

'''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        A  :





						def __init__(	self	,    snake_case_	,    snake_case_=1_3	,    snake_case_=7	,    snake_case_=6	,    snake_case_=1_7	,    snake_case_=2_3	,    snake_case_=1_1	,    snake_case_=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 __lowerCAmelCase   (	self			)			->							Union[str, 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=1_0_0_0			)
										_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 __lowerCAmelCase   (	self			)			->							List[str]:
										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 __lowerCAmelCase   (	self	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    snake_case_	,    )			->							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 __lowerCAmelCase   (	self			)			->							Union[str, Any]:
										_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        A  (	__A  ,    __A  ,    __A  ,    unittest.TestCase ):
						__UpperCAmelCase				:	Any   =   (DecisionTransformerModel,) if is_torch_available() else ()
						__UpperCAmelCase				:	Dict   =   ()
						__UpperCAmelCase				:	Dict   =   {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

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





						def __lowerCAmelCase   (	self			)			->							int:
										_a        =   DecisionTransformerModelTester(self			)
										_a        =   ConfigTester(self	,    config_class=__A	,    hidden_size=3_7			)





						def __lowerCAmelCase   (	self			)			->							Optional[Any]:
										self.config_tester.run_common_tests()





						def __lowerCAmelCase   (	self			)			->							Tuple:
										_a        =   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__A			)





						@slow
						def __lowerCAmelCase   (	self			)			->							Optional[int]:
										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_a        =   DecisionTransformerModel.from_pretrained(__A			)
														self.assertIsNotNone(__A			)





						def __lowerCAmelCase   (	self			)			->							Union[str, 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        A  (	unittest.TestCase ):





						@slow
						def __lowerCAmelCase   (	self			)			->							Dict:
										_a        =   2  # number of steps of autoregressive prediction we will perform
										_a        =   1_0  # 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			)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE							    =							{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'WavLMForAudioFrameClassification',
      'WavLMForCTC',
      'WavLMForSequenceClassification',
      'WavLMForXVector',
      'WavLMModel',
      'WavLMPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



