
'''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):





       """simple docstring"""




       def 							UpperCamelCase					(				self							,				A__   )     ->      List[str]:
          _SCREAMING_SNAKE_CASE  			=					3
          _SCREAMING_SNAKE_CASE  			=					2_50

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






       def 							UpperCamelCase					(				self   )     ->      Optional[Any]:
          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._get_tensors(5   )

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

          self.assertFalse(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._get_tensors(9   )
          self.assertFalse(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._get_tensors(10   )
          self.assertTrue(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )






       def 							UpperCamelCase					(				self   )     ->      List[Any]:
          _SCREAMING_SNAKE_CASE  			=					MaxLengthCriteria(max_length=10   )

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._get_tensors(5   )
          self.assertFalse(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._get_tensors(9   )
          self.assertFalse(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._get_tensors(10   )
          self.assertTrue(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )






       def 							UpperCamelCase					(				self   )     ->      Union[str, Any]:
          _SCREAMING_SNAKE_CASE  			=					MaxNewTokensCriteria(start_length=5							,				max_new_tokens=5   )

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._get_tensors(5   )
          self.assertFalse(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._get_tensors(9   )
          self.assertFalse(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )

          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._get_tensors(10   )
          self.assertTrue(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )

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






       def 							UpperCamelCase					(				self   )     ->      str:
          _SCREAMING_SNAKE_CASE			,				_SCREAMING_SNAKE_CASE  			=					self._get_tensors(5   )

          _SCREAMING_SNAKE_CASE  			=					MaxTimeCriteria(max_time=0.1   )
          self.assertFalse(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )

          _SCREAMING_SNAKE_CASE  			=					MaxTimeCriteria(max_time=0.1							,				initial_timestamp=time.time() - 0.2   )
          self.assertTrue(criteria(_lowerCAmelCase							,				_lowerCAmelCase   )   )






       def 							UpperCamelCase					(				self   )     ->      Optional[int]:
          validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )							,				10   )

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

          _SCREAMING_SNAKE_CASE  			=					validate_stopping_criteria(StoppingCriteriaList()							,				11   )

          self.assertEqual(len(_lowerCAmelCase   )							,				1   )












from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__					:  Optional[int]      							=				logging.get_logger(__name__)

lowerCamelCase__					:  Tuple      							=				{
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "swinv2"

 lowercase_							       =       {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }



 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Optional[Any]=224							,							_lowerCAmelCase    :						Optional[int]=4							,							_lowerCAmelCase    :						Tuple=3							,							_lowerCAmelCase    :						Tuple=96							,							_lowerCAmelCase    :						Dict=[2, 2, 6, 2]							,							_lowerCAmelCase    :						Optional[Any]=[3, 6, 12, 24]							,							_lowerCAmelCase    :						str=7							,							_lowerCAmelCase    :						List[Any]=4.0							,							_lowerCAmelCase    :						List[str]=True							,							_lowerCAmelCase    :						List[Any]=0.0							,							_lowerCAmelCase    :						List[Any]=0.0							,							_lowerCAmelCase    :						Any=0.1							,							_lowerCAmelCase    :						List[Any]="gelu"							,							_lowerCAmelCase    :						str=False							,							_lowerCAmelCase    :						str=0.02							,							_lowerCAmelCase    :						List[Any]=1E-5							,							_lowerCAmelCase    :						str=32							,							**_lowerCAmelCase    :						List[Any]							,							):
       super().__init__(**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     image_size
       SCREAMING_SNAKE_CASE_      		=     patch_size
       SCREAMING_SNAKE_CASE_      		=     num_channels
       SCREAMING_SNAKE_CASE_      		=     embed_dim
       SCREAMING_SNAKE_CASE_      		=     depths
       SCREAMING_SNAKE_CASE_      		=     len(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     num_heads
       SCREAMING_SNAKE_CASE_      		=     window_size
       SCREAMING_SNAKE_CASE_      		=     mlp_ratio
       SCREAMING_SNAKE_CASE_      		=     qkv_bias
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     drop_path_rate
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     use_absolute_embeddings
       SCREAMING_SNAKE_CASE_      		=     layer_norm_eps
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       SCREAMING_SNAKE_CASE_      		=     int(embed_dim * 2 ** (len(_lowerCAmelCase		) - 1)		)
       SCREAMING_SNAKE_CASE_      		=     (0, 0, 0, 0)