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       lowercase__		:


						def __init__( self			:       Dict					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Any=13					,	UpperCamelCase__			:       Any=7					,	UpperCamelCase__			:       Tuple=6					,	UpperCamelCase__			:       int=17					,	UpperCamelCase__			:       List[str]=23					,	UpperCamelCase__			:       Optional[Any]=11					,	UpperCamelCase__			:       List[Any]=True					,	):





												'''simple docstring'''





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


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							floats_tensor((self.batch_size, self.seq_length, self.state_dim)		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							floats_tensor((self.batch_size, self.seq_length, self.act_dim)		)
												SCREAMING_SNAKE_CASE							:					Dict           =							floats_tensor((self.batch_size, self.seq_length, 1)		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							floats_tensor((self.batch_size, self.seq_length, 1)		)
												SCREAMING_SNAKE_CASE							:					str           =							ids_tensor((self.batch_size, self.seq_length)					,	vocab_size=1000		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							random_attention_mask((self.batch_size, self.seq_length)		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							self.get_config()

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


						def  __A ( self			:       Dict		):





												'''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			:       str					,	UpperCamelCase__			:       Any					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       Union[str, Any]					,	UpperCamelCase__			:       Tuple					,	UpperCamelCase__			:       List[Any]					,	UpperCamelCase__			:       Dict					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							DecisionTransformerModel(config=UpperCamelCase__		)
												model.to(UpperCamelCase__		)
												model.eval()
												SCREAMING_SNAKE_CASE							:					List[str]           =							model(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												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			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							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
												)       ,						
												)							:					Union[str, Any]           =							config_and_inputs
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{
												    '''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       lowercase__		(     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  __A ( self			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							DecisionTransformerModelTester(self		)
												SCREAMING_SNAKE_CASE							:					Any           =							ConfigTester(self					,	config_class=UpperCamelCase__					,	hidden_size=37		)


						def  __A ( self			:       int		):





												'''simple docstring'''





												self.config_tester.run_common_tests()


						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*UpperCamelCase__		)


						@slow
						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





												for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							DecisionTransformerModel.from_pretrained(UpperCamelCase__		)
																		self.assertIsNotNone(UpperCamelCase__		)



						def  __A ( self			:       Dict		):





												'''simple docstring'''





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

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

																		SCREAMING_SNAKE_CASE							:					str           =							[
																		    '''states''',
																		    '''actions''',
																		    '''rewards''',
																		    '''returns_to_go''',
																		    '''timesteps''',
																		    '''attention_mask''',
																		]

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






@require_torch
class       lowercase__		(     unittest.TestCase):


						@slow
						def  __A ( self			:       Dict		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.tensor(
												    [[0.24_2793, -0.2869_3074, 0.874_2613], [0.6781_5274, -0.0810_1085, -0.1295_2147]]					,	device=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.tensor(UpperCamelCase__					,	device=UpperCamelCase__					,	dtype=torch.floataa		).reshape(1					,	1					,	1		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							state
												SCREAMING_SNAKE_CASE							:					Any           =							torch.zeros(1					,	0					,	config.act_dim					,	device=UpperCamelCase__					,	dtype=torch.floataa		)
												SCREAMING_SNAKE_CASE							:					int           =							torch.zeros(1					,	0					,	device=UpperCamelCase__					,	dtype=torch.floataa		)
												SCREAMING_SNAKE_CASE							:					Any           =							torch.tensor(0					,	device=UpperCamelCase__					,	dtype=torch.long		).reshape(1					,	1		)

												for step in range(UpperCamelCase__		):
																		SCREAMING_SNAKE_CASE							:					str           =							torch.cat([actions, torch.zeros(1					,	1					,	config.act_dim					,	device=UpperCamelCase__		)]					,	dim=1		)
																		SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							torch.cat([rewards, torch.zeros(1					,	1					,	device=UpperCamelCase__		)]					,	dim=1		)

																		SCREAMING_SNAKE_CASE							:					Tuple           =							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							:					str           =							model(
																								    states=UpperCamelCase__					,	actions=UpperCamelCase__					,	rewards=UpperCamelCase__					,	returns_to_go=UpperCamelCase__					,	timesteps=UpperCamelCase__					,	attention_mask=UpperCamelCase__					,	return_dict=UpperCamelCase__					,	)

																		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							:					str           =							(  # env.step(action)
																		    torch.randn(1					,	1					,	config.state_dim		).to(device=UpperCamelCase__					,	dtype=torch.floataa		),
																		    1.0,
																		    False,
																		    {},
																		)

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






import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	FunnelTokenizer
						UpperCamelCase_  =	FunnelTokenizerFast
						UpperCamelCase_  =	True
						UpperCamelCase_  =	True


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[
												    '''<unk>''',
												    '''<cls>''',
												    '''<sep>''',
												    '''want''',
												    '''##want''',
												    '''##ed''',
												    '''wa''',
												    '''un''',
												    '''runn''',
												    '''##ing''',
												    ''',''',
												    '''low''',
												    '''lowest''',
												]
												SCREAMING_SNAKE_CASE							:					List[str]           =							os.path.join(self.tmpdirname					,	VOCAB_FILES_NAMES['''vocab_file''']		)
												with open(self.vocab_file					,	'''w'''					,	encoding='''utf-8'''		) as vocab_writer:
																		vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizer.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       int					,	**UpperCamelCase__			:       List[Any]		):





												'''simple docstring'''





												return FunnelTokenizerFast.from_pretrained(self.tmpdirname					,	**UpperCamelCase__		)


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[str]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							'''UNwant\u00E9d,running'''
												SCREAMING_SNAKE_CASE							:					int           =							'''unwanted, running'''
												return input_text, output_text


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.tokenizer_class(self.vocab_file		)

												SCREAMING_SNAKE_CASE							:					int           =							tokenizer.tokenize('''UNwant\u00E9d,running'''		)
												self.assertListEqual(UpperCamelCase__					,	['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']		)
												self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)					,	[7, 4, 5, 10, 8, 9]		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Dict           =							self.get_tokenizers(do_lower_case=UpperCamelCase__		)
												for tokenizer in tokenizers:
																		SCREAMING_SNAKE_CASE							:					int           =							tokenizer('''UNwant\u00E9d,running'''		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							len(inputs['''input_ids''']		) - 1
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len		)

																		SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer('''UNwant\u00E9d,running'''					,	'''UNwant\u00E9d,running'''		)
																		self.assertListEqual(inputs['''token_type_ids''']					,	[2] + [0] * sentence_len + [1] * sentence_len		)





