






'''simple docstring'''

from datetime import datetime as dt
import os

from github import Github


UpperCamelCase      					=[
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]



def 				snake_case       (  )							->					str:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				Github(os.environ["""GITHUB_TOKEN"""]						)
      UpperCamelCase_					: Union[str, Any]            =				g.get_repo("""huggingface/transformers"""						)
      UpperCamelCase_					: Tuple            =				repo.get_issues(state="""open"""						)

      for issue in open_issues:
            UpperCamelCase_					: str            =				sorted([comment for comment in issue.get_comments()]     , key=lambda a_						: i.created_at     , reverse=a_						)
            UpperCamelCase_					: List[str]            =				comments[0] if len(a_						) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()						)
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state="""closed"""						)
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()						)
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  issue.create_comment(
                      """This issue has been automatically marked as stale because it has not had """
                      """recent activity. If you think this still needs to be addressed """
                      """please comment on this thread.\n\nPlease note that issues that do not follow the """
                      """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
                      """are likely to be ignored."""						)


if __name__ == "__main__":
      main()














'''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      A     :






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=13      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=6      ,  __lowerCAmelCase=17      ,  __lowerCAmelCase=23      ,  __lowerCAmelCase=11      ,  __lowerCAmelCase=True      ,  ):
       UpperCamelCase_					: Tuple            =				parent
       UpperCamelCase_					: List[Any]            =				batch_size
       UpperCamelCase_					: List[str]            =				seq_length
       UpperCamelCase_					: Dict            =				act_dim
       UpperCamelCase_					: Optional[int]            =				state_dim
       UpperCamelCase_					: Dict            =				hidden_size
       UpperCamelCase_					: str            =				max_length
       UpperCamelCase_					: int            =				is_training
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[str]            =				floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
       UpperCamelCase_					: Dict            =				floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
       UpperCamelCase_					: Union[str, Any]            =				floats_tensor((self.batch_size, self.seq_length, 1)			)
       UpperCamelCase_					: List[str]            =				floats_tensor((self.batch_size, self.seq_length, 1)			)
       UpperCamelCase_					: Optional[Any]            =				ids_tensor((self.batch_size, self.seq_length)      ,  vocab_size=10_00			)
       UpperCamelCase_					: Optional[Any]            =				random_attention_mask((self.batch_size, self.seq_length)			)

       UpperCamelCase_					: Tuple            =				self.get_config()

       return (
           config,
           states,
           actions,
           rewards,
           returns_to_go,
           timesteps,
           attention_mask,
       )
 def 	_UpperCAmelCase							(				self			):
       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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  ):
       UpperCamelCase_					: Tuple            =				DecisionTransformerModel(config=__lowerCAmelCase			)
       model.to(__lowerCAmelCase			)
       model.eval()
       UpperCamelCase_					: str            =				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 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      
       )					: str            =				config_and_inputs
       UpperCamelCase_					: int            =				{
           """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      A     (   SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    Tuple			  =    (DecisionTransformerModel,) if is_torch_available() else ()
 __a      :    Union[str, Any]			  =    ()
 __a      :    int			  =    {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

 # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
 __a      :    Optional[Any]			  =    False

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __a      :    int			  =    False
 __a      :    Tuple			  =    False
 __a      :    Dict			  =    False
 __a      :    Union[str, Any]			  =    False
 __a      :    int			  =    False
 __a      :    List[Any]			  =    False
 __a      :    List[str]			  =    False
 __a      :    Dict			  =    False
 __a      :    Optional[Any]			  =    False
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				DecisionTransformerModelTester(self			)
       UpperCamelCase_					: Dict            =				ConfigTester(self      ,  config_class=__lowerCAmelCase      ,  hidden_size=37			)
 def 	_UpperCAmelCase							(				self			):
       self.config_tester.run_common_tests()
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[int]            =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCAmelCase			)
 @slow
 def 	_UpperCAmelCase							(				self			):
       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_					: Union[str, Any]            =				DecisionTransformerModel.from_pretrained(__lowerCAmelCase			)
             self.assertIsNotNone(__lowerCAmelCase			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_     ,      UpperCamelCase_					: str            =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCamelCase_					: Union[str, Any]            =				model_class(__lowerCAmelCase			)
             UpperCamelCase_					: Optional[int]            =				inspect.signature(model.forward			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase_					: Any            =				[*signature.parameters.keys()]

             UpperCamelCase_					: List[str]            =				[
                 """states""",
                 """actions""",
                 """rewards""",
                 """returns_to_go""",
                 """timesteps""",
                 """attention_mask""",
             ]

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






@require_torch
class      A     (   unittest.TestCase    ):






 """simple docstring"""





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

       UpperCamelCase_					: List[str]            =				torch.tensor(
           [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]      ,  device=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				torch.tensor(__lowerCAmelCase      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			).reshape(1      ,  1      ,  1			)
       UpperCamelCase_					: Optional[int]            =				state
       UpperCamelCase_					: int            =				torch.zeros(1      ,  0      ,  config.act_dim      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			)
       UpperCamelCase_					: Optional[int]            =				torch.zeros(1      ,  0      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			)
       UpperCamelCase_					: Optional[Any]            =				torch.tensor(0      ,  device=__lowerCAmelCase      ,  dtype=torch.long			).reshape(1      ,  1			)

       for step in range(__lowerCAmelCase			):
             UpperCamelCase_					: List[Any]            =				torch.cat([actions, torch.zeros(1      ,  1      ,  config.act_dim      ,  device=__lowerCAmelCase			)]      ,  dim=1			)
             UpperCamelCase_					: Optional[int]            =				torch.cat([rewards, torch.zeros(1      ,  1      ,  device=__lowerCAmelCase			)]      ,  dim=1			)

             UpperCamelCase_					: Union[str, Any]            =				torch.ones(1      ,  states.shape[1]			).to(dtype=torch.long      ,  device=states.device			)

             with torch.no_grad():
                   UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				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			)			)
             UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Union[str, Any]            =				(  # env.step(action)
                 torch.randn(1      ,  1      ,  config.state_dim			).to(device=__lowerCAmelCase      ,  dtype=torch.floataa			),
                 1.0,
                 False,
                 {},
             )

             UpperCamelCase_					: Any            =				action_pred[0, -1]
             UpperCamelCase_					: int            =				torch.cat([states, state]      ,  dim=1			)
             UpperCamelCase_					: str            =				returns_to_go[0, -1] - reward
             UpperCamelCase_					: int            =				torch.cat([returns_to_go, pred_return.reshape(1      ,  1      ,  1			)]      ,  dim=1			)
             UpperCamelCase_					: int            =				torch.cat(
                 [timesteps, torch.ones((1, 1)      ,  device=__lowerCAmelCase      ,  dtype=torch.long			) * (step + 1)]      ,  dim=1			)






