code: 
  '''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   snake_case_       (							_a     :							List[str] ,					_a     :							Optional[Any] ,					_a     :							List[Any] ,					_a     :							Tuple ,					_a     :							str ,					_a     :							Union[str, Any]			):
        # we don't care about the past rewards in this model
  
        snake_case_						       =							states.reshape(1 ,					-1 ,					model.config.state_dim			)
        snake_case_						       =							actions.reshape(1 ,					-1 ,					model.config.act_dim			)
        snake_case_						       =							returns_to_go.reshape(1 ,					-1 ,					1			)
        snake_case_						       =							timesteps.reshape(1 ,					-1			)
  
        if model.config.max_length is not None:
              snake_case_						       =							states[:, -model.config.max_length :]
              snake_case_						       =							actions[:, -model.config.max_length :]
              snake_case_						       =							returns_to_go[:, -model.config.max_length :]
              snake_case_						       =							timesteps[:, -model.config.max_length :]
  
              # pad all tokens to sequence length
              snake_case_						       =							torch.cat(
                  [torch.zeros(model.config.max_length - states.shape[1]			), torch.ones(states.shape[1]			)]			)
              snake_case_						       =							attention_mask.to(dtype=torch.long ,					device=states.device			).reshape(1 ,					-1			)
              snake_case_						       =							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			)
              snake_case_						       =							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			)
              snake_case_						       =							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			)
              snake_case_						       =							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:
              snake_case_						       =							None
  
        snake_case_       ,	snake_case_       ,	snake_case_						       =							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
  
  A           =   gym.make("Hopper-v3")
  A           =   env.observation_space.shape[0]
  A           =   env.action_space.shape[0]
  A           =   1_0_0_0
  A           =   "cuda"
  A           =   10_00.0  # normalization for rewards/returns
  A           =   3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
  A           =   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           =   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           =   torch.from_numpy(state_mean).to(device=device)
  A           =   torch.from_numpy(state_std).to(device=device)
  
  # Create the decision transformer model
  A           =   DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
  A           =   model.to(device)
  model.eval()
  
  for ep in range(1_0):
       A    ,     A           =   0, 0
       A           =   env.reset()
       A           =   torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
       A           =   torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
       A           =   torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
       A           =   torch.zeros(0, device=device, dtype=torch.floataa)
  
       A           =   torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
       for t in range(max_ep_len):
            env.render()
            # add padding
            A           =   torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
            A           =   torch.cat([rewards, torch.zeros(1, device=device)])
  
            A           =   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           =   action
            A           =   action.detach().cpu().numpy()
  
            A    ,     A    ,     A    ,     A           =   env.step(action)
  
            A           =   torch.from_numpy(state).to(device=device).reshape(1, state_dim)
            A           =   torch.cat([states, cur_state], dim=0)
            A           =   reward
  
            A           =   target_return[0, -1] - (reward / scale)
            A           =   torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
            A           =   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
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  import json
  import os
  import pickle
  import subprocess
  from hashlib import mda
  from pathlib import Path
  from tempfile import gettempdir
  from textwrap import dedent
  from types import CodeType, FunctionType
  from unittest import TestCase
  from unittest.mock import patch
  
  import pytest
  from multiprocess import Pool
  from packaging import version
  
  import datasets
  from datasets.fingerprint import Hasher, fingerprint_transform
  from datasets.table import InMemoryTable
  
  from .utils import (
      require_regex,
      require_spacy,
      require_spacy_model,
      require_tiktoken,
      require_torch,
      require_transformers,
  )
  
  
  
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : int	, lowerCAmelCase_  : Optional[Any]    )   ->       Tuple:
          snake_case_						       =							foo
  
  
    def __call__(self  : List[Any]    )   ->       Optional[int]:
          return self.foo
  
  
  
  class  lowerCAmelCase__   (       datasets.Dataset       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    @fingerprint_transform(inplace=lowerCAmelCase_    )
    def 		_lowerCamelCase	(self  : Tuple	, lowerCAmelCase_  : Any	, *lowerCAmelCase_  : Union[str, Any]	, **lowerCAmelCase_  : Any    )   ->       str:
          return DatasetChild(self.data	, fingerprint=lowerCAmelCase_    )
  
  
    @fingerprint_transform(inplace=lowerCAmelCase_    )
    def 		_lowerCamelCase	(self  : str	, lowerCAmelCase_  : Optional[Any]	, *lowerCAmelCase_  : List[Any]	, **lowerCAmelCase_  : Any    )   ->       List[str]:
          return DatasetChild(self.data	, fingerprint=lowerCAmelCase_    )
  
  
  
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : int	, lowerCAmelCase_  : List[str]    )   ->       Dict:
          snake_case_						       =							callable
  
    def __call__(self  : List[str]	, *lowerCAmelCase_  : Any	, **lowerCAmelCase_  : Tuple    )   ->       Dict:
          if self.callable is not None:
                return self.callable(*lowerCAmelCase_	, **lowerCAmelCase_    )
  
  
    def __getstate__(self  : List[str]    )   ->       Tuple:
          raise pickle.PicklingError()
  
  
  
  class  lowerCAmelCase__   (       UpperCamelCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    @require_transformers
    @pytest.mark.integration
    def 		_lowerCamelCase	(self  : str    )   ->       str:
          from transformers import AutoTokenizer
  
          def encode(lowerCAmelCase_  : str    ):
                return tokenizer(lowerCAmelCase_    )
  
          # TODO: add hash consistency tests across sessions
          snake_case_						       =							AutoTokenizer.from_pretrained("bert-base-uncased"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lambda lowerCAmelCase_    : tokenizer(lowerCAmelCase_    )    )    ).hexdigest()
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							AutoTokenizer.from_pretrained("bert-base-cased"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lambda lowerCAmelCase_    : tokenizer(lowerCAmelCase_    )    )    ).hexdigest()
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							AutoTokenizer.from_pretrained("bert-base-uncased"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lambda lowerCAmelCase_    : tokenizer(lowerCAmelCase_    )    )    ).hexdigest()
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    @require_transformers
    @pytest.mark.integration
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       Optional[int]:
          from transformers import AutoTokenizer
  
          snake_case_						       =							AutoTokenizer.from_pretrained("gpt2"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          tokenizer("Hello world !"    )  # call once to change the tokenizer's cache
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
  
    @require_regex
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       Optional[int]:
          import regex
  
          snake_case_						       =							regex.Regex("foo"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							regex.Regex("bar"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							regex.Regex("foo"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
  
  
  class  lowerCAmelCase__   (       UpperCamelCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       Optional[Any]:
          def func():
                return foo
  
          snake_case_						       =							[0]
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							[1]
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							[0]
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : str    )   ->       Tuple:
          def func():
                pass
  
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
  
          def func():
                pass
  
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : List[Any]    )   ->       Union[str, Any]:
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([0]    )    )    ).hexdigest()
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([1]    )    )    ).hexdigest()
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([0]    )    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       int:
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([0]    ).__call__    )    ).hexdigest()
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([1]    ).__call__    )    ).hexdigest()
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(Foo([0]    ).__call__    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : int    )   ->       Optional[Any]:
          snake_case_						       =							(
              "co_argcount",
              "co_kwonlyargcount",
              "co_nlocals",
              "co_stacksize",
              "co_flags",
              "co_code",
              "co_consts",
              "co_names",
              "co_varnames",
              "co_filename",
              "co_name",
              "co_firstlineno",
              "co_lnotab",
              "co_freevars",
              "co_cellvars",
          )
  
          def _create_code_pyaa(*lowerCAmelCase_  : Any    ):
                if hasattr(args[-3]	, "encode"    ):
                      snake_case_						       =							list(lowerCAmelCase_    )
                      snake_case_						       =							args[-3].encode()  # co_lnotab
                      snake_case_						       =							args[-10].encode()  # co_code
                if hasattr(lowerCAmelCase_	, "co_posonlyargcount"    ):
                      if len(lowerCAmelCase_    ) == 16:
                            return CodeType(*lowerCAmelCase_    )
                      elif len(lowerCAmelCase_    ) == 15:
                            return CodeType(args[0]	, 0	, *args[1:]    )
                      return CodeType(args[0]	, 0	, 0	, *args[1:]    )
                elif hasattr(lowerCAmelCase_	, "co_kwonlyargcount"    ):
                      if len(lowerCAmelCase_    ) == 16:
                            return CodeType(args[0]	, *args[2:]    )
                      elif len(lowerCAmelCase_    ) == 15:
                            return CodeType(*lowerCAmelCase_    )
                      return CodeType(args[0]	, 0	, *args[1:]    )
                if len(lowerCAmelCase_    ) == 16:
                      return CodeType(args[0]	, *args[3:]    )
                elif len(lowerCAmelCase_    ) == 15:
                      return CodeType(args[0]	, *args[2:]    )
                return CodeType(*lowerCAmelCase_    )
  
          def create_ipython_func(lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : Any    ):
                def func():
                      return returned_obj
  
                snake_case_						       =							func.__code__
                # Use _create_code from dill in order to make it work for different python versions
                if datasets.config.PY_VERSION < version.parse("3.8"    ):
                      snake_case_						       =							_create_code_pyaa(
                          *[getattr(lowerCAmelCase_	, lowerCAmelCase_    ) if k != "co_filename" else co_filename for k in code_args_pyaa]    )
                else:
                      snake_case_						       =							code.replace(co_filename=lowerCAmelCase_    )
                return FunctionType(lowerCAmelCase_	, func.__globals__	, func.__name__	, func.__defaults__	, func.__closure__    )
  
          snake_case_       ,	snake_case_						       =							"<ipython-input-2-e0383a102aae>", [0]
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
          snake_case_       ,	snake_case_						       =							"<ipython-input-2-e0383a102aae>", [1]
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
          snake_case_       ,	snake_case_						       =							"<ipython-input-5-713f6613acf3>", [0]
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
          snake_case_       ,	snake_case_						       =							os.path.join(gettempdir()	, "ipykernel_12345"	, "321456789.py"    ), [0]
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
          snake_case_       ,	snake_case_						       =							os.path.join(gettempdir()	, "ipykernel_12345"	, "321456789.py"    ), [1]
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
          snake_case_       ,	snake_case_						       =							os.path.join(gettempdir()	, "ipykernel_12345"	, "654123987.py"    ), [0]
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(create_ipython_func(lowerCAmelCase_	, lowerCAmelCase_    )    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
  
    def 		_lowerCamelCase	(self  : int    )   ->       List[Any]:
          snake_case_       ,	snake_case_						       =							[0], [1]
  
          def func():
                return foo, bar
  
          snake_case_						       =							"__main__"
  
          def globalvars_mocka_side_effect(lowerCAmelCase_  : str	, *lowerCAmelCase_  : Tuple	, **lowerCAmelCase_  : List[Any]    ):
                return {"foo": foo, "bar": bar}
  
          def globalvars_mocka_side_effect(lowerCAmelCase_  : List[str]	, *lowerCAmelCase_  : List[str]	, **lowerCAmelCase_  : Optional[Any]    ):
                return {"bar": bar, "foo": foo}
  
          with patch("dill.detect.globalvars"	, side_effect=lowerCAmelCase_    ) as globalvars_mocka:
                snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
                self.assertGreater(globalvars_mocka.call_count	, 0    )
          with patch("dill.detect.globalvars"	, side_effect=lowerCAmelCase_    ) as globalvars_mocka:
                snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
                self.assertGreater(globalvars_mocka.call_count	, 0    )
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
  
  
  class  lowerCAmelCase__   (       UpperCamelCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       Tuple:
          snake_case_						       =							Hasher.hash("hello"    )
          snake_case_						       =							Hasher.hash("hello"    )
          snake_case_						       =							Hasher.hash("there"    )
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : Any    )   ->       int:
          snake_case_						       =							Hasher.hash(Foo("hello"    )    )
          snake_case_						       =							Hasher.hash(Foo("hello"    )    )
          snake_case_						       =							Hasher.hash(Foo("there"    )    )
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : List[Any]    )   ->       List[Any]:
          snake_case_						       =							Hasher()
          for x in ["hello", Foo("hello"    )]:
                hasher.update(lowerCAmelCase_    )
          snake_case_						       =							hasher.hexdigest()
          snake_case_						       =							Hasher()
          for x in ["hello", Foo("hello"    )]:
                hasher.update(lowerCAmelCase_    )
          snake_case_						       =							hasher.hexdigest()
          snake_case_						       =							Hasher()
          for x in ["there", Foo("there"    )]:
                hasher.update(lowerCAmelCase_    )
          snake_case_						       =							hasher.hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    def 		_lowerCamelCase	(self  : str    )   ->       List[Any]:
          with self.assertRaises(pickle.PicklingError    ):
                Hasher.hash(UnpicklableCallable(Foo("hello"    )    )    )
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       Optional[int]:
          snake_case_						       =							"abc"
          snake_case_						       =							[string, string]  # two strings have the same ids
          snake_case_						       =							[string, string]
          snake_case_						       =							json.loads(f'''["{string}", "{string}"]'''    )  # two strings have different ids
          self.assertIs(obja[0]	, lowerCAmelCase_    )
          self.assertIs(obja[0]	, obja[1]    )
          self.assertIs(obja[0]	, lowerCAmelCase_    )
          self.assertIs(obja[0]	, obja[1]    )
          self.assertIsNot(obja[0]	, lowerCAmelCase_    )
          self.assertIsNot(obja[0]	, obja[1]    )
          snake_case_						       =							Hasher.hash(lowerCAmelCase_    )
          snake_case_						       =							Hasher.hash(lowerCAmelCase_    )
          snake_case_						       =							Hasher.hash(lowerCAmelCase_    )
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    @require_tiktoken
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       Union[str, Any]:
          import tiktoken
  
          snake_case_						       =							tiktoken.get_encoding("gpt2"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							tiktoken.get_encoding("r50k_base"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							tiktoken.get_encoding("gpt2"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    @require_torch
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       str:
          import torch
  
          snake_case_						       =							torch.tensor([1.0]    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							torch.tensor([2.0]    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							torch.tensor([1.0]    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
  
    @require_spacy
    @require_spacy_model("en_core_web_sm"    )
    @require_spacy_model("fr_core_news_sm"    )
    @pytest.mark.integration
    def 		_lowerCamelCase	(self  : int    )   ->       Union[str, Any]:
          import spacy
  
          snake_case_						       =							spacy.load("en_core_web_sm"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							spacy.load("fr_core_news_sm"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          snake_case_						       =							spacy.load("en_core_web_sm"    )
          snake_case_						       =							mda(datasets.utils.py_utils.dumps(lowerCAmelCase_    )    ).hexdigest()
          self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertNotEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
  @pytest.mark.integration
  def   snake_case_       (							_a     :							Path			):
        snake_case_						       =							tmp_path / "dir1"
        snake_case_						       =							tmp_path / "dir2"
        dira.mkdir()
        dira.mkdir()
        snake_case_						       =							"script.py"
        snake_case_						       =							dedent(
            "\n    from datasets.fingerprint import Hasher\n    def foo():\n        pass\n    print(Hasher.hash(foo))\n    "			)
        snake_case_						       =							dira / script_filename
        snake_case_						       =							dira / script_filename
        with script_patha.open("w"			) as f:
              f.write(_a			)
        with script_patha.open("w"			) as f:
              f.write(_a			)
        snake_case_						       =							subprocess.check_output(["python", str(_a			)]			)
        snake_case_						       =							subprocess.check_output(["python", str(_a			)]			)
        assert fingerprinta == fingerprinta
  def   snake_case_       (							):
        snake_case_						       =							{"a": [0, 1, 2]}
        snake_case_						       =							DatasetChild(InMemoryTable.from_pydict(_a			)			)
        snake_case_						       =							dataset.funca()._fingerprint
        assert expected_fingerprint == dataset.funca()._fingerprint
        assert expected_fingerprint != dataset.funca()._fingerprint
  
        with Pool(2			) as p:
              assert expected_fingerprint == p.apply_async(dataset.funca			).get()._fingerprint
              assert expected_fingerprint != p.apply_async(dataset.funca			).get()._fingerprint
  def   snake_case_       (							):
        snake_case_						       =							{"a": [0, 1, 2]}
  
  
  
        class  lowerCAmelCase__   (       datasets.Dataset       ):
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          @fingerprint_transform(inplace=lowerCAmelCase_    )
          def 		_lowerCamelCase	(self  : int	, lowerCAmelCase_  : str    )   ->       List[Any]:
                return DummyDatasetChild(self.data	, fingerprint=lowerCAmelCase_    )
  
        snake_case_						       =							DummyDatasetChild(InMemoryTable.from_pydict(_a			)			).func()
  
  
  
        class  lowerCAmelCase__   (       datasets.Dataset       ):
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          @fingerprint_transform(inplace=lowerCAmelCase_	, version="1.0.0"    )
          def 		_lowerCamelCase	(self  : Dict	, lowerCAmelCase_  : Any    )   ->       Any:
                return DummyDatasetChild(self.data	, fingerprint=lowerCAmelCase_    )
  
        snake_case_						       =							DummyDatasetChild(InMemoryTable.from_pydict(_a			)			).func()
  
  
  
        class  lowerCAmelCase__   (       datasets.Dataset       ):
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          @fingerprint_transform(inplace=lowerCAmelCase_	, version="2.0.0"    )
          def 		_lowerCamelCase	(self  : Union[str, Any]	, lowerCAmelCase_  : Any    )   ->       int:
                return DummyDatasetChild(self.data	, fingerprint=lowerCAmelCase_    )
  
        snake_case_						       =							DummyDatasetChild(InMemoryTable.from_pydict(_a			)			).func()
  
        assert len({fingeprint_no_version, fingeprint_a, fingeprint_a}			) == 3
  
  
  
  
  def   snake_case_       (							):
        # AttributeError: module 'dill._dill' has no attribute 'stack'
        snake_case_						       =							Hasher()
        hasher.update(lambda _a			: x			)
  
  