


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 ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=13 ,					__SCREAMING_SNAKE_CASE=7 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=17 ,					__SCREAMING_SNAKE_CASE=23 ,					__SCREAMING_SNAKE_CASE=11 ,					__SCREAMING_SNAKE_CASE=True ,					):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		parent
              UpperCamelCase    :	Optional[Any]   	=		batch_size
              UpperCamelCase    :	Optional[int]   	=		seq_length
              UpperCamelCase    :	Any   	=		act_dim
              UpperCamelCase    :	int   	=		state_dim
              UpperCamelCase    :	Dict   	=		hidden_size
              UpperCamelCase    :	Dict   	=		max_length
              UpperCamelCase    :	Tuple   	=		is_training






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		floats_tensor((self.batch_size, self.seq_length, self.state_dim)						)
              UpperCamelCase    :	List[Any]   	=		floats_tensor((self.batch_size, self.seq_length, self.act_dim)						)
              UpperCamelCase    :	Tuple   	=		floats_tensor((self.batch_size, self.seq_length, 1)						)
              UpperCamelCase    :	List[Any]   	=		floats_tensor((self.batch_size, self.seq_length, 1)						)
              UpperCamelCase    :	List[Any]   	=		ids_tensor((self.batch_size, self.seq_length) ,					vocab_size=1_000						)
              UpperCamelCase    :	Dict   	=		random_attention_mask((self.batch_size, self.seq_length)						)

              UpperCamelCase    :	Tuple   	=		self.get_config()

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






       def  _lowercase   (     self						):




              """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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		DecisionTransformerModel(config=__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	Union[str, Any]   	=		model(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.prepare_config_and_inputs()
              (
                  (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, (
                  UpperCamelCase
              )	, 
              )    :	Optional[Any]   	=		config_and_inputs
              UpperCamelCase    :	Tuple   	=		{
                  '''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_	(							_a,     _a,     _a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[Any]							=					(DecisionTransformerModel,) if is_torch_available() else ()
       __UpperCamelCase      :     List[str]							=					()
       __UpperCamelCase      :     List[str]							=					{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

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






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		DecisionTransformerModelTester(self						)
              UpperCamelCase    :	str   	=		ConfigTester(self ,					config_class=__SCREAMING_SNAKE_CASE ,					hidden_size=37						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.config_tester.run_common_tests()






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     UpperCamelCase    :	Optional[int]   	=		DecisionTransformerModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
                     self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	Tuple   	=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase    :	Union[str, Any]   	=		model_class(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Any   	=		inspect.signature(model.forward						)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     UpperCamelCase    :	List[str]   	=		[*signature.parameters.keys()]

                     UpperCamelCase    :	int   	=		[
                         '''states''',
                         '''actions''',
                         '''rewards''',
                         '''returns_to_go''',
                         '''timesteps''',
                         '''attention_mask''',
                     ]

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




@require_torch
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




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

              UpperCamelCase    :	Dict   	=		torch.tensor(
                  [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]] ,					device=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[str]   	=		torch.tensor(__SCREAMING_SNAKE_CASE ,					device=__SCREAMING_SNAKE_CASE ,					dtype=torch.floataa						).reshape(1 ,					1 ,					1						)
              UpperCamelCase    :	Optional[Any]   	=		state
              UpperCamelCase    :	List[str]   	=		torch.zeros(1 ,					0 ,					config.act_dim ,					device=__SCREAMING_SNAKE_CASE ,					dtype=torch.floataa						)
              UpperCamelCase    :	Optional[Any]   	=		torch.zeros(1 ,					0 ,					device=__SCREAMING_SNAKE_CASE ,					dtype=torch.floataa						)
              UpperCamelCase    :	List[str]   	=		torch.tensor(0 ,					device=__SCREAMING_SNAKE_CASE ,					dtype=torch.long						).reshape(1 ,					1						)

              for step in range(__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	List[Any]   	=		torch.cat([actions, torch.zeros(1 ,					1 ,					config.act_dim ,					device=__SCREAMING_SNAKE_CASE						)] ,					dim=1						)
                     UpperCamelCase    :	Tuple   	=		torch.cat([rewards, torch.zeros(1 ,					1 ,					device=__SCREAMING_SNAKE_CASE						)] ,					dim=1						)

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

                     with torch.no_grad():
                            UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	Any   	=		model(
                                states=__SCREAMING_SNAKE_CASE ,					actions=__SCREAMING_SNAKE_CASE ,					rewards=__SCREAMING_SNAKE_CASE ,					returns_to_go=__SCREAMING_SNAKE_CASE ,					timesteps=__SCREAMING_SNAKE_CASE ,					attention_mask=__SCREAMING_SNAKE_CASE ,					return_dict=__SCREAMING_SNAKE_CASE ,					)

                     self.assertEqual(action_pred.shape ,					actions.shape						)
                     self.assertTrue(torch.allclose(action_pred[0, -1] ,					expected_outputs[step] ,					atol=1e-4						)						)
                     UpperCamelCase	, UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	int   	=		(  # env.step(action)
                         torch.randn(1 ,					1 ,					config.state_dim						).to(device=__SCREAMING_SNAKE_CASE ,					dtype=torch.floataa						),
                         1.0,
                         False,
                         {},
                     )

                     UpperCamelCase    :	str   	=		action_pred[0, -1]
                     UpperCamelCase    :	str   	=		torch.cat([states, state] ,					dim=1						)
                     UpperCamelCase    :	Any   	=		returns_to_go[0, -1] - reward
                     UpperCamelCase    :	Optional[int]   	=		torch.cat([returns_to_go, pred_return.reshape(1 ,					1 ,					1						)] ,					dim=1						)
                     UpperCamelCase    :	Union[str, Any]   	=		torch.cat(
                         [timesteps, torch.ones((1, 1) ,					device=__SCREAMING_SNAKE_CASE ,					dtype=torch.long						) * (step + 1)] ,					dim=1						)





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class    UpperCAmelCase_	(							_a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     str							=					DebertaTokenizer
       __UpperCamelCase      :     Optional[int]							=					True
       __UpperCamelCase      :     Optional[int]							=					DebertaTokenizerFast






       def  _lowercase   (     self						):




              """simple docstring"""




              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              UpperCamelCase    :	Optional[int]   	=		[
                  '''l''',
                  '''o''',
                  '''w''',
                  '''e''',
                  '''r''',
                  '''s''',
                  '''t''',
                  '''i''',
                  '''d''',
                  '''n''',
                  '''\u0120''',
                  '''\u0120l''',
                  '''\u0120n''',
                  '''\u0120lo''',
                  '''\u0120low''',
                  '''er''',
                  '''\u0120lowest''',
                  '''\u0120newer''',
                  '''\u0120wider''',
                  '''[UNK]''',
              ]
              UpperCamelCase    :	Tuple   	=		dict(zip(__SCREAMING_SNAKE_CASE ,					range(len(__SCREAMING_SNAKE_CASE						)						)						)						)
              UpperCamelCase    :	Any   	=		['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
              UpperCamelCase    :	List[Any]   	=		{'''unk_token''': '''[UNK]'''}

              UpperCamelCase    :	Union[str, Any]   	=		os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''vocab_file''']						)
              UpperCamelCase    :	Optional[int]   	=		os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''merges_file''']						)
              with open(self.vocab_file ,					'''w''' ,					encoding='''utf-8'''						) as fp:
                     fp.write(json.dumps(__SCREAMING_SNAKE_CASE						) + '''\n'''						)
              with open(self.merges_file ,					'''w''' ,					encoding='''utf-8'''						) as fp:
                     fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              kwargs.update(self.special_tokens_map						)
              return self.tokenizer_class.from_pretrained(self.tmpdirname ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		'''lower newer'''
              UpperCamelCase    :	Union[str, Any]   	=		'''lower newer'''
              return input_text, output_text






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		self.get_tokenizer()
              UpperCamelCase    :	int   	=		'''lower newer'''
              UpperCamelCase    :	Union[str, Any]   	=		['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
              UpperCamelCase    :	Tuple   	=		tokenizer.tokenize(__SCREAMING_SNAKE_CASE						)
              self.assertListEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[Any]   	=		tokens + [tokenizer.unk_token]
              UpperCamelCase    :	Optional[int]   	=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						) ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		self.get_tokenizer()
              UpperCamelCase    :	Optional[Any]   	=		tokenizer('''Hello''' ,					'''World'''						)
              UpperCamelCase    :	List[str]   	=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
              self.assertListEqual(tokd['''token_type_ids'''] ,					__SCREAMING_SNAKE_CASE						)






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''						)

              UpperCamelCase    :	Optional[Any]   	=		tokenizer.encode('''sequence builders''' ,					add_special_tokens=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		tokenizer.encode('''multi-sequence build''' ,					add_special_tokens=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Dict   	=		tokenizer.encode(
                  '''sequence builders''' ,					add_special_tokens=__SCREAMING_SNAKE_CASE ,					add_prefix_space=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		tokenizer.encode(
                  '''sequence builders''' ,					'''multi-sequence build''' ,					add_special_tokens=__SCREAMING_SNAKE_CASE ,					add_prefix_space=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[str]   	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              assert encoded_sentence == encoded_text_from_decode
              assert encoded_pair == encoded_pair_from_decode






       @slow
       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		[self.tokenizer_class]
              if self.test_rust_tokenizer:
                     tokenizer_classes.append(self.rust_tokenizer_class						)

              for tokenizer_class in tokenizer_classes:
                     UpperCamelCase    :	Optional[int]   	=		tokenizer_class.from_pretrained('''microsoft/deberta-base'''						)

                     UpperCamelCase    :	str   	=		[
                         '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                         '''ALBERT incorporates two parameter reduction techniques''',
                         '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                         ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                         ''' vocabulary embedding.''',
                     ]

                     UpperCamelCase    :	Union[str, Any]   	=		tokenizer(__SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Union[str, Any]   	=		[tokenizer.decode(__SCREAMING_SNAKE_CASE ,					skip_special_tokens=__SCREAMING_SNAKE_CASE						) for seq in encoding['''input_ids''']]

                     # fmt: off
                     UpperCamelCase    :	int   	=		{
                         '''input_ids''': [
                             [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
                         ],
                         '''token_type_ids''': [
                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                         ],
                         '''attention_mask''': [
                             [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                             [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, 1, 1, 1]
                         ]
                     }
                     # fmt: on

                     UpperCamelCase    :	List[str]   	=		[
                         '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                         '''ALBERT incorporates two parameter reduction techniques''',
                         '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                         ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                         ''' vocabulary embedding.''',
                     ]

                     self.assertDictEqual(encoding.data ,					__SCREAMING_SNAKE_CASE						)

                     for expected, decoded in zip(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                            self.assertEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

