


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   :						int				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						Optional[Any]=13				, _UpperCAmelCase   :						Union[str, Any]=7				, _UpperCAmelCase   :						Optional[Any]=6				, _UpperCAmelCase   :						int=17				, _UpperCAmelCase   :						List[Any]=23				, _UpperCAmelCase   :						List[Any]=11				, _UpperCAmelCase   :						Dict=True				, )						->       List[str]:




							"""simple docstring"""


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

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




							"""simple docstring"""


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

							lowercase__					      =		self.get_config()

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

					def   lowerCamelCase__					(self   :						str		)						->       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   lowerCamelCase__					(self   :						str				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Optional[Any]				, _UpperCAmelCase   :						Optional[int]				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Dict				, )						->       int:




							"""simple docstring"""


							lowercase__					      =		DecisionTransformerModel(config=lowercase_		)
							model.to(lowercase_		)
							model.eval()
							lowercase__					      =		model(lowercase_				, lowercase_				, lowercase_				, lowercase_				, lowercase_				, lowercase_		)

							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   lowerCamelCase__					(self   :						Dict		)						->       Tuple:




							"""simple docstring"""


							lowercase__					      =		self.prepare_config_and_inputs()
							(
							    (
							    lowercase__		
							)      ,     (
							    lowercase__		
							)      ,     (
							    lowercase__		
							)      ,     (
							    lowercase__		
							)      ,     (
							    lowercase__		
							)      ,     (
							    lowercase__		
							)      ,     (
							    lowercase__		
							)      ,     		
							)					      =		config_and_inputs
							lowercase__					      =		{
							    """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							):
					'''simple docstring'''






					A__      =			(DecisionTransformerModel,) if is_torch_available() else ()
					A__      =			()
					A__      =			{'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

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

					def   lowerCamelCase__					(self   :						List[str]		)						->       Any:




							"""simple docstring"""


							lowercase__					      =		DecisionTransformerModelTester(self		)
							lowercase__					      =		ConfigTester(self				, config_class=lowercase_				, hidden_size=37		)

					def   lowerCamelCase__					(self   :						Tuple		)						->       Tuple:




							"""simple docstring"""


							self.config_tester.run_common_tests()

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




							"""simple docstring"""


							lowercase__					      =		self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase_		)

					@slow
					def   lowerCamelCase__					(self   :						List[Any]		)						->       int:




							"""simple docstring"""


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


					def   lowerCamelCase__					(self   :						Tuple		)						->       Optional[int]:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase__					      =		[
									    """states""",
									    """actions""",
									    """rewards""",
									    """returns_to_go""",
									    """timesteps""",
									    """attention_mask""",
									]

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






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






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




							"""simple docstring"""


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

							lowercase__					      =		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=lowercase_		)

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

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

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

									with torch.no_grad():
											lowercase__      ,     lowercase__      ,     lowercase__					      =		model(
											    states=lowercase_				, actions=lowercase_				, rewards=lowercase_				, returns_to_go=lowercase_				, timesteps=lowercase_				, attention_mask=lowercase_				, return_dict=lowercase_				, )

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

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





A	:		Tuple    =       'ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def 			UpperCamelCase (     )       ->  None:
		"""simple docstring"""




		lowercase__					      =		input("""Enter message: """					)
		lowercase__					      =		input("""Enter key [alphanumeric]: """					)
		lowercase__					      =		input("""Encrypt/Decrypt [e/d]: """					)

		if mode.lower().startswith("""e"""					):
				lowercase__					      =		"""encrypt"""
				lowercase__					      =		encrypt_message(__magic_name__   ,   __magic_name__					)
		elif mode.lower().startswith("""d"""					):
				lowercase__					      =		"""decrypt"""
				lowercase__					      =		decrypt_message(__magic_name__   ,   __magic_name__					)

		print(f'''\n{mode.title()}ed message:'''					)
		print(__magic_name__					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		return translate_message(__magic_name__   ,   __magic_name__   ,   """encrypt"""					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		return translate_message(__magic_name__   ,   __magic_name__   ,   """decrypt"""					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							str   ,   __magic_name__					:							str					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		[]
		lowercase__					      =		0
		lowercase__					      =		key.upper()

		for symbol in message:
				lowercase__					      =		LETTERS.find(symbol.upper()					)
				if num != -1:
						if mode == "encrypt":
								num += LETTERS.find(key[key_index]					)
						elif mode == "decrypt":
								num -= LETTERS.find(key[key_index]					)

						num %= len(__magic_name__					)

						if symbol.isupper():
								translated.append(LETTERS[num]					)
						elif symbol.islower():
								translated.append(LETTERS[num].lower()					)

						key_index += 1
						if key_index == len(__magic_name__					):
								lowercase__					      =		0
				else:
						translated.append(__magic_name__					)
		return "".join(__magic_name__					)


if __name__ == "__main__":
	main()

