




"""simple docstring"""





import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
					import torch

					from transformers.generation import (
					    MaxLengthCriteria,
					    MaxNewTokensCriteria,
					    MaxTimeCriteria,
					    StoppingCriteriaList,
					    validate_stopping_criteria,
					)



@require_torch
class      a__				(	unittest.TestCase):


			def       a_     (      self      :    str      ,  UpperCamelCase_      :    List[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: Tuple		=					3
						__UpperCAmelCase	: List[Any]		=					250

						__UpperCAmelCase	: Optional[int]		=					ids_tensor((batch_size, length)      ,  a_)
						__UpperCAmelCase	: List[str]		=					torch.ones((batch_size, length)      ,  device=a_      ,  dtype=torch.float) / length
						return input_ids, scores


			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						__UpperCAmelCase	: Dict		=					self._get_tensors(5)

						__UpperCAmelCase	: str		=					StoppingCriteriaList(
						    [
						        MaxLengthCriteria(max_length=10),
						        MaxTimeCriteria(max_time=0.1),
						    ])

						self.assertFalse(criteria(a_      ,  a_))

						__UpperCAmelCase	: Tuple		=					self._get_tensors(9)
						self.assertFalse(criteria(a_      ,  a_))

						__UpperCAmelCase	: Any		=					self._get_tensors(10)
						self.assertTrue(criteria(a_      ,  a_))


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					MaxLengthCriteria(max_length=10)

						__UpperCAmelCase	: Tuple		=					self._get_tensors(5)
						self.assertFalse(criteria(a_      ,  a_))

						__UpperCAmelCase	: Any		=					self._get_tensors(9)
						self.assertFalse(criteria(a_      ,  a_))

						__UpperCAmelCase	: Dict		=					self._get_tensors(10)
						self.assertTrue(criteria(a_      ,  a_))


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					MaxNewTokensCriteria(start_length=5      ,  max_new_tokens=5)

						__UpperCAmelCase	: str		=					self._get_tensors(5)
						self.assertFalse(criteria(a_      ,  a_))

						__UpperCAmelCase	: List[Any]		=					self._get_tensors(9)
						self.assertFalse(criteria(a_      ,  a_))

						__UpperCAmelCase	: Union[str, Any]		=					self._get_tensors(10)
						self.assertTrue(criteria(a_      ,  a_))

						__UpperCAmelCase	: List[Any]		=					StoppingCriteriaList([criteria])
						self.assertEqual(criteria_list.max_length      ,  10)


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					self._get_tensors(5)

						__UpperCAmelCase	: Optional[Any]		=					MaxTimeCriteria(max_time=0.1)
						self.assertFalse(criteria(a_      ,  a_))

						__UpperCAmelCase	: List[str]		=					MaxTimeCriteria(max_time=0.1      ,  initial_timestamp=time.time() - 0.2)
						self.assertTrue(criteria(a_      ,  a_))


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])      ,  10)

						with self.assertWarns(a_):
									validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])      ,  11)

						__UpperCAmelCase	: Tuple		=					validate_stopping_criteria(StoppingCriteriaList()      ,  11)

						self.assertEqual(len(a_)      ,  1)








"""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


A   =       logging.get_logger(__name__)

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



class      a__				(	__magic_name__   ):
			lowercase_    =	"table-transformer"
			lowercase_    =	["past_key_values"]
			lowercase_    =	{
			    "hidden_size": "d_model",
			    "num_attention_heads": "encoder_attention_heads",
			}


			def __init__(      self      :    List[Any]      ,  UpperCamelCase_      :    int=True      ,  UpperCamelCase_      :    Tuple=None      ,  UpperCamelCase_      :    Optional[int]=3      ,  UpperCamelCase_      :    Any=100      ,  UpperCamelCase_      :    Dict=6      ,  UpperCamelCase_      :    List[str]=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=6      ,  UpperCamelCase_      :    Dict=2048      ,  UpperCamelCase_      :    str=8      ,  UpperCamelCase_      :    Any=0.0      ,  UpperCamelCase_      :    str=0.0      ,  UpperCamelCase_      :    Union[str, Any]=True      ,  UpperCamelCase_      :    Optional[int]="relu"      ,  UpperCamelCase_      :    str=256      ,  UpperCamelCase_      :    str=0.1      ,  UpperCamelCase_      :    Tuple=0.0      ,  UpperCamelCase_      :    Dict=0.0      ,  UpperCamelCase_      :    Dict=0.02      ,  UpperCamelCase_      :    Optional[Any]=1.0      ,  UpperCamelCase_      :    Optional[int]=False      ,  UpperCamelCase_      :    int="sine"      ,  UpperCamelCase_      :    Optional[Any]="resnet50"      ,  UpperCamelCase_      :    List[str]=True      ,  UpperCamelCase_      :    int=False      ,  UpperCamelCase_      :    Tuple=1      ,  UpperCamelCase_      :    Any=5      ,  UpperCamelCase_      :    Optional[Any]=2      ,  UpperCamelCase_      :    int=1      ,  UpperCamelCase_      :    List[str]=1      ,  UpperCamelCase_      :    List[str]=5      ,  UpperCamelCase_      :    Union[str, Any]=2      ,  UpperCamelCase_      :    Optional[int]=0.1      ,  **UpperCamelCase_      :    int      ,  ):


						"""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	: int		=					CONFIG_MAPPING["resnet"](out_features=["stage4"])
									elif isinstance(UpperCamelCase_      ,  UpperCamelCase_):
												__UpperCAmelCase	: List[Any]		=					backbone_config.get("model_type")
												__UpperCAmelCase	: List[Any]		=					CONFIG_MAPPING[backbone_model_type]
												__UpperCAmelCase	: Optional[int]		=					config_class.from_dict(UpperCamelCase_)
									# set timm attributes to None
									__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase	: List[Any]		=					None, None, None

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


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.encoder_attention_heads


			@property
			def       a_     (      self      :    int):


						"""simple docstring"""




						return self.d_model






class      a__				(	__magic_name__   ):
			lowercase_    =	version.parse("1.11"   )


			@property
			def       a_     (      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       a_     (      self      :    int):


						"""simple docstring"""




						return 1e-5


			@property
			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						return 12


