






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


__UpperCAmelCase     							=						logging.get_logger(__name__)

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



class   lowerCamelCase				(a__      ):
		_snake_case   :   int							=  '''table-transformer'''
		_snake_case   :   List[Any]							=  ['''past_key_values''']
		_snake_case   :   Tuple							=  {
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		}



		def __init__(							self    ,		_UpperCamelCase=True    ,		_UpperCamelCase=None    ,		_UpperCamelCase=3    ,		_UpperCamelCase=1_0_0    ,		_UpperCamelCase=6    ,		_UpperCamelCase=2_0_4_8    ,		_UpperCamelCase=8    ,		_UpperCamelCase=6    ,		_UpperCamelCase=2_0_4_8    ,		_UpperCamelCase=8    ,		_UpperCamelCase=0.0    ,		_UpperCamelCase=0.0    ,		_UpperCamelCase=True    ,		_UpperCamelCase="relu"    ,		_UpperCamelCase=2_5_6    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.0    ,		_UpperCamelCase=0.0    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=1.0    ,		_UpperCamelCase=False    ,		_UpperCamelCase="sine"    ,		_UpperCamelCase="resnet50"    ,		_UpperCamelCase=True    ,		_UpperCamelCase=False    ,		_UpperCamelCase=1    ,		_UpperCamelCase=5    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1    ,		_UpperCamelCase=1    ,		_UpperCamelCase=5    ,		_UpperCamelCase=2    ,		_UpperCamelCase=0.1    ,		**_UpperCamelCase    ,		)     ->							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_   :				int						=						CONFIG_MAPPING['''resnet'''](out_features=['stage4']       )
												elif isinstance(_lowerCamelCase    ,		_lowerCamelCase       ):
																	UpperCAmelCase_   :				str						=						backbone_config.get('model_type'       )
																	UpperCAmelCase_   :				Optional[int]						=						CONFIG_MAPPING[backbone_model_type]
																	UpperCAmelCase_   :				List[str]						=						config_class.from_dict(_lowerCamelCase       )
												# set timm attributes to None
												UpperCAmelCase_   :				Union[str, Any]						=						None, None, None

							UpperCAmelCase_   :				Optional[Any]						=						use_timm_backbone
							UpperCAmelCase_   :				Optional[int]						=						backbone_config
							UpperCAmelCase_   :				Optional[Any]						=						num_channels
							UpperCAmelCase_   :				Dict						=						num_queries
							UpperCAmelCase_   :				str						=						d_model
							UpperCAmelCase_   :				List[str]						=						encoder_ffn_dim
							UpperCAmelCase_   :				int						=						encoder_layers
							UpperCAmelCase_   :				Optional[Any]						=						encoder_attention_heads
							UpperCAmelCase_   :				List[str]						=						decoder_ffn_dim
							UpperCAmelCase_   :				Any						=						decoder_layers
							UpperCAmelCase_   :				List[str]						=						decoder_attention_heads
							UpperCAmelCase_   :				Tuple						=						dropout
							UpperCAmelCase_   :				Optional[Any]						=						attention_dropout
							UpperCAmelCase_   :				Any						=						activation_dropout
							UpperCAmelCase_   :				List[Any]						=						activation_function
							UpperCAmelCase_   :				Dict						=						init_std
							UpperCAmelCase_   :				Any						=						init_xavier_std
							UpperCAmelCase_   :				List[Any]						=						encoder_layerdrop
							UpperCAmelCase_   :				int						=						decoder_layerdrop
							UpperCAmelCase_   :				Any						=						encoder_layers
							UpperCAmelCase_   :				List[str]						=						auxiliary_loss
							UpperCAmelCase_   :				List[Any]						=						position_embedding_type
							UpperCAmelCase_   :				Optional[Any]						=						backbone
							UpperCAmelCase_   :				Tuple						=						use_pretrained_backbone
							UpperCAmelCase_   :				List[Any]						=						dilation
							# Hungarian matcher
							UpperCAmelCase_   :				List[str]						=						class_cost
							UpperCAmelCase_   :				str						=						bbox_cost
							UpperCAmelCase_   :				Union[str, Any]						=						giou_cost
							# Loss coefficients
							UpperCAmelCase_   :				Any						=						mask_loss_coefficient
							UpperCAmelCase_   :				Optional[int]						=						dice_loss_coefficient
							UpperCAmelCase_   :				Dict						=						bbox_loss_coefficient
							UpperCAmelCase_   :				int						=						giou_loss_coefficient
							UpperCAmelCase_   :				int						=						eos_coefficient
							super().__init__(is_encoder_decoder=_lowerCamelCase    ,		**_lowerCamelCase       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							str:
							return self.encoder_attention_heads



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							return self.d_model





class   lowerCamelCase				(a__      ):
		_snake_case   :   Dict							=  version.parse('''1.11'''      )



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



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



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							return 1_2














import os
import re
import shutil
import sys
import tempfile
import unittest

import black


__UpperCAmelCase     							=						os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
__UpperCAmelCase     							=						'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'



class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						tempfile.mkdtemp()
							os.makedirs(os.path.join(self.diffusers_dir    ,		'schedulers/'       )       )
							UpperCAmelCase_   :				int						=						self.diffusers_dir
							shutil.copy(
							    os.path.join(_UpperCamelCase    ,		'src/diffusers/schedulers/scheduling_ddpm.py'       )    ,		os.path.join(self.diffusers_dir    ,		'schedulers/scheduling_ddpm.py'       )    ,		)



		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				str						=						'src/diffusers'
							shutil.rmtree(self.diffusers_dir       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase=None       )     ->							Optional[Any]:
							UpperCAmelCase_   :				Dict						=						comment + f"\nclass {class_name}(nn.Module):\n" + class_code
							if overwrite_result is not None:
												UpperCAmelCase_   :				Optional[Any]						=						comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
							UpperCAmelCase_   :				Any						=						black.Mode(target_versions={black.TargetVersion.PYaa}    ,		line_length=1_1_9       )
							UpperCAmelCase_   :				Optional[Any]						=						black.format_str(_UpperCamelCase    ,		mode=_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						os.path.join(self.diffusers_dir    ,		'new_code.py'       )
							with open(_UpperCamelCase    ,		'w'    ,		newline='\n'       ) as f:
												f.write(_UpperCamelCase       )
							if overwrite_result is None:
												self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase       )       ) == 0       )
							else:
												check_copies.is_copy_consistent(f.name    ,		overwrite=_UpperCamelCase       )
												with open(_UpperCamelCase    ,		'r'       ) as f:
																	self.assertTrue(f.read()    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				List[Any]						=						check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'       )
							self.assertEqual(_UpperCamelCase    ,		_UpperCamelCase       )



		def  __UpperCAmelCase      (							self       )     ->							str:
							# Base copy consistency
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,		'DDPMSchedulerOutput'    ,		REFERENCE_CODE + '\n'    ,		)

							# With no empty line at the end
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'    ,		'DDPMSchedulerOutput'    ,		_UpperCamelCase    ,		)

							# Copy consistency with rename
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,		'TestSchedulerOutput'    ,		re.sub('DDPM'    ,		'Test'    ,		_UpperCamelCase       )    ,		)

							# Copy consistency with a really long name
							UpperCAmelCase_   :				Optional[int]						=						'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
							self.check_copy_consistency(
							    f"# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"    ,		f"{long_class_name}SchedulerOutput"    ,		re.sub('Bert'    ,		_UpperCamelCase    ,		_UpperCamelCase       )    ,		)

							# Copy consistency with overwrite
							self.check_copy_consistency(
							    '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'    ,		'TestSchedulerOutput'    ,		_UpperCamelCase    ,		overwrite_result=re.sub('DDPM'    ,		'Test'    ,		_UpperCamelCase       )    ,		)






