
import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible






class        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''safety_checker/pytorch_model.bin''',
													    '''safety_checker/model.safetensors''',
													    '''vae/diffusion_pytorch_model.bin''',
													    '''vae/diffusion_pytorch_model.safetensors''',
													    '''text_encoder/pytorch_model.bin''',
													    '''text_encoder/model.safetensors''',
													    '''unet/diffusion_pytorch_model.bin''',
													    '''unet/diffusion_pytorch_model.safetensors''',
													]
													self.assertTrue(is_safetensors_compatible(SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   List[str]:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''unet/diffusion_pytorch_model.bin''',
													    '''unet/diffusion_pytorch_model.safetensors''',
													]
													self.assertTrue(is_safetensors_compatible(SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''safety_checker/pytorch_model.bin''',
													    '''safety_checker/model.safetensors''',
													    '''vae/diffusion_pytorch_model.bin''',
													    '''vae/diffusion_pytorch_model.safetensors''',
													    '''text_encoder/pytorch_model.bin''',
													    '''text_encoder/model.safetensors''',
													    '''unet/diffusion_pytorch_model.bin''',
													    # Removed: 'unet/diffusion_pytorch_model.safetensors',
													]
													self.assertFalse(is_safetensors_compatible(SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''text_encoder/pytorch_model.bin''',
													    '''text_encoder/model.safetensors''',
													]
													self.assertTrue(is_safetensors_compatible(SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''safety_checker/pytorch_model.bin''',
													    '''safety_checker/model.safetensors''',
													    '''vae/diffusion_pytorch_model.bin''',
													    '''vae/diffusion_pytorch_model.safetensors''',
													    '''text_encoder/pytorch_model.bin''',
													    # Removed: 'text_encoder/model.safetensors',
													    '''unet/diffusion_pytorch_model.bin''',
													    '''unet/diffusion_pytorch_model.safetensors''',
													]
													self.assertFalse(is_safetensors_compatible(SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''safety_checker/pytorch_model.fp16.bin''',
													    '''safety_checker/model.fp16.safetensors''',
													    '''vae/diffusion_pytorch_model.fp16.bin''',
													    '''vae/diffusion_pytorch_model.fp16.safetensors''',
													    '''text_encoder/pytorch_model.fp16.bin''',
													    '''text_encoder/model.fp16.safetensors''',
													    '''unet/diffusion_pytorch_model.fp16.bin''',
													    '''unet/diffusion_pytorch_model.fp16.safetensors''',
													]
													__UpperCamelCase       =			'''fp16'''
													self.assertTrue(is_safetensors_compatible(SCREAMING_SNAKE_CASE_     ,	variant=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''unet/diffusion_pytorch_model.fp16.bin''',
													    '''unet/diffusion_pytorch_model.fp16.safetensors''',
													]
													__UpperCamelCase       =			'''fp16'''
													self.assertTrue(is_safetensors_compatible(SCREAMING_SNAKE_CASE_     ,	variant=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''unet/diffusion_pytorch_model.bin''',
													    '''unet/diffusion_pytorch_model.safetensors''',
													]
													__UpperCamelCase       =			'''fp16'''
													self.assertTrue(is_safetensors_compatible(SCREAMING_SNAKE_CASE_     ,	variant=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''safety_checker/pytorch_model.fp16.bin''',
													    '''safety_checker/model.fp16.safetensors''',
													    '''vae/diffusion_pytorch_model.fp16.bin''',
													    '''vae/diffusion_pytorch_model.fp16.safetensors''',
													    '''text_encoder/pytorch_model.fp16.bin''',
													    '''text_encoder/model.fp16.safetensors''',
													    '''unet/diffusion_pytorch_model.fp16.bin''',
													    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
													]
													__UpperCamelCase       =			'''fp16'''
													self.assertFalse(is_safetensors_compatible(SCREAMING_SNAKE_CASE_     ,	variant=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   Optional[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''text_encoder/pytorch_model.fp16.bin''',
													    '''text_encoder/model.fp16.safetensors''',
													]
													__UpperCamelCase       =			'''fp16'''
													self.assertTrue(is_safetensors_compatible(SCREAMING_SNAKE_CASE_     ,	variant=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''text_encoder/pytorch_model.bin''',
													    '''text_encoder/model.safetensors''',
													]
													__UpperCamelCase       =			'''fp16'''
													self.assertTrue(is_safetensors_compatible(SCREAMING_SNAKE_CASE_     ,	variant=SCREAMING_SNAKE_CASE_			)			)



							def     A__		(  self			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			[
													    '''safety_checker/pytorch_model.fp16.bin''',
													    '''safety_checker/model.fp16.safetensors''',
													    '''vae/diffusion_pytorch_model.fp16.bin''',
													    '''vae/diffusion_pytorch_model.fp16.safetensors''',
													    '''text_encoder/pytorch_model.fp16.bin''',
													    # 'text_encoder/model.fp16.safetensors',
													    '''unet/diffusion_pytorch_model.fp16.bin''',
													    '''unet/diffusion_pytorch_model.fp16.safetensors''',
													]
													__UpperCamelCase       =			'''fp16'''
													self.assertFalse(is_safetensors_compatible(SCREAMING_SNAKE_CASE_     ,	variant=SCREAMING_SNAKE_CASE_			)			)






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


lowercase__							:							Tuple									=			logging.get_logger(__name__)

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






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






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



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=100     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=6     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=8     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=256     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1.0     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_="sine"     ,	SCREAMING_SNAKE_CASE_="resnet50"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=0.1     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''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(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																									__UpperCamelCase       =			backbone_config.get('''model_type'''			)
																									__UpperCamelCase       =			CONFIG_MAPPING[backbone_model_type]
																									__UpperCamelCase       =			config_class.from_dict(SCREAMING_SNAKE_CASE_			)
																			# set timm attributes to None
																			__UpperCamelCase  ,							__UpperCamelCase  ,							__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=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.encoder_attention_heads



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self.d_model






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  version.parse('1.11'    )



							@property
							def     A__		(  self			)->   Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






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



							@property
							def     A__		(  self			)->   float:





													'''simple docstring'''






													return 1E-5



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return 12




