






'''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       __lowercase					:

	def __init__(      self   :						Optional[Any]  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						Any=13  ,					UpperCAmelCase_   :						int=7  ,					UpperCAmelCase_   :						Union[str, Any]=6  ,					UpperCAmelCase_   :						List[Any]=17  ,					UpperCAmelCase_   :						Optional[int]=23  ,					UpperCAmelCase_   :						Optional[Any]=11  ,					UpperCAmelCase_   :						Union[str, Any]=True  ,					):
			UpperCamelCase__				:					str					=					parent
			UpperCamelCase__				:					Optional[int]					=					batch_size
			UpperCamelCase__				:					int					=					seq_length
			UpperCamelCase__				:					Optional[Any]					=					act_dim
			UpperCamelCase__				:					str					=					state_dim
			UpperCamelCase__				:					Optional[int]					=					hidden_size
			UpperCamelCase__				:					Any					=					max_length
			UpperCamelCase__				:					str					=					is_training

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

			UpperCamelCase__				:					int					=					self.get_config()

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

	def      __UpperCamelCase							(      self   :						List[Any]):
			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   :						Any  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Tuple  ,					UpperCAmelCase_   :						Optional[Any]  ,					UpperCAmelCase_   :						List[str]  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						int  ,					):
			UpperCamelCase__				:					List[str]					=					DecisionTransformerModel(config=UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					List[Any]					=					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      __UpperCamelCase							(      self   :						Optional[int]):
			UpperCamelCase__				:					Optional[int]					=					self.prepare_config_and_inputs()
			(
			    (
			    UpperCamelCase__
			),	(
			    UpperCamelCase__
			),	(
			    UpperCamelCase__
			),	(
			    UpperCamelCase__
			),	(
			    UpperCamelCase__
			),	(
			    UpperCamelCase__
			),	(
			    UpperCamelCase__
			),	
			)				:					Dict					=					config_and_inputs
			UpperCamelCase__				:					List[str]					=					{
			    '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					(__lowerCamelCase   ,				__lowerCamelCase   ,				__lowerCamelCase   ,				unittest.TestCase    ):
	_lowerCamelCase              =  (DecisionTransformerModel,) if is_torch_available() else ()
	_lowerCamelCase              =  ()
	_lowerCamelCase              =  {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

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

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

	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					List[str]					=					DecisionTransformerModelTester(self)
			UpperCamelCase__				:					Optional[int]					=					ConfigTester(self  ,					config_class=UpperCAmelCase_  ,					hidden_size=37)

	def      __UpperCamelCase							(      self   :						Any):
			self.config_tester.run_common_tests()

	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Tuple					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_)

	@slow
	def      __UpperCamelCase							(      self   :						Any):
			for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCamelCase__				:					Any					=					DecisionTransformerModel.from_pretrained(UpperCAmelCase_)
					self.assertIsNotNone(UpperCAmelCase_)





	def      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__,	UpperCamelCase__				:					str					=					self.model_tester.prepare_config_and_inputs_for_common()

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

					UpperCamelCase__				:					Union[str, Any]					=					[
					    'states',
					    'actions',
					    'rewards',
					    'returns_to_go',
					    'timesteps',
					    'attention_mask',
					]

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







@require_torch
class       __lowercase					(unittest.TestCase    ):

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

			UpperCamelCase__				:					int					=					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=UpperCAmelCase_)

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

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

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

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

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












'''simple docstring'''




import argparse
import struct
import unittest







class       __lowercase					:

	def __init__(      self   :						Tuple  ,					UpperCAmelCase_   :						bytes):
			UpperCamelCase__				:					Dict					=					data

			# Initialize hash values
			UpperCamelCase__				:					Any					=					[
			    0X6A_09E_667,
			    0XBB_67A_E85,
			    0X3C_6EF_372,
			    0XA5_4FF_53A,
			    0X51_0E5_27F,
			    0X9B_056_88C,
			    0X1F_83D_9AB,
			    0X5B_E0C_D19,
			]

			# Initialize round constants
			UpperCamelCase__				:					List[Any]					=					[
			    0X42_8A2_F98,
			    0X71_374_491,
			    0XB5_C0F_BCF,
			    0XE9_B5D_BA5,
			    0X39_56C_25B,
			    0X59_F11_1F1,
			    0X92_3F8_2A4,
			    0XAB_1C5_ED5,
			    0XD8_07A_A98,
			    0X12_835_B01,
			    0X24_318_5BE,
			    0X55_0C7_DC3,
			    0X72_BE5_D74,
			    0X80_DEB_1FE,
			    0X9B_DC0_6A7,
			    0XC1_9BF_174,
			    0XE4_9B6_9C1,
			    0XEF_BE4_786,
			    0X0F_C19_DC6,
			    0X24_0CA_1CC,
			    0X2D_E92_C6F,
			    0X4A_748_4AA,
			    0X5C_B0A_9DC,
			    0X76_F98_8DA,
			    0X98_3E5_152,
			    0XA8_31C_66D,
			    0XB0_032_7C8,
			    0XBF_597_FC7,
			    0XC6_E00_BF3,
			    0XD5_A79_147,
			    0X06_CA6_351,
			    0X14_292_967,
			    0X27_B70_A85,
			    0X2E_1B2_138,
			    0X4D_2C6_DFC,
			    0X53_380_D13,
			    0X65_0A7_354,
			    0X76_6A0_ABB,
			    0X81_C2C_92E,
			    0X92_722_C85,
			    0XA2_BFE_8A1,
			    0XA8_1A6_64B,
			    0XC2_4B8_B70,
			    0XC7_6C5_1A3,
			    0XD1_92E_819,
			    0XD6_990_624,
			    0XF4_0E3_585,
			    0X10_6AA_070,
			    0X19_A4C_116,
			    0X1E_376_C08,
			    0X27_487_74C,
			    0X34_B0B_CB5,
			    0X39_1C0_CB3,
			    0X4E_D8A_A4A,
			    0X5B_9CC_A4F,
			    0X68_2E6_FF3,
			    0X74_8F8_2EE,
			    0X78_A56_36F,
			    0X84_C87_814,
			    0X8C_C70_208,
			    0X90_BEF_FFA,
			    0XA4_506_CEB,
			    0XBE_F9A_3F7,
			    0XC6_717_8F2,
			]

			UpperCamelCase__				:					Tuple					=					self.preprocessing(self.data)
			self.final_hash()

	@staticmethod
	def      __UpperCamelCase							(      UpperCAmelCase_   :						bytes):
			UpperCamelCase__				:					List[Any]					=					B'\x80' + (B'\x00' * (63 - (len(UpperCAmelCase_) + 8) % 64))
			UpperCamelCase__				:					List[Any]					=					struct.pack('>Q'  ,					(len(UpperCAmelCase_) * 8))
			return data + padding + big_endian_integer

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			# Convert into blocks of 64 bytes
			UpperCamelCase__				:					int					=					[
			    self.preprocessed_data[x : x + 64]
			    for x in range(0  ,					len(self.preprocessed_data)  ,					64)
			]

			for block in self.blocks:
					# Convert the given block into a list of 4 byte integers
					UpperCamelCase__				:					Tuple					=					list(struct.unpack('>16L'  ,					UpperCAmelCase_))
					# add 48 0-ed integers
					words += [0] * 48

					UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					str					=					self.hashes

					for index in range(0  ,					64):
							if index > 15:
									# modify the zero-ed indexes at the end of the array
									UpperCamelCase__				:					Dict					=					(
									    self.ror(words[index - 15]  ,					7)
									    ^ self.ror(words[index - 15]  ,					18)
									    ^ (words[index - 15] >> 3)
									)
									UpperCamelCase__				:					Tuple					=					(
									    self.ror(words[index - 2]  ,					17)
									    ^ self.ror(words[index - 2]  ,					19)
									    ^ (words[index - 2] >> 10)
									)

									UpperCamelCase__				:					int					=					(
									    words[index - 16] + sa + words[index - 7] + sa
									) % 0X100_000_000

							# Compression
							UpperCamelCase__				:					Optional[Any]					=					self.ror(UpperCAmelCase_  ,					6) ^ self.ror(UpperCAmelCase_  ,					11) ^ self.ror(UpperCAmelCase_  ,					25)
							UpperCamelCase__				:					List[str]					=					(e & f) ^ ((~e & 0XFF_FFF_FFF) & g)
							UpperCamelCase__				:					List[Any]					=					(
							    h + sa + ch + self.round_constants[index] + words[index]
							) % 0X100_000_000
							UpperCamelCase__				:					List[str]					=					self.ror(UpperCAmelCase_  ,					2) ^ self.ror(UpperCAmelCase_  ,					13) ^ self.ror(UpperCAmelCase_  ,					22)
							UpperCamelCase__				:					Dict					=					(a & b) ^ (a & c) ^ (b & c)
							UpperCamelCase__				:					List[str]					=					(sa + maj) % 0X100_000_000

							UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					Tuple					=					(
							    g,
							    f,
							    e,
							    ((d + tempa) % 0X100_000_000),
							    c,
							    b,
							    a,
							    ((tempa + tempa) % 0X100_000_000),
							)

					UpperCamelCase__				:					List[Any]					=					[a, b, c, d, e, f, g, h]

					# Modify final values
					UpperCamelCase__				:					Optional[Any]					=					[
					    ((element + mutated_hash_values[index]) % 0X100_000_000)
					    for index, element in enumerate(self.hashes)
					]

			UpperCamelCase__				:					Any					=					''.join([hex(UpperCAmelCase_)[2:].zfill(8) for value in self.hashes])





	def      __UpperCamelCase							(      self   :						int  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						int):
			return 0XFF_FFF_FFF & (value << (32 - rotations)) | (value >> rotations)







class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						int):
			import hashlib

			UpperCamelCase__				:					str					=					bytes('Test String'  ,					'utf-8')
			self.assertEqual(SHAaaa(UpperCAmelCase_).hash  ,					hashlib.shaaaa(UpperCAmelCase_).hexdigest())







def       __UpperCAmelCase						(  )							->     None:
		import doctest

		doctest.testmod()

		UpperCamelCase__				:					Union[str, Any]					=					argparse.ArgumentParser()
		parser.add_argument(
		    '-s'     ,      '--string'     ,      dest='input_string'     ,      default='Hello World!! Welcome to Cryptography'     ,      help='Hash the string'     ,      )
		parser.add_argument(
		    '-f'     ,      '--file'     ,      dest='input_file'     ,      help='Hash contents of a file')

		UpperCamelCase__				:					List[str]					=					parser.parse_args()

		UpperCamelCase__				:					str					=					args.input_string

		# hash input should be a bytestring
		if args.input_file:
				with open(args.input_file     ,      'rb') as f:
						UpperCamelCase__				:					Any					=					f.read()
		else:
				UpperCamelCase__				:					List[Any]					=					bytes(lowerCamelCase_     ,      'utf-8')

		print(SHAaaa(lowerCamelCase_).hash)


if __name__ == "__main__":
		main()




