





"""simple docstring"""





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 		lowercase					:





      def __init__(       self      ,						lowercase      ,						lowercase=13      ,						lowercase=7      ,						lowercase=6      ,						lowercase=17      ,						lowercase=23      ,						lowercase=11      ,						lowercase=True      ,						)		->     Any:
            lowerCAmelCase  						=       parent
            lowerCAmelCase  						=       batch_size
            lowerCAmelCase  						=       seq_length
            lowerCAmelCase  						=       act_dim
            lowerCAmelCase  						=       state_dim
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       max_length
            lowerCAmelCase  						=       is_training





      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
            lowerCAmelCase  						=       floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
            lowerCAmelCase  						=       floats_tensor((self.batch_size, self.seq_length, 1) )
            lowerCAmelCase  						=       floats_tensor((self.batch_size, self.seq_length, 1) )
            lowerCAmelCase  						=       ids_tensor((self.batch_size, self.seq_length)      ,						vocab_size=1_000 )
            lowerCAmelCase  						=       random_attention_mask((self.batch_size, self.seq_length) )

            lowerCAmelCase  						=       self.get_config()

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





      def     _snake_case			(       self )		->     List[str]:
            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     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     int:
            lowerCAmelCase  						=       DecisionTransformerModel(config=lowercase )
            model.to(lowercase )
            model.eval()
            lowerCAmelCase  						=       model(lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase )

            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     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       self.prepare_config_and_inputs()
            (
                (
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		
            )  						=       config_and_inputs
            lowerCAmelCase  						=       {
                """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 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase     ,  _UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      (DecisionTransformerModel,) if is_torch_available() else ()
      _SCREAMING_SNAKE_CASE					=      ()
      _SCREAMING_SNAKE_CASE					=      {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

      # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      False





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       DecisionTransformerModelTester(self )
            lowerCAmelCase  						=       ConfigTester(self      ,						config_class=lowercase      ,						hidden_size=37 )





      def     _snake_case			(       self )		->     int:
            self.config_tester.run_common_tests()





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase )





      @slow
      def     _snake_case			(       self )		->     str:
            for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase  						=       DecisionTransformerModel.from_pretrained(lowercase )
                  self.assertIsNotNone(lowercase )





      def     _snake_case			(       self )		->     Optional[Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()

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

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

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







@require_torch
class 		lowercase					(					unittest.TestCase					):





      @slow
      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       2  # number of steps of autoregressive prediction we will perform
            lowerCAmelCase  						=       10  # defined by the RL environment, may be normalized
            lowerCAmelCase  						=       DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert""" )
            lowerCAmelCase  						=       model.to(lowercase )
            lowerCAmelCase  						=       model.config
            torch.manual_seed(0 )
            lowerCAmelCase  						=       torch.randn(1      ,						1      ,						config.state_dim ).to(device=lowercase      ,						dtype=torch.floataa )  # env.reset()

            lowerCAmelCase  						=       torch.tensor(
                [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]      ,						device=lowercase )

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

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

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

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

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

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










'''simple docstring'''

from __future__ import annotations

from typing import Generic, TypeVar

a_  :				List[str]						= TypeVar("""T""")





class      snake_case    (    Generic[T]			):





  """simple docstring"""




  def __init__(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  data
   lowerCamelCase_      =  self
   lowerCamelCase_      =  0





class      snake_case    (    Generic[T]			):





  """simple docstring"""




  def __init__(    self     ):







   """simple docstring"""
   # map from node name to the node object
   lowerCamelCase_      =  {}






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # create a new set with x as its member
   lowerCamelCase_      =  DisjointSetTreeNode(UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # find the set x belongs to (with path-compression)
   lowerCamelCase_      =  self.map[data]
   if elem_ref != elem_ref.parent:
    lowerCamelCase_      =  self.find_set(elem_ref.parent.data     )
   return elem_ref.parent






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   # helper function for union operation
   if nodea.rank > nodea.rank:
    lowerCamelCase_      =  nodea
   else:
    lowerCamelCase_      =  nodea
    if nodea.rank == nodea.rank:
     nodea.rank += 1







  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   # merge 2 disjoint sets
   self.link(self.find_set(UpperCamelCase     )			,  self.find_set(UpperCamelCase     )     )





class      snake_case    (    Generic[T]			):





  """simple docstring"""




  def __init__(    self     ):







   """simple docstring"""
   # connections: map from the node to the neighbouring nodes (with weights)
   lowerCamelCase_      =  {}






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   # add a node ONLY if its not present in the graph
   if node not in self.connections:
    lowerCamelCase_      =  {}






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   # add an edge with the given weight
   self.add_node(UpperCamelCase     )
   self.add_node(UpperCamelCase     )
   lowerCamelCase_      =  weight
   lowerCamelCase_      =  weight







  def   snake_case		(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  []
   lowerCamelCase_      =  set()
   for start in self.connections:
    for end in self.connections[start]:
     if (start, end) not in seen:
      seen.add((end, start)     )
      edges.append((start, end, self.connections[start][end])     )
   edges.sort(key=lambda UpperCamelCase     : x[2]     )

   # creating the disjoint set
   lowerCamelCase_      =  DisjointSetTree[T]()
   for node in self.connections:
    disjoint_set.make_set(UpperCamelCase     )

   # MST generation
   lowerCamelCase_      =  0
   lowerCamelCase_      =  0
   lowerCamelCase_      =  GraphUndirectedWeighted[T]()
   while num_edges < len(self.connections     ) - 1:
    lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_      =  edges[index]
    index += 1
    lowerCamelCase_      =  disjoint_set.find_set(UpperCamelCase     )
    lowerCamelCase_      =  disjoint_set.find_set(UpperCamelCase     )
    if parent_u != parent_v:
     num_edges += 1
     graph.add_edge(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )
     disjoint_set.union(UpperCamelCase			,  UpperCamelCase     )
   return graph



