




'''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      ,							UpperCamelCase      ,							UpperCamelCase=13      ,							UpperCamelCase=7      ,							UpperCamelCase=6      ,							UpperCamelCase=17      ,							UpperCamelCase=23      ,							UpperCamelCase=11      ,							UpperCamelCase=True      ,							)				->    Any:
					__a			=					parent
					__a			=					batch_size
					__a			=					seq_length
					__a			=					act_dim
					__a			=					state_dim
					__a			=					hidden_size
					__a			=					max_length
					__a			=					is_training







			def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
					__a			=					floats_tensor((self.batch_size, self.seq_length, self.state_dim)      )
					__a			=					floats_tensor((self.batch_size, self.seq_length, self.act_dim)      )
					__a			=					floats_tensor((self.batch_size, self.seq_length, 1)      )
					__a			=					floats_tensor((self.batch_size, self.seq_length, 1)      )
					__a			=					ids_tensor((self.batch_size, self.seq_length)      ,							vocab_size=1000      )
					__a			=					random_attention_mask((self.batch_size, self.seq_length)      )

					__a			=					self.get_config()

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







			def 		UpperCamelCase__			(	self      )				->    Optional[int]:
					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      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							)				->    Any:
					__a			=					DecisionTransformerModel(config=__A      )
					model.to(__A      )
					model.eval()
					__a			=					model(__A      ,							__A      ,							__A      ,							__A      ,							__A      ,							__A      )

					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      )				->    Tuple:
					__a			=					self.prepare_config_and_inputs()
					(
					    (
					    __a
					)	,       (
					    __a
					)	,       (
					    __a
					)	,       (
					    __a
					)	,       (
					    __a
					)	,       (
					    __a
					)	,       (
					    __a
					)	,       
					)			=					config_and_inputs
					__a			=					{
					    '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						(  __magic_name__       ,							__magic_name__       ,							__magic_name__       ,							unittest.TestCase ):
			_a							=      (DecisionTransformerModel,) if is_torch_available() else ()
			_a							=      ()
			_a							=      {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

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







			def 		UpperCamelCase__			(	self      )				->    Optional[int]:
					__a			=					DecisionTransformerModelTester(self      )
					__a			=					ConfigTester(self      ,							config_class=__A      ,							hidden_size=37      )







			def 		UpperCamelCase__			(	self      )				->    Dict:
					self.config_tester.run_common_tests()







			def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
					__a			=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__A      )







			@slow
			def 		UpperCamelCase__			(	self      )				->    str:
					for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__a			=					DecisionTransformerModel.from_pretrained(__A      )
							self.assertIsNotNone(__A      )







			def 		UpperCamelCase__			(	self      )				->    List[Any]:
					__a	,       __a			=					self.model_tester.prepare_config_and_inputs_for_common()

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

							__a			=					[
							    'states',
							    'actions',
							    'rewards',
							    'returns_to_go',
							    'timesteps',
							    'attention_mask',
							]

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





@require_torch
class    __lowercase						(  unittest.TestCase ):







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

					__a			=					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=__A      )

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

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

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

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

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

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









'''simple docstring'''


import os
from datetime import datetime as dt

from github import Github


UpperCAmelCase_ 		=      [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "enhancement",
    "new pipeline/model",
    "new scheduler",
    "wip",
]


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					Github(os.environ['GITHUB_TOKEN']			)
  __a			=					g.get_repo('huggingface/diffusers'			)
  __a			=					repo.get_issues(state='open'			)

  for issue in open_issues:
    __a			=					sorted(issue.get_comments()				,					key=lambda a_			: i.created_at				,					reverse=a_			)
    __a			=					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()			)
    ):
      # Closes the issue after 7 days of inactivity since the Stalebot notification.
      issue.edit(state='closed'			)
    elif (
        "stale" in issue.get_labels()
        and last_comment is not None
        and last_comment.user.login != "github-actions[bot]"
    ):
      # Opens the issue if someone other than Stalebot commented.
      issue.edit(state='open'			)
      issue.remove_from_labels('stale'			)
    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()			)
    ):
      # Post a Stalebot notification after 23 days of inactivity.
      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/diffusers/blob/main/CONTRIBUTING.md) '
          'are likely to be ignored.'			)
      issue.add_to_labels('stale'			)


if __name__ == "__main__":
  main()



