


'''simple docstring'''





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

import torch

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




@dataclass
class 				UpperCAmelCase			(			__a						):
 UpperCAmelCase				=					42
 UpperCAmelCase				=					42




class 				UpperCAmelCase			(			__a						, __a						):
 UpperCAmelCase				=					1


 @register_to_config
 def __init__(      self							:	Dict			,       __lowerCamelCase							:	int = 2_0_0_0			,       __lowerCamelCase							:	float = 0.15			,       __lowerCamelCase							:	float = 0.01			,       __lowerCamelCase							:	float = 1_3_4_8.0			,       __lowerCamelCase							:	float = 1e-5			,       __lowerCamelCase							:	int = 1			,       ):
    UpperCAmelCase__	:List[Any]      =      sigma_max

    # setable values
    UpperCAmelCase__	:str      =      None

    self.set_sigmas(lowercase_			,       lowercase_			,       lowercase_			,       lowercase_ )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	torch.FloatTensor			,       __lowerCamelCase							:	Optional[int] = None ):
    return sample


 def        __SCREAMING_SNAKE_CASE					(      self							:	Optional[int]			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	float = None			,       __lowerCamelCase							:	Union[str, torch.device] = None ):
    UpperCAmelCase__	:Any      =      sampling_eps if sampling_eps is not None else self.config.sampling_eps

    UpperCAmelCase__	:str      =      torch.linspace(1			,       lowercase_			,       lowercase_			,       device=lowercase_ )


 def        __SCREAMING_SNAKE_CASE					(      self							:	int			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	float = None			,       __lowerCamelCase							:	float = None			,       __lowerCamelCase							:	float = None ):
    UpperCAmelCase__	:Tuple      =      sigma_min if sigma_min is not None else self.config.sigma_min
    UpperCAmelCase__	:str      =      sigma_max if sigma_max is not None else self.config.sigma_max
    UpperCAmelCase__	:Dict      =      sampling_eps if sampling_eps is not None else self.config.sampling_eps
    if self.timesteps is None:
       self.set_timesteps(lowercase_			,       lowercase_ )

    UpperCAmelCase__	:Union[str, Any]      =      sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
    UpperCAmelCase__	:int      =      torch.exp(torch.linspace(math.log(lowercase_ )			,       math.log(lowercase_ )			,       lowercase_ ) )
    UpperCAmelCase__	:List[str]      =      torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Any			,       __lowerCamelCase							:	Tuple			,       __lowerCamelCase							:	Union[str, Any] ):
    return torch.where(
        timesteps == 0			,       torch.zeros_like(t.to(timesteps.device ) )			,       self.discrete_sigmas[timesteps - 1].to(timesteps.device )			,       )


 def        __SCREAMING_SNAKE_CASE					(      self							:	List[str]			,       __lowerCamelCase							:	torch.FloatTensor			,       __lowerCamelCase							:	int			,       __lowerCamelCase							:	torch.FloatTensor			,       __lowerCamelCase							:	Optional[torch.Generator] = None			,       __lowerCamelCase							:	bool = True			,       ):


    if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )

    UpperCAmelCase__	:Dict      =      timestep * torch.ones(
        sample.shape[0]			,       device=sample.device )  # torch.repeat_interleave(timestep, sample.shape[0])
    UpperCAmelCase__	:str      =      (timestep * (len(self.timesteps ) - 1)).long()

    # mps requires indices to be in the same device, so we use cpu as is the default with cuda
    UpperCAmelCase__	:Tuple      =      timesteps.to(self.discrete_sigmas.device )

    UpperCAmelCase__	:Any      =      self.discrete_sigmas[timesteps].to(sample.device )
    UpperCAmelCase__	:Tuple      =      self.get_adjacent_sigma(lowercase_			,       lowercase_ ).to(sample.device )
    UpperCAmelCase__	:int      =      torch.zeros_like(lowercase_ )
    UpperCAmelCase__	:Union[str, Any]      =      (sigma**2 - adjacent_sigma**2) ** 0.5

    # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
    # also equation 47 shows the analog from SDE models to ancestral sampling methods
    UpperCAmelCase__	:List[Any]      =      diffusion.flatten()
    while len(diffusion.shape ) < len(sample.shape ):
       UpperCAmelCase__	:Tuple      =      diffusion.unsqueeze(-1 )
    UpperCAmelCase__	:List[str]      =      drift - diffusion**2 * model_output

    #  equation 6: sample noise for the diffusion term of
    UpperCAmelCase__	:Dict      =      randn_tensor(
        sample.shape			,       layout=sample.layout			,       generator=lowercase_			,       device=sample.device			,       dtype=sample.dtype )
    UpperCAmelCase__	:int      =      sample - drift  # subtract because `dt` is a small negative timestep
    # TODO is the variable diffusion the correct scaling term for the noise?
    UpperCAmelCase__	:Optional[Any]      =      prev_sample_mean + diffusion * noise  # add impact of diffusion field g

    if not return_dict:
       return (prev_sample, prev_sample_mean)

    return SdeVeOutput(prev_sample=lowercase_			,       prev_sample_mean=lowercase_ )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Dict			,       __lowerCamelCase							:	torch.FloatTensor			,       __lowerCamelCase							:	torch.FloatTensor			,       __lowerCamelCase							:	Optional[torch.Generator] = None			,       __lowerCamelCase							:	bool = True			,       ):


    if self.timesteps is None:
       raise ValueError(
           '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler''' )

    # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
    # sample noise for correction
    UpperCAmelCase__	:int      =      randn_tensor(sample.shape			,       layout=sample.layout			,       generator=lowercase_ ).to(sample.device )

    # compute step size from the model_output, the noise, and the snr
    UpperCAmelCase__	:int      =      torch.norm(model_output.reshape(model_output.shape[0]			,       -1 )			,       dim=-1 ).mean()
    UpperCAmelCase__	:Optional[int]      =      torch.norm(noise.reshape(noise.shape[0]			,       -1 )			,       dim=-1 ).mean()
    UpperCAmelCase__	:Dict      =      (self.config.snr * noise_norm / grad_norm) ** 2 * 2
    UpperCAmelCase__	:List[str]      =      step_size * torch.ones(sample.shape[0] ).to(sample.device )
    # self.repeat_scalar(step_size, sample.shape[0])

    # compute corrected sample: model_output term and noise term
    UpperCAmelCase__	:Optional[int]      =      step_size.flatten()
    while len(step_size.shape ) < len(sample.shape ):
       UpperCAmelCase__	:Tuple      =      step_size.unsqueeze(-1 )
    UpperCAmelCase__	:List[str]      =      sample + step_size * model_output
    UpperCAmelCase__	:List[str]      =      prev_sample_mean + ((step_size * 2) ** 0.5) * noise

    if not return_dict:
       return (prev_sample,)

    return SchedulerOutput(prev_sample=lowercase_ )


 def        __SCREAMING_SNAKE_CASE					(      self							:	Tuple			,       __lowerCamelCase							:	torch.FloatTensor			,       __lowerCamelCase							:	torch.FloatTensor			,       __lowerCamelCase							:	torch.FloatTensor			,       ):
    UpperCAmelCase__	:Optional[int]      =      timesteps.to(original_samples.device )
    UpperCAmelCase__	:Any      =      self.discrete_sigmas.to(original_samples.device )[timesteps]
    UpperCAmelCase__	:Tuple      =      (
        noise * sigmas[:, None, None, None]
        if noise is not None
        else torch.randn_like(lowercase_ ) * sigmas[:, None, None, None]
    )
    UpperCAmelCase__	:str      =      noise + original_samples
    return noisy_samples





 def __len__(      self							:	Union[str, Any] ):
    return self.config.num_train_timesteps




'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''table-transformer'''
  A_    							=						['''past_key_values''']
  A_    							=						{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
  }




  def __init__(		self      :       str   ,				lowercase_      :       str=True   ,				lowercase_      :       List[str]=None   ,				lowercase_      :       Dict=3   ,				lowercase_      :       Optional[Any]=100   ,				lowercase_      :       Optional[int]=6   ,				lowercase_      :       Optional[Any]=2_048   ,				lowercase_      :       List[Any]=8   ,				lowercase_      :       Optional[Any]=6   ,				lowercase_      :       int=2_048   ,				lowercase_      :       Any=8   ,				lowercase_      :       Optional[Any]=0.0   ,				lowercase_      :       int=0.0   ,				lowercase_      :       Dict=True   ,				lowercase_      :       int="relu"   ,				lowercase_      :       Tuple=256   ,				lowercase_      :       Dict=0.1   ,				lowercase_      :       List[str]=0.0   ,				lowercase_      :       List[str]=0.0   ,				lowercase_      :       int=0.0_2   ,				lowercase_      :       List[str]=1.0   ,				lowercase_      :       Optional[int]=False   ,				lowercase_      :       List[Any]="sine"   ,				lowercase_      :       Optional[int]="resnet50"   ,				lowercase_      :       Union[str, Any]=True   ,				lowercase_      :       Union[str, Any]=False   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Any=5   ,				lowercase_      :       Optional[int]=2   ,				lowercase_      :       Dict=1   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Dict=5   ,				lowercase_      :       Optional[int]=2   ,				lowercase_      :       Optional[Any]=0.1   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


    if backbone_config is not None and use_timm_backbone:
      raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""      )

    if not use_timm_backbone:
      if backbone_config is None:
        logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""      )
        lowercase_     =       CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]      )
      elif isinstance(lowercase_   ,				lowercase_      ):
        lowercase_     =       backbone_config.get("""model_type"""      )
        lowercase_     =       CONFIG_MAPPING[backbone_model_type]
        lowercase_     =       config_class.from_dict(lowercase_      )
      # set timm attributes to None
      lowercase_     ,	lowercase_     ,	lowercase_     =       None, None, None

    lowercase_     =       use_timm_backbone
    lowercase_     =       backbone_config
    lowercase_     =       num_channels
    lowercase_     =       num_queries
    lowercase_     =       d_model
    lowercase_     =       encoder_ffn_dim
    lowercase_     =       encoder_layers
    lowercase_     =       encoder_attention_heads
    lowercase_     =       decoder_ffn_dim
    lowercase_     =       decoder_layers
    lowercase_     =       decoder_attention_heads
    lowercase_     =       dropout
    lowercase_     =       attention_dropout
    lowercase_     =       activation_dropout
    lowercase_     =       activation_function
    lowercase_     =       init_std
    lowercase_     =       init_xavier_std
    lowercase_     =       encoder_layerdrop
    lowercase_     =       decoder_layerdrop
    lowercase_     =       encoder_layers
    lowercase_     =       auxiliary_loss
    lowercase_     =       position_embedding_type
    lowercase_     =       backbone
    lowercase_     =       use_pretrained_backbone
    lowercase_     =       dilation
    # Hungarian matcher
    lowercase_     =       class_cost
    lowercase_     =       bbox_cost
    lowercase_     =       giou_cost
    # Loss coefficients
    lowercase_     =       mask_loss_coefficient
    lowercase_     =       dice_loss_coefficient
    lowercase_     =       bbox_loss_coefficient
    lowercase_     =       giou_loss_coefficient
    lowercase_     =       eos_coefficient
    super().__init__(is_encoder_decoder=lowercase_   ,				**lowercase_      )




  @property
  def 		lowerCamelCase__  (		self      :       Tuple      ):







    '''simple docstring'''


    return self.encoder_attention_heads





  @property
  def 		lowerCamelCase__  (		self      :       Dict      ):







    '''simple docstring'''


    return self.d_model






class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						version.parse('''1.11'''      )




  @property
  def 		lowerCamelCase__  (		self      :       int      ):







    '''simple docstring'''


    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
            ("""pixel_mask""", {0: """batch"""}),
        ]      )




  @property
  def 		lowerCamelCase__  (		self      :       List[Any]      ):







    '''simple docstring'''


    return 1e-5





  @property
  def 		lowerCamelCase__  (		self      :       Union[str, Any]      ):







    '''simple docstring'''


    return 12

