





'''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     lowerCAmelCase__ :

       '''simple docstring'''

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

       def 			__snake_case      ( self					:				Optional[Any]						):
              UpperCAmelCase  =						floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
              UpperCAmelCase  =						floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
              UpperCAmelCase  =						floats_tensor((self.batch_size, self.seq_length, 1)						)
              UpperCAmelCase  =						floats_tensor((self.batch_size, self.seq_length, 1)						)
              UpperCAmelCase  =						ids_tensor((self.batch_size, self.seq_length)       ,     vocab_size=1000						)
              UpperCAmelCase  =						random_attention_mask((self.batch_size, self.seq_length)						)

              UpperCAmelCase  =						self.get_config()

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

       def 			__snake_case      ( self					:				List[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 			__snake_case      ( self					:				Dict       ,     a__					:				Union[str, Any]       ,     a__					:				Dict       ,     a__					:				Dict       ,     a__					:				Dict       ,     a__					:				List[Any]       ,     a__					:				List[str]       ,     a__					:				Union[str, Any]       ,     ):
              UpperCAmelCase  =						DecisionTransformerModel(config=a__						)
              model.to(a__						)
              model.eval()
              UpperCAmelCase  =						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 			__snake_case      ( self					:				Union[str, Any]						):
              UpperCAmelCase  =						self.prepare_config_and_inputs()
              (
                  (
                  UpperCAmelCase
              ),					(
                  UpperCAmelCase
              ),					(
                  UpperCAmelCase
              ),					(
                  UpperCAmelCase
              ),					(
                  UpperCAmelCase
              ),					(
                  UpperCAmelCase
              ),					(
                  UpperCAmelCase
              ),					
              )  =						config_and_inputs
              UpperCAmelCase  =						{
                  '''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     lowerCAmelCase__ (				UpperCAmelCase_      , UpperCAmelCase_      , UpperCAmelCase_      , unittest.TestCase    ):

       '''simple docstring'''
       _lowerCamelCase								=(DecisionTransformerModel,) if is_torch_available() else ()
       _lowerCamelCase								=()
       _lowerCamelCase								={"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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

       def 			__snake_case      ( self					:				Union[str, Any]						):
              UpperCAmelCase  =						DecisionTransformerModelTester(self						)
              UpperCAmelCase  =						ConfigTester(self       ,     config_class=a__       ,     hidden_size=37						)

       def 			__snake_case      ( self					:				str						):
              self.config_tester.run_common_tests()

       def 			__snake_case      ( self					:				str						):
              UpperCAmelCase  =						self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*a__						)

       @slow
       def 			__snake_case      ( self					:				List[str]						):
              for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCAmelCase  =						DecisionTransformerModel.from_pretrained(a__						)
                     self.assertIsNotNone(a__						)



       def 			__snake_case      ( self					:				List[str]						):
              UpperCAmelCase,					UpperCAmelCase  =						self.model_tester.prepare_config_and_inputs_for_common()

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

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

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






@require_torch
class     lowerCAmelCase__ (				unittest.TestCase    ):

       '''simple docstring'''

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

              UpperCAmelCase  =						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__						)

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

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

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

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

                     UpperCAmelCase  =						action_pred[0, -1]
                     UpperCAmelCase  =						torch.cat([states, state]       ,     dim=1						)
                     UpperCAmelCase  =						returns_to_go[0, -1] - reward
                     UpperCAmelCase  =						torch.cat([returns_to_go, pred_return.reshape(1       ,     1       ,     1						)]       ,     dim=1						)
                     UpperCAmelCase  =						torch.cat(
                         [timesteps, torch.ones((1, 1)       ,     device=a__       ,     dtype=torch.long						) * (step + 1)]       ,     dim=1						)










"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowerCamelCase_							      = {
    '''<''': operator.lt,
    '''<=''': operator.le,
    '''==''': operator.eq,
    '''!=''': operator.ne,
    '''>=''': operator.ge,
    '''>''': operator.gt,
}

def 							snake_case (  A__      ,A__      ,A__      ,A__      ,A__      ,A__		):
			if got_ver is None or want_ver is None:
						raise ValueError(
						    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
						    F""" reinstalling {pkg}."""		)
			if not ops[op](version.parse(A__		)      ,version.parse(A__		)		):
						raise ImportError(
						    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""		)

def 							snake_case (  A__      ,A__ = None		):
			UpperCAmelCase_	:					int     	=   F"""\n{hint}""" if hint is not None else ""

			# non-versioned check
			if re.match(r"^[\w_\-\d]+$"      ,A__		):
						UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   requirement, None, None
			else:
						UpperCAmelCase_	:					Optional[int]     	=   re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)"      ,A__		)
						if not match:
									raise ValueError(
									    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
									    F""" got {requirement}"""		)
						UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   match[0]
						UpperCAmelCase_	:					Optional[Any]     	=   want_full.split(","		)  # there could be multiple requirements
						UpperCAmelCase_	:					int     	=   {}
						for w in want_range:
									UpperCAmelCase_	:					str     	=   re.findall(r"^([\s!=<>]{1,2})(.+)"      ,A__		)
									if not match:
												raise ValueError(
												    "requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
												    F""" but got {requirement}"""		)
									UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   match[0]
									UpperCAmelCase_	:					Union[str, Any]     	=   want_ver
									if op not in ops:
												raise ValueError(F"""{requirement}: need one of {list(ops.keys()		)}, but got {op}"""		)

    # special case
			if pkg == "python":
						UpperCAmelCase_	:					List[Any]     	=   ".".join([str(A__		) for x in sys.version_info[:3]]		)
						for op, want_ver in wanted.items():
									_compare_versions(A__      ,A__      ,A__      ,A__      ,A__      ,A__		)
						return

			# check if any version is installed
			try:
						UpperCAmelCase_	:					str     	=   importlib.metadata.version(A__		)
			except importlib.metadata.PackageNotFoundError:
						raise importlib.metadata.PackageNotFoundError(
						    F"""The '{requirement}' distribution was not found and is required by this application. {hint}"""		)

			# check that the right version is installed if version number or a range was provided
			if want_ver is not None:
						for op, want_ver in wanted.items():
									_compare_versions(A__      ,A__      ,A__      ,A__      ,A__      ,A__		)




def 							snake_case (  A__		):
			UpperCAmelCase_	:					Any     	=   "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
			return require_version(A__      ,A__		)



