
"""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 			__magic_name__       :
       def __init__(     self ,				A_ ,				A_=13 ,				A_=7 ,				A_=6 ,				A_=17 ,				A_=23 ,				A_=11 ,				A_=True ,				)     ->	List[str]:





         """simple docstring"""




         _lowercase:			Optional[int]       =     parent
         _lowercase:			str       =     batch_size
         _lowercase:			Union[str, Any]       =     seq_length
         _lowercase:			List[str]       =     act_dim
         _lowercase:			List[str]       =     state_dim
         _lowercase:			Tuple       =     hidden_size
         _lowercase:			List[Any]       =     max_length
         _lowercase:			List[str]       =     is_training
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			List[Any]       =     floats_tensor((self.batch_size, self.seq_length, self.state_dim)       )
         _lowercase:			List[Any]       =     floats_tensor((self.batch_size, self.seq_length, self.act_dim)       )
         _lowercase:			Union[str, Any]       =     floats_tensor((self.batch_size, self.seq_length, 1)       )
         _lowercase:			List[Any]       =     floats_tensor((self.batch_size, self.seq_length, 1)       )
         _lowercase:			int       =     ids_tensor((self.batch_size, self.seq_length) ,				vocab_size=1000       )
         _lowercase:			Any       =     random_attention_mask((self.batch_size, self.seq_length)       )

         _lowercase:			List[Any]       =     self.get_config()

         return (
             config,
             states,
             actions,
             rewards,
             returns_to_go,
             timesteps,
             attention_mask,
         )
       def 							lowercase_							(     self       )     ->	List[Any]:





         """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 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				)     ->	Tuple:





         """simple docstring"""




         _lowercase:			Dict       =     DecisionTransformerModel(config=A_       )
         model.to(A_       )
         model.eval()
         _lowercase:			List[Any]       =     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 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			Any       =     self.prepare_config_and_inputs()
         (
             (
             _lowercase					
         ) ,				(
             _lowercase					
         ) ,				(
             _lowercase					
         ) ,				(
             _lowercase					
         ) ,				(
             _lowercase					
         ) ,				(
             _lowercase					
         ) ,				(
             _lowercase					
         ) ,									
         ):			List[str]       =     config_and_inputs
         _lowercase:			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 			__magic_name__       (       lowercase_   ,					lowercase_   ,					lowercase_   ,					unittest.TestCase ):
       UpperCamelCase_   =		(DecisionTransformerModel,) if is_torch_available() else ()
       UpperCamelCase_   =		()
       UpperCamelCase_   =		{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

       # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       UpperCamelCase_   =		False
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     DecisionTransformerModelTester(self       )
         _lowercase:			Dict       =     ConfigTester(self ,				config_class=A_ ,				hidden_size=37       )
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         self.config_tester.run_common_tests()
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			int       =     self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_       )
       @slow
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _lowercase:			Optional[Any]       =     DecisionTransformerModel.from_pretrained(A_       )
           self.assertIsNotNone(A_       )



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase ,				_lowercase:			Tuple       =     self.model_tester.prepare_config_and_inputs_for_common()

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

           _lowercase:			Optional[int]       =     [
               '''states''',
               '''actions''',
               '''rewards''',
               '''returns_to_go''',
               '''timesteps''',
               '''attention_mask''',
           ]

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


@require_torch
class 			__magic_name__       (       unittest.TestCase ):
       @slow
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			Any       =     2  # number of steps of autoregressive prediction we will perform
         _lowercase:			Tuple       =     10  # defined by the RL environment, may be normalized
         _lowercase:			List[str]       =     DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''       )
         _lowercase:			Any       =     model.to(A_       )
         _lowercase:			int       =     model.config
         torch.manual_seed(0       )
         _lowercase:			Optional[Any]       =     torch.randn(1 ,				1 ,				config.state_dim       ).to(device=A_ ,				dtype=torch.floataa       )  # env.reset()

         _lowercase:			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=A_       )

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

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

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

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

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




"""simple docstring"""




import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


A__   :					Dict							=   logging.get_logger(__name__)

A__   :					int							=   {
    'Intel/dpt-large': 'https://huggingface.co/Intel/dpt-large/resolve/main/config.json',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		'''dpt'''
       def __init__(     self ,				A_=768 ,				A_=12 ,				A_=12 ,				A_=3072 ,				A_="gelu" ,				A_=0.0 ,				A_=0.0 ,				A_=0.02 ,				A_=1E-12 ,				A_=384 ,				A_=16 ,				A_=3 ,				A_=False ,				A_=True ,				A_=[2, 5, 8, 11] ,				A_="project" ,				A_=[4, 2, 1, 0.5] ,				A_=[96, 192, 384, 768] ,				A_=256 ,				A_=-1 ,				A_=False ,				A_=True ,				A_=0.4 ,				A_=255 ,				A_=0.1 ,				A_=[1, 1024, 24, 24] ,				A_=[0, 1] ,				A_=None ,				**A_ ,				)     ->	int:





         """simple docstring"""




         super().__init__(**A_       )

         _lowercase:			Union[str, Any]       =     hidden_size
         _lowercase:			str       =     is_hybrid

         if self.is_hybrid:
           if backbone_config is None:
             logger.info('''Initializing the config with a `BiT` backbone.'''       )
             _lowercase:			Dict       =     {
                 '''global_padding''': '''same''',
                 '''layer_type''': '''bottleneck''',
                 '''depths''': [3, 4, 9],
                 '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
                 '''embedding_dynamic_padding''': True,
             }
             _lowercase:			List[str]       =     BitConfig(**A_       )
           elif isinstance(A_ ,				A_       ):
             logger.info('''Initializing the config with a `BiT` backbone.'''       )
             _lowercase:			Dict       =     BitConfig(**A_       )
           elif isinstance(A_ ,				A_       ):
             _lowercase:			str       =     backbone_config
           else:
             raise ValueError(
                 f'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''       )

           _lowercase:			Dict       =     backbone_featmap_shape
           _lowercase:			Optional[int]       =     neck_ignore_stages

           if readout_type != "project":
             raise ValueError('''Readout type must be \'project\' when using `DPT-hybrid` mode.'''       )
         else:
           _lowercase:			Any       =     None
           _lowercase:			str       =     None
           _lowercase:			Optional[Any]       =     []

         _lowercase:			Tuple       =     num_hidden_layers
         _lowercase:			Optional[int]       =     num_attention_heads
         _lowercase:			Any       =     intermediate_size
         _lowercase:			List[Any]       =     hidden_act
         _lowercase:			Union[str, Any]       =     hidden_dropout_prob
         _lowercase:			Tuple       =     attention_probs_dropout_prob
         _lowercase:			Dict       =     initializer_range
         _lowercase:			Dict       =     layer_norm_eps
         _lowercase:			Any       =     image_size
         _lowercase:			Optional[int]       =     patch_size
         _lowercase:			Optional[int]       =     num_channels
         _lowercase:			int       =     qkv_bias
         _lowercase:			List[str]       =     backbone_out_indices
         if readout_type not in ["ignore", "add", "project"]:
           raise ValueError('''Readout_type must be one of [\'ignore\', \'add\', \'project\']'''       )
         _lowercase:			Union[str, Any]       =     readout_type
         _lowercase:			Optional[Any]       =     reassemble_factors
         _lowercase:			List[str]       =     neck_hidden_sizes
         _lowercase:			Tuple       =     fusion_hidden_size
         _lowercase:			int       =     head_in_index
         _lowercase:			Optional[int]       =     use_batch_norm_in_fusion_residual
         # auxiliary head attributes (semantic segmentation)
         _lowercase:			int       =     use_auxiliary_head
         _lowercase:			Dict       =     auxiliary_loss_weight
         _lowercase:			List[Any]       =     semantic_loss_ignore_index
         _lowercase:			Dict       =     semantic_classifier_dropout



       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Optional[int]       =     copy.deepcopy(self.__dict__       )

         if output["backbone_config"] is not None:
           _lowercase:			List[str]       =     self.backbone_config.to_dict()

         _lowercase:			List[Any]       =     self.__class__.model_type
         return output
