

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:			Any				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Optional[int]=13				,				__lowerCAmelCase:			Any=7				,				__lowerCAmelCase:			str=6				,				__lowerCAmelCase:			Optional[Any]=17				,				__lowerCAmelCase:			List[Any]=23				,				__lowerCAmelCase:			Any=11				,				__lowerCAmelCase:			str=True				,				) -> Union[str, Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       parent
    __UpperCAmelCase						    =       batch_size
    __UpperCAmelCase						    =       seq_length
    __UpperCAmelCase						    =       act_dim
    __UpperCAmelCase						    =       state_dim
    __UpperCAmelCase						    =       hidden_size
    __UpperCAmelCase						    =       max_length
    __UpperCAmelCase						    =       is_training






  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Union[str, Any]:




    '''simple docstring'''




    __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=1_000   )
    __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 	_UpperCAmelCase				(				self:			List[str]   ) -> Optional[int]:




    '''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:			List[Any]				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Optional[Any]				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			Optional[int]				,				) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       DecisionTransformerModel(config=__lowerCAmelCase   )
    model.to(__lowerCAmelCase   )
    model.eval()
    __UpperCAmelCase						    =       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 	_UpperCAmelCase				(				self:			Optional[int]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.prepare_config_and_inputs()
    (
        __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 			UpperCAmelCase__    (		snake_case		,	snake_case		,	snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	Optional[int]    		= (DecisionTransformerModel,) if is_torch_available() else ()
  lowerCAmelCase__		:	Optional[int]    		= ()
  lowerCAmelCase__		:	Dict    		= {'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__		:	Optional[Any]    		= False
  lowerCAmelCase__		:	List[Any]    		= False
  lowerCAmelCase__		:	Union[str, Any]    		= False
  lowerCAmelCase__		:	Dict    		= False
  lowerCAmelCase__		:	int    		= False
  lowerCAmelCase__		:	str    		= False
  lowerCAmelCase__		:	Union[str, Any]    		= False
  lowerCAmelCase__		:	Dict    		= False
  lowerCAmelCase__		:	Dict    		= False






  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       DecisionTransformerModelTester(self   )
    __UpperCAmelCase						    =       ConfigTester(self				,				config_class=__lowerCAmelCase				,				hidden_size=37   )






  def 	_UpperCAmelCase				(				self:			int   ) -> Any:




    '''simple docstring'''




    self.config_tester.run_common_tests()






  def 	_UpperCAmelCase				(				self:			List[Any]   ) -> Any:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase   )






  @slow
  def 	_UpperCAmelCase				(				self:			int   ) -> Tuple:




    '''simple docstring'''




    for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      __UpperCAmelCase						    =       DecisionTransformerModel.from_pretrained(__lowerCAmelCase   )
      self.assertIsNotNone(__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			Dict   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __UpperCAmelCase						    =       model_class(__lowerCAmelCase   )
      __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(__lowerCAmelCase   )]				,				__lowerCAmelCase   )






@require_torch
class 			UpperCAmelCase__    (		unittest.TestCase					):



  """simple docstring"""






  @slow
  def 	_UpperCAmelCase				(				self:			Union[str, Any]   ) -> List[str]:




    '''simple docstring'''




    __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(__lowerCAmelCase   )
    __UpperCAmelCase						    =       model.config
    torch.manual_seed(0   )
    __UpperCAmelCase						    =       torch.randn(1				,				1				,				config.state_dim   ).to(device=__lowerCAmelCase				,				dtype=torch.floataa   )  # env.reset()

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

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

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

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

      with torch.no_grad():
        __UpperCAmelCase						    =       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						    =       (  # env.step(action)
          torch.randn(1				,				1				,				config.state_dim   ).to(device=__lowerCAmelCase				,				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=__lowerCAmelCase				,				dtype=torch.long   ) * (step + 1)]				,				dim=1   )




from sklearn.metrics import fa_score

import datasets


a_							=      """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""


a_							=      """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'f1': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['f1'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results['f1'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
        >>> print(round(results['f1'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'f1': array([0.8, 0. , 0. ])}
"""


a_							=      """
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,	_KWARGS_DESCRIPTION					)
class 			UpperCAmelCase__    (		datasets.Metric					):



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			int   ) -> Dict:




    '''simple docstring'''




    return datasets.MetricInfo(
        description=_DESCRIPTION				,				citation=_CITATION				,				inputs_description=_KWARGS_DESCRIPTION				,				features=datasets.Features(
            {
                "predictions": datasets.Sequence(datasets.Value("int32"   )   ),
                "references": datasets.Sequence(datasets.Value("int32"   )   ),
            }
            if self.config_name == "multilabel"
            else {
                "predictions": datasets.Value("int32"   ),
                "references": datasets.Value("int32"   ),
            }   )				,				reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]				,				)






  def 	_UpperCAmelCase				(				self:			Any				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[Any]=None				,				__lowerCAmelCase:			Optional[Any]=1				,				__lowerCAmelCase:			List[Any]="binary"				,				__lowerCAmelCase:			List[Any]=None   ) -> Optional[int]:




    '''simple docstring'''




    __UpperCAmelCase						    =       fa_score(
        __lowerCAmelCase				,				__lowerCAmelCase				,				labels=__lowerCAmelCase				,				pos_label=__lowerCAmelCase				,				average=__lowerCAmelCase				,				sample_weight=__lowerCAmelCase   )
    return {"f1": float(__lowerCAmelCase   ) if score.size == 1 else score}



