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


lowercase        =  logging.get_logger(__name__)

lowercase        =  {
    '''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
        '''https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'''
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    '''trajectory_transformer'''
					SCREAMING_SNAKE_CASE			    =    ['''past_key_values''']
					SCREAMING_SNAKE_CASE			    =    {
					    '''hidden_size''': '''n_embd''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}

					def __init__(	self					:     List[str]   ,					__UpperCamelCase					:     Optional[int]=1_0_0   ,					__UpperCamelCase					:     List[Any]=5   ,					__UpperCamelCase					:     Any=1   ,					__UpperCamelCase					:     Any=1   ,					__UpperCamelCase					:     Tuple=2_4_9   ,					__UpperCamelCase					:     Tuple=6   ,					__UpperCamelCase					:     str=1_7   ,					__UpperCamelCase					:     Any=2_5   ,					__UpperCamelCase					:     List[str]=4   ,					__UpperCamelCase					:     Dict=4   ,					__UpperCamelCase					:     Any=1_2_8   ,					__UpperCamelCase					:     List[str]=0.1   ,					__UpperCamelCase					:     Optional[Any]=0.1   ,					__UpperCamelCase					:     int=0.1   ,					__UpperCamelCase					:     Any=0.0006   ,					__UpperCamelCase					:     List[str]=5_1_2   ,					__UpperCamelCase					:     Dict=0.02   ,					__UpperCamelCase					:     Union[str, Any]=1E-12   ,					__UpperCamelCase					:     Any=1   ,					__UpperCamelCase					:     str=True   ,					__UpperCamelCase					:     Union[str, Any]=1   ,					__UpperCamelCase					:     Tuple=5_0_2_5_6   ,					__UpperCamelCase					:     str=5_0_2_5_6   ,					**__UpperCamelCase					:     List[Any]   ,					):
												lowerCamelCase_							  =					vocab_size
												lowerCamelCase_							  =					action_weight
												lowerCamelCase_							  =					reward_weight
												lowerCamelCase_							  =					value_weight
												lowerCamelCase_							  =					max_position_embeddings
												lowerCamelCase_							  =					block_size
												lowerCamelCase_							  =					action_dim
												lowerCamelCase_							  =					observation_dim
												lowerCamelCase_							  =					transition_dim
												lowerCamelCase_							  =					learning_rate
												lowerCamelCase_							  =					n_layer
												lowerCamelCase_							  =					n_head
												lowerCamelCase_							  =					n_embd
												lowerCamelCase_							  =					embd_pdrop
												lowerCamelCase_							  =					attn_pdrop
												lowerCamelCase_							  =					resid_pdrop
												lowerCamelCase_							  =					initializer_range
												lowerCamelCase_							  =					layer_norm_eps
												lowerCamelCase_							  =					kaiming_initializer_range
												lowerCamelCase_							  =					use_cache
												super().__init__(pad_token_id=__UpperCamelCase   ,					bos_token_id=__UpperCamelCase   ,					eos_token_id=__UpperCamelCase   ,					**__UpperCamelCase   )







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    lowercase__      (	self					:     Optional[Any]   ):
												# For consistency across different places the DisjunctiveConstraint is called,
												# dc.token_ids is a list of integers. It is also initialized only by integers.

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

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

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

					def    lowercase__      (	self					:     List[Any]   ):
												# 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).
												lowerCamelCase_							  =					[[1, 2], [1, 2, 3, 4]]

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

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					[[1, 2, 3], [1, 2, 4]]

												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )

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



					def    lowercase__      (	self					:     Tuple   ):
												lowerCamelCase_							  =					[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

												lowerCamelCase_							  =					DisjunctiveConstraint(__UpperCamelCase   )

												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]   )





