



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


__A       =						logging.get_logger(__name__)

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






class 		lowercase						(			_SCREAMING_SNAKE_CASE):






	"""simple docstring"""


	a__				: List[Any]		       =							"table-transformer"
	a__				: Optional[int]		       =							["past_key_values"]
	a__				: Any		       =							{
	    "hidden_size": "d_model",
	    "num_attention_heads": "encoder_attention_heads",
	}




	def __init__(     self     :						Tuple						,  __UpperCAmelCase     :						Union[str, Any]=True						,  __UpperCAmelCase     :						Optional[Any]=None						,  __UpperCAmelCase     :						Union[str, Any]=3						,  __UpperCAmelCase     :						int=100						,  __UpperCAmelCase     :						str=6						,  __UpperCAmelCase     :						List[str]=2_048						,  __UpperCAmelCase     :						Optional[Any]=8						,  __UpperCAmelCase     :						int=6						,  __UpperCAmelCase     :						Tuple=2_048						,  __UpperCAmelCase     :						Union[str, Any]=8						,  __UpperCAmelCase     :						Tuple=0.0						,  __UpperCAmelCase     :						List[str]=0.0						,  __UpperCAmelCase     :						str=True						,  __UpperCAmelCase     :						Optional[int]="relu"						,  __UpperCAmelCase     :						Tuple=256						,  __UpperCAmelCase     :						Dict=0.1						,  __UpperCAmelCase     :						int=0.0						,  __UpperCAmelCase     :						List[Any]=0.0						,  __UpperCAmelCase     :						str=0.02						,  __UpperCAmelCase     :						List[Any]=1.0						,  __UpperCAmelCase     :						Dict=False						,  __UpperCAmelCase     :						List[str]="sine"						,  __UpperCAmelCase     :						int="resnet50"						,  __UpperCAmelCase     :						Union[str, Any]=True						,  __UpperCAmelCase     :						str=False						,  __UpperCAmelCase     :						List[Any]=1						,  __UpperCAmelCase     :						List[str]=5						,  __UpperCAmelCase     :						Optional[int]=2						,  __UpperCAmelCase     :						str=1						,  __UpperCAmelCase     :						Optional[int]=1						,  __UpperCAmelCase     :						Tuple=5						,  __UpperCAmelCase     :						List[str]=2						,  __UpperCAmelCase     :						int=0.1						,  **__UpperCAmelCase     :						List[str]						,  )      ->			Union[str, Any]:
							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(__UpperCAmelCase						,  __UpperCAmelCase					):
																			UpperCAmelCase_=       backbone_config.get("""model_type"""					)
																			UpperCAmelCase_=       CONFIG_MAPPING[backbone_model_type]
																			UpperCAmelCase_=       config_class.from_dict(__UpperCAmelCase					)
													# 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=__UpperCAmelCase						,  **__UpperCAmelCase					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			int:
							return self.encoder_attention_heads




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			int:
							return self.d_model






class 		lowercase						(			_SCREAMING_SNAKE_CASE):






	"""simple docstring"""


	a__				: Dict		       =							version.parse("1.11")




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							        ("""pixel_mask""", {0: """batch"""}),
							    ]					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			float:
							return 1E-5




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			int:
							return 12









from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Dict						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						List[Any]					)      ->			List[str]:
							super().__init__()

							# make sure scheduler can always be converted to DDIM
							UpperCAmelCase_=       DDIMScheduler.from_config(scheduler.config					)

							self.register_modules(unet=__UpperCAmelCase						,  scheduler=__UpperCAmelCase					)




	@torch.no_grad()
	def __call__(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						int = 1						,  __UpperCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None						,  __UpperCAmelCase     :						float = 0.0						,  __UpperCAmelCase     :						int = 50						,  __UpperCAmelCase     :						Optional[bool] = None						,  __UpperCAmelCase     :						Optional[str] = "pil"						,  __UpperCAmelCase     :						bool = True						,  )      ->			Union[ImagePipelineOutput, Tuple]:

							# Sample gaussian noise to begin loop
							if isinstance(self.unet.config.sample_size						,  __UpperCAmelCase					):
													UpperCAmelCase_=       (
													    batch_size,
													    self.unet.config.in_channels,
													    self.unet.config.sample_size,
													    self.unet.config.sample_size,
													)
							else:
													UpperCAmelCase_=       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					) and len(__UpperCAmelCase					) != batch_size:
													raise ValueError(
													    F"""You have passed a list of generators of length {len(__UpperCAmelCase					)}, but requested an effective batch"""
													    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""					)

							UpperCAmelCase_=       randn_tensor(__UpperCAmelCase						,  generator=__UpperCAmelCase						,  device=self.device						,  dtype=self.unet.dtype					)

							# set step values
							self.scheduler.set_timesteps(__UpperCAmelCase					)

							for t in self.progress_bar(self.scheduler.timesteps					):
													# 1. predict noise model_output
													UpperCAmelCase_=       self.unet(__UpperCAmelCase						,  __UpperCAmelCase					).sample

													# 2. predict previous mean of image x_t-1 and add variance depending on eta
													# eta corresponds to η in paper and should be between [0, 1]
													# do x_t -> x_t-1
													UpperCAmelCase_=       self.scheduler.step(
													    __UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						,  eta=__UpperCAmelCase						,  use_clipped_model_output=__UpperCAmelCase						,  generator=__UpperCAmelCase					).prev_sample

							UpperCAmelCase_=       (image / 2 + 0.5).clamp(0						,  1					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					).numpy()
							if output_type == "pil":
													UpperCAmelCase_=       self.numpy_to_pil(__UpperCAmelCase					)

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=__UpperCAmelCase					)




