




import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
  import faiss




@require_faiss
class    __lowerCAmelCase       (							lowerCAmelCase):






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :List[Any]      	=						tempfile.mkdtemp()
         lowercase    :str      	=						8

         # DPR tok
         lowercase    :List[Any]      	=						[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "[PAD]",
             "[MASK]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         lowercase    :Any      	=						os.path.join(self.tmpdirname       ,					"dpr_tokenizer"				)
         os.makedirs(_lowerCAmelCase       ,					exist_ok=_lowerCAmelCase				)
         lowercase    :Union[str, Any]      	=						os.path.join(_lowerCAmelCase       ,					DPR_VOCAB_FILES_NAMES["vocab_file"]				)
         with open(self.vocab_file       ,					"w"       ,					encoding="utf-8"				) as vocab_writer:
           vocab_writer.write("".join([x + "\n" for x in vocab_tokens]				)				)

         # BART tok
         lowercase    :Optional[int]      	=						[
             "l",
             "o",
             "w",
             "e",
             "r",
             "s",
             "t",
             "i",
             "d",
             "n",
             "\u0120",
             "\u0120l",
             "\u0120n",
             "\u0120lo",
             "\u0120low",
             "er",
             "\u0120lowest",
             "\u0120newer",
             "\u0120wider",
             "<unk>",
         ]
         lowercase    :List[str]      	=						dict(zip(_lowerCAmelCase       ,					range(len(_lowerCAmelCase				)				)				)				)
         lowercase    :Union[str, Any]      	=						["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
         lowercase    :int      	=						{"unk_token": "<unk>"}

         lowercase    :Dict      	=						os.path.join(self.tmpdirname       ,					"bart_tokenizer"				)
         os.makedirs(_lowerCAmelCase       ,					exist_ok=_lowerCAmelCase				)
         lowercase    :Union[str, Any]      	=						os.path.join(_lowerCAmelCase       ,					BART_VOCAB_FILES_NAMES["vocab_file"]				)
         lowercase    :Optional[int]      	=						os.path.join(_lowerCAmelCase       ,					BART_VOCAB_FILES_NAMES["merges_file"]				)
         with open(self.vocab_file       ,					"w"       ,					encoding="utf-8"				) as fp:
           fp.write(json.dumps(_lowerCAmelCase				) + "\n"				)
         with open(self.merges_file       ,					"w"       ,					encoding="utf-8"				) as fp:
           fp.write("\n".join(_lowerCAmelCase				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,					"dpr_tokenizer"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,					"dpr_tokenizer"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,					"bart_tokenizer"				)				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]				):
         shutil.rmtree(self.tmpdirname				)






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :Any      	=						Dataset.from_dict(
             {
                 "id": ["0", "1"],
                 "text": ["foo", "bar"],
                 "title": ["Foo", "Bar"],
                 "embeddings": [np.ones(self.retrieval_vector_size				), 2 * np.ones(self.retrieval_vector_size				)],
             }				)
         dataset.add_faiss_index("embeddings"       ,					string_factory="Flat"       ,					metric_type=faiss.METRIC_INNER_PRODUCT				)
         return dataset






       def 					SCREAMING_SNAKE_CASE    (   self:					int				):
         lowercase    :str      	=						self.get_dummy_dataset()
         lowercase    :Optional[Any]      	=						RagConfig(
             retrieval_vector_size=self.retrieval_vector_size       ,					question_encoder=DPRConfig().to_dict()       ,					generator=BartConfig().to_dict()       ,					)
         with patch("transformers.models.rag.retrieval_rag.load_dataset"				) as mock_load_dataset:
           lowercase    :List[str]      	=						dataset
           lowercase    :List[Any]      	=						RagRetriever(
               _lowerCAmelCase       ,					question_encoder_tokenizer=self.get_dpr_tokenizer()       ,					generator_tokenizer=self.get_bart_tokenizer()       ,					)
         return retriever






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict       ,					_lowerCAmelCase:					bool				):
         lowercase    :List[str]      	=						self.get_dummy_dataset()
         lowercase    :Union[str, Any]      	=						RagConfig(
             retrieval_vector_size=self.retrieval_vector_size       ,					question_encoder=DPRConfig().to_dict()       ,					generator=BartConfig().to_dict()       ,					index_name="custom"       ,					)
         if from_disk:
           lowercase    :Dict      	=						os.path.join(self.tmpdirname       ,					"dataset"				)
           lowercase    :Any      	=						os.path.join(self.tmpdirname       ,					"index.faiss"				)
           dataset.get_index("embeddings"				).save(os.path.join(self.tmpdirname       ,					"index.faiss"				)				)
           dataset.drop_index("embeddings"				)
           dataset.save_to_disk(os.path.join(self.tmpdirname       ,					"dataset"				)				)
           del dataset
           lowercase    :Optional[Any]      	=						RagRetriever(
               _lowerCAmelCase       ,					question_encoder_tokenizer=self.get_dpr_tokenizer()       ,					generator_tokenizer=self.get_bart_tokenizer()       ,					)
         else:
           lowercase    :Any      	=						RagRetriever(
               _lowerCAmelCase       ,					question_encoder_tokenizer=self.get_dpr_tokenizer()       ,					generator_tokenizer=self.get_bart_tokenizer()       ,					index=CustomHFIndex(config.retrieval_vector_size       ,					_lowerCAmelCase				)       ,					)
         return retriever






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         lowercase    :List[Any]      	=						Dataset.from_dict(
             {
                 "id": ["0", "1"],
                 "text": ["foo", "bar"],
                 "title": ["Foo", "Bar"],
                 "embeddings": [np.ones(self.retrieval_vector_size + 1				), 2 * np.ones(self.retrieval_vector_size + 1				)],
             }				)
         dataset.add_faiss_index("embeddings"       ,					string_factory="Flat"       ,					metric_type=faiss.METRIC_INNER_PRODUCT				)

         lowercase    :Optional[Any]      	=						os.path.join(self.tmpdirname       ,					"hf_bert_base.hnswSQ8_correct_phi_128.c_index"				)
         dataset.save_faiss_index("embeddings"       ,					index_file_name + ".index.dpr"				)
         pickle.dump(dataset["id"]       ,					open(index_file_name + ".index_meta.dpr"       ,					"wb"				)				)

         lowercase    :List[str]      	=						os.path.join(self.tmpdirname       ,					"psgs_w100.tsv.pkl"				)
         lowercase    :int      	=						{sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
         pickle.dump(_lowerCAmelCase       ,					open(_lowerCAmelCase       ,					"wb"				)				)

         lowercase    :Optional[Any]      	=						RagConfig(
             retrieval_vector_size=self.retrieval_vector_size       ,					question_encoder=DPRConfig().to_dict()       ,					generator=BartConfig().to_dict()       ,					index_name="legacy"       ,					index_path=self.tmpdirname       ,					)
         lowercase    :Union[str, Any]      	=						RagRetriever(
             _lowerCAmelCase       ,					question_encoder_tokenizer=self.get_dpr_tokenizer()       ,					generator_tokenizer=self.get_bart_tokenizer()				)
         return retriever






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         lowercase    :Any      	=						1
         lowercase    :Tuple      	=						self.get_dummy_canonical_hf_index_retriever()
         lowercase    :Tuple      	=						np.array(
             [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,					dtype=np.floataa				)
         lowercase    :List[str]      	=						retriever.retrieve(_lowerCAmelCase       ,					n_docs=_lowerCAmelCase				)
         self.assertEqual(retrieved_doc_embeds.shape       ,					(2, n_docs, self.retrieval_vector_size)				)
         self.assertEqual(len(_lowerCAmelCase				)       ,					2				)
         self.assertEqual(sorted(doc_dicts[0]				)       ,					["embeddings", "id", "text", "title"]				)
         self.assertEqual(len(doc_dicts[0]["id"]				)       ,					_lowerCAmelCase				)
         self.assertEqual(doc_dicts[0]["id"][0]       ,					"1"				)  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]["id"][0]       ,					"0"				)  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()       ,					[[1], [0]]				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :Any      	=						self.get_dummy_canonical_hf_index_retriever()
         with tempfile.TemporaryDirectory() as tmp_dirname:
           with patch("transformers.models.rag.retrieval_rag.load_dataset"				) as mock_load_dataset:
             lowercase    :Optional[Any]      	=						self.get_dummy_dataset()
             retriever.save_pretrained(_lowerCAmelCase				)
             lowercase    :str      	=						RagRetriever.from_pretrained(_lowerCAmelCase				)
             self.assertIsInstance(_lowerCAmelCase       ,					_lowerCAmelCase				)
             lowercase    :Union[str, Any]      	=						np.array(
                 [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,					dtype=np.floataa				)
             lowercase    :Union[str, Any]      	=						retriever.retrieve(_lowerCAmelCase       ,					n_docs=1				)
             self.assertTrue(out is not None				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         lowercase    :Dict      	=						1
         lowercase    :Union[str, Any]      	=						self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCAmelCase				)
         lowercase    :Tuple      	=						np.array(
             [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,					dtype=np.floataa				)
         lowercase    :str      	=						retriever.retrieve(_lowerCAmelCase       ,					n_docs=_lowerCAmelCase				)
         self.assertEqual(retrieved_doc_embeds.shape       ,					(2, n_docs, self.retrieval_vector_size)				)
         self.assertEqual(len(_lowerCAmelCase				)       ,					2				)
         self.assertEqual(sorted(doc_dicts[0]				)       ,					["embeddings", "id", "text", "title"]				)
         self.assertEqual(len(doc_dicts[0]["id"]				)       ,					_lowerCAmelCase				)
         self.assertEqual(doc_dicts[0]["id"][0]       ,					"1"				)  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]["id"][0]       ,					"0"				)  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()       ,					[[1], [0]]				)






       def 					SCREAMING_SNAKE_CASE    (   self:					str				):
         lowercase    :Dict      	=						self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCAmelCase				)
         with tempfile.TemporaryDirectory() as tmp_dirname:
           retriever.save_pretrained(_lowerCAmelCase				)
           lowercase    :Tuple      	=						RagRetriever.from_pretrained(_lowerCAmelCase				)
           self.assertIsInstance(_lowerCAmelCase       ,					_lowerCAmelCase				)
           lowercase    :Tuple      	=						np.array(
               [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,					dtype=np.floataa				)
           lowercase    :int      	=						retriever.retrieve(_lowerCAmelCase       ,					n_docs=1				)
           self.assertTrue(out is not None				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Any				):
         lowercase    :Tuple      	=						1
         lowercase    :Any      	=						self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCAmelCase				)
         lowercase    :Any      	=						np.array(
             [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,					dtype=np.floataa				)
         lowercase    :Optional[int]      	=						retriever.retrieve(_lowerCAmelCase       ,					n_docs=_lowerCAmelCase				)
         self.assertEqual(retrieved_doc_embeds.shape       ,					(2, n_docs, self.retrieval_vector_size)				)
         self.assertEqual(len(_lowerCAmelCase				)       ,					2				)
         self.assertEqual(sorted(doc_dicts[0]				)       ,					["embeddings", "id", "text", "title"]				)
         self.assertEqual(len(doc_dicts[0]["id"]				)       ,					_lowerCAmelCase				)
         self.assertEqual(doc_dicts[0]["id"][0]       ,					"1"				)  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]["id"][0]       ,					"0"				)  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()       ,					[[1], [0]]				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :Union[str, Any]      	=						self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCAmelCase				)
         with tempfile.TemporaryDirectory() as tmp_dirname:
           retriever.save_pretrained(_lowerCAmelCase				)
           lowercase    :List[str]      	=						RagRetriever.from_pretrained(_lowerCAmelCase				)
           self.assertIsInstance(_lowerCAmelCase       ,					_lowerCAmelCase				)
           lowercase    :Tuple      	=						np.array(
               [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,					dtype=np.floataa				)
           lowercase    :Optional[Any]      	=						retriever.retrieve(_lowerCAmelCase       ,					n_docs=1				)
           self.assertTrue(out is not None				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]				):
         lowercase    :Optional[Any]      	=						1
         lowercase    :Optional[Any]      	=						self.get_dummy_legacy_index_retriever()
         lowercase    :Union[str, Any]      	=						np.array(
             [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,					dtype=np.floataa				)
         lowercase    :Tuple      	=						retriever.retrieve(_lowerCAmelCase       ,					n_docs=_lowerCAmelCase				)
         self.assertEqual(retrieved_doc_embeds.shape       ,					(2, n_docs, self.retrieval_vector_size)				)
         self.assertEqual(len(_lowerCAmelCase				)       ,					2				)
         self.assertEqual(sorted(doc_dicts[0]				)       ,					["text", "title"]				)
         self.assertEqual(len(doc_dicts[0]["text"]				)       ,					_lowerCAmelCase				)
         self.assertEqual(doc_dicts[0]["text"][0]       ,					"bar"				)  # max inner product is reached with second doc
         self.assertEqual(doc_dicts[1]["text"][0]       ,					"foo"				)  # max inner product is reached with first doc
         self.assertListEqual(doc_ids.tolist()       ,					[[1], [0]]				)






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         lowercase    :Dict      	=						self.get_dummy_legacy_index_retriever()
         with tempfile.TemporaryDirectory() as tmp_dirname:
           retriever.save_pretrained(_lowerCAmelCase				)
           lowercase    :str      	=						RagRetriever.from_pretrained(_lowerCAmelCase				)
           self.assertIsInstance(_lowerCAmelCase       ,					_lowerCAmelCase				)
           lowercase    :str      	=						np.array(
               [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,					dtype=np.floataa				)
           lowercase    :Any      	=						retriever.retrieve(_lowerCAmelCase       ,					n_docs=1				)
           self.assertTrue(out is not None				)






       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple				):
         import torch

         lowercase    :Dict      	=						1
         lowercase    :List[Any]      	=						self.get_dummy_canonical_hf_index_retriever()
         lowercase    :str      	=						[[5, 7], [10, 11]]
         lowercase    :str      	=						np.array(
             [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,					dtype=np.floataa				)
         lowercase    :Dict      	=						retriever(_lowerCAmelCase       ,					_lowerCAmelCase       ,					prefix=retriever.config.generator.prefix       ,					n_docs=_lowerCAmelCase				)
         lowercase    :Tuple      	=						(
             out["context_input_ids"],
             out["context_attention_mask"],
             out["retrieved_doc_embeds"],
         )
         self.assertEqual(retrieved_doc_embeds.shape       ,					(2, n_docs, self.retrieval_vector_size)				)
         self.assertIsInstance(_lowerCAmelCase       ,					_lowerCAmelCase				)
         self.assertIsInstance(_lowerCAmelCase       ,					_lowerCAmelCase				)
         self.assertIsInstance(_lowerCAmelCase       ,					np.ndarray				)

         lowercase    :Optional[Any]      	=						retriever(
             _lowerCAmelCase       ,					_lowerCAmelCase       ,					prefix=retriever.config.generator.prefix       ,					n_docs=_lowerCAmelCase       ,					return_tensors="pt"       ,					)
         lowercase    :List[Any]      	=						(  # noqa: F841
             out["context_input_ids"],
             out["context_attention_mask"],
             out["retrieved_doc_embeds"],
             out["doc_ids"],
         )
         self.assertEqual(retrieved_doc_embeds.shape       ,					(2, n_docs, self.retrieval_vector_size)				)
         self.assertIsInstance(_lowerCAmelCase       ,					torch.Tensor				)
         self.assertIsInstance(_lowerCAmelCase       ,					torch.Tensor				)
         self.assertIsInstance(_lowerCAmelCase       ,					torch.Tensor				)






       @require_torch
       @require_tokenizers
       @require_sentencepiece
       def 					SCREAMING_SNAKE_CASE    (   self:					List[Any]				):
         lowercase    :List[str]      	=						self.get_dpr_ctx_encoder_tokenizer()
         lowercase    :Optional[int]      	=						1
         lowercase    :Any      	=						self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCAmelCase				)
         retriever.set_ctx_encoder_tokenizer(_lowerCAmelCase				)

         lowercase    :List[str]      	=						[[5, 7], [10, 11]]
         lowercase    :List[str]      	=						np.array(
             [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]       ,					dtype=np.floataa				)
         lowercase    :List[str]      	=						retriever(_lowerCAmelCase       ,					_lowerCAmelCase       ,					prefix=retriever.config.generator.prefix       ,					n_docs=_lowerCAmelCase				)

         self.assertEqual(
             len(_lowerCAmelCase				)       ,					6				)  # check whether the retriever output consist of 6 attributes including tokenized docs
         self.assertEqual(
             all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")				)       ,					_lowerCAmelCase				)  # check for doc token related keys in dictionary.









from collections.abc import Generator
from math import sin



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

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



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

  lowercase    :List[Any]      	=						format(lowerCamelCase,			"08x"   )[-8:]
  lowercase    :Tuple      	=						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       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Union[str, Any]      	=						B""
  for char in message:
    bit_string += format(lowerCamelCase,			"08b"   ).encode("utf-8"   )
  lowercase    :str      	=						format(len(lowerCamelCase   ),			"064b"   ).encode("utf-8"   )

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

  return bit_string



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

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



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

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



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  return (a + b) % 2**32



def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase   ):
  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       UpperCAmelCase__  (     lowerCamelCase   ):
  lowercase    :Tuple      	=						preprocess(lowerCamelCase   )

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

  # Starting states
  lowercase    :Optional[Any]      	=						0X6745_2301
  lowercase    :Union[str, Any]      	=						0XEFCD_AB89
  lowercase    :Optional[int]      	=						0X98BA_DCFE
  lowercase    :str      	=						0X1032_5476

  lowercase    :str      	=						[
      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(lowerCamelCase   ):
    lowercase    :str      	=						aa
    lowercase    :int      	=						ba
    lowercase    :int      	=						ca
    lowercase    :List[str]      	=						da

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

    # Add hashed chunk to running total
    lowercase    :Optional[Any]      	=						sum_aa(lowerCamelCase,			lowerCamelCase   )
    lowercase    :List[Any]      	=						sum_aa(lowerCamelCase,			lowerCamelCase   )
    lowercase    :List[str]      	=						sum_aa(lowerCamelCase,			lowerCamelCase   )
    lowercase    :Union[str, Any]      	=						sum_aa(lowerCamelCase,			lowerCamelCase   )

  lowercase    :int      	=						reformat_hex(lowerCamelCase   ) + reformat_hex(lowerCamelCase   ) + reformat_hex(lowerCamelCase   ) + reformat_hex(lowerCamelCase   )
  return digest


if __name__ == "__main__":
     import doctest

     doctest.testmod()



