





'''simple docstring'''






from collections.abc import Generator
from math import sin






def      __A						(							a_						:  bytes	):







		if len(a_	) != 3_2:
				raise ValueError("Input must be of length 32"	)

		lowerCAmelCase     :    Tuple						=   b""
		for i in [3, 2, 1, 0]:
				little_endian += string_aa[8 * i : 8 * i + 8]
		return little_endian






def      __A						(							a_						:  int	):







		if i < 0:
				raise ValueError("Input must be non-negative"	)

		lowerCAmelCase     :    Optional[Any]						=   format(a_      ,"08x"	)[-8:]
		lowerCAmelCase     :    List[str]						=   b""
		for i in [3, 2, 1, 0]:
				little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("utf-8"	)
		return little_endian_hex






def      __A						(							a_						:  bytes	):
		lowerCAmelCase     :    Union[str, Any]						=   b""
		for char in message:
				bit_string += format(a_      ,"08b"	).encode("utf-8"	)
		lowerCAmelCase     :    Dict						=   format(len(a_	)      ,"064b"	).encode("utf-8"	)

		# Pad bit_string to a multiple of 512 chars
		bit_string += b"1"
		while len(a_	) % 5_1_2 != 4_4_8:
				bit_string += b"0"
		bit_string += to_little_endian(start_len[3_2:]	) + to_little_endian(start_len[:3_2]	)

		return bit_string






def      __A						(							a_						:  bytes	):







		if len(a_	) % 5_1_2 != 0:
				raise ValueError("Input must have length that's a multiple of 512"	)

		for pos in range(0      ,len(a_	)      ,5_1_2	):
				lowerCAmelCase     :    Optional[Any]						=   bit_string[pos : pos + 5_1_2]
				lowerCAmelCase     :    Union[str, Any]						=   []
				for i in range(0      ,5_1_2      ,3_2	):
						block_words.append(int(to_little_endian(block[i : i + 3_2]	)      ,2	)	)
				yield block_words






def      __A						(							a_						:  int	):







		if i < 0:
				raise ValueError("Input must be non-negative"	)

		lowerCAmelCase     :    int						=   format(a_      ,"032b"	)
		lowerCAmelCase     :    Optional[Any]						=   ""
		for c in i_str:
				new_str += "1" if c == "0" else "0"
		return int(a_      ,2	)






def      __A						(							a_						:  int      ,a_						:  int	):
		return (a + b) % 2**3_2






def      __A						(							a_						:  int      ,a_						:  int	):







		if i < 0:
				raise ValueError("Input must be non-negative"	)
		if shift < 0:
				raise ValueError("Shift must be non-negative"	)
		return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2






def      __A						(							a_						:  bytes	):
		lowerCAmelCase     :    str						=   preprocess(a_	)

		lowerCAmelCase     :    Union[str, Any]						=   [int(2**3_2 * abs(sin(i + 1	)	)	) for i in range(6_4	)]

		# Starting states
		lowerCAmelCase     :    int						=   0x67_452_301
		lowerCAmelCase     :    int						=   0xEF_CDA_B89
		lowerCAmelCase     :    Optional[int]						=   0x98_BAD_CFE
		lowerCAmelCase     :    List[str]						=   0x10_325_476

		lowerCAmelCase     :    List[Any]						=   [
		    7,
		    1_2,
		    1_7,
		    2_2,
		    7,
		    1_2,
		    1_7,
		    2_2,
		    7,
		    1_2,
		    1_7,
		    2_2,
		    7,
		    1_2,
		    1_7,
		    2_2,
		    5,
		    9,
		    1_4,
		    2_0,
		    5,
		    9,
		    1_4,
		    2_0,
		    5,
		    9,
		    1_4,
		    2_0,
		    5,
		    9,
		    1_4,
		    2_0,
		    4,
		    1_1,
		    1_6,
		    2_3,
		    4,
		    1_1,
		    1_6,
		    2_3,
		    4,
		    1_1,
		    1_6,
		    2_3,
		    4,
		    1_1,
		    1_6,
		    2_3,
		    6,
		    1_0,
		    1_5,
		    2_1,
		    6,
		    1_0,
		    1_5,
		    2_1,
		    6,
		    1_0,
		    1_5,
		    2_1,
		    6,
		    1_0,
		    1_5,
		    2_1,
		]

		# Process bit string in chunks, each with 16 32-char words
		for block_words in get_block_words(a_	):
				lowerCAmelCase     :    Tuple						=   aa
				lowerCAmelCase     :    Dict						=   ba
				lowerCAmelCase     :    Union[str, Any]						=   ca
				lowerCAmelCase     :    str						=   da

				# Hash current chunk
				for i in range(6_4	):
						if i <= 1_5:
								# f = (b & c) | (not_32(b) & d)     # Alternate definition for f
								lowerCAmelCase     :    List[str]						=   d ^ (b & (c ^ d))
								lowerCAmelCase     :    int						=   i
						elif i <= 3_1:
								# f = (d & b) | (not_32(d) & c)     # Alternate definition for f
								lowerCAmelCase     :    List[Any]						=   c ^ (d & (b ^ c))
								lowerCAmelCase     :    Optional[Any]						=   (5 * i + 1) % 1_6
						elif i <= 4_7:
								lowerCAmelCase     :    Union[str, Any]						=   b ^ c ^ d
								lowerCAmelCase     :    str						=   (3 * i + 5) % 1_6
						else:
								lowerCAmelCase     :    Union[str, Any]						=   c ^ (b | not_aa(a_	))
								lowerCAmelCase     :    str						=   (7 * i) % 1_6
						lowerCAmelCase     :    Dict						=   (f + a + added_consts[i] + block_words[g]) % 2**3_2
						lowerCAmelCase     :    List[str]						=   d
						lowerCAmelCase     :    Any						=   c
						lowerCAmelCase     :    int						=   b
						lowerCAmelCase     :    Optional[int]						=   sum_aa(a_      ,left_rotate_aa(a_      ,shift_amounts[i]	)	)

				# Add hashed chunk to running total
				lowerCAmelCase     :    Optional[int]						=   sum_aa(a_      ,a_	)
				lowerCAmelCase     :    int						=   sum_aa(a_      ,a_	)
				lowerCAmelCase     :    Optional[Any]						=   sum_aa(a_      ,a_	)
				lowerCAmelCase     :    Any						=   sum_aa(a_      ,a_	)

		lowerCAmelCase     :    List[str]						=   reformat_hex(a_	) + reformat_hex(a_	) + reformat_hex(a_	) + reformat_hex(a_	)
		return digest


if __name__ == "__main__":
			import doctest

			doctest.testmod()










'''simple docstring'''






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 		lowerCamelCase  (      _A      ,   _A      ,   _A       ):



	@register_to_config
	def __init__(   self		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_		,      a_ = False		,      ):
			super().__init__()

			lowerCAmelCase     :    int						=   nn.Embedding(a_		,      a_	)

			lowerCAmelCase     :    int						=   nn.Embedding(a_		,      a_	)
			lowerCAmelCase     :    int						=   False

			lowerCAmelCase     :    int						=   nn.Dropout(p=a_	)

			lowerCAmelCase     :    str						=   TaConfig(
			    vocab_size=a_		,      d_model=a_		,      num_heads=a_		,      d_kv=a_		,      d_ff=a_		,      dropout_rate=a_		,      feed_forward_proj=a_		,      is_decoder=a_		,      is_encoder_decoder=a_		,      )

			lowerCAmelCase     :    Any						=   nn.ModuleList()
			for lyr_num in range(a_	):
					lowerCAmelCase     :    Optional[Any]						=   TaBlock(a_	)
					self.encoders.append(a_	)

			lowerCAmelCase     :    List[str]						=   TaLayerNorm(a_	)
			lowerCAmelCase     :    Union[str, Any]						=   nn.Dropout(p=a_	)





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Optional[Any]						=   self.token_embedder(a_	)

			lowerCAmelCase     :    int						=   encoder_input_tokens.shape[1]
			lowerCAmelCase     :    Optional[int]						=   torch.arange(a_		,      device=encoder_input_tokens.device	)
			x += self.position_encoding(a_	)

			lowerCAmelCase     :    List[str]						=   self.dropout_pre(a_	)

			# inverted the attention mask
			lowerCAmelCase     :    List[Any]						=   encoder_input_tokens.size()
			lowerCAmelCase     :    Dict						=   self.get_extended_attention_mask(a_		,      a_	)

			for lyr in self.encoders:
					lowerCAmelCase     :    Dict						=   lyr(a_		,      a_	)[0]
			lowerCAmelCase     :    int						=   self.layer_norm(a_	)

			return self.dropout_post(a_	), encoder_inputs_mask



