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


_SCREAMING_SNAKE_CASE      						=	logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      						=	{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}
class SCREAMING_SNAKE_CASE_						(	_UpperCAmelCase						):
   __lowerCAmelCase      =							"""table-transformer"""
   __lowerCAmelCase      =							["""past_key_values"""]
   __lowerCAmelCase      =							{
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }







   def __init__(			self		:		List[Any]					,					lowerCamelCase_		:		Dict=True					,					lowerCamelCase_		:		Tuple=None					,					lowerCamelCase_		:		Tuple=3					,					lowerCamelCase_		:		int=100					,					lowerCamelCase_		:		List[Any]=6					,					lowerCamelCase_		:		Any=2048					,					lowerCamelCase_		:		int=8					,					lowerCamelCase_		:		Optional[int]=6					,					lowerCamelCase_		:		Optional[int]=2048					,					lowerCamelCase_		:		Dict=8					,					lowerCamelCase_		:		int=0.0					,					lowerCamelCase_		:		List[str]=0.0					,					lowerCamelCase_		:		int=True					,					lowerCamelCase_		:		Optional[int]="relu"					,					lowerCamelCase_		:		List[str]=256					,					lowerCamelCase_		:		int=0.1					,					lowerCamelCase_		:		Optional[int]=0.0					,					lowerCamelCase_		:		Dict=0.0					,					lowerCamelCase_		:		List[str]=0.0_2					,					lowerCamelCase_		:		Optional[int]=1.0					,					lowerCamelCase_		:		List[str]=False					,					lowerCamelCase_		:		Optional[int]="sine"					,					lowerCamelCase_		:		Any="resnet50"					,					lowerCamelCase_		:		Any=True					,					lowerCamelCase_		:		Any=False					,					lowerCamelCase_		:		List[str]=1					,					lowerCamelCase_		:		Optional[Any]=5					,					lowerCamelCase_		:		int=2					,					lowerCamelCase_		:		str=1					,					lowerCamelCase_		:		List[Any]=1					,					lowerCamelCase_		:		Tuple=5					,					lowerCamelCase_		:		Optional[Any]=2					,					lowerCamelCase_		:		Any=0.1					,					**lowerCamelCase_		:		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."""							)
      UpperCamelCase          =	CONFIG_MAPPING['''resnet'''](out_features=["""stage4"""]							)
     elif isinstance(A_					,					A_							):
      UpperCamelCase          =	backbone_config.get("""model_type"""							)
      UpperCamelCase          =	CONFIG_MAPPING[backbone_model_type]
      UpperCamelCase          =	config_class.from_dict(A_							)
     # set timm attributes to None
     UpperCamelCase          =	None, None, None

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







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





    """simple docstring"""


    return self.encoder_attention_heads







   @property
   def 		lowerCamelCase_     (			self		:		Optional[Any]							):





    """simple docstring"""


    return self.d_model





class SCREAMING_SNAKE_CASE_						(	_UpperCAmelCase						):
   __lowerCAmelCase      =							version.parse("""1.11"""						)







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





    """simple docstring"""


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







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





    """simple docstring"""


    return 1E-5







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





    """simple docstring"""


    return 12



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   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  None







class   __lowercase      (_UpperCAmelCase		,   _UpperCAmelCase ):
 _UpperCamelCase			  =  2
 @register_to_config
 def __init__(							self      ,							A_ = 0.02      ,							A_ = 100      ,							A_ = 1.007      ,							A_ = 80      ,							A_ = 0.05      ,							A_ = 50      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					sigma_max

       # setable values
       __lowerCAmelCase					:						int					=					None
       __lowerCAmelCase					:						np.IntTensor					=					None
       __lowerCAmelCase					:						torch.FloatTensor					=					None  # sigma(t_i)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->torch.FloatTensor:



       '''simple docstring'''





       return sample
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_ = None					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					num_inference_steps
       __lowerCAmelCase					:						Dict					=					np.arange(0      ,							self.num_inference_steps					)[::-1].copy()
       __lowerCAmelCase					:						Optional[Any]					=					torch.from_numpy(A_					).to(A_					)
       __lowerCAmelCase					:						Tuple					=					[
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in self.timesteps
       ]
       __lowerCAmelCase					:						Optional[int]					=					torch.tensor(A_      ,							dtype=torch.floataa      ,							device=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_ = None					)					->Tuple[torch.FloatTensor, float]:



       '''simple docstring'''





       if self.config.s_min <= sigma <= self.config.s_max:
             __lowerCAmelCase					:						List[str]					=					min(self.config.s_churn / self.num_inference_steps      ,							2**0.5 - 1					)
       else:
             __lowerCAmelCase					:						List[str]					=					0

       # sample eps ~ N(0, S_noise^2 * I)
       __lowerCAmelCase					:						int					=					self.config.s_noise * randn_tensor(sample.shape      ,							generator=A_					).to(sample.device					)
       __lowerCAmelCase					:						str					=					sigma + gamma * sigma
       __lowerCAmelCase					:						Any					=					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						Union[str, Any]					=					sample_hat + sigma_hat * model_output
       __lowerCAmelCase					:						int					=					(sample_hat - pred_original_sample) / sigma_hat
       __lowerCAmelCase					:						Tuple					=					sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_      ,							A_ = True      ,							)					->Union[KarrasVeOutput, Tuple]:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					sample_prev + sigma_prev * model_output
       __lowerCAmelCase					:						List[Any]					=					(sample_prev - pred_original_sample) / sigma_prev
       __lowerCAmelCase					:						Any					=					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
             return (sample_prev, derivative)

       return KarrasVeOutput(
           prev_sample=A_      ,							derivative=A_      ,							pred_original_sample=A_					)





 def 	UpperCamelCase__  (							self      ,							A_      ,							A_      ,							A_					)					->Any:



       '''simple docstring'''





       raise NotImplementedError()


