






'''simple docstring'''



import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE							=						get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_       (     __A					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      ReformerTokenizer
      UpperCamelCase_             =      ReformerTokenizerFast
      UpperCamelCase_             =      True
      UpperCamelCase_             =      False
      UpperCamelCase_             =      True






      def 					A__   (			self      :		Dict		)	->	Tuple:


       '''simple docstring'''

       super().setUp()

       lowercase       : Optional[Any]						=ReformerTokenizer(UpperCAmelCase  ,   keep_accents=UpperCAmelCase		)
       tokenizer.save_pretrained(self.tmpdirname		)






      def 					A__   (			self      :		Optional[int]		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : List[Any]						='''<s>'''
       lowercase       : List[Any]						=1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase		)  ,   UpperCAmelCase		)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase		)  ,   UpperCAmelCase		)






      def 					A__   (			self      :		Optional[Any]		)	->	Any:


       '''simple docstring'''

       lowercase       : Any						=list(self.get_tokenizer().get_vocab().keys()		)

       self.assertEqual(vocab_keys[0]  ,   '''<unk>'''		)
       self.assertEqual(vocab_keys[1]  ,   '''<s>'''		)
       self.assertEqual(vocab_keys[-1]  ,   '''j'''		)
       self.assertEqual(len(UpperCAmelCase		)  ,   1000		)






      def 					A__   (			self      :		Tuple		)	->	str:


       '''simple docstring'''

       self.assertEqual(self.get_tokenizer().vocab_size  ,   1000		)






      def 					A__   (			self      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       if not self.test_rust_tokenizer:
        return

       lowercase       : List[str]						=self.get_tokenizer()
       lowercase       : int						=self.get_rust_tokenizer()

       lowercase       : Any						='''I was born in 92000, and this is falsé.'''

       lowercase       : Optional[Any]						=tokenizer.tokenize(UpperCAmelCase		)
       lowercase       : List[Any]						=rust_tokenizer.tokenize(UpperCAmelCase		)
       self.assertListEqual(UpperCAmelCase  ,   UpperCAmelCase		)

       lowercase       : List[Any]						=tokenizer.encode(UpperCAmelCase  ,   add_special_tokens=UpperCAmelCase		)
       lowercase       : Optional[int]						=rust_tokenizer.encode(UpperCAmelCase  ,   add_special_tokens=UpperCAmelCase		)
       self.assertListEqual(UpperCAmelCase  ,   UpperCAmelCase		)

       lowercase       : Optional[Any]						=self.get_rust_tokenizer()
       lowercase       : str						=tokenizer.encode(UpperCAmelCase		)
       lowercase       : Any						=rust_tokenizer.encode(UpperCAmelCase		)
       self.assertListEqual(UpperCAmelCase  ,   UpperCAmelCase		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		int=15		)	->	Optional[int]:


       '''simple docstring'''

       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
        with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
         lowercase       : Tuple						=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase  ,   **UpperCAmelCase		)

         # Simple input
         lowercase       : List[str]						='''This is a simple input'''
         lowercase       : List[Any]						=['''This is a simple input 1''', '''This is a simple input 2''']
         lowercase       : Optional[int]						=('''This is a simple input''', '''This is a pair''')
         lowercase       : List[Any]						=[
             ('''This is a simple input 1''', '''This is a simple input 2'''),
             ('''This is a simple pair 1''', '''This is a simple pair 2'''),
         ]

         # Simple input tests
         self.assertRaises(UpperCAmelCase  ,   tokenizer_r.encode  ,   UpperCAmelCase  ,   max_length=UpperCAmelCase  ,   padding='''max_length'''		)

         # Simple input
         self.assertRaises(UpperCAmelCase  ,   tokenizer_r.encode_plus  ,   UpperCAmelCase  ,   max_length=UpperCAmelCase  ,   padding='''max_length'''		)

         # Simple input
         self.assertRaises(
             UpperCAmelCase  ,   tokenizer_r.batch_encode_plus  ,   UpperCAmelCase  ,   max_length=UpperCAmelCase  ,   padding='''max_length'''  ,   )

         # Pair input
         self.assertRaises(UpperCAmelCase  ,   tokenizer_r.encode  ,   UpperCAmelCase  ,   max_length=UpperCAmelCase  ,   padding='''max_length'''		)

         # Pair input
         self.assertRaises(UpperCAmelCase  ,   tokenizer_r.encode_plus  ,   UpperCAmelCase  ,   max_length=UpperCAmelCase  ,   padding='''max_length'''		)

         # Pair input
         self.assertRaises(
             UpperCAmelCase  ,   tokenizer_r.batch_encode_plus  ,   UpperCAmelCase  ,   max_length=UpperCAmelCase  ,   padding='''max_length'''  ,   )






      def 					A__   (			self      :		str		)	->	int:


       '''simple docstring'''

       pass






      def 					A__   (			self      :		List[Any]		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : Any						=ReformerTokenizer(UpperCAmelCase  ,   keep_accents=UpperCAmelCase		)

       lowercase       : str						=tokenizer.tokenize('''This is a test'''		)
       self.assertListEqual(UpperCAmelCase  ,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']		)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(UpperCAmelCase		)  ,   [285, 46, 10, 170, 382]  ,   )

       lowercase       : Any						=tokenizer.tokenize('''I was born in 92000, and this is falsé.'''		)
       self.assertListEqual(
           UpperCAmelCase  ,   [
               SPIECE_UNDERLINE + '''I''',
               SPIECE_UNDERLINE + '''was''',
               SPIECE_UNDERLINE + '''b''',
               '''or''',
               '''n''',
               SPIECE_UNDERLINE + '''in''',
               SPIECE_UNDERLINE + '''''',
               '''9''',
               '''2''',
               '''0''',
               '''0''',
               '''0''',
               ''',''',
               SPIECE_UNDERLINE + '''and''',
               SPIECE_UNDERLINE + '''this''',
               SPIECE_UNDERLINE + '''is''',
               SPIECE_UNDERLINE + '''f''',
               '''al''',
               '''s''',
               '''é''',
               '''.''',
           ]  ,   )
       lowercase       : Dict						=tokenizer.convert_tokens_to_ids(UpperCAmelCase		)
       self.assertListEqual(
           UpperCAmelCase  ,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]  ,   )

       lowercase       : str						=tokenizer.convert_ids_to_tokens(UpperCAmelCase		)
       self.assertListEqual(
           UpperCAmelCase  ,   [
               SPIECE_UNDERLINE + '''I''',
               SPIECE_UNDERLINE + '''was''',
               SPIECE_UNDERLINE + '''b''',
               '''or''',
               '''n''',
               SPIECE_UNDERLINE + '''in''',
               SPIECE_UNDERLINE + '''''',
               '''<unk>''',
               '''2''',
               '''0''',
               '''0''',
               '''0''',
               ''',''',
               SPIECE_UNDERLINE + '''and''',
               SPIECE_UNDERLINE + '''this''',
               SPIECE_UNDERLINE + '''is''',
               SPIECE_UNDERLINE + '''f''',
               '''al''',
               '''s''',
               '''<unk>''',
               '''.''',
           ]  ,   )






      @cached_property
      def 					A__   (			self      :		str		)	->	Optional[int]:


       '''simple docstring'''

       return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''		)






      @slow
      def 					A__   (			self      :		str		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Tuple						='''Hello World!'''
       lowercase       : Any						=[126, 32, 262, 152, 38, 72, 287]

       self.assertListEqual(UpperCAmelCase  ,   self.big_tokenizer.encode(UpperCAmelCase		)		)






      @slow
      def 					A__   (			self      :		Optional[Any]		)	->	Tuple:


       '''simple docstring'''

       lowercase       : str						=(
           '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
           ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
       )
       lowercase       : Union[str, Any]						=[
           108,
           265,
           24,
           111,
           4,
           258,
           156,
           35,
           28,
           275,
           3,
           259,
           297,
           260,
           84,
           4,
           35,
           110,
           44,
           8,
           259,
           91,
           268,
           21,
           11,
           209,
           274,
           109,
           266,
           277,
           117,
           86,
           93,
           315,
           258,
           278,
           258,
           277,
           258,
           0,
           258,
           288,
           258,
           319,
           258,
           0,
           258,
           0,
           258,
           0,
           258,
           0,
           258,
           287,
           258,
           315,
           258,
           289,
           258,
           278,
           99,
           269,
           266,
           262,
           8,
           259,
           241,
           4,
           217,
           230,
           268,
           266,
           55,
           168,
           106,
           75,
           193,
           266,
           223,
           27,
           49,
           26,
           282,
           25,
           264,
           299,
           19,
           26,
           0,
           258,
           277,
           117,
           86,
           93,
           176,
           183,
           270,
           11,
           262,
           42,
           61,
           265,
       ]

       self.assertListEqual(UpperCAmelCase  ,   self.big_tokenizer.encode(UpperCAmelCase		)		)






      @require_torch
      @slow
      def 					A__   (			self      :		Optional[Any]		)	->	List[Any]:


       '''simple docstring'''

       import torch

       from transformers import ReformerConfig, ReformerModel

       # Build sequence
       lowercase       : List[Any]						=list(self.big_tokenizer.get_vocab().keys()		)[:10]
       lowercase       : Union[str, Any]						=''' '''.join(UpperCAmelCase		)
       lowercase       : int						=self.big_tokenizer.encode_plus(UpperCAmelCase  ,   return_tensors='''pt'''		)
       lowercase       : List[str]						=self.big_tokenizer.batch_encode_plus([sequence, sequence]  ,   return_tensors='''pt'''		)

       lowercase       : Optional[Any]						=ReformerConfig()
       # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
       lowercase       : str						=encoded_sequence['''input_ids'''].shape
       lowercase       : Any						=ReformerModel(UpperCAmelCase		)

       # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
       assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

       with torch.no_grad():
        model(**UpperCAmelCase		)
        model(**UpperCAmelCase		)






      @slow
      def 					A__   (			self      :		int		)	->	Dict:


       '''simple docstring'''

       lowercase       : Optional[Any]						={'''input_ids''': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
       # fmt: on

       # This tokenizer does not know some characters like ")".
       # That is the reason why we use very simple texts here.
       # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
       lowercase       : Optional[Any]						=[
           '''This is a very simple sentence.''',
           '''The quick brown fox jumps over the lazy dog.''',
       ]

       self.tokenizer_integration_test_util(
           expected_encoding=UpperCAmelCase  ,   model_name='''google/reformer-crime-and-punishment'''  ,   revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a'''  ,   padding=UpperCAmelCase  ,   sequences=UpperCAmelCase  ,   )








'''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 __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A=13					,  A=7					,  A=6					,  A=17					,  A=23					,  A=11					,  A=True					,  )       ->	Tuple:


      """simple docstring"""



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



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    floats_tensor((self.batch_size, self.seq_length, 1)			)
      _a           =    ids_tensor((self.batch_size, self.seq_length)					,  vocab_size=1_000			)
      _a           =    random_attention_mask((self.batch_size, self.seq_length)			)

      _a           =    self.get_config()

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



 def    a__    (self			)       ->	str:


      """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    a__    (self					,  A					,  A					,  A					,  A					,  A					,  A					,  A					,  )       ->	List[Any]:


      """simple docstring"""



      _a           =    DecisionTransformerModel(config=A			)
      model.to(A			)
      model.eval()
      _a           =    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    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.prepare_config_and_inputs()
      (
          (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       (
          _a
      )    ,       
      )           =    config_and_inputs
      _a           =    {
          '''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 __A     (    A  ,  A  ,  A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			Optional[Any]		=			(DecisionTransformerModel,) if is_torch_available() else ()
 __lowerCamelCase       :			List[str]		=			()
 __lowerCamelCase       :			Tuple		=			{'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			List[str]		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			str		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Tuple		=			False
 __lowerCamelCase       :			Dict		=			False
 __lowerCamelCase       :			List[str]		=			False



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      _a           =    DecisionTransformerModelTester(self			)
      _a           =    ConfigTester(self					,  config_class=A					,  hidden_size=37			)



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


      """simple docstring"""



      self.config_tester.run_common_tests()



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


      """simple docstring"""



      _a           =    self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A			)



 @slow
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



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



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


      """simple docstring"""



      _a    ,       _a           =    self.model_tester.prepare_config_and_inputs_for_common()

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

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

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






@require_torch
class __A     (    unittest.TestCase		):



 '''simple docstring'''







 @slow
 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    2  # number of steps of autoregressive prediction we will perform
      _a           =    10  # defined by the RL environment, may be normalized
      _a           =    DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''			)
      _a           =    model.to(A			)
      _a           =    model.config
      torch.manual_seed(0			)
      _a           =    torch.randn(1					,  1					,  config.state_dim			).to(device=A					,  dtype=torch.floataa			)  # env.reset()

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

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

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

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

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

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



