'''simple docstring'''




import copy
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/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							="conditional_detr"
     lowerCamelCase  :					Dict							=["past_key_values"]
     lowerCamelCase  :					List[Any]							={
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(			self		:		Optional[Any]  ,    a		:		Optional[int]=True  ,    a		:		str=None  ,    a		:		Union[str, Any]=3  ,    a		:		int=3_00  ,    a		:		int=6  ,    a		:		Optional[int]=20_48  ,    a		:		str=8  ,    a		:		Optional[Any]=6  ,    a		:		Any=20_48  ,    a		:		Dict=8  ,    a		:		List[str]=0.0  ,    a		:		Any=0.0  ,    a		:		List[str]=True  ,    a		:		Tuple="relu"  ,    a		:		int=2_56  ,    a		:		int=0.1  ,    a		:		Optional[int]=0.0  ,    a		:		List[Any]=0.0  ,    a		:		List[str]=0.02  ,    a		:		str=1.0  ,    a		:		List[str]=False  ,    a		:		Any="sine"  ,    a		:		List[str]="resnet50"  ,    a		:		Dict=True  ,    a		:		Dict=False  ,    a		:		int=2  ,    a		:		Optional[int]=5  ,    a		:		Dict=2  ,    a		:		Any=1  ,    a		:		Dict=1  ,    a		:		int=2  ,    a		:		str=5  ,    a		:		str=2  ,    a		:		Union[str, Any]=0.25  ,    **a		:		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.'''   )
                 __lowerCamelCase          =  CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
             elif isinstance(a  ,    a   ):
                 __lowerCamelCase          =  backbone_config.get('''model_type'''   )
                 __lowerCamelCase          =  CONFIG_MAPPING[backbone_model_type]
                 __lowerCamelCase          =  config_class.from_dict(a   )

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






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         return self.encoder_attention_heads






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         return self.d_model






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  copy.deepcopy(self.__dict__   )
         if self.backbone_config is not None:
             __lowerCamelCase          =  self.backbone_config.to_dict()
         __lowerCamelCase          =  self.__class__.model_type
         return output




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					List[str]							=version.parse("1.11"			)






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



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






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple   ):





         """simple docstring"""



         return 1e-5






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Any   ):





         """simple docstring"""



         return 12

'''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        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		int   ):





         """simple docstring"""



         __lowerCamelCase          =  3
         __lowerCamelCase          =  2_50

         __lowerCamelCase          =  ids_tensor((batch_size, length)  ,    a   )
         __lowerCamelCase          =  torch.ones((batch_size, length)  ,    device=a  ,    dtype=torch.float   ) / length
         return input_ids, scores






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )

         __lowerCamelCase          =  StoppingCriteriaList(
             [
                 MaxLengthCriteria(max_length=10   ),
                 MaxTimeCriteria(max_time=0.1   ),
             ]   )

         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )






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





         """simple docstring"""



         __lowerCamelCase          =  MaxLengthCriteria(max_length=10   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  MaxNewTokensCriteria(start_length=5  ,    max_new_tokens=5   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(9   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(10   )
         self.assertTrue(criteria(a  ,    a   )   )

         __lowerCamelCase          =  StoppingCriteriaList([criteria]   )
         self.assertEqual(criteria_list.max_length  ,    10   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		int   ):





         """simple docstring"""



         __lowerCamelCase     ,  __lowerCamelCase          =  self._get_tensors(5   )

         __lowerCamelCase          =  MaxTimeCriteria(max_time=0.1   )
         self.assertFalse(criteria(a  ,    a   )   )

         __lowerCamelCase          =  MaxTimeCriteria(max_time=0.1  ,    initial_timestamp=time.time() - 0.2   )
         self.assertTrue(criteria(a  ,    a   )   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]   ):





         """simple docstring"""



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

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

         __lowerCamelCase          =  validate_stopping_criteria(StoppingCriteriaList()  ,    11   )

         self.assertEqual(len(a   )  ,    1   )
