




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				:






       def __init__(      self:    Optional[int]				,    _lowerCAmelCase:    Union[str, Any]				,    _lowerCAmelCase:    str=13				,    _lowerCAmelCase:    str=7				,    _lowerCAmelCase:    List[str]=6				,    _lowerCAmelCase:    List[Any]=17				,    _lowerCAmelCase:    Any=23				,    _lowerCAmelCase:    str=11				,    _lowerCAmelCase:    List[str]=True				,    ):
         lowercase			:Union[str, Any]							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:List[str]							=  seq_length
         lowercase			:Any							=  act_dim
         lowercase			:int							=  state_dim
         lowercase			:Tuple							=  hidden_size
         lowercase			:Optional[int]							=  max_length
         lowercase			:List[str]							=  is_training






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase			:Dict							=  floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
         lowercase			:Dict							=  floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
         lowercase			:Dict							=  floats_tensor((self.batch_size, self.seq_length, 1) )
         lowercase			:Optional[Any]							=  floats_tensor((self.batch_size, self.seq_length, 1) )
         lowercase			:str							=  ids_tensor((self.batch_size, self.seq_length)				,    vocab_size=10_00 )
         lowercase			:Tuple							=  random_attention_mask((self.batch_size, self.seq_length) )

         lowercase			:Tuple							=  self.get_config()

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






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, 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     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    Any				,    _lowerCAmelCase:    List[str]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    Union[str, Any]				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    Optional[int]				,    _lowerCAmelCase:    List[Any]				,    ):
         lowercase			:List[str]							=  DecisionTransformerModel(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:List[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     SCREAMING_SNAKE_CASE			(      self:    Optional[Any] ):
         lowercase			:Tuple							=  self.prepare_config_and_inputs()
         (
             (
             lowercase
         )		,			(
             lowercase
         )		,			(
             lowercase
         )		,			(
             lowercase
         )		,			(
             lowercase
         )		,			(
             lowercase
         )		,			(
             lowercase
         )		,			
         )			:Tuple							=  config_and_inputs
         lowercase			:List[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				(							lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase					,       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     SCREAMING_SNAKE_CASE			(      self:    List[Any] ):
         lowercase			:List[Any]							=  DecisionTransformerModelTester(self )
         lowercase			:Optional[int]							=  ConfigTester(self				,    config_class=_lowerCAmelCase				,    hidden_size=37 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         self.config_tester.run_common_tests()






       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         lowercase			:str							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase )






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowercase			:Optional[int]							=  DecisionTransformerModel.from_pretrained(_lowerCAmelCase )
           self.assertIsNotNone(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase		,			lowercase			:Union[str, Any]							=  self.model_tester.prepare_config_and_inputs_for_common()

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

           lowercase			:int							=  [
               "states",
               "actions",
               "rewards",
               "returns_to_go",
               "timesteps",
               "attention_mask",
           ]

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







@require_torch
class       __lowerCAmelCase				(							unittest.TestCase):






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

         lowercase			:List[Any]							=  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 )

         lowercase			:Dict							=  torch.tensor(_lowerCAmelCase				,    device=_lowerCAmelCase				,    dtype=torch.floataa ).reshape(1				,    1				,    1 )
         lowercase			:Union[str, Any]							=  state
         lowercase			:Optional[int]							=  torch.zeros(1				,    0				,    config.act_dim				,    device=_lowerCAmelCase				,    dtype=torch.floataa )
         lowercase			:Union[str, Any]							=  torch.zeros(1				,    0				,    device=_lowerCAmelCase				,    dtype=torch.floataa )
         lowercase			:Optional[int]							=  torch.tensor(0				,    device=_lowerCAmelCase				,    dtype=torch.long ).reshape(1				,    1 )

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

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

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

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_UpperCAmelCase		:  int	=   {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  List[str]	=   [
         "SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "SwinForImageClassification",
         "SwinForMaskedImageModeling",
         "SwinModel",
         "SwinPreTrainedModel",
         "SwinBackbone",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  int	=   [
         "TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFSwinForImageClassification",
         "TFSwinForMaskedImageModeling",
         "TFSwinModel",
         "TFSwinPreTrainedModel",
     ]

if TYPE_CHECKING:
     from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_swin import (
              SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              SwinBackbone,
              SwinForImageClassification,
              SwinForMaskedImageModeling,
              SwinModel,
              SwinPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_swin import (
              TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
              TFSwinModel,
              TFSwinPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase		:  int	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



