
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 	UpperCAmelCase_ :
	"""simple docstring"""

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







	def       SCREAMING_SNAKE_CASE__     (     self:     Union[str, Any]			):
								_lowerCAmelCase :Union[str, Any]      	=						floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
								_lowerCAmelCase :Tuple      	=						floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
								_lowerCAmelCase :Any      	=						floats_tensor((self.batch_size, self.seq_length, 1)			)
								_lowerCAmelCase :List[Any]      	=						floats_tensor((self.batch_size, self.seq_length, 1)			)
								_lowerCAmelCase :str      	=						ids_tensor((self.batch_size, self.seq_length)							,   vocab_size=1000			)
								_lowerCAmelCase :Any      	=						random_attention_mask((self.batch_size, self.seq_length)			)

								_lowerCAmelCase :int      	=						self.get_config()

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







	def       SCREAMING_SNAKE_CASE__     (     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       SCREAMING_SNAKE_CASE__     (     self:     Any							,   _UpperCAmelCase:     int							,   _UpperCAmelCase:     Optional[Any]							,   _UpperCAmelCase:     str							,   _UpperCAmelCase:     Dict							,   _UpperCAmelCase:     List[Any]							,   _UpperCAmelCase:     int							,   _UpperCAmelCase:     str							,   ):
								_lowerCAmelCase :Any      	=						DecisionTransformerModel(config=__a			)
								model.to(__a			)
								model.eval()
								_lowerCAmelCase :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:     List[Any]			):
								_lowerCAmelCase :Optional[Any]      	=						self.prepare_config_and_inputs()
								(
								    _lowerCAmelCase		
								) :int      	=						config_and_inputs
								_lowerCAmelCase :int      	=						{
								    """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 	UpperCAmelCase_ (a__			,  a__			,  a__			,  unittest.TestCase							):
	"""simple docstring"""

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

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

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







	def       SCREAMING_SNAKE_CASE__     (     self:     List[str]			):
								_lowerCAmelCase :int      	=						DecisionTransformerModelTester(self			)
								_lowerCAmelCase :int      	=						ConfigTester(self							,   config_class=__a							,   hidden_size=37			)







	def       SCREAMING_SNAKE_CASE__     (     self:     List[str]			):
								self.config_tester.run_common_tests()







	def       SCREAMING_SNAKE_CASE__     (     self:     List[str]			):
								_lowerCAmelCase :Optional[int]      	=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a			)







	@slow
	def       SCREAMING_SNAKE_CASE__     (     self:     List[str]			):
								for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															_lowerCAmelCase :Union[str, Any]      	=						DecisionTransformerModel.from_pretrained(__a			)
															self.assertIsNotNone(__a			)







	def       SCREAMING_SNAKE_CASE__     (     self:     Tuple			):
								_lowerCAmelCase :Dict      	=						self.model_tester.prepare_config_and_inputs_for_common()

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

															_lowerCAmelCase :Optional[Any]      	=						[
															    """states""",
															    """actions""",
															    """rewards""",
															    """returns_to_go""",
															    """timesteps""",
															    """attention_mask""",
															]

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





@require_torch
class 	UpperCAmelCase_ (unittest.TestCase							):
	"""simple docstring"""

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

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

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

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

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

															_lowerCAmelCase :Dict      	=						action_pred[0, -1]
															_lowerCAmelCase :Optional[int]      	=						torch.cat([states, state]							,   dim=1			)
															_lowerCAmelCase :Tuple      	=						returns_to_go[0, -1] - reward
															_lowerCAmelCase :Tuple      	=						torch.cat([returns_to_go, pred_return.reshape(1							,   1							,   1			)]							,   dim=1			)
															_lowerCAmelCase :List[str]      	=						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


lowercase      :   Optional[int]      =       {
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase      :   int      =       [
          """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PegasusXForConditionalGeneration""",
          """PegasusXModel""",
          """PegasusXPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_pegasus_x import (
                PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
                PegasusXForConditionalGeneration,
                PegasusXModel,
                PegasusXPreTrainedModel,
            )


else:
      import sys

      lowercase      :   Optional[Any]      =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



