





"""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      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


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

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

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

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]	)		->				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    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       List[str]					,   __a   :       Optional[int]					,   __a   :       str					,   __a   :       Union[str, Any]					,   __a   :       Optional[int]					,   __a   :       str					,   __a   :       Tuple					,   )		->				Tuple:
        _UpperCamelCase      :	Any						=       DecisionTransformerModel(config=__a	)
        model.to(__a	)
        model.eval()
        _UpperCamelCase      :	Tuple						=       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    __SCREAMING_SNAKE_CASE				(		self   :       int	)		->				str:
        _UpperCamelCase      :	Any						=       self.prepare_config_and_inputs()
        (
            (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  (
            _UpperCamelCase
        ),  
        )      :	Union[str, Any]						=       config_and_inputs
        _UpperCamelCase      :	Tuple						=       {
            "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      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase		,  _UpperCamelCase		,  _UpperCamelCase		,  unittest.TestCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Optional[Any]	     =					(DecisionTransformerModel,) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE__							:List[str]	     =					()
  SCREAMING_SNAKE_CASE__							:int	     =					{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Any	)		->				Union[str, Any]:
        _UpperCamelCase      :	List[Any]						=       DecisionTransformerModelTester(self	)
        _UpperCamelCase      :	Any						=       ConfigTester(self					,   config_class=__a					,   hidden_size=37	)

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

  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]	)		->				Tuple:
        _UpperCamelCase      :	List[str]						=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__a	)

  @slow
  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Union[str, Any]:
        for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _UpperCamelCase      :	int						=       DecisionTransformerModel.from_pretrained(__a	)
              self.assertIsNotNone(__a	)


  def    __SCREAMING_SNAKE_CASE				(		self   :       Union[str, Any]	)		->				Union[str, Any]:
        _UpperCamelCase,  _UpperCamelCase      :	Union[str, Any]						=       self.model_tester.prepare_config_and_inputs_for_common()

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

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

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





@require_torch
class      __SCREAMING_SNAKE_CASE    (						unittest.TestCase   ):







  '''simple docstring'''


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

        _UpperCamelCase      :	int						=       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	)

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

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

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

              with torch.no_grad():
                    _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	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	)	)
              _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	Dict						=       (  # env.step(action)
                  torch.randn(1					,   1					,   config.state_dim	).to(device=__a					,   dtype=torch.floataa	),
                  1.0,
                  False,
                  {},
              )

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







"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCamelCase__				  =   {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   ["Mask2FormerImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__				  =   [
      "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "Mask2FormerForUniversalSegmentation",
      "Mask2FormerModel",
      "Mask2FormerPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_maskaformer import MaskaFormerImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskaformer import (
        MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskaFormerForUniversalSegmentation,
        MaskaFormerModel,
        MaskaFormerPreTrainedModel,
    )


else:
  import sys

  lowerCamelCase__				  =   _LazyModule(__name__, globals()["__file__"], _import_structure)
