
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  ):




	'''simple docstring'''


	def       SCREAMING_SNAKE_CASE__		(			self			):
		# For consistency across different places the DisjunctiveConstraint is called,
		# dc.token_ids is a list of integers. It is also initialized only by integers.

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		# We can't have constraints that are complete subsets of another. This leads to a preverse
		# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
		# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
		# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
		# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
		lowercase	    =       [[1, 2], [1, 2, 3, 4]]

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       [[1, 2, 3], [1, 2, 4]]

		lowercase	    =       DisjunctiveConstraint(snake_case			)

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

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

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





	def       SCREAMING_SNAKE_CASE__		(			self			):
		lowercase	    =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

		lowercase	    =       DisjunctiveConstraint(snake_case			)

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

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

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

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

		dc.reset()

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

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

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







"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''vit_mae'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[int]=768       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			Optional[Any]=12       ,				_UpperCAmelCase			:			Optional[int]=3_072       ,				_UpperCAmelCase			:			Any="gelu"       ,				_UpperCAmelCase			:			Optional[Any]=0.0       ,				_UpperCAmelCase			:			Optional[int]=0.0       ,				_UpperCAmelCase			:			Dict=0.02       ,				_UpperCAmelCase			:			List[Any]=1E-1_2       ,				_UpperCAmelCase			:			Optional[Any]=224       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=3       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=512       ,				_UpperCAmelCase			:			int=8       ,				_UpperCAmelCase			:			List[Any]=2_048       ,				_UpperCAmelCase			:			Optional[Any]=0.75       ,				_UpperCAmelCase			:			List[str]=False       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       super().__init__(**_UpperCAmelCase						)

       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_act
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  image_size
       _A     =  patch_size
       _A     =  num_channels
       _A     =  qkv_bias
       _A     =  decoder_num_attention_heads
       _A     =  decoder_hidden_size
       _A     =  decoder_num_hidden_layers
       _A     =  decoder_intermediate_size
       _A     =  mask_ratio
       _A     =  norm_pix_loss

