





'''simple docstring'''


from collections.abc import Generator
from math import sin


def 						__a(SCREAMING_SNAKE_CASE_	:       bytes     ):



    '''simple docstring'''





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

    _lowerCAmelCase						      =   B""
    for i in [3, 2, 1, 0]:
        little_endian += string_aa[8 * i : 8 * i + 8]
    return little_endian


def 						__a(SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





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

    _lowerCAmelCase						      =   format(SCREAMING_SNAKE_CASE_		,							"08x"     )[-8:]
    _lowerCAmelCase						      =   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(SCREAMING_SNAKE_CASE_	:       bytes     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   B""
    for char in message:
        bit_string += format(SCREAMING_SNAKE_CASE_		,							"08b"     ).encode("utf-8"     )
    _lowerCAmelCase						      =   format(len(SCREAMING_SNAKE_CASE_     )		,							"064b"     ).encode("utf-8"     )

    # Pad bit_string to a multiple of 512 chars
    bit_string += b"1"
    while len(SCREAMING_SNAKE_CASE_     ) % 512 != 448:
        bit_string += b"0"
    bit_string += to_little_endian(start_len[32:]     ) + to_little_endian(start_len[:32]     )

    return bit_string


def 						__a(SCREAMING_SNAKE_CASE_	:       bytes     ):



    '''simple docstring'''





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

    for pos in range(0		,							len(SCREAMING_SNAKE_CASE_     )		,							512     ):
        _lowerCAmelCase						      =   bit_string[pos : pos + 512]
        _lowerCAmelCase						      =   []
        for i in range(0		,							512		,							32     ):
            block_words.append(int(to_little_endian(block[i : i + 32]     )		,							2     )     )
        yield block_words


def 						__a(SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





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

    _lowerCAmelCase						      =   format(SCREAMING_SNAKE_CASE_		,							"032b"     )
    _lowerCAmelCase						      =   ""
    for c in i_str:
        new_str += "1" if c == "0" else "0"
    return int(SCREAMING_SNAKE_CASE_		,							2     )


def 						__a(SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    return (a + b) % 2**32


def 						__a(SCREAMING_SNAKE_CASE_	:       int		,							SCREAMING_SNAKE_CASE_	:       int     ):



    '''simple docstring'''





    if i < 0:
        raise ValueError("Input must be non-negative"     )
    if shift < 0:
        raise ValueError("Shift must be non-negative"     )
    return ((i << shift) ^ (i >> (32 - shift))) % 2**32


def 						__a(SCREAMING_SNAKE_CASE_	:       bytes     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   preprocess(SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   [int(2**32 * abs(sin(i + 1     )     )     ) for i in range(64     )]

    # Starting states
    _lowerCAmelCase						      =   0X67452301
    _lowerCAmelCase						      =   0XEFCDAB89
    _lowerCAmelCase						      =   0X98BADCFE
    _lowerCAmelCase						      =   0X10325476

    _lowerCAmelCase						      =   [
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        7,
        12,
        17,
        22,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        5,
        9,
        14,
        20,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        4,
        11,
        16,
        23,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
        6,
        10,
        15,
        21,
    ]

    # Process bit string in chunks, each with 16 32-char words
    for block_words in get_block_words(SCREAMING_SNAKE_CASE_     ):
        _lowerCAmelCase						      =   aa
        _lowerCAmelCase						      =   ba
        _lowerCAmelCase						      =   ca
        _lowerCAmelCase						      =   da

        # Hash current chunk
        for i in range(64     ):
            if i <= 15:
                # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                _lowerCAmelCase						      =   d ^ (b & (c ^ d))
                _lowerCAmelCase						      =   i
            elif i <= 31:
                # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                _lowerCAmelCase						      =   c ^ (d & (b ^ c))
                _lowerCAmelCase						      =   (5 * i + 1) % 16
            elif i <= 47:
                _lowerCAmelCase						      =   b ^ c ^ d
                _lowerCAmelCase						      =   (3 * i + 5) % 16
            else:
                _lowerCAmelCase						      =   c ^ (b | not_aa(SCREAMING_SNAKE_CASE_     ))
                _lowerCAmelCase						      =   (7 * i) % 16
            _lowerCAmelCase						      =   (f + a + added_consts[i] + block_words[g]) % 2**32
            _lowerCAmelCase						      =   d
            _lowerCAmelCase						      =   c
            _lowerCAmelCase						      =   b
            _lowerCAmelCase						      =   sum_aa(SCREAMING_SNAKE_CASE_		,							left_rotate_aa(SCREAMING_SNAKE_CASE_		,							shift_amounts[i]     )     )

        # Add hashed chunk to running total
        _lowerCAmelCase						      =   sum_aa(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   sum_aa(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   sum_aa(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )
        _lowerCAmelCase						      =   sum_aa(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )

    _lowerCAmelCase						      =   reformat_hex(SCREAMING_SNAKE_CASE_     ) + reformat_hex(SCREAMING_SNAKE_CASE_     ) + reformat_hex(SCREAMING_SNAKE_CASE_     ) + reformat_hex(SCREAMING_SNAKE_CASE_     )
    return digest


if __name__ == "__main__":
       import doctest

       doctest.testmod()







from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class      _UpperCAmelCase      (							A__      ,   A__      ,   A__  ):
     UpperCamelCase__		     =					[r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']




     @register_to_config
     def __init__(					self			,    a__			,    a__			,    a__ = None			,    a__ = 5_0_2_5_7			,    a__ = 1_0_2_4			,    a__ = 7_6_8			,    a__ = 1_2			,    a__ = 1_2			,    a__ = None			,    a__ = "gelu_new"			,    a__ = 0.1			,    a__ = 0.1			,    a__ = 0.1			,    a__ = 1e-5			,    a__ = 0.0_2			,    a__ = True			,    a__ = True			,    a__ = False			,    a__ = False			,    ):
         super().__init__()

         A__					=       prefix_length

         if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
             raise ValueError(
                 F"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
                 F" `n_embd`: {n_embd} are not equal.")

         A__					=       prefix_inner_dim
         A__					=       prefix_hidden_dim

         A__					=       (
             nn.Linear(self.prefix_inner_dim			,    self.prefix_hidden_dim)
             if self.prefix_hidden_dim is not None
             else nn.Identity()
         )
         A__					=       (
             nn.Linear(self.prefix_hidden_dim			,    a__) if self.prefix_hidden_dim is not None else nn.Identity()
         )

         A__					=       GPTaConfig(
             vocab_size=a__			,    n_positions=a__			,    n_embd=a__			,    n_layer=a__			,    n_head=a__			,    n_inner=a__			,    activation_function=a__			,    resid_pdrop=a__			,    embd_pdrop=a__			,    attn_pdrop=a__			,    layer_norm_epsilon=a__			,    initializer_range=a__			,    scale_attn_weights=a__			,    use_cache=a__			,    scale_attn_by_inverse_layer_idx=a__			,    reorder_and_upcast_attn=a__			,    )
         A__					=       GPTaLMHeadModel(a__)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__ = None			,    a__ = None			,    ):
         A__					=       self.transformer.transformer.wte(a__)
         A__					=       self.encode_prefix(a__)
         A__					=       self.decode_prefix(a__)
         A__					=       torch.cat((prefix_embeds, embedding_text)			,    dim=1)

         if labels is not None:
             A__					=       self.get_dummy_token(input_ids.shape[0]			,    input_ids.device)
             A__					=       torch.cat((dummy_token, input_ids)			,    dim=1)
         A__					=       self.transformer(inputs_embeds=a__			,    labels=a__			,    attention_mask=a__)
         if self.prefix_hidden_dim is not None:
             return out, hidden
         else:
             return out




     def 		snake_case_		(					self			,    a__			,    a__):
         return torch.zeros(a__			,    self.prefix_length			,    dtype=torch.intaa			,    device=a__)




     def 		snake_case_		(					self			,    a__):
         return self.encode_prefix(a__)




     @torch.no_grad()
     def 		snake_case_		(					self			,    a__			,    a__			,    a__):
         A__					=       torch.split(a__			,    1			,    dim=0)
         A__					=       []
         A__					=       []
         for feature in features:
             A__					=       self.decode_prefix(feature.to(a__))  # back to the clip feature
             # Only support beam search for now
             A__			,						A__					=       self.generate_beam(
                 input_embeds=a__			,    device=a__			,    eos_token_id=a__)
             generated_tokens.append(output_tokens[0])
             generated_seq_lengths.append(seq_lengths[0])
         A__					=       torch.stack(a__)
         A__					=       torch.stack(a__)
         return generated_tokens, generated_seq_lengths




     @torch.no_grad()
     def 		snake_case_		(					self			,    a__=None			,    a__=None			,    a__=None			,    a__ = 5			,    a__ = 6_7			,    a__ = 1.0			,    a__ = None			,    ):
         A__					=       eos_token_id
         A__					=       None
         A__					=       None
         A__					=       torch.ones(a__			,    device=a__			,    dtype=torch.int)
         A__					=       torch.zeros(a__			,    device=a__			,    dtype=torch.bool)

         if input_embeds is not None:
             A__					=       input_embeds
         else:
             A__					=       self.transformer.transformer.wte(a__)

         for i in range(a__):
             A__					=       self.transformer(inputs_embeds=a__)
             A__					=       outputs.logits
             A__					=       logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
             A__					=       logits.softmax(-1).log()

             if scores is None:
                 A__			,						A__					=       logits.topk(a__			,    -1)
                 A__					=       generated.expand(a__			,    *generated.shape[1:])
                 A__			,						A__					=       next_tokens.permute(1			,    0), scores.squeeze(0)
                 if tokens is None:
                     A__					=       next_tokens
                 else:
                     A__					=       tokens.expand(a__			,    *tokens.shape[1:])
                     A__					=       torch.cat((tokens, next_tokens)			,    dim=1)
             else:
                 A__					=       -float(np.inf)
                 A__					=       0
                 A__					=       scores[:, None] + logits
                 seq_lengths[~is_stopped] += 1
                 A__					=       scores_sum / seq_lengths[:, None]
                 A__			,						A__					=       scores_sum_average.view(-1).topk(a__			,    -1)
                 A__					=       next_tokens // scores_sum.shape[1]
                 A__					=       seq_lengths[next_tokens_source]
                 A__					=       next_tokens % scores_sum.shape[1]
                 A__					=       next_tokens.unsqueeze(1)
                 A__					=       tokens[next_tokens_source]
                 A__					=       torch.cat((tokens, next_tokens)			,    dim=1)
                 A__					=       generated[next_tokens_source]
                 A__					=       scores_sum_average * seq_lengths
                 A__					=       is_stopped[next_tokens_source]

             A__					=       self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0]			,    1			,    -1)
             A__					=       torch.cat((generated, next_token_embed)			,    dim=1)
             A__					=       is_stopped + next_tokens.eq(a__).squeeze()
             if is_stopped.all():
                 break

         A__					=       scores / seq_lengths
         A__					=       scores.argsort(descending=a__)
         # tokens tensors are already padded to max_seq_length
         A__					=       [tokens[i] for i in order]
         A__					=       torch.stack(a__			,    dim=0)
         A__					=       torch.tensor([seq_lengths[i] for i in order]			,    dtype=seq_lengths.dtype)
         return output_texts, seq_lengths
