



'''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 	lowerCAmelCase_:
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase=13  ,__UpperCAmelCase=7  ,__UpperCAmelCase=6  ,__UpperCAmelCase=17  ,__UpperCAmelCase=23  ,__UpperCAmelCase=11  ,__UpperCAmelCase=True  ,)   ->		int:
      lowerCAmelCase__			:  int											= parent
      lowerCAmelCase__			:  List[Any]											= batch_size
      lowerCAmelCase__			:  Dict											= seq_length
      lowerCAmelCase__			:  Dict											= act_dim
      lowerCAmelCase__			:  int											= state_dim
      lowerCAmelCase__			:  Dict											= hidden_size
      lowerCAmelCase__			:  List[str]											= max_length
      lowerCAmelCase__			:  Union[str, Any]											= is_training




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  List[str]											= floats_tensor((self.batch_size, self.seq_length, self.state_dim)				)
      lowerCAmelCase__			:  Optional[int]											= floats_tensor((self.batch_size, self.seq_length, self.act_dim)				)
      lowerCAmelCase__			:  Optional[Any]											= floats_tensor((self.batch_size, self.seq_length, 1)				)
      lowerCAmelCase__			:  Tuple											= floats_tensor((self.batch_size, self.seq_length, 1)				)
      lowerCAmelCase__			:  Dict											= ids_tensor((self.batch_size, self.seq_length)  ,vocab_size=1000				)
      lowerCAmelCase__			:  Optional[int]											= random_attention_mask((self.batch_size, self.seq_length)				)

      lowerCAmelCase__			:  Optional[Any]											= self.get_config()

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




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      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  ,)   ->		Tuple:
      lowerCAmelCase__			:  Optional[int]											= DecisionTransformerModel(config=snake_case_				)
      model.to(snake_case_				)
      model.eval()
      lowerCAmelCase__			:  List[Any]											= model(snake_case_  ,snake_case_  ,snake_case_  ,snake_case_  ,snake_case_  ,snake_case_				)

      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				)   ->		Optional[int]:
      lowerCAmelCase__			:  Union[str, Any]											= self.prepare_config_and_inputs()
      (
          lowerCAmelCase__    
      )			:  List[Any]											= config_and_inputs
      lowerCAmelCase__			:  Union[str, Any]											= {
          """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 	lowerCAmelCase_(    _a						,			_a						,			_a						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Any           =				(DecisionTransformerModel,) if is_torch_available() else ()
  __lowercase							:				Dict           =				()
  __lowercase							:				Dict           =				{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

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




  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  Union[str, Any]											= DecisionTransformerModelTester(self				)
      lowerCAmelCase__			:  Tuple											= ConfigTester(self  ,config_class=snake_case_  ,hidden_size=37				)




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




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  Tuple											= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		int:
      for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowerCAmelCase__			:  Tuple											= DecisionTransformerModel.from_pretrained(snake_case_				)
          self.assertIsNotNone(snake_case_				)






  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  Tuple											= self.model_tester.prepare_config_and_inputs_for_common()

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

          lowerCAmelCase__			:  Union[str, Any]											= [
              """states""",
              """actions""",
              """rewards""",
              """returns_to_go""",
              """timesteps""",
              """attention_mask""",
          ]

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







@require_torch
class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




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

      lowerCAmelCase__			:  List[str]											= torch.tensor(
          [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]  ,device=snake_case_				)

      lowerCAmelCase__			:  Optional[Any]											= torch.tensor(snake_case_  ,device=snake_case_  ,dtype=torch.floataa				).reshape(1  ,1  ,1				)
      lowerCAmelCase__			:  Union[str, Any]											= state
      lowerCAmelCase__			:  List[str]											= torch.zeros(1  ,0  ,config.act_dim  ,device=snake_case_  ,dtype=torch.floataa				)
      lowerCAmelCase__			:  List[Any]											= torch.zeros(1  ,0  ,device=snake_case_  ,dtype=torch.floataa				)
      lowerCAmelCase__			:  List[str]											= torch.tensor(0  ,device=snake_case_  ,dtype=torch.long				).reshape(1  ,1				)

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

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

          with torch.no_grad():
              lowerCAmelCase__			:  str											= model(
                  states=snake_case_  ,actions=snake_case_  ,rewards=snake_case_  ,returns_to_go=snake_case_  ,timesteps=snake_case_  ,attention_mask=snake_case_  ,return_dict=snake_case_  ,)

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

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







'''simple docstring'''





from PIL import Image
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__				,       lowerCAmelCase__			:  Dict											= image.size
    lowerCAmelCase__			:  int											= 0
    lowerCAmelCase__			:  int											= image.load()
    for i in range(UpperCamelCase      ):
        for j in range(UpperCamelCase      ):
            lowerCAmelCase__			:  int											= pixels[j, i]
            mean += pixel
    mean //= width * height

    for j in range(UpperCamelCase      ):
        for i in range(UpperCamelCase      ):
            lowerCAmelCase__			:  Dict											= 255 if pixels[i, j] > mean else 0
    return image


if __name__ == "__main__":
 _lowerCAmelCase										=     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
 image.save('''output_image_path''')


