code: 
  
  import importlib
  from typing import List, Optional, Union
  
  from seqeval.metrics import accuracy_score, classification_report
  
  import datasets
  
  
  __A   :     Union[str, Any]      =			'''\
  @inproceedings{ramshaw-marcus-1995-text,
      title = "Text Chunking using Transformation-Based Learning",
      author = "Ramshaw, Lance  and
        Marcus, Mitch",
      booktitle = "Third Workshop on Very Large Corpora",
      year = "1995",
      url = "https://www.aclweb.org/anthology/W95-0107",
  }
  @misc{seqeval,
    title={{seqeval}: A Python framework for sequence labeling evaluation},
    url={https://github.com/chakki-works/seqeval},
    note={Software available from https://github.com/chakki-works/seqeval},
    author={Hiroki Nakayama},
    year={2018},
  }
  '''
  
  __A   :     str      =			'''\
  seqeval is a Python framework for sequence labeling evaluation.
  seqeval can evaluate the performance of chunking tasks such as named-entity recognition, part-of-speech tagging, semantic role labeling and so on.
  
  This is well-tested by using the Perl script conlleval, which can be used for
  measuring the performance of a system that has processed the CoNLL-2000 shared task data.
  
  seqeval supports following formats:
  IOB1
  IOB2
  IOE1
  IOE2
  IOBES
  
  See the [README.md] file at https://github.com/chakki-works/seqeval for more information.
  '''
  
  __A   :     int      =			'''
  Produces labelling scores along with its sufficient statistics
  from a source against one or more references.
  
  Args:
      predictions: List of List of predicted labels (Estimated targets as returned by a tagger)
      references: List of List of reference labels (Ground truth (correct) target values)
      suffix: True if the IOB prefix is after type, False otherwise. default: False
      scheme: Specify target tagging scheme. Should be one of ["IOB1", "IOB2", "IOE1", "IOE2", "IOBES", "BILOU"].
          default: None
      mode: Whether to count correct entity labels with incorrect I/B tags as true positives or not.
          If you want to only count exact matches, pass mode="strict". default: None.
      sample_weight: Array-like of shape (n_samples,), weights for individual samples. default: None
      zero_division: Which value to substitute as a metric value when encountering zero division. Should be on of 0, 1,
          "warn". "warn" acts as 0, but the warning is raised.
  
  Returns:
      \'scores\': dict. Summary of the scores for overall and per type
          Overall:
              \'accuracy\': accuracy,
              \'precision\': precision,
              \'recall\': recall,
              \'f1\': F1 score, also known as balanced F-score or F-measure,
          Per type:
              \'precision\': precision,
              \'recall\': recall,
              \'f1\': F1 score, also known as balanced F-score or F-measure
  Examples:
  
      >>> predictions = [[\'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
      >>> references = [[\'O\', \'O\', \'O\', \'B-MISC\', \'I-MISC\', \'I-MISC\', \'O\'], [\'B-PER\', \'I-PER\', \'O\']]
      >>> seqeval = datasets.load_metric("seqeval")
      >>> results = seqeval.compute(predictions=predictions, references=references)
      >>> print(list(results.keys()))
      [\'MISC\', \'PER\', \'overall_precision\', \'overall_recall\', \'overall_f1\', \'overall_accuracy\']
      >>> print(results["overall_f1"])
      0.5
      >>> print(results["PER"]["f1"])
      1.0
  '''
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,						_KWARGS_DESCRIPTION )
  class    __A				(							datasets.Metric ):
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ):
          return datasets.MetricInfo(
              description=_DESCRIPTION    ,				citation=_CITATION    ,				homepage='https://github.com/chakki-works/seqeval'    ,				inputs_description=_KWARGS_DESCRIPTION    ,				features=datasets.Features(
                  {
                      'predictions': datasets.Sequence(datasets.Value('string'    ,				id='label'    )    ,				id='sequence'    ),
                      'references': datasets.Sequence(datasets.Value('string'    ,				id='label'    )    ,				id='sequence'    ),
                  }    )    ,				codebase_urls=['https://github.com/chakki-works/seqeval']    ,				reference_urls=['https://github.com/chakki-works/seqeval']    ,				)
  
  
  
  
  
  
      def 	lowercase__ (	self			:    str    ,				UpperCAmelCase_			:    Union[str, Any]    ,				UpperCAmelCase_			:    int    ,				UpperCAmelCase_			:    bool = False    ,				UpperCAmelCase_			:    Optional[str] = None    ,				UpperCAmelCase_			:    Optional[str] = None    ,				UpperCAmelCase_			:    Optional[List[int]] = None    ,				UpperCAmelCase_			:    Union[str, int] = "warn"    ,				):
          if scheme is not None:
              try:
                  lowerCAmelCase				:			int    			=      importlib.import_module('seqeval.scheme'    )
                  lowerCAmelCase				:			Dict    			=      getattr(UpperCAmelCase_    ,				UpperCAmelCase_    )
              except AttributeError:
                  raise ValueError(f"Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {scheme}"    )
          lowerCAmelCase				:			Optional[int]    			=      classification_report(
              y_true=UpperCAmelCase_    ,				y_pred=UpperCAmelCase_    ,				suffix=UpperCAmelCase_    ,				output_dict=UpperCAmelCase_    ,				scheme=UpperCAmelCase_    ,				mode=UpperCAmelCase_    ,				sample_weight=UpperCAmelCase_    ,				zero_division=UpperCAmelCase_    ,				)
          report.pop('macro avg'    )
          report.pop('weighted avg'    )
          lowerCAmelCase				:			List[Any]    			=      report.pop('micro avg'    )
  
          lowerCAmelCase				:			int    			=      {
              type_name: {
                  'precision': score['precision'],
                  'recall': score['recall'],
                  'f1': score['f1-score'],
                  'number': score['support'],
              }
              for type_name, score in report.items()
          }
          lowerCAmelCase				:			int    			=      overall_score['precision']
          lowerCAmelCase				:			List[str]    			=      overall_score['recall']
          lowerCAmelCase				:			Tuple    			=      overall_score['f1-score']
          lowerCAmelCase				:			int    			=      accuracy_score(y_true=UpperCAmelCase_    ,				y_pred=UpperCAmelCase_    )
  
          return scores
  
  
  
  
  
  
style_context: 
  
  import gym
  import numpy as np
  import torch
  from mujoco_py import GlfwContext
  
  from transformers import DecisionTransformerModel
  
  
  GlfwContext(offscreen=True)  # Create a window to init GLFW.
  def 		SCREAMING_SNAKE_CASE__				(							_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase				)					-> str:
  
  
  
      '''simple docstring'''
  
  
  
  
  
  
      lowerCAmelCase				:			Optional[Any]    			=      states.reshape(1,      -1,      model.config.state_dim				)
      lowerCAmelCase				:			List[Any]    			=      actions.reshape(1,      -1,      model.config.act_dim				)
      lowerCAmelCase				:			List[Any]    			=      returns_to_go.reshape(1,      -1,      1				)
      lowerCAmelCase				:			Dict    			=      timesteps.reshape(1,      -1				)
  
      if model.config.max_length is not None:
          lowerCAmelCase				:			Optional[Any]    			=      states[:, -model.config.max_length :]
          lowerCAmelCase				:			Union[str, Any]    			=      actions[:, -model.config.max_length :]
          lowerCAmelCase				:			Dict    			=      returns_to_go[:, -model.config.max_length :]
          lowerCAmelCase				:			List[str]    			=      timesteps[:, -model.config.max_length :]
  
          # pad all tokens to sequence length
          lowerCAmelCase				:			List[str]    			=      torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]				), torch.ones(states.shape[1]				)]				)
          lowerCAmelCase				:			List[Any]    			=      attention_mask.to(dtype=torch.long,      device=states.device				).reshape(1,      -1				)
          lowerCAmelCase				:			Any    			=      torch.cat(
              [
                  torch.zeros(
                      (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim),      device=states.device,      ),
                  states,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			int    			=      torch.cat(
              [
                  torch.zeros(
                      (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim),      device=actions.device,      ),
                  actions,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			Union[str, Any]    			=      torch.cat(
              [
                  torch.zeros(
                      (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1),      device=returns_to_go.device,      ),
                  returns_to_go,
              ],      dim=1,      ).to(dtype=torch.floataa				)
          lowerCAmelCase				:			Tuple    			=      torch.cat(
              [
                  torch.zeros(
                      (timesteps.shape[0], model.config.max_length - timesteps.shape[1]),      device=timesteps.device				),
                  timesteps,
              ],      dim=1,      ).to(dtype=torch.long				)
      else:
          lowerCAmelCase				:			Optional[Any]    			=      None
  
      lowerCAmelCase			,  lowerCAmelCase			,  lowerCAmelCase				:			List[str]    			=      model(
          states=_UpperCAmelCase,      actions=_UpperCAmelCase,      rewards=_UpperCAmelCase,      returns_to_go=_UpperCAmelCase,      timesteps=_UpperCAmelCase,      attention_mask=_UpperCAmelCase,      return_dict=_UpperCAmelCase,      )
  
      return action_preds[0, -1]
  
  
  # build the environment
  
  __A   :     Dict      =			gym.make('''Hopper-v3''')
  __A   :     Any      =			env.observation_space.shape[0]
  __A   :     Optional[Any]      =			env.action_space.shape[0]
  __A   :     Any      =			1000
  __A   :     Tuple      =			'''cuda'''
  __A   :     Optional[int]      =			10_00.0  # normalization for rewards/returns
  __A   :     Optional[Any]      =			3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  __A   :     str      =			np.array(
      [
          1.31_12_79,
          -0.08_46_95_21,
          -0.5_38_27_19,
          -0.07_20_15_76,
          0.04_93_23_66,
          2.1_06_68_56,
          -0.15_01_73_54,
          0.00_87_83_45,
          -0.2_84_81_86,
          -0.18_54_00_96,
          -0.28_46_12_86,
      ]
  )
  __A   :     Dict      =			np.array(
      [
          0.17_79_07_51,
          0.05_44_46_21,
          0.21_29_71_39,
          0.14_53_04_19,
          0.6_12_44_44,
          0.85_17_44_65,
          1.4_51_52_52,
          0.6_75_16_96,
          1.53_62_39,
          1.6_16_07_46,
          5.6_07_25_36,
      ]
  )
  __A   :     int      =			torch.from_numpy(state_mean).to(device=device)
  __A   :     Tuple      =			torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  __A   :     List[Any]      =			DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-medium''')
  __A   :     Any      =			model.to(device)
  model.eval()
  
  for ep in range(10):
   __A     ,      __A   :     Optional[int]      =			0, 0
   __A   :     List[str]      =			env.reset()
   __A   :     Optional[int]      =			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
   __A   :     str      =			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
   __A   :     Optional[int]      =			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
   __A   :     Any      =			torch.zeros(0, device=device, dtype=torch.floataa)
  
   __A   :     List[Any]      =			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
   for t in range(max_ep_len):
    env.render()
    # add padding
    __A   :     Union[str, Any]      =			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
    __A   :     List[Any]      =			torch.cat([rewards, torch.zeros(1, device=device)])
  
    __A   :     int      =			get_action(
        model,
        (states.to(dtype=torch.floataa) - state_mean) / state_std,
        actions.to(dtype=torch.floataa),
        rewards.to(dtype=torch.floataa),
        target_return.to(dtype=torch.floataa),
        timesteps.to(dtype=torch.long),
    )
    __A   :     List[str]      =			action
    __A   :     List[str]      =			action.detach().cpu().numpy()
  
    __A     ,      __A     ,      __A     ,      __A   :     List[str]      =			env.step(action)
  
    __A   :     Union[str, Any]      =			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
    __A   :     Any      =			torch.cat([states, cur_state], dim=0)
    __A   :     Dict      =			reward
  
    __A   :     str      =			target_return[0, -1] - (reward / scale)
    __A   :     Dict      =			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
    __A   :     List[Any]      =			torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)
  
    episode_return += reward
    episode_length += 1
  
    if done:
     break
  
  
  
  
  
  