






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       ,A__       ,A__=13       ,A__=7       ,A__=6       ,A__=17       ,A__=23       ,A__=11       ,A__=True       ,):
						_A	:				Optional[int]   =      parent
						_A	:				Union[str, Any]   =      batch_size
						_A	:				str   =      seq_length
						_A	:				Dict   =      act_dim
						_A	:				int   =      state_dim
						_A	:				Union[str, Any]   =      hidden_size
						_A	:				Tuple   =      max_length
						_A	:				Dict   =      is_training







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

						_A	:				str   =      self.get_config()

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







		def  A__      (					self		):
						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  A__      (					self       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,):
						_A	:				str   =      DecisionTransformerModel(config=A__		)
						model.to(A__		)
						model.eval()
						_A	:				str   =      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  A__      (					self		):
						_A	:				Dict   =      self.prepare_config_and_inputs()
						(
						    (
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				(
						    _A
						)  ,				
						)	:				Optional[int]   =      config_and_inputs
						_A	:				str   =      {
						    '''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__ (							__snake_case     ,				__snake_case     ,				__snake_case     ,				unittest.TestCase							):
		__snake_case      :       int     =   (DecisionTransformerModel,) if is_torch_available() else ()
		__snake_case      :       Union[str, Any]     =   ()
		__snake_case      :       Tuple     =   {"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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







		def  A__      (					self		):
						_A	:				Any   =      DecisionTransformerModelTester(self		)
						_A	:				List[str]   =      ConfigTester(self       ,config_class=A__       ,hidden_size=37		)







		def  A__      (					self		):
						self.config_tester.run_common_tests()







		def  A__      (					self		):
						_A	:				List[str]   =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A__		)







		@slow
		def  A__      (					self		):
						for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_A	:				List[Any]   =      DecisionTransformerModel.from_pretrained(A__		)
										self.assertIsNotNone(A__		)







		def  A__      (					self		):
						_A  ,				_A	:				Tuple   =      self.model_tester.prepare_config_and_inputs_for_common()

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

										_A	:				Dict   =      [
										    '''states''',
										    '''actions''',
										    '''rewards''',
										    '''returns_to_go''',
										    '''timesteps''',
										    '''attention_mask''',
										]

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






@require_torch
class 						UpperCAmelCase__ (							unittest.TestCase							):







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

						_A	:				str   =      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__		)

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

						for step in range(A__		):
										_A	:				int   =      torch.cat([actions, torch.zeros(1       ,1       ,config.act_dim       ,device=A__		)]       ,dim=1		)
										_A	:				Optional[int]   =      torch.cat([rewards, torch.zeros(1       ,1       ,device=A__		)]       ,dim=1		)

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

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

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











from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Optional[Any]								={
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[int]     =   "pegasus"
		__snake_case      :       List[str]     =   ["past_key_values"]
		__snake_case      :       Union[str, Any]     =   {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







		def __init__(					self       ,A__=50265       ,A__=1024       ,A__=12       ,A__=4096       ,A__=16       ,A__=12       ,A__=4096       ,A__=16       ,A__=0.0       ,A__=0.0       ,A__=True       ,A__=True       ,A__="gelu"       ,A__=1024       ,A__=0.1       ,A__=0.0       ,A__=0.0       ,A__=0.02       ,A__=0       ,A__=False       ,A__=0       ,A__=1       ,A__=1       ,**A__       ,):
						_A	:				List[str]   =      vocab_size
						_A	:				List[Any]   =      max_position_embeddings
						_A	:				Union[str, Any]   =      d_model
						_A	:				Union[str, Any]   =      encoder_ffn_dim
						_A	:				Union[str, Any]   =      encoder_layers
						_A	:				int   =      encoder_attention_heads
						_A	:				List[Any]   =      decoder_ffn_dim
						_A	:				Optional[int]   =      decoder_layers
						_A	:				Tuple   =      decoder_attention_heads
						_A	:				List[str]   =      dropout
						_A	:				Tuple   =      attention_dropout
						_A	:				Union[str, Any]   =      activation_dropout
						_A	:				int   =      activation_function
						_A	:				Union[str, Any]   =      init_std
						_A	:				Any   =      encoder_layerdrop
						_A	:				int   =      decoder_layerdrop
						_A	:				Tuple   =      use_cache
						_A	:				Dict   =      encoder_layers
						_A	:				List[str]   =      scale_embedding  # scale factor will be sqrt(d_model) if True
						super().__init__(
						    pad_token_id=A__       ,eos_token_id=A__       ,is_encoder_decoder=A__       ,decoder_start_token_id=A__       ,forced_eos_token_id=A__       ,**A__       ,)







		@property
		def  A__      (					self		):
						return self.encoder_attention_heads







		@property
		def  A__      (					self		):
						return self.d_model



