



"""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        __snake_case       (	unittest.TestCase):




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]						,					__lowerCAmelCase     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= 3
         _lowerCamelCase						:				str    			= 2_5_0

         _lowerCamelCase						:				Optional[Any]    			= ids_tensor((batch_size, length)						,					__lowerCAmelCase		)
         _lowerCamelCase						:				List[str]    			= torch.ones((batch_size, length)						,					device=__lowerCAmelCase						,					dtype=torch.float		) / length
         return input_ids, scores




  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






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

         _lowerCamelCase						:				str    			= StoppingCriteriaList(
             [
                 MaxLengthCriteria(max_length=1_0		),
                 MaxTimeCriteria(max_time=0.1		),
             ]		)

         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				List[Any]    			= self._get_tensors(9		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				Any    			= self._get_tensors(1_0		)
         self.assertTrue(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[Any]    			= MaxLengthCriteria(max_length=1_0		)

         _lowerCamelCase			,    _lowerCamelCase						:				Optional[int]    			= self._get_tensors(5		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				Any    			= self._get_tensors(9		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				int    			= self._get_tensors(1_0		)
         self.assertTrue(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= MaxNewTokensCriteria(start_length=5						,					max_new_tokens=5		)

         _lowerCamelCase			,    _lowerCamelCase						:				Tuple    			= self._get_tensors(5		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				Tuple    			= self._get_tensors(9		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				Any    			= self._get_tensors(1_0		)
         self.assertTrue(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase						:				str    			= StoppingCriteriaList([criteria]		)
         self.assertEqual(criteria_list.max_length						,					1_0		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase			,    _lowerCamelCase						:				Optional[Any]    			= self._get_tensors(5		)

         _lowerCamelCase						:				List[str]    			= MaxTimeCriteria(max_time=0.1		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase						:				Dict    			= MaxTimeCriteria(max_time=0.1						,					initial_timestamp=time.time() - 0.2		)
         self.assertTrue(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0		)]		)						,					1_0		)

         with self.assertWarns(__lowerCAmelCase		):
                validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0		)]		)						,					1_1		)

         _lowerCamelCase						:				Dict    			= validate_stopping_criteria(StoppingCriteriaList()						,					1_1		)

         self.assertEqual(len(__lowerCAmelCase		)						,					1		)







'''simple docstring'''





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


__a          =		logging.get_logger(__name__)

__a          =		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"glpn"




    def __init__(    self					:     Optional[Any] ,     snake_case_					:     List[str]=3 ,     snake_case_					:     Dict=4 ,     snake_case_					:     List[Any]=[2, 2, 2, 2] ,     snake_case_					:     int=[8, 4, 2, 1] ,     snake_case_					:     List[str]=[32, 64, 160, 256] ,     snake_case_					:     Tuple=[7, 3, 3, 3] ,     snake_case_					:     List[Any]=[4, 2, 2, 2] ,     snake_case_					:     Tuple=[1, 2, 5, 8] ,     snake_case_					:     List[str]=[4, 4, 4, 4] ,     snake_case_					:     Optional[int]="gelu" ,     snake_case_					:     Dict=0.0 ,     snake_case_					:     Union[str, Any]=0.0 ,     snake_case_					:     List[Any]=0.02 ,     snake_case_					:     Tuple=0.1 ,     snake_case_					:     Any=1E-6 ,     snake_case_					:     Dict=64 ,     snake_case_					:     Tuple=10 ,     snake_case_					:     List[Any]=-1 ,     **snake_case_					:     Optional[Any] ,     ):
          super().__init__(**snake_case_						)

          snake_case__  :    Optional[Any]    						=      num_channels
          snake_case__  :    Dict    						=      num_encoder_blocks
          snake_case__  :    Tuple    						=      depths
          snake_case__  :    Union[str, Any]    						=      sr_ratios
          snake_case__  :    Tuple    						=      hidden_sizes
          snake_case__  :    Optional[Any]    						=      patch_sizes
          snake_case__  :    int    						=      strides
          snake_case__  :    List[Any]    						=      mlp_ratios
          snake_case__  :    Optional[int]    						=      num_attention_heads
          snake_case__  :    Dict    						=      hidden_act
          snake_case__  :    int    						=      hidden_dropout_prob
          snake_case__  :    Optional[Any]    						=      attention_probs_dropout_prob
          snake_case__  :    str    						=      initializer_range
          snake_case__  :    List[str]    						=      drop_path_rate
          snake_case__  :    int    						=      layer_norm_eps
          snake_case__  :    Tuple    						=      decoder_hidden_size
          snake_case__  :    List[Any]    						=      max_depth
          snake_case__  :    Dict    						=      head_in_index
