



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			:





       def __init__(   self		:		Optional[Any]      ,				__lowercase		:		Any      ,				__lowercase		:		Any=13      ,				__lowercase		:		int=7      ,				__lowercase		:		Union[str, Any]=6      ,				__lowercase		:		List[Any]=17      ,				__lowercase		:		Optional[int]=23      ,				__lowercase		:		Optional[Any]=11      ,				__lowercase		:		Union[str, Any]=True      ,				):

           '''simple docstring'''
           __a       =		parent
           __a       =		batch_size
           __a       =		seq_length
           __a       =		act_dim
           __a       =		state_dim
           __a       =		hidden_size
           __a       =		max_length
           __a       =		is_training





       def 						UpperCamelCase_					(   self		:		List[str]	):

           '''simple docstring'''
           __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=1000	)
           __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 						UpperCamelCase_					(   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 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Union[str, Any]      ,				__lowercase		:		str      ,				__lowercase		:		Tuple      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		List[str]      ,				__lowercase		:		Any      ,				__lowercase		:		int      ,				):

           '''simple docstring'''
           __a       =		DecisionTransformerModel(config=UpperCAmelCase_	)
           model.to(UpperCAmelCase_	)
           model.eval()
           __a       =		model(UpperCAmelCase_      ,				UpperCAmelCase_      ,				UpperCAmelCase_      ,				UpperCAmelCase_      ,				UpperCAmelCase_      ,				UpperCAmelCase_	)

           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 						UpperCamelCase_					(   self		:		Optional[int]	):

           '''simple docstring'''
           __a       =		self.prepare_config_and_inputs()
           (
               __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    SCREAMING_SNAKE_CASE			(							__lowerCamelCase			,      __lowerCamelCase			,      __lowerCamelCase			,      unittest.TestCase					):
       __lowerCamelCase	:	Any    =(DecisionTransformerModel,) if is_torch_available() else ()
       __lowerCamelCase	:	List[Any]    =()
       __lowerCamelCase	:	int    ={'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

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





       def 						UpperCamelCase_					(   self		:		Tuple	):

           '''simple docstring'''
           __a       =		DecisionTransformerModelTester(self	)
           __a       =		ConfigTester(self      ,				config_class=UpperCAmelCase_      ,				hidden_size=37	)





       def 						UpperCamelCase_					(   self		:		Any	):

           '''simple docstring'''
           self.config_tester.run_common_tests()





       def 						UpperCamelCase_					(   self		:		int	):

           '''simple docstring'''
           __a       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_	)





       @slow
       def 						UpperCamelCase_					(   self		:		Any	):

           '''simple docstring'''
           for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a       =		DecisionTransformerModel.from_pretrained(UpperCAmelCase_	)
               self.assertIsNotNone(UpperCAmelCase_	)







       def 						UpperCamelCase_					(   self		:		Dict	):

           '''simple docstring'''
           __a       =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a       =		model_class(UpperCAmelCase_	)
               __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(UpperCAmelCase_	)]      ,				UpperCAmelCase_	)






@require_torch
class    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





       @slow
       def 						UpperCamelCase_					(   self		:		Tuple	):

           '''simple docstring'''
           __a       =		2  # number of steps of autoregressive prediction we will perform
           __a       =		10  # defined by the RL environment, may be normalized
           __a       =		DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""	)
           __a       =		model.to(UpperCAmelCase_	)
           __a       =		model.config
           torch.manual_seed(0	)
           __a       =		torch.randn(1      ,				1      ,				config.state_dim	).to(device=UpperCAmelCase_      ,				dtype=torch.floataa	)  # env.reset()

           __a       =		torch.tensor(
               [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]      ,				device=UpperCAmelCase_	)

           __a       =		torch.tensor(UpperCAmelCase_      ,				device=UpperCAmelCase_      ,				dtype=torch.floataa	).reshape(1      ,				1      ,				1	)
           __a       =		state
           __a       =		torch.zeros(1      ,				0      ,				config.act_dim      ,				device=UpperCAmelCase_      ,				dtype=torch.floataa	)
           __a       =		torch.zeros(1      ,				0      ,				device=UpperCAmelCase_      ,				dtype=torch.floataa	)
           __a       =		torch.tensor(0      ,				device=UpperCAmelCase_      ,				dtype=torch.long	).reshape(1      ,				1	)

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

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

               with torch.no_grad():
                   __a       =		model(
                       states=UpperCAmelCase_      ,				actions=UpperCAmelCase_      ,				rewards=UpperCAmelCase_      ,				returns_to_go=UpperCAmelCase_      ,				timesteps=UpperCAmelCase_      ,				attention_mask=UpperCAmelCase_      ,				return_dict=UpperCAmelCase_      ,				)

               self.assertEqual(action_pred.shape      ,				actions.shape	)
               self.assertTrue(torch.allclose(action_pred[0, -1]      ,				expected_outputs[step]      ,				atol=1E-4	)	)
               __a       =		(  # env.step(action)
                   torch.randn(1      ,				1      ,				config.state_dim	).to(device=UpperCAmelCase_      ,				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=UpperCAmelCase_      ,				dtype=torch.long	) * (step + 1)]      ,				dim=1	)





from typing import TYPE_CHECKING

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


lowerCamelCase__			= {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}

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

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

if TYPE_CHECKING:
						from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
						import sys

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