



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

import numpy as np
import torch

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


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







@dataclass
class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : torch.FloatTensor







class       _snake_case				(     __snake_case ,							__snake_case ):






   '''simple docstring'''






   A__      : List[str]       =						1


   @register_to_config
   def __init__(    self:     Optional[int]  ,lowerCamelCase_:     int = 40  ,lowerCamelCase_:     float = 0.0_0_2  ,lowerCamelCase_:     float = 8_0.0  ,lowerCamelCase_:     float = 0.5  ,lowerCamelCase_:     float = 1.0  ,lowerCamelCase_:     float = 7.0  ,lowerCamelCase_:     bool = True  ,)	->      List[Any]:
       # standard deviation of the initial noise distribution
       UpperCAmelCase_     :       Tuple  		=					sigma_max

       UpperCAmelCase_     :       int  		=					np.linspace(0  ,1  ,lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[int]  		=					self._convert_to_karras(lowerCamelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					self.sigma_to_t(lowerCamelCase_							)

       # setable values
       UpperCAmelCase_     :       Optional[Any]  		=					None
       UpperCAmelCase_     :       Any  		=					torch.from_numpy(lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[Any]  		=					torch.from_numpy(lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					False
       UpperCAmelCase_     :       Dict  		=					False


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     List[str]=None							)	->      Optional[Any]:
       if schedule_timesteps is None:
           UpperCAmelCase_     :       str  		=					self.timesteps

       UpperCAmelCase_     :       str  		=					(schedule_timesteps == timestep).nonzero()
       return indices.item()


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     Union[float, torch.FloatTensor]							)	->      torch.FloatTensor:
       # Get sigma corresponding to timestep
       if isinstance(lowerCamelCase_  ,torch.Tensor							):
           UpperCAmelCase_     :       Any  		=					timestep.to(self.timesteps.device							)
       UpperCAmelCase_     :       Optional[Any]  		=					self.index_for_timestep(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					self.sigmas[step_idx]

       UpperCAmelCase_     :       Tuple  		=					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       UpperCAmelCase_     :       Any  		=					True
       return sample


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Union[float, np.ndarray]							)	->      Tuple:
       if not isinstance(lowerCamelCase_  ,np.ndarray							):
           UpperCAmelCase_     :       str  		=					np.array(lowerCamelCase_  ,dtype=np.floataa							)

       UpperCAmelCase_     :       Dict  		=					1000 * 0.2_5 * np.log(sigmas + 1e-44							)

       return timesteps


   def       A__    (    self:     List[Any]  ,lowerCamelCase_:     Optional[int] = None  ,lowerCamelCase_:     Union[str, torch.device] = None  ,lowerCamelCase_:     Optional[List[int]] = None  ,)	->      Optional[Any]:
       if num_inference_steps is None and timesteps is None:
           raise ValueError("""Exactly one of `num_inference_steps` or `timesteps` must be supplied."""							)

       if num_inference_steps is not None and timesteps is not None:
           raise ValueError("""Can only pass one of `num_inference_steps` or `timesteps`."""							)

       # Follow DDPMScheduler custom timesteps logic
       if timesteps is not None:
           for i in range(1  ,len(lowerCamelCase_							)							):
               if timesteps[i] >= timesteps[i - 1]:
                   raise ValueError("""`timesteps` must be in descending order."""							)

           if timesteps[0] >= self.config.num_train_timesteps:
               raise ValueError(
                   F'''`timesteps` must start before `self.config.train_timesteps`:'''
                   F''' {self.config.num_train_timesteps}.'''							)

           UpperCAmelCase_     :       List[Any]  		=					np.array(lowerCamelCase_  ,dtype=np.intaa							)
           UpperCAmelCase_     :       Tuple  		=					True
       else:
           if num_inference_steps > self.config.num_train_timesteps:
               raise ValueError(
                   F'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
                   F''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
                   F''' maximal {self.config.num_train_timesteps} timesteps.'''							)

           UpperCAmelCase_     :       Any  		=					num_inference_steps

           UpperCAmelCase_     :       Any  		=					self.config.num_train_timesteps // self.num_inference_steps
           UpperCAmelCase_     :       Optional[int]  		=					(np.arange(0  ,lowerCamelCase_							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
           UpperCAmelCase_     :       int  		=					False

       # Map timesteps to Karras sigmas directly for multistep sampling
       # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
       UpperCAmelCase_     :       List[str]  		=					self.config.num_train_timesteps
       UpperCAmelCase_     :       Any  		=					timesteps[::-1].copy()
       UpperCAmelCase_     :       int  		=					ramp / (num_train_timesteps - 1)
       UpperCAmelCase_     :       Tuple  		=					self._convert_to_karras(lowerCamelCase_							)
       UpperCAmelCase_     :       Dict  		=					self.sigma_to_t(lowerCamelCase_							)

       UpperCAmelCase_     :       List[str]  		=					np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
       UpperCAmelCase_     :       Union[str, Any]  		=					torch.from_numpy(lowerCamelCase_							).to(device=lowerCamelCase_							)

       if str(lowerCamelCase_							).startswith("""mps"""							):
           # mps does not support float64
           UpperCAmelCase_     :       Optional[Any]  		=					torch.from_numpy(lowerCamelCase_							).to(lowerCamelCase_  ,dtype=torch.floataa							)
       else:
           UpperCAmelCase_     :       Optional[int]  		=					torch.from_numpy(lowerCamelCase_							).to(device=lowerCamelCase_							)


   def       A__    (    self:     str  ,lowerCamelCase_:     Any							)	->      Optional[Any]:
       UpperCAmelCase_     :       float  		=					self.config.sigma_min
       UpperCAmelCase_     :       float  		=					self.config.sigma_max

       UpperCAmelCase_     :       Tuple  		=					self.config.rho
       UpperCAmelCase_     :       str  		=					sigma_min ** (1 / rho)
       UpperCAmelCase_     :       Optional[int]  		=					sigma_max ** (1 / rho)
       UpperCAmelCase_     :       str  		=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     int							)	->      Tuple:
       UpperCAmelCase_     :       List[Any]  		=					self.config.sigma_data

       UpperCAmelCase_     :       int  		=					sigma_data**2 / (sigma**2 + sigma_data**2)
       UpperCAmelCase_     :       str  		=					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out


   def       A__    (    self:     Optional[Any]  ,lowerCamelCase_:     List[Any]							)	->      List[Any]:
       UpperCAmelCase_     :       List[Any]  		=					self.config.sigma_min
       UpperCAmelCase_     :       List[str]  		=					self.config.sigma_data

       UpperCAmelCase_     :       Union[str, Any]  		=					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       UpperCAmelCase_     :       Any  		=					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     Union[float, torch.FloatTensor]  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     Optional[torch.Generator] = None  ,lowerCamelCase_:     bool = True  ,)	->      Union[CMStochasticIterativeSchedulerOutput, Tuple]:

       if (
           isinstance(lowerCamelCase_  ,lowerCamelCase_							)
           or isinstance(lowerCamelCase_  ,torch.IntTensor							)
           or isinstance(lowerCamelCase_  ,torch.LongTensor							)
       ):
           raise ValueError(
               (
                   """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
                   F''' `{self.__class__}.step()` is not supported. Make sure to pass'''
                   """ one of the `scheduler.timesteps` as a timestep."""
               )  ,)

       if not self.is_scale_input_called:
           logger.warning(
               """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
               """See `StableDiffusionPipeline` for a usage example."""							)

       if isinstance(lowerCamelCase_  ,torch.Tensor							):
           UpperCAmelCase_     :       List[str]  		=					timestep.to(self.timesteps.device							)

       UpperCAmelCase_     :       Optional[Any]  		=					self.config.sigma_min
       UpperCAmelCase_     :       List[str]  		=					self.config.sigma_max

       UpperCAmelCase_     :       int  		=					self.index_for_timestep(lowerCamelCase_							)

       # sigma_next corresponds to next_t in original implementation
       UpperCAmelCase_     :       Optional[Any]  		=					self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
           UpperCAmelCase_     :       Dict  		=					self.sigmas[step_index + 1]
       else:
           # Set sigma_next to sigma_min
           UpperCAmelCase_     :       int  		=					self.sigmas[-1]

       # Get scalings for boundary conditions
       UpperCAmelCase_		, UpperCAmelCase_     :       Any  		=					self.get_scalings_for_boundary_condition(lowerCamelCase_							)

       # 1. Denoise model output using boundary conditions
       UpperCAmelCase_     :       Optional[Any]  		=					c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
           UpperCAmelCase_     :       int  		=					denoised.clamp(-1  ,1							)

       # 2. Sample z ~ N(0, s_noise^2 * I)
       # Noise is not used for onestep sampling.
       if len(self.timesteps							) > 1:
           UpperCAmelCase_     :       Optional[int]  		=					randn_tensor(
               model_output.shape  ,dtype=model_output.dtype  ,device=model_output.device  ,generator=lowerCamelCase_							)
       else:
           UpperCAmelCase_     :       Union[str, Any]  		=					torch.zeros_like(lowerCamelCase_							)
       UpperCAmelCase_     :       Any  		=					noise * self.config.s_noise

       UpperCAmelCase_     :       str  		=					sigma_next.clamp(min=lowerCamelCase_  ,max=lowerCamelCase_							)

       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       UpperCAmelCase_     :       Tuple  		=					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

       if not return_dict:
           return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=lowerCamelCase_							)


   def       A__    (    self:     str  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     torch.FloatTensor  ,lowerCamelCase_:     torch.FloatTensor  ,)	->      torch.FloatTensor:
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCAmelCase_     :       int  		=					self.sigmas.to(device=original_samples.device  ,dtype=original_samples.dtype							)
       if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_							):
           # mps does not support float64
           UpperCAmelCase_     :       List[str]  		=					self.timesteps.to(original_samples.device  ,dtype=torch.floataa							)
           UpperCAmelCase_     :       Dict  		=					timesteps.to(original_samples.device  ,dtype=torch.floataa							)
       else:
           UpperCAmelCase_     :       int  		=					self.timesteps.to(original_samples.device							)
           UpperCAmelCase_     :       Tuple  		=					timesteps.to(original_samples.device							)

       UpperCAmelCase_     :       Tuple  		=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

       UpperCAmelCase_     :       Any  		=					sigmas[step_indices].flatten()
       while len(sigma.shape							) < len(original_samples.shape							):
           UpperCAmelCase_     :       Dict  		=					sigma.unsqueeze(-1							)

       UpperCAmelCase_     :       List[str]  		=					original_samples + noise * sigma
       return noisy_samples






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





from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


UpperCamelCase_									=   logging.get_logger(__name__)

UpperCamelCase_									=   {
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
      import detectrona







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : List[Any]       =						"layoutlmv2"


   def __init__(    self:     Tuple  ,lowerCamelCase_:     Dict=30522  ,lowerCamelCase_:     str=768  ,lowerCamelCase_:     Any=12  ,lowerCamelCase_:     Tuple=12  ,lowerCamelCase_:     int=3072  ,lowerCamelCase_:     int="gelu"  ,lowerCamelCase_:     Dict=0.1  ,lowerCamelCase_:     List[Any]=0.1  ,lowerCamelCase_:     List[Any]=512  ,lowerCamelCase_:     Tuple=2  ,lowerCamelCase_:     Dict=0.0_2  ,lowerCamelCase_:     Dict=1e-12  ,lowerCamelCase_:     Dict=0  ,lowerCamelCase_:     List[str]=1024  ,lowerCamelCase_:     Optional[Any]=128  ,lowerCamelCase_:     List[Any]=32  ,lowerCamelCase_:     List[Any]=True  ,lowerCamelCase_:     Dict=256  ,lowerCamelCase_:     int=64  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     Tuple=[7, 7, 256]  ,lowerCamelCase_:     Union[str, Any]=128  ,lowerCamelCase_:     Any=128  ,lowerCamelCase_:     int=True  ,lowerCamelCase_:     Union[str, Any]=True  ,lowerCamelCase_:     Optional[int]=False  ,lowerCamelCase_:     List[str]=None  ,**lowerCamelCase_:     str  ,)	->      Any:
       super().__init__(
           vocab_size=lowerCamelCase_  ,hidden_size=lowerCamelCase_  ,num_hidden_layers=lowerCamelCase_  ,num_attention_heads=lowerCamelCase_  ,intermediate_size=lowerCamelCase_  ,hidden_act=lowerCamelCase_  ,hidden_dropout_prob=lowerCamelCase_  ,attention_probs_dropout_prob=lowerCamelCase_  ,max_position_embeddings=lowerCamelCase_  ,type_vocab_size=lowerCamelCase_  ,initializer_range=lowerCamelCase_  ,layer_norm_eps=lowerCamelCase_  ,pad_token_id=lowerCamelCase_  ,**lowerCamelCase_  ,)
       UpperCAmelCase_     :       Optional[int]  		=					max_ad_position_embeddings
       UpperCAmelCase_     :       Optional[int]  		=					max_rel_pos
       UpperCAmelCase_     :       Optional[int]  		=					rel_pos_bins
       UpperCAmelCase_     :       List[str]  		=					fast_qkv
       UpperCAmelCase_     :       int  		=					max_rel_ad_pos
       UpperCAmelCase_     :       int  		=					rel_ad_pos_bins
       UpperCAmelCase_     :       Optional[Any]  		=					convert_sync_batchnorm
       UpperCAmelCase_     :       str  		=					image_feature_pool_shape
       UpperCAmelCase_     :       List[Any]  		=					coordinate_size
       UpperCAmelCase_     :       int  		=					shape_size
       UpperCAmelCase_     :       Optional[Any]  		=					has_relative_attention_bias
       UpperCAmelCase_     :       str  		=					has_spatial_attention_bias
       UpperCAmelCase_     :       Tuple  		=					has_visual_segment_embedding
       UpperCAmelCase_     :       Optional[Any]  		=					(
           detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
       )


   @classmethod
   def       A__    (    self:     Any							)	->      Dict:
       return {
           "MODEL.MASK_ON": True,
           "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
           "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
           "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
           "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
           "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
           "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
           "MODEL.POST_NMS_TOPK_TEST": 1000,
           "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
           "MODEL.ROI_HEADS.NUM_CLASSES": 5,
           "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
           "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
           "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
           "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
           "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
           "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
           "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
           "MODEL.RESNETS.DEPTH": 101,
           "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
           "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
           "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
           "MODEL.RESNETS.NUM_GROUPS": 32,
           "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
           "MODEL.RESNETS.STRIDE_IN_1X1": False,
       }






   def       A__    (    self:     Tuple							)	->      int:
       UpperCAmelCase_     :       Any  		=					detectrona.config.get_cfg()
       for k, v in self.detectrona_config_args.items():
           UpperCAmelCase_     :       Optional[int]  		=					k.split("""."""							)
           UpperCAmelCase_     :       List[str]  		=					detectrona_config
           for attribute in attributes[:-1]:
               UpperCAmelCase_     :       Optional[Any]  		=					getattr(lowerCamelCase_  ,lowerCamelCase_							)
           setattr(lowerCamelCase_  ,attributes[-1]  ,lowerCamelCase_							)

       return detectrona_config
