



"""simple docstring"""

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
			import torch

			from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__=None   ,		lowercase__="resnet50"   ,		lowercase__=3   ,		lowercase__=32   ,		lowercase__=3   ,		lowercase__=True   ,		lowercase__=True   ,		):
											snake_case_					:		Union[str, Any] 					=					parent
											snake_case_					:		Optional[Any] 					=					out_indices if out_indices is not None else [4]
											snake_case_					:		int 					=					stage_names
											snake_case_					:		str 					=					out_features
											snake_case_					:		Optional[Any] 					=					backbone
											snake_case_					:		Tuple 					=					batch_size
											snake_case_					:		Union[str, Any] 					=					image_size
											snake_case_					:		List[str] 					=					num_channels
											snake_case_					:		str 					=					use_pretrained_backbone
											snake_case_					:		List[str] 					=					is_training







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[Any] 					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
											snake_case_					:		List[str] 					=					self.get_config()

											return config, pixel_values







						def 						__UpperCamelCase							(self       ):
											return TimmBackboneConfig(
											    image_size=self.image_size   ,		num_channels=self.num_channels   ,		out_features=self.out_features   ,		out_indices=self.out_indices   ,		stage_names=self.stage_names   ,		use_pretrained_backbone=self.use_pretrained_backbone   ,		backbone=self.backbone   ,		)







						def 						__UpperCamelCase							(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		List[str] 					=					TimmBackbone(config=lowercase__       )
											model.to(lowercase__       )
											model.eval()
											with torch.no_grad():
																snake_case_					:		str 					=					model(lowercase__       )
											self.parent.assertEqual(
											    result.feature_map[-1].shape   ,		(self.batch_size, model.channels[-1], 14, 14)   ,		)







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Union[str, Any] 					=					self.prepare_config_and_inputs()
											snake_case_					,			snake_case_					:		str 					=					config_and_inputs
											snake_case_					:		str 					=					{"""pixel_values""": pixel_values}
											return config, inputs_dict







@require_torch
@require_timm
class    __lowercase     (					_UpperCAmelCase					,    _UpperCAmelCase					,    _UpperCAmelCase					,    unittest.TestCase):







						"""simple docstring"""







						_A		:							Union[str, Any]							       =		(TimmBackbone,) if is_torch_available() else ()
						_A		:							Union[str, Any]							       =		{"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
						_A		:							Optional[Any]							       =		False
						_A		:							Dict							       =		False
						_A		:							List[Any]							       =		False
						_A		:							int							       =		False







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[str] 					=					TimmBackboneModelTester(self       )
											snake_case_					:		int 					=					ConfigTester(self   ,		config_class=lowercase__   ,		has_text_modality=lowercase__       )







						def 						__UpperCamelCase							(self       ):
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					"""resnet18"""
											snake_case_					:		Union[str, Any] 					=					"""microsoft/resnet-18"""

											snake_case_					:		Optional[Any] 					=					AutoBackbone.from_pretrained(lowercase__   ,		use_timm_backbone=lowercase__       )
											snake_case_					:		Optional[Any] 					=					AutoBackbone.from_pretrained(lowercase__       )

											self.assertEqual(len(timm_model.out_features       )   ,		len(transformers_model.out_features       )       )
											self.assertEqual(len(timm_model.stage_names       )   ,		len(transformers_model.stage_names       )       )
											self.assertEqual(timm_model.channels   ,		transformers_model.channels       )
											# Out indices are set to the last layer by default. For timm models, we don't know
											# the number of layers in advance, so we set it to (-1,), whereas for transformers
											# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
											self.assertEqual(timm_model.out_indices   ,		(-1,)       )
											self.assertEqual(transformers_model.out_indices   ,		[len(timm_model.stage_names       ) - 1]       )

											snake_case_					:		Tuple 					=					AutoBackbone.from_pretrained(lowercase__   ,		use_timm_backbone=lowercase__   ,		out_indices=[1, 2, 3]       )
											snake_case_					:		List[Any] 					=					AutoBackbone.from_pretrained(lowercase__   ,		out_indices=[1, 2, 3]       )

											self.assertEqual(timm_model.out_indices   ,		transformers_model.out_indices       )
											self.assertEqual(len(timm_model.out_features       )   ,		len(transformers_model.out_features       )       )
											self.assertEqual(timm_model.channels   ,		transformers_model.channels       )







						@unittest.skip("""TimmBackbone doesn't support feed forward chunking"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone initialization is managed on the timm side"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""model weights aren't tied in TimmBackbone."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""model weights aren't tied in TimmBackbone."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""TimmBackbone doesn't support output_attentions."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""Safetensors is not supported by timm."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""       )
						def 						__UpperCamelCase							(self       ):
											pass







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		List[Any] 					=					self.model_tester.prepare_config_and_inputs_for_common()

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

																snake_case_					:		Dict 					=					["""pixel_values"""]
																self.assertListEqual(arg_names[:1]   ,		lowercase__       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		Union[str, Any] 					=					self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_					:		Union[str, Any] 					=					True
											snake_case_					:		List[str] 					=					self.has_attentions

											# no need to test all models as different heads yield the same functionality
											snake_case_					:		str 					=					self.all_model_classes[0]
											snake_case_					:		str 					=					model_class(lowercase__       )
											model.to(lowercase__       )

											snake_case_					:		Optional[Any] 					=					self._prepare_for_class(lowercase__   ,		lowercase__       )
											snake_case_					:		List[Any] 					=					model(**lowercase__       )
											snake_case_					:		str 					=					outputs[0][-1]

											# Encoder-/Decoder-only models
											snake_case_					:		int 					=					outputs.hidden_states[0]
											hidden_states.retain_grad()

											if self.has_attentions:
																snake_case_					:		str 					=					outputs.attentions[0]
																attentions.retain_grad()

											output.flatten()[0].backward(retain_graph=lowercase__       )

											self.assertIsNotNone(hidden_states.grad       )

											if self.has_attentions:
																self.assertIsNotNone(attentions.grad       )







						def 						__UpperCamelCase							(self       ):
											snake_case_					,			snake_case_					:		Optional[Any] 					=					self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																snake_case_					:		Any 					=					model_class(lowercase__       )
																model.to(lowercase__       )
																model.eval()
																snake_case_					:		Tuple 					=					model(**lowercase__       )

																self.assertEqual(len(result.feature_maps       )   ,		len(config.out_indices       )       )
																self.assertEqual(len(model.channels       )   ,		len(config.out_indices       )       )

																# Check output of last stage is taken if out_features=None, out_indices=None
																snake_case_					:		str 					=					copy.deepcopy(lowercase__       )
																snake_case_					:		List[str] 					=					None
																snake_case_					:		Tuple 					=					model_class(lowercase__       )
																model.to(lowercase__       )
																model.eval()
																snake_case_					:		str 					=					model(**lowercase__       )

																self.assertEqual(len(result.feature_maps       )   ,		1       )
																self.assertEqual(len(model.channels       )   ,		1       )

																# Check backbone can be initialized with fresh weights
																snake_case_					:		Dict 					=					copy.deepcopy(lowercase__       )
																snake_case_					:		Any 					=					False
																snake_case_					:		List[str] 					=					model_class(lowercase__       )
																model.to(lowercase__       )
																model.eval()
																snake_case_					:		Any 					=					model(**lowercase__       )









"""simple docstring"""

import argparse
import hashlib  # hashlib is only used inside the Test class
import struct







class    __lowercase     :







						"""simple docstring"""







						def __init__(self   ,		lowercase__       ):
											snake_case_					:		Union[str, Any] 					=					data
											snake_case_					:		List[str] 					=					[0X6_7_4_5_2_3_0_1, 0Xe_f_c_d_a_b_8_9, 0X9_8_b_a_d_c_f_e, 0X1_0_3_2_5_4_7_6, 0Xc_3_d_2_e_1_f_0]







						@staticmethod
						def 						__UpperCamelCase							(lowercase__   ,		lowercase__       ):
											return ((n << b) | (n >> (32 - b))) & 0Xf_f_f_f_f_f_f_f







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					B"""\x80""" + B"""\x00""" * (63 - (len(self.data       ) + 8) % 64)
											snake_case_					:		Tuple 					=					self.data + padding + struct.pack(""">Q"""   ,		8 * len(self.data       )       )
											return padded_data







						def 						__UpperCamelCase							(self       ):
											return [
											    self.padded_data[i : i + 64] for i in range(0   ,		len(self.padded_data       )   ,		64       )
											]







						def 						__UpperCamelCase							(self   ,		lowercase__       ):
											snake_case_					:		int 					=					list(struct.unpack(""">16L"""   ,		lowercase__       )       ) + [0] * 64
											for i in range(16   ,		80       ):
																snake_case_					:		Dict 					=					self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])   ,		1       )
											return w







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		List[Any] 					=					self.padding()
											snake_case_					:		Any 					=					self.split_blocks()
											for block in self.blocks:
																snake_case_					:		Any 					=					self.expand_block(lowercase__       )
																snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		List[Any] 					=					self.h
																for i in range(0   ,		80       ):
																					if 0 <= i < 20:
																										snake_case_					:		Optional[Any] 					=					(b & c) | ((~b) & d)
																										snake_case_					:		List[str] 					=					0X5_a_8_2_7_9_9_9
																					elif 20 <= i < 40:
																										snake_case_					:		Union[str, Any] 					=					b ^ c ^ d
																										snake_case_					:		Tuple 					=					0X6_e_d_9_e_b_a_1
																					elif 40 <= i < 60:
																										snake_case_					:		str 					=					(b & c) | (b & d) | (c & d)
																										snake_case_					:		List[str] 					=					0X8_f_1_b_b_c_d_c
																					elif 60 <= i < 80:
																										snake_case_					:		Tuple 					=					b ^ c ^ d
																										snake_case_					:		str 					=					0Xc_a_6_2_c_1_d_6
																					snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					,			snake_case_					:		Optional[Any] 					=					(
																					    self.rotate(lowercase__   ,		5       ) + f + e + k + expanded_block[i] & 0Xf_f_f_f_f_f_f_f,
																					    a,
																					    self.rotate(lowercase__   ,		30       ),
																					    c,
																					    d,
																					)
																snake_case_					:		Any 					=					(
																    self.h[0] + a & 0Xf_f_f_f_f_f_f_f,
																    self.h[1] + b & 0Xf_f_f_f_f_f_f_f,
																    self.h[2] + c & 0Xf_f_f_f_f_f_f_f,
																    self.h[3] + d & 0Xf_f_f_f_f_f_f_f,
																    self.h[4] + e & 0Xf_f_f_f_f_f_f_f,
																)
											return ("{:08x}" * 5).format(*self.h       )







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Union[str, Any] 					=					b"""Test String"""
					assert SHAaHash(SCREAMING_SNAKE_CASE__			).final_hash() == hashlib.shaa(SCREAMING_SNAKE_CASE__			).hexdigest()  # noqa: S324







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		int 					=					argparse.ArgumentParser(description="""Process some strings or files"""			)
					parser.add_argument(
					    """--string"""				,    dest="""input_string"""				,    default="""Hello World!! Welcome to Cryptography"""				,    help="""Hash the string"""				,    )
					parser.add_argument("""--file"""				,    dest="""input_file"""				,    help="""Hash contents of a file"""			)
					snake_case_					:		Optional[int] 					=					parser.parse_args()
					snake_case_					:		Optional[int] 					=					args.input_string
					# In any case hash input should be a bytestring
					if args.input_file:
										with open(args.input_file				,    """rb"""			) as f:
															snake_case_					:		List[str] 					=					f.read()
					else:
										snake_case_					:		Dict 					=					bytes(SCREAMING_SNAKE_CASE__				,    """utf-8"""			)
					print(SHAaHash(SCREAMING_SNAKE_CASE__			).final_hash()			)


if __name__ == "__main__":
			main()
			import doctest

			doctest.testmod()




