





from abc import ABC, abstractmethod
from typing import List, Optional

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					Tuple )  ->	List[Any]:




           '''simple docstring'''


           self.test()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		0
           SCREAMING_SNAKE_CASE	=		False
           while not completed:
                 if counter == 1:
                       self.reset()
                 SCREAMING_SNAKE_CASE	=		self.advance()
                 if not self.does_advance(lowerCamelCase__ ):
                       raise Exception(
                           """Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.""" )

                 SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.update(lowerCamelCase__ )
                 counter += 1

                 if counter > 10000:
                       raise Exception("""update() does not fulfill the constraint.""" )

           if self.remaining() != 0:
                 raise Exception("""Custom Constraint is not defined correctly.""" )






     @abstractmethod
     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Optional[Any]:




           '''simple docstring'''


           raise NotImplementedError(
               F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )






     @abstractmethod
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					int )  ->	Optional[Any]:




           '''simple docstring'''


           raise NotImplementedError(
               F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )






     @abstractmethod
     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					int )  ->	List[Any]:




           '''simple docstring'''


           raise NotImplementedError(
               F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )






     @abstractmethod
     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Optional[Any]:




           '''simple docstring'''


           raise NotImplementedError(
               F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )






     @abstractmethod
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Dict:




           '''simple docstring'''


           raise NotImplementedError(
               F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )






     @abstractmethod
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					Dict=False )  ->	str:




           '''simple docstring'''


           raise NotImplementedError(
               F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					List[Any]		,lowerCamelCase__		:					List[int] )  ->	List[Any]:




           '''simple docstring'''


           super(lowerCamelCase__		,self ).__init__()

           if not isinstance(lowerCamelCase__		,lowerCamelCase__ ) or len(lowerCamelCase__ ) == 0:
                 raise ValueError(F"""`token_ids` has to be a non-empty list, but is {token_ids}.""" )
           if any((not isinstance(lowerCamelCase__		,lowerCamelCase__ ) or token_id < 0) for token_id in token_ids ):
                 raise ValueError(F"""Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.""" )

           SCREAMING_SNAKE_CASE	=		token_ids

           SCREAMING_SNAKE_CASE	=		len(self.token_ids )
           SCREAMING_SNAKE_CASE	=		-1  # the index of the currently fulfilled step
           SCREAMING_SNAKE_CASE	=		False






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           if self.completed:
                 return None
           return self.token_ids[self.fulfilled_idx + 1]






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					int )  ->	List[Any]:




           '''simple docstring'''


           if not isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                 raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(lowerCamelCase__ )}""" )

           if self.completed:
                 return False

           return token_id == self.token_ids[self.fulfilled_idx + 1]






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					int )  ->	Optional[Any]:




           '''simple docstring'''


           if not isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                 raise ValueError(F"""`token_id` has to be an `int`, but is {token_id} of type {type(lowerCamelCase__ )}""" )

           SCREAMING_SNAKE_CASE	=		False
           SCREAMING_SNAKE_CASE	=		False
           SCREAMING_SNAKE_CASE	=		False

           if self.does_advance(lowerCamelCase__ ):
                 self.fulfilled_idx += 1
                 SCREAMING_SNAKE_CASE	=		True
                 if self.fulfilled_idx == (self.seqlen - 1):
                       SCREAMING_SNAKE_CASE	=		True
                 SCREAMING_SNAKE_CASE	=		completed
           else:
                 # failed to make progress.
                 SCREAMING_SNAKE_CASE	=		True
                 self.reset()
           return stepped, completed, reset






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		False
           SCREAMING_SNAKE_CASE	=		0






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Optional[Any]:




           '''simple docstring'''


           return self.seqlen - (self.fulfilled_idx + 1)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					List[str]=False )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		PhrasalConstraint(self.token_ids )

           if stateful:
                 SCREAMING_SNAKE_CASE	=		self.seqlen
                 SCREAMING_SNAKE_CASE	=		self.fulfilled_idx
                 SCREAMING_SNAKE_CASE	=		self.completed

           return new_constraint

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					Any		,lowerCamelCase__		:					List[List[int]]		,lowerCamelCase__		:					Dict=True )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		max([len(lowerCamelCase__ ) for one in nested_token_ids] )

           SCREAMING_SNAKE_CASE	=		{}
           for token_ids in nested_token_ids:
                 SCREAMING_SNAKE_CASE	=		root
                 for tidx, token_id in enumerate(lowerCamelCase__ ):
                       if token_id not in level:
                             SCREAMING_SNAKE_CASE	=		{}

                       SCREAMING_SNAKE_CASE	=		level[token_id]

           if no_subsets and self.has_subsets(lowerCamelCase__		,lowerCamelCase__ ):
                 raise ValueError(
                     """Each list in `nested_token_ids` can't be a complete subset of another list, but is"""
                     F""" {nested_token_ids}.""" )

           SCREAMING_SNAKE_CASE	=		root






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					Union[str, Any] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.trie

           for current_token in current_seq:
                 SCREAMING_SNAKE_CASE	=		start[current_token]

           SCREAMING_SNAKE_CASE	=		list(start.keys() )

           return next_tokens






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					int )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.next_tokens(lowerCamelCase__ )

           return len(lowerCamelCase__ ) == 0






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					List[Any] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		list(root.values() )
           if len(lowerCamelCase__ ) == 0:
                 return 1
           else:
                 return sum([self.count_leaves(lowerCamelCase__ ) for nn in next_nodes] )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Dict )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.count_leaves(lowerCamelCase__ )
           return len(lowerCamelCase__ ) != leaf_count

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def __init__(							self		:					str		,lowerCamelCase__		:					List[List[int]] )  ->	Optional[int]:




           '''simple docstring'''


           super(lowerCamelCase__		,self ).__init__()

           if not isinstance(lowerCamelCase__		,lowerCamelCase__ ) or len(lowerCamelCase__ ) == 0:
                 raise ValueError(F"""`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.""" )
           if any(not isinstance(lowerCamelCase__		,lowerCamelCase__ ) for token_ids in nested_token_ids ):
                 raise ValueError(F"""`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.""" )
           if any(
               any((not isinstance(lowerCamelCase__		,lowerCamelCase__ ) or token_id < 0) for token_id in token_ids )
               for token_ids in nested_token_ids ):
                 raise ValueError(
                     F"""Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.""" )

           SCREAMING_SNAKE_CASE	=		DisjunctiveTrie(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		nested_token_ids

           SCREAMING_SNAKE_CASE	=		self.trie.max_height
           SCREAMING_SNAKE_CASE	=		[]
           SCREAMING_SNAKE_CASE	=		False






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.trie.next_tokens(self.current_seq )

           if len(lowerCamelCase__ ) == 0:
                 return None
           else:
                 return token_list






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					int )  ->	int:




           '''simple docstring'''


           if not isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                 raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowerCamelCase__ )}""" )

           SCREAMING_SNAKE_CASE	=		self.trie.next_tokens(self.current_seq )

           return token_id in next_tokens






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					int )  ->	Tuple:




           '''simple docstring'''


           if not isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                 raise ValueError(F"""`token_id` is supposed to be type `int`, but is {token_id} of type {type(lowerCamelCase__ )}""" )

           SCREAMING_SNAKE_CASE	=		False
           SCREAMING_SNAKE_CASE	=		False
           SCREAMING_SNAKE_CASE	=		False

           if self.does_advance(lowerCamelCase__ ):
                 self.current_seq.append(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		True
           else:
                 SCREAMING_SNAKE_CASE	=		True
                 self.reset()

           SCREAMING_SNAKE_CASE	=		self.trie.reached_leaf(self.current_seq )
           SCREAMING_SNAKE_CASE	=		completed

           return stepped, completed, reset






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		False
           SCREAMING_SNAKE_CASE	=		[]






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Tuple:




           '''simple docstring'''


           if self.completed:
                 # since this can be completed without reaching max height
                 return 0
           else:
                 return self.seqlen - len(self.current_seq )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str]		,lowerCamelCase__		:					List[Any]=False )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		DisjunctiveConstraint(self.token_ids )

           if stateful:
                 SCREAMING_SNAKE_CASE	=		self.seqlen
                 SCREAMING_SNAKE_CASE	=		self.current_seq
                 SCREAMING_SNAKE_CASE	=		self.completed

           return new_constraint

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					Union[str, Any]		,lowerCamelCase__		:					List[Constraint] )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		constraints

           # max # of steps required to fulfill a given constraint
           SCREAMING_SNAKE_CASE	=		max([c.seqlen for c in constraints] )
           SCREAMING_SNAKE_CASE	=		len(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		False

           self.init_state()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		[]
           SCREAMING_SNAKE_CASE	=		None
           SCREAMING_SNAKE_CASE	=		[constraint.copy(stateful=lowerCamelCase__ ) for constraint in self.constraints]






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		0
           if self.inprogress_constraint:
                 # extra points for having a constraint mid-fulfilled
                 add += self.max_seqlen - self.inprogress_constraint.remaining()

           return (len(self.complete_constraints ) * self.max_seqlen) + add






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		[]
           if self.inprogress_constraint is None:
                 for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
                       SCREAMING_SNAKE_CASE	=		constraint.advance()
                       if isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                             token_list.append(lowerCamelCase__ )
                       elif isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                             token_list.extend(lowerCamelCase__ )
           else:
                 SCREAMING_SNAKE_CASE	=		self.inprogress_constraint.advance()
                 if isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                       token_list.append(lowerCamelCase__ )
                 elif isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                       token_list.extend(lowerCamelCase__ )

           if len(lowerCamelCase__ ) == 0:
                 return None
           else:
                 return token_list






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					Optional[List[int]] )  ->	Union[str, Any]:




           '''simple docstring'''


           self.init_state()

           if token_ids is not None:
                 for token in token_ids:
                       # completes or steps **one** constraint
                       SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.add(lowerCamelCase__ )

                       # the entire list of constraints are fulfilled
                       if self.completed:
                             break






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					int )  ->	Optional[int]:




           '''simple docstring'''


           if not isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                 raise ValueError(F"""`token_id` should be an `int`, but is `{token_id}`.""" )

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		False, False

           if self.completed:
                 SCREAMING_SNAKE_CASE	=		True
                 SCREAMING_SNAKE_CASE	=		False
                 return complete, stepped

           if self.inprogress_constraint is not None:
                 # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
                 # job, simply update the state

                 SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.inprogress_constraint.update(lowerCamelCase__ )
                 if reset:
                       # 1. If the next token breaks the progress, then we must restart.
                       #     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

                       #     But that doesn't mean we self.init_state(), since we only reset the state for this particular
                       #     constraint, not the full list of constraints.

                       self.pending_constraints.append(self.inprogress_constraint.copy(stateful=lowerCamelCase__ ) )
                       SCREAMING_SNAKE_CASE	=		None

                 if complete:
                       # 2. If the next token completes the constraint, move it to completed list, set
                       #     inprogress to None. If there are no pending constraints either, then this full list of constraints
                       #     is complete.

                       self.complete_constraints.append(self.inprogress_constraint )
                       SCREAMING_SNAKE_CASE	=		None

                       if len(self.pending_constraints ) == 0:
                             # we're done!
                             SCREAMING_SNAKE_CASE	=		True

           else:
                 # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
                 # of constraints?

                 for cidx, pending_constraint in enumerate(self.pending_constraints ):
                       if pending_constraint.does_advance(lowerCamelCase__ ):
                             SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		pending_constraint.update(lowerCamelCase__ )

                             if not stepped:
                                   raise Exception(
                                       """`constraint.update(token_id)` is not yielding incremental progress, """
                                       """even though `constraint.does_advance(token_id)` is true.""" )

                             if complete:
                                   self.complete_constraints.append(lowerCamelCase__ )
                                   SCREAMING_SNAKE_CASE	=		None

                             if not complete and stepped:
                                   SCREAMING_SNAKE_CASE	=		pending_constraint

                             if complete or stepped:
                                   # If we made any progress at all, then it's at least not a "pending constraint".

                                   SCREAMING_SNAKE_CASE	=		(
                                       self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
                                   )

                                   if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
                                         # If there's no longer any pending after this and no inprogress either, then we must be
                                         # complete.

                                         SCREAMING_SNAKE_CASE	=		True

                                   break  # prevent accidentally stepping through multiple constraints with just one token.

           return complete, stepped






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					List[Any]=True )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		ConstraintListState(self.constraints )  # we actually never though self.constraints objects
           # throughout this process. So it's at initialization state.

           if stateful:
                 SCREAMING_SNAKE_CASE	=		[
                     constraint.copy(stateful=lowerCamelCase__ ) for constraint in self.complete_constraints
                 ]
                 if self.inprogress_constraint is not None:
                       SCREAMING_SNAKE_CASE	=		self.inprogress_constraint.copy(stateful=lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		[constraint.copy() for constraint in self.pending_constraints]

           return new_state









import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import DecisionTransformerModel
      from transformers.models.decision_transformer.modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      )

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Dict=13		,lowerCamelCase__		:					Tuple=7		,lowerCamelCase__		:					Optional[Any]=6		,lowerCamelCase__		:					List[str]=17		,lowerCamelCase__		:					Union[str, Any]=23		,lowerCamelCase__		:					Tuple=11		,lowerCamelCase__		:					Any=True		,)  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		parent
           SCREAMING_SNAKE_CASE	=		batch_size
           SCREAMING_SNAKE_CASE	=		seq_length
           SCREAMING_SNAKE_CASE	=		act_dim
           SCREAMING_SNAKE_CASE	=		state_dim
           SCREAMING_SNAKE_CASE	=		hidden_size
           SCREAMING_SNAKE_CASE	=		max_length
           SCREAMING_SNAKE_CASE	=		is_training






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
           SCREAMING_SNAKE_CASE	=		floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
           SCREAMING_SNAKE_CASE	=		floats_tensor((self.batch_size, self.seq_length, 1) )
           SCREAMING_SNAKE_CASE	=		floats_tensor((self.batch_size, self.seq_length, 1) )
           SCREAMING_SNAKE_CASE	=		ids_tensor((self.batch_size, self.seq_length)		,vocab_size=1000 )
           SCREAMING_SNAKE_CASE	=		random_attention_mask((self.batch_size, self.seq_length) )

           SCREAMING_SNAKE_CASE	=		self.get_config()

           return (
               config,
               states,
               actions,
               rewards,
               returns_to_go,
               timesteps,
               attention_mask,
           )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           return DecisionTransformerConfig(
               batch_size=self.batch_size		,seq_length=self.seq_length		,act_dim=self.act_dim		,state_dim=self.state_dim		,hidden_size=self.hidden_size		,max_length=self.max_length		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					Any		,lowerCamelCase__		:					Union[str, Any]		,)  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		DecisionTransformerModel(config=lowerCamelCase__ )
           model.to(lowerCamelCase__ )
           model.eval()
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )

           self.parent.assertEqual(result.state_preds.shape		,states.shape )
           self.parent.assertEqual(result.action_preds.shape		,actions.shape )
           self.parent.assertEqual(result.return_preds.shape		,returns_to_go.shape )
           self.parent.assertEqual(
               result.last_hidden_state.shape		,(self.batch_size, self.seq_length * 3, self.hidden_size) )  # seq length *3 as there are 3 modelities: states, returns and actions






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.prepare_config_and_inputs()
           (
               (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      (
               SCREAMING_SNAKE_CASE
           ),      
           )	=		config_and_inputs
           SCREAMING_SNAKE_CASE	=		{
               """states""": states,
               """actions""": actions,
               """rewards""": rewards,
               """returns_to_go""": returns_to_go,
               """timesteps""": timesteps,
               """attention_mask""": attention_mask,
           }
           return config, inputs_dict

@require_torch
class 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Tuple				=    (DecisionTransformerModel,) if is_torch_available() else ()
     __snake_case					:			Dict				=    ()
     __snake_case					:			List[Any]				=    {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

     # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
     __snake_case					:			int				=    False

     # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
     __snake_case					:			Optional[Any]				=    False
     __snake_case					:			Optional[Any]				=    False
     __snake_case					:			int				=    False
     __snake_case					:			str				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			List[Any]				=    False
     __snake_case					:			Optional[int]				=    False
     __snake_case					:			List[Any]				=    False






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		DecisionTransformerModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,hidden_size=37 )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Dict:




           '''simple docstring'''


           self.config_tester.run_common_tests()






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE	=		DecisionTransformerModel.from_pretrained(lowerCamelCase__ )
                 self.assertIsNotNone(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE	=		model_class(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		inspect.signature(model.forward )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 SCREAMING_SNAKE_CASE	=		[*signature.parameters.keys()]

                 SCREAMING_SNAKE_CASE	=		[
                     """states""",
                     """actions""",
                     """rewards""",
                     """returns_to_go""",
                     """timesteps""",
                     """attention_mask""",
                 ]

                 self.assertListEqual(arg_names[: len(lowerCamelCase__ )]		,lowerCamelCase__ )

@require_torch
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		2  # number of steps of autoregressive prediction we will perform
           SCREAMING_SNAKE_CASE	=		10  # defined by the RL environment, may be normalized
           SCREAMING_SNAKE_CASE	=		DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert""" )
           SCREAMING_SNAKE_CASE	=		model.to(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model.config
           torch.manual_seed(0 )
           SCREAMING_SNAKE_CASE	=		torch.randn(1		,1		,config.state_dim ).to(device=lowerCamelCase__		,dtype=torch.floataa )  # env.reset()

           SCREAMING_SNAKE_CASE	=		torch.tensor(
               [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]		,device=lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		torch.tensor(lowerCamelCase__		,device=lowerCamelCase__		,dtype=torch.floataa ).reshape(1		,1		,1 )
           SCREAMING_SNAKE_CASE	=		state
           SCREAMING_SNAKE_CASE	=		torch.zeros(1		,0		,config.act_dim		,device=lowerCamelCase__		,dtype=torch.floataa )
           SCREAMING_SNAKE_CASE	=		torch.zeros(1		,0		,device=lowerCamelCase__		,dtype=torch.floataa )
           SCREAMING_SNAKE_CASE	=		torch.tensor(0		,device=lowerCamelCase__		,dtype=torch.long ).reshape(1		,1 )

           for step in range(lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		torch.cat([actions, torch.zeros(1		,1		,config.act_dim		,device=lowerCamelCase__ )]		,dim=1 )
                 SCREAMING_SNAKE_CASE	=		torch.cat([rewards, torch.zeros(1		,1		,device=lowerCamelCase__ )]		,dim=1 )

                 SCREAMING_SNAKE_CASE	=		torch.ones(1		,states.shape[1] ).to(dtype=torch.long		,device=states.device )

                 with torch.no_grad():
                       SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		model(
                           states=lowerCamelCase__		,actions=lowerCamelCase__		,rewards=lowerCamelCase__		,returns_to_go=lowerCamelCase__		,timesteps=lowerCamelCase__		,attention_mask=lowerCamelCase__		,return_dict=lowerCamelCase__		,)

                 self.assertEqual(action_pred.shape		,actions.shape )
                 self.assertTrue(torch.allclose(action_pred[0, -1]		,expected_outputs[step]		,atol=1e-4 ) )
                 SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		(  # env.step(action)
                     torch.randn(1		,1		,config.state_dim ).to(device=lowerCamelCase__		,dtype=torch.floataa ),
                     1.0,
                     False,
                     {},
                 )

                 SCREAMING_SNAKE_CASE	=		action_pred[0, -1]
                 SCREAMING_SNAKE_CASE	=		torch.cat([states, state]		,dim=1 )
                 SCREAMING_SNAKE_CASE	=		returns_to_go[0, -1] - reward
                 SCREAMING_SNAKE_CASE	=		torch.cat([returns_to_go, pred_return.reshape(1		,1		,1 )]		,dim=1 )
                 SCREAMING_SNAKE_CASE	=		torch.cat(
                     [timesteps, torch.ones((1, 1)		,device=lowerCamelCase__		,dtype=torch.long ) * (step + 1)]		,dim=1 )


