lowercase__      :		Optional[Any]          =     config_class.from_dict(a							)

             lowercase__      :		Tuple          =     backbone_config

             lowercase__      :		Optional[Any]          =     ignore_value
             lowercase__      :		int          =     num_queries
             lowercase__      :		str          =     no_object_weight
             lowercase__      :		str          =     class_weight
             lowercase__      :		Dict          =     mask_weight
             lowercase__      :		str          =     dice_weight
             lowercase__      :		Union[str, Any]          =     contrastive_weight
             lowercase__      :		Tuple          =     contrastive_temperature
             lowercase__      :		List[str]          =     train_num_points
             lowercase__      :		Tuple          =     oversample_ratio
             lowercase__      :		Tuple          =     importance_sample_ratio
             lowercase__      :		Optional[int]          =     init_std
             lowercase__      :		Optional[int]          =     init_xavier_std
             lowercase__      :		List[str]          =     layer_norm_eps
             lowercase__      :		str          =     is_training
             lowercase__      :		Dict          =     use_auxiliary_loss
             lowercase__      :		Any          =     output_auxiliary_logits
             lowercase__      :		str          =     strides
             lowercase__      :		Any          =     task_seq_len
             lowercase__      :		List[Any]          =     text_encoder_width
             lowercase__      :		Dict          =     text_encoder_context_length
             lowercase__      :		Any          =     text_encoder_num_layers
             lowercase__      :		Any          =     text_encoder_vocab_size
             lowercase__      :		str          =     text_encoder_proj_layers
             lowercase__      :		str          =     text_encoder_n_ctx
             lowercase__      :		List[Any]          =     conv_dim
             lowercase__      :		Optional[Any]          =     mask_dim
             lowercase__      :		List[Any]          =     hidden_dim
             lowercase__      :		int          =     encoder_feedforward_dim
             lowercase__      :		List[str]          =     norm
             lowercase__      :		Union[str, Any]          =     encoder_layers
             lowercase__      :		List[Any]          =     decoder_layers
             lowercase__      :		int          =     use_task_norm
             lowercase__      :		Union[str, Any]          =     num_attention_heads
             lowercase__      :		Union[str, Any]          =     dropout
             lowercase__      :		Any          =     dim_feedforward
             lowercase__      :		Optional[int]          =     pre_norm
             lowercase__      :		Tuple          =     enforce_input_proj
             lowercase__      :		List[Any]          =     query_dec_layers
             lowercase__      :		Optional[int]          =     common_stride
             lowercase__      :		str          =     decoder_layers

             super().__init__(**a							)


      def 		_UpperCAmelCase	(       self							)	-> Dict[str, any]:
             lowercase__      :		str          =     copy.deepcopy(self.__dict__							)
             lowercase__      :		Union[str, Any]          =     self.backbone_config.to_dict()
             lowercase__      :		str          =     self.__class__.model_type
             return output


"""simple docstring"""





from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 					UpperCAmelCase_     (   _a):


      def __init__(       self		,      a		,      a		,      a		,      a		,      a		,      a = 0.93		,      a = True		,      )	-> Any:
             lowercase__      :		Union[str, Any]          =     mixing_coeff
             lowercase__      :		Any          =     leapfrog_steps

             super().__init__()
             self.register_modules(
                 vae=a		,      text_encoder=a		,      tokenizer=a		,      unet=a		,      scheduler=a		,      )

             lowercase__      :		List[str]          =     2 ** (len(self.vae.config.block_out_channels							) - 1)
             lowercase__      :		Union[str, Any]          =     VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)


      def 		_UpperCAmelCase	(       self		,      a		,      a = None		,      a = False							)	-> Optional[int]:
             lowercase__      :		str          =     self.tokenizer(
                 a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

             lowercase__      :		str          =     self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

             lowercase__      :		Any          =     prompt_embeds.to(dtype=self.text_encoder.dtype		,      device=self.device							)

             if do_classifier_free_guidance:
                    lowercase__      :		Tuple          =     '' if negative_prompt is None else negative_prompt

                    lowercase__      :		int          =     self.tokenizer(
                        a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

                    lowercase__      :		str          =     self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

                    lowercase__      :		str          =     torch.cat([negative_prompt_embeds, prompt_embeds]							)

             return prompt_embeds


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Optional[Any]:
             lowercase__      :		int          =     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
             lowercase__      :		Tuple          =     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> List[str]:
             lowercase__      :		str          =     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
             lowercase__      :		Union[str, Any]          =     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[str]:
             # as self.alphas_cumprod is always in cpu
             lowercase__      :		Any          =     int(a							)

             lowercase__      :		Any          =     self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

             return alpha_prod, 1 - alpha_prod


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> int:
             lowercase__      :		List[Any]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[Any]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		str          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Tuple          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[int]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

             lowercase__      :		int          =     (base - b_t * model_output) / a_t

             return model_input, next_model_input.to(base.dtype							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> Tuple:
             lowercase__      :		Optional[int]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[int]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		List[Any]          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Union[str, Any]          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[Any]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
             lowercase__      :		List[str]          =     a_t * base + b_t * model_output

             return model_input, next_model_input.to(base.dtype							)


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     1 / self.vae.config.scaling_factor * latents
             lowercase__      :		Union[str, Any]          =     self.vae.decode(a							).sample
             lowercase__      :		int          =     (image / 2 + 0.5).clamp(0		,      1							)
             return image


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      a = None		,      )	-> Dict:
             lowercase__      :		List[str]          =     guidance_scale > 1.0

             lowercase__      :		Union[str, Any]          =     image.to(device=self.device		,      dtype=text_embeds.dtype							)
             lowercase__      :		Tuple          =     self.vae.encode(a							).latent_dist.sample(a							)

             lowercase__      :		Optional[Any]          =     self.vae.config.scaling_factor * latent

             lowercase__      :		Tuple          =     [latent.clone(), latent.clone()]

             for i, t in tqdm(enumerate(a							)		,      total=len(a							)							):
                    lowercase__      :		Tuple          =     self.noise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

                    # j - model_input index, k - base index
                    for j in range(2							):
                           lowercase__      :		Any          =     j ^ 1

                           if self.leapfrog_steps:
                                  if i % 2 == 0:
                                         lowercase__   ,				lowercase__      :		Tuple          =     j, k

                           lowercase__      :		Union[str, Any]          =     coupled_latents[j]
                           lowercase__      :		Tuple          =     coupled_latents[k]

                           lowercase__      :		Dict          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		Union[str, Any]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

                           if do_classifier_free_guidance:
                                  lowercase__   ,				lowercase__      :		Any          =     noise_pred.chunk(2							)
                                  lowercase__      :		Any          =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                           lowercase__   ,				lowercase__      :		Optional[Any]          =     self.noise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		str          =     model_input

             return coupled_latents


      @torch.no_grad()
      def __call__(       self		,      a		,      a		,      a		,      a = 3.0		,      a = 5_0		,      a = 0.8		,      a = None		,      a = None		,      a = "pil"		,      )	-> Dict:
             lowercase__      :		List[Any]          =     guidance_scale > 1.0

             lowercase__      :		List[str]          =     self.image_processor.preprocess(a							)

             lowercase__      :		Union[str, Any]          =     self._encode_prompt(a		,      a		,      a							)
             lowercase__      :		int          =     self._encode_prompt(a		,      a		,      a							)

             self.scheduler.set_timesteps(a		,      self.device							)

             lowercase__      :		Dict          =     num_inference_steps - int(num_inference_steps * strength							)
             lowercase__      :		str          =     self.scheduler.timesteps[t_limit:]
             lowercase__      :		int          =     fwd_timesteps.flip(0							)

             lowercase__      :		int          =     self.prepare_latents(a		,      a		,      a		,      a		,      a							)

             for i, t in tqdm(enumerate(a							)		,      total=len(a							)							):
                    # j - model_input index, k - base index
                    for k in range(2							):
                           lowercase__      :		Optional[Any]          =     k ^ 1

                           if self.leapfrog_steps:
                                  if i % 2 == 1:
                                         lowercase__   ,				lowercase__      :		int          =     j, k

                           lowercase__      :		Optional[int]          =     coupled_latents[j]
                           lowercase__      :		int          =     coupled_latents[k]

                           lowercase__      :		List[Any]          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		List[str]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

                           if do_classifier_free_guidance:
                                  lowercase__   ,				lowercase__      :		int          =     noise_pred.chunk(2							)
                                  lowercase__      :		Any          =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                           lowercase__   ,				lowercase__      :		List[str]          =     self.denoise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		List[str]          =     model_input

                    lowercase__      :		Union[str, Any]          =     self.denoise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

             # either one is fine
             lowercase__      :		Optional[Any]          =     coupled_latents[0]

             if output_type not in ["latent", "pt", "np", "pil"]:
                    lowercase__      :		Any          =     (
                        f"""the output_type {output_type} is outdated. Please make sure to set it to one of these instead: """
                        '`pil`, `np`, `pt`, `latent`'
                    )
                    deprecate('Unsupported output_type'		,      '1.0.0'		,      a		,      standard_warn=a							)
                    lowercase__      :		str          =     'np'

             if output_type == "latent":
                    lowercase__      :		List[str]          =     final_latent
             else:
                    lowercase__      :		Optional[int]          =     self.decode_latents(a							)
                    lowercase__      :		Optional[int]          =     self.image_processor.postprocess(a		,      output_type=a							)

             return image

