






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__							:




						def __init__(       self					:      List[str]				,    _a					:      str				,    _a					:      Optional[Any]=13				,    _a					:      Dict=7				,    _a					:      int=6				,    _a					:      Any=17				,    _a					:      Dict=23				,    _a					:      Union[str, Any]=11				,    _a					:      List[Any]=True				,    )							->     Optional[int]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =floats_tensor((self.batch_size, self.seq_length, self.state_dim)  )
										_SCREAMING_SNAKE_CASE			  =floats_tensor((self.batch_size, self.seq_length, self.act_dim)  )
										_SCREAMING_SNAKE_CASE			  =floats_tensor((self.batch_size, self.seq_length, 1)  )
										_SCREAMING_SNAKE_CASE			  =floats_tensor((self.batch_size, self.seq_length, 1)  )
										_SCREAMING_SNAKE_CASE			  =ids_tensor((self.batch_size, self.seq_length)				,    vocab_size=1000  )
										_SCREAMING_SNAKE_CASE			  =random_attention_mask((self.batch_size, self.seq_length)  )

										_SCREAMING_SNAKE_CASE			  =self.get_config()

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




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     List[Any]:


										"""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 							__UpperCamelCase				(       self					:      str				,    _a					:      Optional[int]				,    _a					:      str				,    _a					:      Union[str, Any]				,    _a					:      Optional[Any]				,    _a					:      Optional[Any]				,    _a					:      List[str]				,    _a					:      Optional[int]				,    )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =DecisionTransformerModel(config=_a  )
										model.to(_a  )
										model.eval()
										_SCREAMING_SNAKE_CASE			  =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 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.prepare_config_and_inputs()
										(
										    (
										    _SCREAMING_SNAKE_CASE				
										)  ,				(
										    _SCREAMING_SNAKE_CASE				
										)  ,				(
										    _SCREAMING_SNAKE_CASE				
										)  ,				(
										    _SCREAMING_SNAKE_CASE				
										)  ,				(
										    _SCREAMING_SNAKE_CASE				
										)  ,				(
										    _SCREAMING_SNAKE_CASE				
										)  ,				(
										    _SCREAMING_SNAKE_CASE				
										)  ,								
										)			  =config_and_inputs
										_SCREAMING_SNAKE_CASE			  ={
										    '''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__							(		UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						(DecisionTransformerModel,) if is_torch_available() else ()
						UpperCAmelCase             =						()
						UpperCAmelCase             =						{"feature-extraction": DecisionTransformerModel} if is_torch_available() else {}

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

						# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False
						UpperCAmelCase             =						False




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


										"""simple docstring"""





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




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


										"""simple docstring"""





										self.config_tester.run_common_tests()




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





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




						@slow
						def 							__UpperCamelCase				(       self					:      Dict  )							->     Union[str, Any]:


										"""simple docstring"""





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




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

@require_torch
class 						A__							(		unittest.TestCase  ):




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


										"""simple docstring"""





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

										_SCREAMING_SNAKE_CASE			  =torch.tensor(
										    [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]				,    device=_a  )

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

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

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

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

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



import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LEDTokenizer
						UpperCAmelCase             =						LEDTokenizerFast
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





										super().setUp()
										_SCREAMING_SNAKE_CASE			  =[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''<unk>''',
										]
										_SCREAMING_SNAKE_CASE			  =dict(zip(_a				,    range(len(_a  )  )  )  )
										_SCREAMING_SNAKE_CASE			  =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										_SCREAMING_SNAKE_CASE			  ={'''unk_token''': '''<unk>'''}

										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										_SCREAMING_SNAKE_CASE			  =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(_a  ) + '''\n'''  )
										with open(self.merges_file				,    '''w'''				,    encoding='''utf-8'''  ) as fp:
														fp.write('''\n'''.join(_a  )  )




						def 							__UpperCamelCase				(       self					:      str				,    **_a					:      int  )							->     Optional[Any]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      List[str]				,    **_a					:      List[Any]  )							->     str:


										"""simple docstring"""





										kwargs.update(self.special_tokens_map  )
										return self.rust_tokenizer_class.from_pretrained(self.tmpdirname				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Any  )							->     Dict:


										"""simple docstring"""





										return "lower newer", "lower newer"




						@cached_property
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Dict:


										"""simple docstring"""





										return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''  )




						@cached_property
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Any:


										"""simple docstring"""





										return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										_SCREAMING_SNAKE_CASE			  =[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    max_length=len(_a  )				,    padding=_a				,    return_tensors='''pt'''  )
														self.assertIsInstance(_a				,    _a  )

														self.assertEqual((2, 9)				,    batch.input_ids.shape  )
														self.assertEqual((2, 9)				,    batch.attention_mask.shape  )
														_SCREAMING_SNAKE_CASE			  =batch.input_ids.tolist()[0]
														self.assertListEqual(_a				,    _a  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    padding=_a				,    return_tensors='''pt'''  )
														self.assertIn('''input_ids'''				,    _a  )
														self.assertIn('''attention_mask'''				,    _a  )
														self.assertNotIn('''labels'''				,    _a  )
														self.assertNotIn('''decoder_attention_mask'''				,    _a  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Dict  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =[
										    '''Summary of the text.''',
										    '''Another summary.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(text_target=_a				,    max_length=32				,    padding='''max_length'''				,    return_tensors='''pt'''  )
														self.assertEqual(32				,    targets['''input_ids'''].shape[1]  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Any  )							->     Union[str, Any]:


										"""simple docstring"""





										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(
														    ['''I am a small frog''' * 1024, '''I am a small frog''']				,    padding=_a				,    truncation=_a				,    return_tensors='''pt'''  )
														self.assertIsInstance(_a				,    _a  )
														self.assertEqual(batch.input_ids.shape				,    (2, 5122)  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''']
										_SCREAMING_SNAKE_CASE			  =[
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    return_tensors='''pt'''  )
														_SCREAMING_SNAKE_CASE			  =tokenizer(text_target=_a				,    return_tensors='''pt'''  )
														_SCREAMING_SNAKE_CASE			  =inputs['''input_ids''']
														_SCREAMING_SNAKE_CASE			  =targets['''input_ids''']
														self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
														self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
														self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
														self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     str:


										"""simple docstring"""





										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
														_SCREAMING_SNAKE_CASE			  =['''Summary of the text.''', '''Another summary.''']
														_SCREAMING_SNAKE_CASE			  =[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

														_SCREAMING_SNAKE_CASE			  =tokenizer(_a				,    padding=_a  )
														_SCREAMING_SNAKE_CASE			  =[[0] * len(_a  ) for x in encoded_output['''input_ids''']]
														_SCREAMING_SNAKE_CASE			  =tokenizer.pad(_a  )
														self.assertSequenceEqual(outputs['''global_attention_mask''']				,    _a  )




						def 							__UpperCamelCase				(       self					:      int  )							->     List[str]:


										"""simple docstring"""





										pass




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


										"""simple docstring"""





										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
																		_SCREAMING_SNAKE_CASE			  =self.rust_tokenizer_class.from_pretrained(_a				,    **_a  )
																		_SCREAMING_SNAKE_CASE			  =self.tokenizer_class.from_pretrained(_a				,    **_a  )
																		_SCREAMING_SNAKE_CASE			  ='''A, <mask> AllenNLP sentence.'''
																		_SCREAMING_SNAKE_CASE			  =tokenizer_r.encode_plus(_a				,    add_special_tokens=_a				,    return_token_type_ids=_a  )
																		_SCREAMING_SNAKE_CASE			  =tokenizer_p.encode_plus(_a				,    add_special_tokens=_a				,    return_token_type_ids=_a  )
																		self.assertEqual(sum(tokens_r['''token_type_ids''']  )				,    sum(tokens_p['''token_type_ids''']  )  )
																		self.assertEqual(
																		    sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  )				,    sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  )				,    )

																		_SCREAMING_SNAKE_CASE			  =tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
																		_SCREAMING_SNAKE_CASE			  =tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )
																		self.assertSequenceEqual(tokens_p['''input_ids''']				,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
																		self.assertSequenceEqual(tokens_r['''input_ids''']				,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

																		self.assertSequenceEqual(
																		    _a				,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
																		self.assertSequenceEqual(
																		    _a				,    ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )