

"""simple docstring"""






import argparse
import struct
import unittest







class 	__UpperCamelCase    :

  def __init__(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    data

      # Initialize hash values
      _lowerCAmelCase       :   Any  =    [
          0x6A09_E667,
          0xBB67_AE85,
          0x3C6E_F372,
          0xA54F_F53A,
          0x510E_527F,
          0x9B05_688C,
          0x1F83_D9AB,
          0x5BE0_CD19,
      ]

      # Initialize round constants
      _lowerCAmelCase       :   str  =    [
          0x428A_2F98,
          0x7137_4491,
          0xB5C0_FBCF,
          0xE9B5_DBA5,
          0x3956_C25B,
          0x59F1_11F1,
          0x923F_82A4,
          0xAB1C_5ED5,
          0xD807_AA98,
          0x1283_5B01,
          0x2431_85BE,
          0x550C_7DC3,
          0x72BE_5D74,
          0x80DE_B1FE,
          0x9BDC_06A7,
          0xC19B_F174,
          0xE49B_69C1,
          0xEFBE_4786,
          0x0FC1_9DC6,
          0x240C_A1CC,
          0x2DE9_2C6F,
          0x4A74_84AA,
          0x5CB0_A9DC,
          0x76F9_88DA,
          0x983E_5152,
          0xA831_C66D,
          0xB003_27C8,
          0xBF59_7FC7,
          0xC6E0_0BF3,
          0xD5A7_9147,
          0x06CA_6351,
          0x1429_2967,
          0x27B7_0A85,
          0x2E1B_2138,
          0x4D2C_6DFC,
          0x5338_0D13,
          0x650A_7354,
          0x766A_0ABB,
          0x81C2_C92E,
          0x9272_2C85,
          0xA2BF_E8A1,
          0xA81A_664B,
          0xC24B_8B70,
          0xC76C_51A3,
          0xD192_E819,
          0xD699_0624,
          0xF40E_3585,
          0x106A_A070,
          0x19A4_C116,
          0x1E37_6C08,
          0x2748_774C,
          0x34B0_BCB5,
          0x391C_0CB3,
          0x4ED8_AA4A,
          0x5B9C_CA4F,
          0x682E_6FF3,
          0x748F_82EE,
          0x78A5_636F,
          0x84C8_7814,
          0x8CC7_0208,
          0x90BE_FFFA,
          0xA450_6CEB,
          0xBEF9_A3F7,
          0xC671_78F2,
      ]

      _lowerCAmelCase       :   Any  =    self.preprocessing(self.data      )
      self.final_hash()

  @staticmethod
  def __lowerCamelCase		(		_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    b'\x80' + (b'\x00' * (63 - (len(_A      ) + 8) % 64))
      _lowerCAmelCase       :   Any  =    struct.pack('>Q'    ,(len(_A      ) * 8)      )
      return data + padding + big_endian_integer

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    [
          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
          _lowerCAmelCase       :   int  =    list(struct.unpack('>16L'    ,_A      )      )
          # add 48 0-ed integers
          words += [0] * 48

          _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    self.hashes

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

                  _lowerCAmelCase       :   str  =    (
                      words[index - 16] + sa + words[index - 7] + sa
                  ) % 0x1_0000_0000

              # Compression
              _lowerCAmelCase       :   Optional[Any]  =    self.ror(_A    ,6      ) ^ self.ror(_A    ,11      ) ^ self.ror(_A    ,25      )
              _lowerCAmelCase       :   int  =    (e & f) ^ ((~e & 0xFFFF_FFFF) & g)
              _lowerCAmelCase       :   int  =    (
                  h + sa + ch + self.round_constants[index] + words[index]
              ) % 0x1_0000_0000
              _lowerCAmelCase       :   Union[str, Any]  =    self.ror(_A    ,2      ) ^ self.ror(_A    ,13      ) ^ self.ror(_A    ,22      )
              _lowerCAmelCase       :   Any  =    (a & b) ^ (a & c) ^ (b & c)
              _lowerCAmelCase       :   Any  =    (sa + maj) % 0x1_0000_0000

              _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Tuple  =    (
                  g,
                  f,
                  e,
                  ((d + tempa) % 0x1_0000_0000),
                  c,
                  b,
                  a,
                  ((tempa + tempa) % 0x1_0000_0000),
              )

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

          # Modify final values
          _lowerCAmelCase       :   int  =    [
              ((element + mutated_hash_values[index]) % 0x1_0000_0000)
              for index, element in enumerate(self.hashes      )
          ]

      _lowerCAmelCase       :   List[str]  =    ''.join([hex(_A      )[2:].zfill(8      ) for value in self.hashes]      )







  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      return 0xFFFF_FFFF & (value << (32 - rotations)) | (value >> rotations)







class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      import hashlib

      _lowerCAmelCase       :   Any  =    bytes('Test String'    ,'utf-8'      )
      self.assertEqual(SHAaaa(_A      ).hash    ,hashlib.shaaaa(_A      ).hexdigest()      )





def 		lowerCamelCase__		(					):

    '''simple docstring'''



    import doctest

    doctest.testmod()

    _lowerCAmelCase       :   Optional[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'   )

    _lowerCAmelCase       :   Tuple  =    parser.parse_args()

    _lowerCAmelCase       :   List[str]  =    args.input_string

    # hash input should be a bytestring
    if args.input_file:
        with open(args.input_file ,   'rb'   ) as f:
            _lowerCAmelCase       :   int  =    f.read()
    else:
        _lowerCAmelCase       :   int  =    bytes(_lowerCamelCase ,   'utf-8'   )

    print(SHAaaa(_lowerCamelCase   ).hash   )


if __name__ == "__main__":
 main()








"""simple docstring"""






from dataclasses import dataclass
from typing import Dict, Optional, Union

import torch
import torch.nn.functional as F
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .attention_processor import AttentionProcessor, AttnProcessor
from .embeddings import TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin







@dataclass
class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		42







class 	__UpperCamelCase    (    a__							,							a__						):

  @register_to_config
  def __init__(		self    ,_A = 32    ,_A = 64    ,_A = 20    ,_A = 768    ,_A=77    ,_A=4    ,_A = 0.0    ,_A = "silu"    ,_A = None    ,_A = None    ,_A = "linear"    ,_A = "prd"    ,_A = None    ,_A = None    ,_A = None    ,):
      '''simple docstring'''

      super().__init__()
      _lowerCAmelCase       :   Any  =    num_attention_heads
      _lowerCAmelCase       :   Optional[int]  =    attention_head_dim
      _lowerCAmelCase       :   Tuple  =    num_attention_heads * attention_head_dim
      _lowerCAmelCase       :   Optional[Any]  =    additional_embeddings

      _lowerCAmelCase       :   Union[str, Any]  =    time_embed_dim or inner_dim
      _lowerCAmelCase       :   Union[str, Any]  =    embedding_proj_dim or embedding_dim
      _lowerCAmelCase       :   Optional[int]  =    clip_embed_dim or embedding_dim

      _lowerCAmelCase       :   int  =    Timesteps(_A    ,_A    ,0      )
      _lowerCAmelCase       :   int  =    TimestepEmbedding(_A    ,_A    ,out_dim=_A    ,act_fn=_A      )

      _lowerCAmelCase       :   List[Any]  =    nn.Linear(_A    ,_A      )

      if embedding_proj_norm_type is None:
          _lowerCAmelCase       :   Optional[Any]  =    None
      elif embedding_proj_norm_type == "layer":
          _lowerCAmelCase       :   List[Any]  =    nn.LayerNorm(_A      )
      else:
          raise ValueError(F"""unsupported embedding_proj_norm_type: {embedding_proj_norm_type}"""      )

      _lowerCAmelCase       :   Tuple  =    nn.Linear(_A    ,_A      )

      if encoder_hid_proj_type is None:
          _lowerCAmelCase       :   int  =    None
      elif encoder_hid_proj_type == "linear":
          _lowerCAmelCase       :   List[Any]  =    nn.Linear(_A    ,_A      )
      else:
          raise ValueError(F"""unsupported encoder_hid_proj_type: {encoder_hid_proj_type}"""      )

      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,num_embeddings + additional_embeddings    ,_A      )      )

      if added_emb_type == "prd":
          _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,1    ,_A      )      )
      elif added_emb_type is None:
          _lowerCAmelCase       :   List[Any]  =    None
      else:
          raise ValueError(
              F"""`added_emb_type`: {added_emb_type} is not supported. Make sure to choose one of `'prd'` or `None`."""      )

      _lowerCAmelCase       :   List[Any]  =    nn.ModuleList(
          [
              BasicTransformerBlock(
                  _A    ,_A    ,_A    ,dropout=_A    ,activation_fn='gelu'    ,attention_bias=_A    ,)
              for d in range(_A      )
          ]      )

      if norm_in_type == "layer":
          _lowerCAmelCase       :   Any  =    nn.LayerNorm(_A      )
      elif norm_in_type is None:
          _lowerCAmelCase       :   Any  =    None
      else:
          raise ValueError(F"""Unsupported norm_in_type: {norm_in_type}."""      )

      _lowerCAmelCase       :   Union[str, Any]  =    nn.LayerNorm(_A      )

      _lowerCAmelCase       :   int  =    nn.Linear(_A    ,_A      )

      _lowerCAmelCase       :   Any  =    torch.full(
          [num_embeddings + additional_embeddings, num_embeddings + additional_embeddings]    ,-1_0_0_0_0.0      )
      causal_attention_mask.triu_(1      )
      _lowerCAmelCase       :   Tuple  =    causal_attention_mask[None, ...]
      self.register_buffer('causal_attention_mask'    ,_A    ,persistent=_A      )

      _lowerCAmelCase       :   Tuple  =    nn.Parameter(torch.zeros(1    ,_A      )      )
      _lowerCAmelCase       :   Dict  =    nn.Parameter(torch.zeros(1    ,_A      )      )

  @property
  # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    {}

      def fn_recursive_add_processors(_A    ,_A    ,_A      ):
          if hasattr(_A    ,'set_processor'      ):
              _lowerCAmelCase       :   str  =    module.processor

          for sub_name, child in module.named_children():
              fn_recursive_add_processors(F"""{name}.{sub_name}"""    ,_A    ,_A      )

          return processors

      for name, module in self.named_children():
          fn_recursive_add_processors(_A    ,_A    ,_A      )

      return processors

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    len(self.attn_processors.keys()      )

      if isinstance(_A    ,_A      ) and len(_A      ) != count:
          raise ValueError(
              F"""A dict of processors was passed, but the number of processors {len(_A      )} does not match the"""
              F""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""      )

      def fn_recursive_attn_processor(_A    ,_A    ,_A      ):
          if hasattr(_A    ,'set_processor'      ):
              if not isinstance(_A    ,_A      ):
                  module.set_processor(_A      )
              else:
                  module.set_processor(processor.pop(F"""{name}.processor"""      )      )

          for sub_name, child in module.named_children():
              fn_recursive_attn_processor(F"""{name}.{sub_name}"""    ,_A    ,_A      )

      for name, module in self.named_children():
          fn_recursive_attn_processor(_A    ,_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.set_attn_processor(AttnProcessor()      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A = None    ,_A = None    ,_A = True    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    hidden_states.shape[0]

      _lowerCAmelCase       :   int  =    timestep
      if not torch.is_tensor(_A      ):
          _lowerCAmelCase       :   str  =    torch.tensor([timesteps]    ,dtype=torch.long    ,device=hidden_states.device      )
      elif torch.is_tensor(_A      ) and len(timesteps.shape      ) == 0:
          _lowerCAmelCase       :   Dict  =    timesteps[None].to(hidden_states.device      )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      _lowerCAmelCase       :   Optional[int]  =    timesteps * torch.ones(_A    ,dtype=timesteps.dtype    ,device=timesteps.device      )

      _lowerCAmelCase       :   Dict  =    self.time_proj(_A      )

      # timesteps does not contain any weights and will always return f32 tensors
      # but time_embedding might be fp16, so we need to cast here.
      _lowerCAmelCase       :   Any  =    timesteps_projected.to(dtype=self.dtype      )
      _lowerCAmelCase       :   Optional[Any]  =    self.time_embedding(_A      )

      if self.embedding_proj_norm is not None:
          _lowerCAmelCase       :   int  =    self.embedding_proj_norm(_A      )

      _lowerCAmelCase       :   str  =    self.embedding_proj(_A      )
      if self.encoder_hidden_states_proj is not None and encoder_hidden_states is not None:
          _lowerCAmelCase       :   str  =    self.encoder_hidden_states_proj(_A      )
      elif self.encoder_hidden_states_proj is not None and encoder_hidden_states is None:
          raise ValueError('`encoder_hidden_states_proj` requires `encoder_hidden_states` to be set'      )

      _lowerCAmelCase       :   Any  =    self.proj_in(_A      )

      _lowerCAmelCase       :   Dict  =    self.positional_embedding.to(hidden_states.dtype      )

      _lowerCAmelCase       :   List[Any]  =    []
      _lowerCAmelCase       :   Optional[Any]  =    0

      if encoder_hidden_states is not None:
          additional_embeds.append(_A      )
          additional_embeddings_len += encoder_hidden_states.shape[1]

      if len(proj_embeddings.shape      ) == 2:
          _lowerCAmelCase       :   int  =    proj_embeddings[:, None, :]

      if len(hidden_states.shape      ) == 2:
          _lowerCAmelCase       :   Any  =    hidden_states[:, None, :]

      _lowerCAmelCase       :   int  =    additional_embeds + [
          proj_embeddings,
          time_embeddings[:, None, :],
          hidden_states,
      ]

      if self.prd_embedding is not None:
          _lowerCAmelCase       :   List[str]  =    self.prd_embedding.to(hidden_states.dtype      ).expand(_A    ,-1    ,-1      )
          additional_embeds.append(_A      )

      _lowerCAmelCase       :   List[str]  =    torch.cat(
          _A    ,dim=1    ,)

      # Allow positional_embedding to not include the `addtional_embeddings` and instead pad it with zeros for these additional tokens
      _lowerCAmelCase       :   Tuple  =    additional_embeddings_len + proj_embeddings.shape[1] + 1
      if positional_embeddings.shape[1] < hidden_states.shape[1]:
          _lowerCAmelCase       :   Any  =    F.pad(
              _A    ,(
                  0,
                  0,
                  additional_embeddings_len,
                  self.prd_embedding.shape[1] if self.prd_embedding is not None else 0,
              )    ,value=0.0    ,)

      _lowerCAmelCase       :   int  =    hidden_states + positional_embeddings

      if attention_mask is not None:
          _lowerCAmelCase       :   Optional[Any]  =    (1 - attention_mask.to(hidden_states.dtype      )) * -1_0_0_0_0.0
          _lowerCAmelCase       :   Union[str, Any]  =    F.pad(_A    ,(0, self.additional_embeddings)    ,value=0.0      )
          _lowerCAmelCase       :   Tuple  =    (attention_mask[:, None, :] + self.causal_attention_mask).to(hidden_states.dtype      )
          _lowerCAmelCase       :   Optional[Any]  =    attention_mask.repeat_interleave(self.config.num_attention_heads    ,dim=0      )

      if self.norm_in is not None:
          _lowerCAmelCase       :   Any  =    self.norm_in(_A      )

      for block in self.transformer_blocks:
          _lowerCAmelCase       :   int  =    block(_A    ,attention_mask=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.norm_out(_A      )

      if self.prd_embedding is not None:
          _lowerCAmelCase       :   Optional[int]  =    hidden_states[:, -1]
      else:
          _lowerCAmelCase       :   Any  =    hidden_states[:, additional_embeddings_len:]

      _lowerCAmelCase       :   Optional[int]  =    self.proj_to_clip_embeddings(_A      )

      if not return_dict:
          return (predicted_image_embedding,)

      return PriorTransformerOutput(predicted_image_embedding=_A      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    (prior_latents * self.clip_std) + self.clip_mean
      return prior_latents





