



"""simple docstring"""







from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


__snake_case	:				List[Any]  					=			{
    """id""": """5733be284776f41900661182""",
    """title""": """University_of_Notre_Dame""",
    """context""": """Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.""",
    """question""": """To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?""",
    """answers""": {"""text""": ["""Saint Bernadette Soubirous"""], """answer_start""": [515]},
}



@pytest.mark.parametrize(
    '''task_cls'''		,							[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]		,							)
def   a_	(			__a      ):
     A__				=   task_cls()
     A__				=   asdict(lowerCAmelCase__      )
     A__				=   task_template_from_dict(lowerCAmelCase__      )
     assert task == reloaded

class       UpperCamelCase  :





     """simple docstring"""






     def 						A__  (					self :							List[Any] ):
          A__				=   LanguageModeling(text_column='''input_text''' )
          assert {"input_text": "text"} == task.column_mapping





     def 						A__  (					self :							int ):
          A__				=   Features({'''text''': Value('''string''' )} )
          A__				=   {'text_column': 'input_text'}
          A__				=   LanguageModeling.from_dict(_lowerCamelCase )
          assert "language-modeling" == task.task
          assert input_schema == task.input_schema


class       UpperCamelCase  (					snake_case__				):





     """simple docstring"""






     def 						A__  (					self :							Optional[int] ):
          A__				=   sorted(['''pos''', '''neg'''] )





     def 						A__  (					self :							Dict ):
          A__				=   TextClassification(text_column='''input_text'''	,      label_column='''input_label''' )
          self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}	,      task.column_mapping )





     def 						A__  (					self :							Optional[int] ):
          A__				=   Features({'''text''': Value('''string''' )} )
          # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
          A__				=   Features({'''labels''': ClassLabel} )
          A__				=   {'text_column': 'input_text', 'label_column': 'input_labels'}
          A__				=   TextClassification.from_dict(_lowerCamelCase )
          self.assertEqual('''text-classification'''	,      task.task )
          self.assertEqual(_lowerCamelCase	,      task.input_schema )
          self.assertEqual(_lowerCamelCase	,      task.label_schema )





     def 						A__  (					self :							Tuple ):
          A__				=   TextClassification(text_column='''input_text'''	,      label_column='''input_label''' )
          self.assertEqual(task.label_schema['''labels''']	,      _lowerCamelCase )
          A__				=   task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels )} ) )
          self.assertEqual(task.label_schema['''labels''']	,      ClassLabel(names=self.labels ) )


class       UpperCamelCase  (					snake_case__				):





     """simple docstring"""






     def 						A__  (					self :							Optional[Any] ):
          A__				=   QuestionAnsweringExtractive(
              context_column='''input_context'''	,      question_column='''input_question'''	,      answers_column='''input_answers''' )
          self.assertDictEqual(
              {'''input_context''': '''context''', '''input_question''': '''question''', '''input_answers''': '''answers'''}	,      task.column_mapping )





     def 						A__  (					self :							Tuple ):
          A__				=   Features({'''question''': Value('''string''' ), '''context''': Value('''string''' )} )
          A__				=   Features(
              {
                  '''answers''': Sequence(
                      {
                          '''text''': Value('''string''' ),
                          '''answer_start''': Value('''int32''' ),
                      } )
              } )
          A__				=   {
              'context_column': 'input_input_context',
              'question_column': 'input_question',
              'answers_column': 'input_answers',
          }
          A__				=   QuestionAnsweringExtractive.from_dict(_lowerCamelCase )
          self.assertEqual('''question-answering-extractive'''	,      task.task )
          self.assertEqual(_lowerCamelCase	,      task.input_schema )
          self.assertEqual(_lowerCamelCase	,      task.label_schema )


class       UpperCamelCase  (					snake_case__				):





     """simple docstring"""






     def 						A__  (					self :							List[Any] ):
          A__				=   Summarization(text_column='''input_text'''	,      summary_column='''input_summary''' )
          self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}	,      task.column_mapping )





     def 						A__  (					self :							Tuple ):
          A__				=   Features({'''text''': Value('''string''' )} )
          A__				=   Features({'''summary''': Value('''string''' )} )
          A__				=   {'text_column': 'input_text', 'summary_column': 'input_summary'}
          A__				=   Summarization.from_dict(_lowerCamelCase )
          self.assertEqual('''summarization'''	,      task.task )
          self.assertEqual(_lowerCamelCase	,      task.input_schema )
          self.assertEqual(_lowerCamelCase	,      task.label_schema )


class       UpperCamelCase  (					snake_case__				):





     """simple docstring"""






     def 						A__  (					self :							List[str] ):
          A__				=   AutomaticSpeechRecognition(audio_column='''input_audio'''	,      transcription_column='''input_transcription''' )
          self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}	,      task.column_mapping )





     def 						A__  (					self :							Dict ):
          A__				=   Features({'''audio''': Audio()} )
          A__				=   Features({'''transcription''': Value('''string''' )} )
          A__				=   {
              'audio_column': 'input_audio',
              'transcription_column': 'input_transcription',
          }
          A__				=   AutomaticSpeechRecognition.from_dict(_lowerCamelCase )
          self.assertEqual('''automatic-speech-recognition'''	,      task.task )
          self.assertEqual(_lowerCamelCase	,      task.input_schema )
          self.assertEqual(_lowerCamelCase	,      task.label_schema )


class       UpperCamelCase  (					snake_case__				):





     """simple docstring"""






     def 						A__  (					self :							Optional[int] ):
          A__				=   sorted(['''pos''', '''neg'''] )





     def 						A__  (					self :							Optional[int] ):
          A__				=   AudioClassification(audio_column='''input_audio'''	,      label_column='''input_label''' )
          self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}	,      task.column_mapping )





     def 						A__  (					self :							Tuple ):
          A__				=   Features({'''audio''': Audio()} )
          A__				=   Features({'''labels''': ClassLabel} )
          A__				=   {
              'audio_column': 'input_image',
              'label_column': 'input_label',
          }
          A__				=   AudioClassification.from_dict(_lowerCamelCase )
          self.assertEqual('''audio-classification'''	,      task.task )
          self.assertEqual(_lowerCamelCase	,      task.input_schema )
          self.assertEqual(_lowerCamelCase	,      task.label_schema )





     def 						A__  (					self :							Any ):
          A__				=   AudioClassification(audio_column='''input_audio'''	,      label_column='''input_label''' )
          self.assertEqual(task.label_schema['''labels''']	,      _lowerCamelCase )
          A__				=   task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels )} ) )
          self.assertEqual(task.label_schema['''labels''']	,      ClassLabel(names=self.labels ) )


class       UpperCamelCase  (					snake_case__				):





     """simple docstring"""






     def 						A__  (					self :							int ):
          A__				=   sorted(['''pos''', '''neg'''] )





     def 						A__  (					self :							List[str] ):
          A__				=   ImageClassification(image_column='''input_image'''	,      label_column='''input_label''' )
          self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}	,      task.column_mapping )





     def 						A__  (					self :							List[str] ):
          A__				=   Features({'''image''': Image()} )
          A__				=   Features({'''labels''': ClassLabel} )
          A__				=   {
              'image_column': 'input_image',
              'label_column': 'input_label',
          }
          A__				=   ImageClassification.from_dict(_lowerCamelCase )
          self.assertEqual('''image-classification'''	,      task.task )
          self.assertEqual(_lowerCamelCase	,      task.input_schema )
          self.assertEqual(_lowerCamelCase	,      task.label_schema )





     def 						A__  (					self :							Optional[Any] ):
          A__				=   ImageClassification(image_column='''input_image'''	,      label_column='''input_label''' )
          self.assertEqual(task.label_schema['''labels''']	,      _lowerCamelCase )
          A__				=   task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels )} ) )
          self.assertEqual(task.label_schema['''labels''']	,      ClassLabel(names=self.labels ) )


class       UpperCamelCase  (					snake_case__				):





     """simple docstring"""






     def 						A__  (					self :							int ):
          A__				=   DatasetInfo(task_templates=QuestionAnsweringExtractive() )
          A__				=   Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}	,      info=_lowerCamelCase )
          assert isinstance(dataset.info.task_templates	,      _lowerCamelCase )
          assert len(dataset.info.task_templates ) == 1

          def keep_task(_lowerCamelCase :							List[Any] ):
               return x

          def dont_keep_task(_lowerCamelCase :							int ):
               A__				=   deepcopy(_lowerCamelCase )
               A__				=   0
               return out

          A__				=   dataset.map(_lowerCamelCase )
          assert mapped_dataset.info.task_templates == dataset.info.task_templates
          # reload from cache
          A__				=   dataset.map(_lowerCamelCase )
          assert mapped_dataset.info.task_templates == dataset.info.task_templates

          A__				=   dataset.map(_lowerCamelCase )
          assert mapped_dataset.info.task_templates == []
          # reload from cache
          A__				=   dataset.map(_lowerCamelCase )
          assert mapped_dataset.info.task_templates == []





     def 						A__  (					self :							int ):
          A__				=   Features({'''text''': Value('''string''' ), '''label''': ClassLabel(names=('''pos''', '''neg''') )} )
          A__				=   TextClassification(text_column='''text'''	,      label_column='''label''' )
          A__				=   DatasetInfo(features=_lowerCamelCase	,      task_templates=_lowerCamelCase )
          A__				=   Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}	,      info=_lowerCamelCase )

          def process(_lowerCamelCase :							List[Any] ):
               return example

          A__				=   dataset.remove_columns('''label''' )
          A__				=   modified_dataset.map(_lowerCamelCase )
          assert mapped_dataset.info.task_templates == []








"""simple docstring"""







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   a_	(			__a		,							__a		,							__a		,							__a		,							__a		,							__a      ):
     # we don't care about the past rewards in this model

     A__				=   states.reshape(1		,							-1		,							model.config.state_dim      )
     A__				=   actions.reshape(1		,							-1		,							model.config.act_dim      )
     A__				=   returns_to_go.reshape(1		,							-1		,							1      )
     A__				=   timesteps.reshape(1		,							-1      )

     if model.config.max_length is not None:
          A__				=   states[:, -model.config.max_length :]
          A__				=   actions[:, -model.config.max_length :]
          A__				=   returns_to_go[:, -model.config.max_length :]
          A__				=   timesteps[:, -model.config.max_length :]

          # pad all tokens to sequence length
          A__				=   torch.cat(
              [torch.zeros(model.config.max_length - states.shape[1]      ), torch.ones(states.shape[1]      )]      )
          A__				=   attention_mask.to(dtype=torch.long		,							device=states.device      ).reshape(1		,							-1      )
          A__				=   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      )
          A__				=   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      )
          A__				=   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      )
          A__				=   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:
          A__				=   None

     A__	,     A__	,     A__				=   model(
         states=__a		,							actions=__a		,							rewards=__a		,							returns_to_go=__a		,							timesteps=__a		,							attention_mask=__a		,							return_dict=__a		,							)

     return action_preds[0, -1]


# build the environment

__snake_case	:				List[str]  					=			gym.make('Hopper-v3')
__snake_case	:				Optional[Any]  					=			env.observation_space.shape[0]
__snake_case	:				List[str]  					=			env.action_space.shape[0]
__snake_case	:				Optional[int]  					=			1_000
__snake_case	:				Any  					=			'cuda'
__snake_case	:				str  					=			1_000.0  # normalization for rewards/returns
__snake_case	:				str  					=			3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__snake_case	:				int  					=			np.array(
    [
        1.311_279,
        -0.08_469_521,
        -0.5_382_719,
        -0.07_201_576,
        0.04_932_366,
        2.1_066_856,
        -0.15_017_354,
        0.00_878_345,
        -0.2_848_186,
        -0.18_540_096,
        -0.28_461_286,
    ]
)
__snake_case	:				str  					=			np.array(
    [
        0.17_790_751,
        0.05_444_621,
        0.21_297_139,
        0.14_530_419,
        0.6_124_444,
        0.85_174_465,
        1.4_515_252,
        0.6_751_696,
        1.536_239,
        1.6_160_746,
        5.6_072_536,
    ]
)
__snake_case	:				str  					=			torch.from_numpy(state_mean).to(device=device)
__snake_case	:				str  					=			torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__snake_case	:				str  					=			DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
__snake_case	:				str  					=			model.to(device)
model.eval()

for ep in range(10):
 __snake_case					,  __snake_case	:				Union[str, Any]  					=			0, 0
 __snake_case	:				List[Any]  					=			env.reset()
 __snake_case	:				List[str]  					=			torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
 __snake_case	:				Union[str, Any]  					=			torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
 __snake_case	:				List[str]  					=			torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
 __snake_case	:				str  					=			torch.zeros(0, device=device, dtype=torch.floataa)

 __snake_case	:				int  					=			torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
 for t in range(max_ep_len):
  env.render()
  # add padding
  __snake_case	:				Dict  					=			torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
  __snake_case	:				Dict  					=			torch.cat([rewards, torch.zeros(1, device=device)])

  __snake_case	:				Any  					=			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),
  )
  __snake_case	:				List[str]  					=			action
  __snake_case	:				int  					=			action.detach().cpu().numpy()

  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case	:				Union[str, Any]  					=			env.step(action)

  __snake_case	:				Tuple  					=			torch.from_numpy(state).to(device=device).reshape(1, state_dim)
  __snake_case	:				Dict  					=			torch.cat([states, cur_state], dim=0)
  __snake_case	:				Optional[Any]  					=			reward

  __snake_case	:				Union[str, Any]  					=			target_return[0, -1] - (reward / scale)
  __snake_case	:				Dict  					=			torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
  __snake_case	:				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



