

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					:



   def __init__(							self							:					str,		__A							:					Dict,		__A							:					List[Any]=1_3,		__A							:					List[Any]=7,		__A							:					Tuple=6,		__A							:					Optional[int]=1_7,		__A							:					List[Any]=2_3,		__A							:					str=1_1,		__A							:					Optional[int]=True,		):
     UpperCAmelCase	:							List[Any]					=			parent
     UpperCAmelCase	:							Optional[Any]					=			batch_size
     UpperCAmelCase	:							List[str]					=			seq_length
     UpperCAmelCase	:							List[str]					=			act_dim
     UpperCAmelCase	:							Tuple					=			state_dim
     UpperCAmelCase	:							List[Any]					=			hidden_size
     UpperCAmelCase	:							Dict					=			max_length
     UpperCAmelCase	:							List[str]					=			is_training



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase	:							int					=			floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
     UpperCAmelCase	:							int					=			floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
     UpperCAmelCase	:							List[Any]					=			floats_tensor((self.batch_size, self.seq_length, 1)						)
     UpperCAmelCase	:							Union[str, Any]					=			floats_tensor((self.batch_size, self.seq_length, 1)						)
     UpperCAmelCase	:							Tuple					=			ids_tensor((self.batch_size, self.seq_length),		vocab_size=1_0_0_0						)
     UpperCAmelCase	:							List[str]					=			random_attention_mask((self.batch_size, self.seq_length)						)

     UpperCAmelCase	:							Any					=			self.get_config()

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



   def 	__magic_name__   (							self							:					Dict						):
     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 	__magic_name__   (							self							:					Union[str, Any],		__A							:					Optional[Any],		__A							:					Any,		__A							:					Optional[Any],		__A							:					Optional[Any],		__A							:					List[Any],		__A							:					Any,		__A							:					Any,		):
     UpperCAmelCase	:							List[Any]					=			DecisionTransformerModel(config=_lowerCamelCase						)
     model.to(_lowerCamelCase						)
     model.eval()
     UpperCAmelCase	:							Optional[int]					=			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 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							Tuple					=			self.prepare_config_and_inputs()
     (
         UpperCAmelCase    
     )	:							Dict					=			config_and_inputs
     UpperCAmelCase	:							Union[str, 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  __UpperCAmelCase					(	a__      ,  a__      ,  a__      ,  unittest.TestCase						):
   UpperCamelCase       					=					(DecisionTransformerModel,) if is_torch_available() else ()
   UpperCamelCase       					=					()
   UpperCamelCase       					=					{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

   # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False



   def 	__magic_name__   (							self							:					Optional[Any]						):
     UpperCAmelCase	:							List[str]					=			DecisionTransformerModelTester(self						)
     UpperCAmelCase	:							Union[str, Any]					=			ConfigTester(self,		config_class=_lowerCamelCase,		hidden_size=3_7						)



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



   def 	__magic_name__   (							self							:					List[str]						):
     UpperCAmelCase	:							Optional[Any]					=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCamelCase						)



   @slow
   def 	__magic_name__   (							self							:					Any						):
     for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase	:							Dict					=			DecisionTransformerModel.from_pretrained(_lowerCamelCase						)
       self.assertIsNotNone(_lowerCamelCase						)





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

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

       UpperCAmelCase	:							Any					=			[
           '''states''',
           '''actions''',
           '''rewards''',
           '''returns_to_go''',
           '''timesteps''',
           '''attention_mask''',
       ]

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






@require_torch
class  __UpperCAmelCase					(	unittest.TestCase						):



   @slow
   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							str					=			2  # number of steps of autoregressive prediction we will perform
     UpperCAmelCase	:							List[Any]					=			1_0  # defined by the RL environment, may be normalized
     UpperCAmelCase	:							int					=			DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''						)
     UpperCAmelCase	:							List[Any]					=			model.to(_lowerCamelCase						)
     UpperCAmelCase	:							List[Any]					=			model.config
     torch.manual_seed(0						)
     UpperCAmelCase	:							str					=			torch.randn(1,		1,		config.state_dim						).to(device=_lowerCamelCase,		dtype=torch.floataa						)  # env.reset()

     UpperCAmelCase	:							Optional[int]					=			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=_lowerCamelCase						)

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

     for step in range(_lowerCamelCase						):
       UpperCAmelCase	:							Any					=			torch.cat([actions, torch.zeros(1,		1,		config.act_dim,		device=_lowerCamelCase						)],		dim=1						)
       UpperCAmelCase	:							str					=			torch.cat([rewards, torch.zeros(1,		1,		device=_lowerCamelCase						)],		dim=1						)

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

       with torch.no_grad():
         UpperCAmelCase	:							int					=			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						)						)
       UpperCAmelCase	:							Optional[int]					=			(  # env.step(action)
           torch.randn(1,		1,		config.state_dim						).to(device=_lowerCamelCase,		dtype=torch.floataa						),
           1.0,
           False,
           {},
       )

       UpperCAmelCase	:							Dict					=			action_pred[0, -1]
       UpperCAmelCase	:							Optional[Any]					=			torch.cat([states, state],		dim=1						)
       UpperCAmelCase	:							Tuple					=			returns_to_go[0, -1] - reward
       UpperCAmelCase	:							Any					=			torch.cat([returns_to_go, pred_return.reshape(1,		1,		1						)],		dim=1						)
       UpperCAmelCase	:							Optional[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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase    :       str        =							{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       List[Any]        =							["XLNetTokenizer"]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       Optional[Any]        =							["XLNetTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       Dict        =							[
         "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "XLNetForMultipleChoice",
         "XLNetForQuestionAnswering",
         "XLNetForQuestionAnsweringSimple",
         "XLNetForSequenceClassification",
         "XLNetForTokenClassification",
         "XLNetLMHeadModel",
         "XLNetModel",
         "XLNetPreTrainedModel",
         "load_tf_weights_in_xlnet",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _lowerCamelCase    :       Union[str, Any]        =							[
         "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFXLNetForMultipleChoice",
         "TFXLNetForQuestionAnsweringSimple",
         "TFXLNetForSequenceClassification",
         "TFXLNetForTokenClassification",
         "TFXLNetLMHeadModel",
         "TFXLNetMainLayer",
         "TFXLNetModel",
         "TFXLNetPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlnet import XLNetTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_xlnet_fast import XLNetTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_xlnet import (
              XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              XLNetForMultipleChoice,
              XLNetForQuestionAnswering,
              XLNetForQuestionAnsweringSimple,
              XLNetForSequenceClassification,
              XLNetForTokenClassification,
              XLNetLMHeadModel,
              XLNetModel,
              XLNetPreTrainedModel,
              load_tf_weights_in_xlnet,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_xlnet import (
              TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFXLNetForMultipleChoice,
              TFXLNetForQuestionAnsweringSimple,
              TFXLNetForSequenceClassification,
              TFXLNetForTokenClassification,
              TFXLNetLMHeadModel,
              TFXLNetMainLayer,
              TFXLNetModel,
              TFXLNetPreTrainedModel,
          )

else:
     import sys

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





