




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch

					from transformers.generation import DisjunctiveConstraint
@require_torch
class 			a__    (					unittest.TestCase      ):






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [[1, 2, 4], [1, 2, 3, 4]]
											__lowerCAmelCase				   =       DisjunctiveConstraint(_A			)
											self.assertTrue(isinstance(dc.token_ids		,			_A			)			)

											with self.assertRaises(_A			):
																DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]			)			)

											with self.assertRaises(_A			):
																DisjunctiveConstraint([torch.LongTensor([1, 2, 4]			), torch.LongTensor([1, 2, 3, 4, 5]			)]			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [[1, 2], [1, 2, 3, 4]]

											with self.assertRaises(_A			):
																DisjunctiveConstraint(_A			)  # fails here






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [[1, 2, 3], [1, 2, 4]]

											__lowerCAmelCase				   =       DisjunctiveConstraint(_A			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       dc.update(1			)
											__lowerCAmelCase				   =       stepped is True and completed is False and reset is False
											self.assertTrue(_A			)
											self.assertTrue(not dc.completed			)
											self.assertTrue(dc.current_seq == [1]			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       dc.update(2			)
											__lowerCAmelCase				   =       stepped is True and completed is False and reset is False
											self.assertTrue(_A			)
											self.assertTrue(not dc.completed			)
											self.assertTrue(dc.current_seq == [1, 2]			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       dc.update(3			)
											__lowerCAmelCase				   =       stepped is True and completed is True and reset is False
											self.assertTrue(_A			)
											self.assertTrue(dc.completed			)  # Completed!
											self.assertTrue(dc.current_seq == [1, 2, 3]			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

											__lowerCAmelCase				   =       DisjunctiveConstraint(_A			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       dc.update(1			)
											self.assertTrue(not dc.completed			)
											self.assertTrue(dc.current_seq == [1]			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       dc.update(2			)
											self.assertTrue(not dc.completed			)
											self.assertTrue(dc.current_seq == [1, 2]			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       dc.update(4			)
											self.assertTrue(not dc.completed			)
											self.assertTrue(dc.current_seq == [1, 2, 4]			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       dc.update(5			)
											self.assertTrue(dc.completed			)  # Completed!
											self.assertTrue(dc.current_seq == [1, 2, 4, 5]			)

											dc.reset()

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       dc.update(1			)
											self.assertTrue(not dc.completed			)
											self.assertTrue(dc.remaining() == 3			)
											self.assertTrue(dc.current_seq == [1]			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       dc.update(2			)
											self.assertTrue(not dc.completed			)
											self.assertTrue(dc.remaining() == 2			)
											self.assertTrue(dc.current_seq == [1, 2]			)

											__lowerCAmelCase   ,							__lowerCAmelCase   ,							__lowerCAmelCase				   =       dc.update(5			)
											self.assertTrue(dc.completed			)  # Completed!
											self.assertTrue(dc.remaining() == 0			)
											self.assertTrue(dc.current_seq == [1, 2, 5]			)









'''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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       'resnet'
 __lowerCamelCase    :       Any          =       ['basic', 'bottleneck']



 def __init__(self ,   A=3 ,   A=64 ,   A=[256, 512, 1_024, 2_048] ,   A=[3, 4, 6, 3] ,   A="bottleneck" ,   A="relu" ,   A=False ,   A=None ,   A=None ,   **A ,   )    ->     Dict:


      """simple docstring"""



      super().__init__(**A       )
      if layer_type not in self.layer_types:
           raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
      _a      =  num_channels
      _a      =  embedding_size
      _a      =  hidden_sizes
      _a      =  depths
      _a      =  layer_type
      _a      =  hidden_act
      _a      =  downsample_in_first_stage
      _a      =  ['''stem'''] + [f'''stage{idx}''' for idx in range(1 ,   len(A       ) + 1       )]
      _a  ,    _a      =  get_aligned_output_features_output_indices(
          out_features=A ,   out_indices=A ,   stage_names=self.stage_names       )






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       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'''}),
          ]       )



 @property
 def 					a__  (self       )    ->     float:


      """simple docstring"""



      return 1E-3



