




import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin






@dataclass
class       A      (		__A		):
     """simple docstring"""

     _snake_case:						torch.FloatTensor
     _snake_case:						torch.FloatTensor

def       lowerCAmelCase_      (	lowerCamelCase:							Any  ,					lowerCamelCase:							int=0.999  ,					lowerCamelCase:							Any="cosine"  ,					)       ->      int:


      '''simple docstring'''




      if alpha_transform_type == "cosine":

            def alpha_bar_fn(lowerCamelCase:							List[Any]    ):
                  return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(lowerCamelCase:							str    ):
                  return math.exp(t * -12.0    )

      else:
            raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )

      lowerCamelCase__   	=					[]
      for i in range(lowerCamelCase    ):
            lowerCamelCase__   	=					i / num_diffusion_timesteps
            lowerCamelCase__   	=					(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(lowerCamelCase    ) / alpha_bar_fn(lowerCamelCase    )  ,					lowerCamelCase    )    )
      return torch.tensor(lowerCamelCase  ,					dtype=torch.floataa    )






class       A      (		__A      ,    __A		):
     """simple docstring"""

     _snake_case:						Any  =1




     @register_to_config
     def __init__(     self	: Optional[int]						,	A__	: List[Any] = 1_0_0_0						,	A__	: Union[str, Any] = 0.0001						,	A__	: Optional[Any] = 0.02						,	A__	: Optional[Any] = "linear"						,	A__	: Any = 0.0						,	A__	: int = None						,	A__	: Dict = True						,	)					->							Tuple:

           if trained_betas is not None:
                 lowerCamelCase__   	=					torch.from_numpy(__snake_case      )
           elif beta_schedule == "linear":
                 lowerCamelCase__   	=					torch.linspace(__snake_case						,	__snake_case						,	__snake_case						,	dtype=torch.floataa      )
           elif beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 lowerCamelCase__   	=					(
                     torch.linspace(beta_start**0.5						,	beta_end**0.5						,	__snake_case						,	dtype=torch.floataa      ) ** 2
                 )
           elif beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 lowerCamelCase__   	=					betas_for_alpha_bar(__snake_case      )
           elif beta_schedule == "sigmoid":
                 # GeoDiff sigmoid schedule
                 lowerCamelCase__   	=					torch.linspace(-6						,	6						,	__snake_case      )
                 lowerCamelCase__   	=					torch.sigmoid(__snake_case      ) * (beta_end - beta_start) + beta_start
           else:
                 raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""      )

           lowerCamelCase__   	=					1.0 - self.betas
           lowerCamelCase__   	=					torch.cumprod(self.alphas						,	dim=0      )
           lowerCamelCase__   	=					torch.tensor(1.0      )

           lowerCamelCase__   	=					torch.tensor(1.0      )

           # standard deviation of the initial noise distribution
           lowerCamelCase__   	=					1.0

           # setable values
           lowerCamelCase__   	=					None
           lowerCamelCase__   	=					torch.from_numpy(np.arange(0						,	__snake_case      )[::-1].copy()      )

           lowerCamelCase__   	=					eta




     def 			__snake_case (     self	: int						,	A__	: Optional[int]						,	A__	: Dict = None      )					->							torch.FloatTensor:
           return sample




     def 			__snake_case (     self	: Tuple						,	A__	: int						,	A__	: Optional[Any] = 1_0						,	A__	: Dict = 1_0						,	A__	: Dict = None						,	)					->							List[str]:
           lowerCamelCase__   	=					min(self.config.num_train_timesteps						,	__snake_case      )
           lowerCamelCase__   	=					num_inference_steps

           lowerCamelCase__   	=					[]

           lowerCamelCase__   	=					{}
           for j in range(0						,	num_inference_steps - jump_length						,	__snake_case      ):
                 lowerCamelCase__   	=					jump_n_sample - 1

           lowerCamelCase__   	=					num_inference_steps
           while t >= 1:
                 lowerCamelCase__   	=					t - 1
                 timesteps.append(__snake_case      )

                 if jumps.get(__snake_case						,	0      ) > 0:
                       lowerCamelCase__   	=					jumps[t] - 1
                       for _ in range(__snake_case      ):
                             lowerCamelCase__   	=					t + 1
                             timesteps.append(__snake_case      )

           lowerCamelCase__   	=					np.array(__snake_case      ) * (self.config.num_train_timesteps // self.num_inference_steps)
           lowerCamelCase__   	=					torch.from_numpy(__snake_case      ).to(__snake_case      )




     def 			__snake_case (     self	: List[str]						,	A__	: Optional[int]      )					->							int:
           lowerCamelCase__   	=					t - self.config.num_train_timesteps // self.num_inference_steps

           lowerCamelCase__   	=					self.alphas_cumprod[t]
           lowerCamelCase__   	=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
           lowerCamelCase__   	=					1 - alpha_prod_t
           lowerCamelCase__   	=					1 - alpha_prod_t_prev

           # For t > 0, compute predicted variance βt (see formula (6) and (7) from
           # https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
           # previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
           # variance to pred_sample
           # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
           # without eta.
           # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
           lowerCamelCase__   	=					(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

           return variance




     def 			__snake_case (     self	: List[str]						,	A__	: List[str]						,	A__	: Dict						,	A__	: int						,	A__	: Optional[Any]						,	A__	: Optional[Any]						,	A__	: Dict = None						,	A__	: str = True						,	)					->							Union[RePaintSchedulerOutput, Tuple]:
           lowerCamelCase__   	=					timestep
           lowerCamelCase__   	=					timestep - self.config.num_train_timesteps // self.num_inference_steps

           # 1. compute alphas, betas
           lowerCamelCase__   	=					self.alphas_cumprod[t]
           lowerCamelCase__   	=					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
           lowerCamelCase__   	=					1 - alpha_prod_t

           # 2. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
           lowerCamelCase__   	=					(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                 lowerCamelCase__   	=					torch.clamp(__snake_case						,	-1						,	1      )

           # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
           # substitute formula (7) in the algorithm coming from DDPM paper
           # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
           # DDIM schedule gives the same results as DDPM with eta = 1.0
           # Noise is being reused in 7. and 8., but no impact on quality has
           # been observed.

           # 5. Add noise
           lowerCamelCase__   	=					model_output.device
           lowerCamelCase__   	=					randn_tensor(model_output.shape						,	generator=__snake_case						,	device=__snake_case						,	dtype=model_output.dtype      )
           lowerCamelCase__   	=					self.eta * self._get_variance(__snake_case      ) ** 0.5

           lowerCamelCase__   	=					0
           if t > 0 and self.eta > 0:
                 lowerCamelCase__   	=					std_dev_t * noise

           # 6. compute "direction pointing to x_t" of formula (12)
           # from https://arxiv.org/pdf/2010.02502.pdf
           lowerCamelCase__   	=					(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

           # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowerCamelCase__   	=					alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

           # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
           lowerCamelCase__   	=					(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

           # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
           lowerCamelCase__   	=					mask * prev_known_part + (1.0 - mask) * prev_unknown_part

           if not return_dict:
                 return (
                     pred_prev_sample,
                     pred_original_sample,
                 )

           return RePaintSchedulerOutput(prev_sample=__snake_case						,	pred_original_sample=__snake_case      )




     def 			__snake_case (     self	: Optional[int]						,	A__	: List[Any]						,	A__	: Any						,	A__	: List[str]=None      )					->							Optional[Any]:
           lowerCamelCase__   	=					self.config.num_train_timesteps // self.num_inference_steps

           for i in range(__snake_case      ):
                 lowerCamelCase__   	=					self.betas[timestep + i]
                 if sample.device.type == "mps":
                       # randn does not work reproducibly on mps
                       lowerCamelCase__   	=					randn_tensor(sample.shape						,	dtype=sample.dtype						,	generator=__snake_case      )
                       lowerCamelCase__   	=					noise.to(sample.device      )
                 else:
                       lowerCamelCase__   	=					randn_tensor(sample.shape						,	generator=__snake_case						,	device=sample.device						,	dtype=sample.dtype      )

                 # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
                 lowerCamelCase__   	=					(1 - beta) ** 0.5 * sample + beta**0.5 * noise

           return sample




     def 			__snake_case (     self	: Any						,	A__	: int						,	A__	: str						,	A__	: Any						,	)					->							torch.FloatTensor:
           raise NotImplementedError('Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.'      )




     def __len__(     self	: Dict      )					->							Optional[Any]:
           return self.config.num_train_timesteps






from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import (
							    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
							    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
							    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
							    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
							)


__lowerCAmelCase			:Optional[Any]	 =     logging.get_logger(__name__)


__lowerCAmelCase			:Optional[Any]	 =     Dict[str, Any]
__lowerCAmelCase			:int	 =     List[Prediction]






@add_end_docstrings(__A     )
class 							_a(     __A     ):







		def __init__(     self			,    *__snake_case			,    **__snake_case					)   ->   List[Any]:


							'''simple docstring'''

							super().__init__(*__snake_case			,    **__snake_case					)

							if self.framework == "tf":
												raise ValueError(f"""The {self.__class__} is only available in PyTorch."""					)

							requires_backends(self			,    "vision"					)
							self.check_model_type(
							    dict(
							        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
							        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()					)					)







		def 							lowercase							(     self			,    **__snake_case					)   ->   Union[str, Any]:


							'''simple docstring'''

							_snake_case							:       str												= {}
							_snake_case							:       int												= {}
							if "subtask" in kwargs:
												_snake_case							:       Any												= kwargs["subtask"]
												_snake_case							:       List[Any]												= kwargs["subtask"]
							if "threshold" in kwargs:
												_snake_case							:       Optional[Any]												= kwargs["threshold"]
							if "mask_threshold" in kwargs:
												_snake_case							:       Any												= kwargs["mask_threshold"]
							if "overlap_mask_area_threshold" in kwargs:
												_snake_case							:       List[Any]												= kwargs["overlap_mask_area_threshold"]

							return preprocess_kwargs, {}, postprocess_kwargs







		def __call__(     self			,    __snake_case			,    **__snake_case					)   ->   Union[Predictions, List[Prediction]]:


							'''simple docstring'''

							return super().__call__(__snake_case			,    **__snake_case					)







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None					)   ->   List[str]:


							'''simple docstring'''

							_snake_case							:       Any												= load_image(__snake_case					)
							_snake_case							:       Tuple												= [(image.height, image.width)]
							if self.model.config.__class__.__name__ == "OneFormerConfig":
												if subtask is None:
																	_snake_case							:       List[Any]												= {}
												else:
																	_snake_case							:       int												= {"task_inputs": [subtask]}
												_snake_case							:       Tuple												= self.image_processor(images=[image]			,    return_tensors="pt"			,    **__snake_case					)
												_snake_case							:       Union[str, Any]												= self.tokenizer(
												    inputs["task_inputs"]			,    padding="max_length"			,    max_length=self.model.config.task_seq_len			,    return_tensors=self.framework			,    )["input_ids"]
							else:
												_snake_case							:       str												= self.image_processor(images=[image]			,    return_tensors="pt"					)
							_snake_case							:       Optional[int]												= target_size
							return inputs







		def 							lowercase							(     self			,    __snake_case					)   ->   Optional[Any]:


							'''simple docstring'''

							_snake_case							:       Dict												= model_inputs.pop("target_size"					)
							_snake_case							:       Union[str, Any]												= self.model(**__snake_case					)
							_snake_case							:       Optional[Any]												= target_size
							return model_outputs







		def 							lowercase							(     self			,    __snake_case			,    __snake_case=None			,    __snake_case=0.9			,    __snake_case=0.5			,    __snake_case=0.5					)   ->   Optional[int]:


							'''simple docstring'''

							_snake_case							:       List[Any]												= None
							if subtask in {"panoptic", None} and hasattr(self.image_processor			,    "post_process_panoptic_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_panoptic_segmentation
							elif subtask in {"instance", None} and hasattr(self.image_processor			,    "post_process_instance_segmentation"					):
												_snake_case							:       str												= self.image_processor.post_process_instance_segmentation

							if fn is not None:
												_snake_case							:       Optional[Any]												= fn(
												    __snake_case			,    threshold=__snake_case			,    mask_threshold=__snake_case			,    overlap_mask_area_threshold=__snake_case			,    target_sizes=model_outputs["target_size"]			,    )[0]

												_snake_case							:       Optional[int]												= []
												_snake_case							:       int												= outputs["segmentation"]

												for segment in outputs["segments_info"]:
																	_snake_case							:       Dict												= (segmentation == segment["id"]) * 2_5_5
																	_snake_case							:       int												= Image.fromarray(mask.numpy().astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       Optional[Any]												= self.model.config.idalabel[segment["label_id"]]
																	_snake_case							:       Optional[int]												= segment["score"]
																	annotation.append({"score": score, "label": label, "mask": mask}					)

							elif subtask in {"semantic", None} and hasattr(self.image_processor			,    "post_process_semantic_segmentation"					):
												_snake_case							:       Tuple												= self.image_processor.post_process_semantic_segmentation(
												    __snake_case			,    target_sizes=model_outputs["target_size"]					)[0]

												_snake_case							:       Tuple												= []
												_snake_case							:       Any												= outputs.numpy()
												_snake_case							:       Tuple												= np.unique(__snake_case					)

												for label in labels:
																	_snake_case							:       str												= (segmentation == label) * 2_5_5
																	_snake_case							:       str												= Image.fromarray(mask.astype(np.uinta					)			,    mode="L"					)
																	_snake_case							:       List[str]												= self.model.config.idalabel[label]
																	annotation.append({"score": None, "label": label, "mask": mask}					)
							else:
												raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model					)}"""					)
							return annotation