


"""simple docstring"""

import flax.linen as nn
import jax.numpy as jnp

from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD







class    __lowercase     (					nn.Module):







						"""simple docstring"""







						_A		:							List[Any]							       =		42
						_A		:							List[Any]							       =		42
						_A		:							Union[str, Any]							       =		0.0
						_A		:							List[Any]							       =		1
						_A		:							str							       =		1
						_A		:							Tuple							       =		True
						_A		:							str							       =		False
						_A		:							Dict							       =		False
						_A		:							List[str]							       =		False
						_A		:							Optional[int]							       =		jnp.floataa







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Optional[int] 					=					[]
											snake_case_					:		Optional[int] 					=					[]

											for i in range(self.num_layers       ):
																snake_case_					:		Optional[int] 					=					self.in_channels if i == 0 else self.out_channels

																snake_case_					:		List[Any] 					=					FlaxResnetBlockaD(
																    in_channels=lowerCamelCase_   ,		out_channels=self.out_channels   ,		dropout_prob=self.dropout   ,		dtype=self.dtype   ,		)
																resnets.append(lowerCamelCase_       )

																snake_case_					:		Any 					=					FlaxTransformeraDModel(
																    in_channels=self.out_channels   ,		n_heads=self.num_attention_heads   ,		d_head=self.out_channels // self.num_attention_heads   ,		depth=1   ,		use_linear_projection=self.use_linear_projection   ,		only_cross_attention=self.only_cross_attention   ,		use_memory_efficient_attention=self.use_memory_efficient_attention   ,		dtype=self.dtype   ,		)
																attentions.append(lowerCamelCase_       )

											snake_case_					:		List[Any] 					=					resnets
											snake_case_					:		List[str] 					=					attentions

											if self.add_downsample:
																snake_case_					:		Union[str, Any] 					=					FlaxDownsampleaD(self.out_channels   ,		dtype=self.dtype       )







						def __call__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__=True       ):
											snake_case_					:		int 					=					()

											for resnet, attn in zip(self.resnets   ,		self.attentions       ):
																snake_case_					:		int 					=					resnet(lowerCamelCase_   ,		lowerCamelCase_   ,		deterministic=lowerCamelCase_       )
																snake_case_					:		str 					=					attn(lowerCamelCase_   ,		lowerCamelCase_   ,		deterministic=lowerCamelCase_       )
																output_states += (hidden_states,)

											if self.add_downsample:
																snake_case_					:		Union[str, Any] 					=					self.downsamplers_a(lowerCamelCase_       )
																output_states += (hidden_states,)

											return hidden_states, output_states







class    __lowercase     (					nn.Module):







						"""simple docstring"""







						_A		:							List[Any]							       =		42
						_A		:							Any							       =		42
						_A		:							Dict							       =		0.0
						_A		:							Tuple							       =		1
						_A		:							int							       =		True
						_A		:							Optional[int]							       =		jnp.floataa







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		str 					=					[]

											for i in range(self.num_layers       ):
																snake_case_					:		Optional[Any] 					=					self.in_channels if i == 0 else self.out_channels

																snake_case_					:		Optional[int] 					=					FlaxResnetBlockaD(
																    in_channels=lowerCamelCase_   ,		out_channels=self.out_channels   ,		dropout_prob=self.dropout   ,		dtype=self.dtype   ,		)
																resnets.append(lowerCamelCase_       )
											snake_case_					:		str 					=					resnets

											if self.add_downsample:
																snake_case_					:		int 					=					FlaxDownsampleaD(self.out_channels   ,		dtype=self.dtype       )







						def __call__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__=True       ):
											snake_case_					:		Optional[int] 					=					()

											for resnet in self.resnets:
																snake_case_					:		int 					=					resnet(lowerCamelCase_   ,		lowerCamelCase_   ,		deterministic=lowerCamelCase_       )
																output_states += (hidden_states,)

											if self.add_downsample:
																snake_case_					:		Optional[int] 					=					self.downsamplers_a(lowerCamelCase_       )
																output_states += (hidden_states,)

											return hidden_states, output_states







class    __lowercase     (					nn.Module):







						"""simple docstring"""







						_A		:							Any							       =		42
						_A		:							int							       =		42
						_A		:							Any							       =		42
						_A		:							str							       =		0.0
						_A		:							int							       =		1
						_A		:							str							       =		1
						_A		:							Tuple							       =		True
						_A		:							Union[str, Any]							       =		False
						_A		:							Tuple							       =		False
						_A		:							Dict							       =		False
						_A		:							List[str]							       =		jnp.floataa







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		int 					=					[]
											snake_case_					:		List[Any] 					=					[]

											for i in range(self.num_layers       ):
																snake_case_					:		List[Any] 					=					self.in_channels if (i == self.num_layers - 1) else self.out_channels
																snake_case_					:		Dict 					=					self.prev_output_channel if i == 0 else self.out_channels

																snake_case_					:		Tuple 					=					FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels   ,		out_channels=self.out_channels   ,		dropout_prob=self.dropout   ,		dtype=self.dtype   ,		)
																resnets.append(lowerCamelCase_       )

																snake_case_					:		List[str] 					=					FlaxTransformeraDModel(
																    in_channels=self.out_channels   ,		n_heads=self.num_attention_heads   ,		d_head=self.out_channels // self.num_attention_heads   ,		depth=1   ,		use_linear_projection=self.use_linear_projection   ,		only_cross_attention=self.only_cross_attention   ,		use_memory_efficient_attention=self.use_memory_efficient_attention   ,		dtype=self.dtype   ,		)
																attentions.append(lowerCamelCase_       )

											snake_case_					:		str 					=					resnets
											snake_case_					:		Tuple 					=					attentions

											if self.add_upsample:
																snake_case_					:		int 					=					FlaxUpsampleaD(self.out_channels   ,		dtype=self.dtype       )







						def __call__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__=True       ):




											for resnet, attn in zip(self.resnets   ,		self.attentions       ):
																# pop res hidden states
																snake_case_					:		Any 					=					res_hidden_states_tuple[-1]
																snake_case_					:		Any 					=					res_hidden_states_tuple[:-1]
																snake_case_					:		Dict 					=					jnp.concatenate((hidden_states, res_hidden_states)   ,		axis=-1       )

																snake_case_					:		Optional[Any] 					=					resnet(lowerCamelCase_   ,		lowerCamelCase_   ,		deterministic=lowerCamelCase_       )
																snake_case_					:		List[str] 					=					attn(lowerCamelCase_   ,		lowerCamelCase_   ,		deterministic=lowerCamelCase_       )

											if self.add_upsample:
																snake_case_					:		Optional[int] 					=					self.upsamplers_a(lowerCamelCase_       )

											return hidden_states







class    __lowercase     (					nn.Module):







						"""simple docstring"""







						_A		:							int							       =		42
						_A		:							Optional[int]							       =		42
						_A		:							Any							       =		42
						_A		:							List[Any]							       =		0.0
						_A		:							Any							       =		1
						_A		:							Dict							       =		True
						_A		:							List[str]							       =		jnp.floataa







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					[]

											for i in range(self.num_layers       ):
																snake_case_					:		Optional[Any] 					=					self.in_channels if (i == self.num_layers - 1) else self.out_channels
																snake_case_					:		List[str] 					=					self.prev_output_channel if i == 0 else self.out_channels

																snake_case_					:		List[Any] 					=					FlaxResnetBlockaD(
																    in_channels=resnet_in_channels + res_skip_channels   ,		out_channels=self.out_channels   ,		dropout_prob=self.dropout   ,		dtype=self.dtype   ,		)
																resnets.append(lowerCamelCase_       )

											snake_case_					:		Union[str, Any] 					=					resnets

											if self.add_upsample:
																snake_case_					:		Optional[Any] 					=					FlaxUpsampleaD(self.out_channels   ,		dtype=self.dtype       )







						def __call__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__=True       ):




											for resnet in self.resnets:
																# pop res hidden states
																snake_case_					:		List[str] 					=					res_hidden_states_tuple[-1]
																snake_case_					:		Tuple 					=					res_hidden_states_tuple[:-1]
																snake_case_					:		Optional[int] 					=					jnp.concatenate((hidden_states, res_hidden_states)   ,		axis=-1       )

																snake_case_					:		str 					=					resnet(lowerCamelCase_   ,		lowerCamelCase_   ,		deterministic=lowerCamelCase_       )

											if self.add_upsample:
																snake_case_					:		Union[str, Any] 					=					self.upsamplers_a(lowerCamelCase_       )

											return hidden_states







class    __lowercase     (					nn.Module):







						"""simple docstring"""







						_A		:							Any							       =		42
						_A		:							List[Any]							       =		0.0
						_A		:							Optional[int]							       =		1
						_A		:							Optional[int]							       =		1
						_A		:							Union[str, Any]							       =		False
						_A		:							int							       =		False
						_A		:							Tuple							       =		jnp.floataa







						def 						__UpperCamelCase							(self       ):
											snake_case_					:		Any 					=					[
											    FlaxResnetBlockaD(
											        in_channels=self.in_channels   ,		out_channels=self.in_channels   ,		dropout_prob=self.dropout   ,		dtype=self.dtype   ,		)
											]

											snake_case_					:		int 					=					[]

											for _ in range(self.num_layers       ):
																snake_case_					:		Tuple 					=					FlaxTransformeraDModel(
																    in_channels=self.in_channels   ,		n_heads=self.num_attention_heads   ,		d_head=self.in_channels // self.num_attention_heads   ,		depth=1   ,		use_linear_projection=self.use_linear_projection   ,		use_memory_efficient_attention=self.use_memory_efficient_attention   ,		dtype=self.dtype   ,		)
																attentions.append(lowerCamelCase_       )

																snake_case_					:		Optional[Any] 					=					FlaxResnetBlockaD(
																    in_channels=self.in_channels   ,		out_channels=self.in_channels   ,		dropout_prob=self.dropout   ,		dtype=self.dtype   ,		)
																resnets.append(lowerCamelCase_       )

											snake_case_					:		Optional[Any] 					=					resnets
											snake_case_					:		Any 					=					attentions







						def __call__(self   ,		lowercase__   ,		lowercase__   ,		lowercase__   ,		lowercase__=True       ):
											snake_case_					:		int 					=					self.resnets[0](lowerCamelCase_   ,		lowerCamelCase_       )
											for attn, resnet in zip(self.attentions   ,		self.resnets[1:]       ):
																snake_case_					:		str 					=					attn(lowerCamelCase_   ,		lowerCamelCase_   ,		deterministic=lowerCamelCase_       )
																snake_case_					:		Optional[int] 					=					resnet(lowerCamelCase_   ,		lowerCamelCase_   ,		deterministic=lowerCamelCase_       )

											return hidden_states









"""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()




