



import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

_lowerCamelCase						    =  logging.getLogger(__name__)

@dataclass
class       _SCREAMING_SNAKE_CASE	:
     lowerCAmelCase					=			field(
         default=128			,    metadata={
             """help""": (
                 """The maximum total input sequence length after tokenization. Sequences longer """
                 """than this will be truncated, sequences shorter will be padded."""
             )
         }			,    )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={
             """help""": (
                 """Whether to pad all samples to `max_seq_length`. """
                 """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
             )
         }			,    )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of training examples to this """
                 """value if set."""
             )
         }			,    )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                 """value if set."""
             )
         }			,    )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={
             """help""": (
                 """For debugging purposes or quicker training, truncate the number of prediction examples to this """
                 """value if set."""
             )
         }			,    )






@dataclass
class       _SCREAMING_SNAKE_CASE	:
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""} )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={"""help""": """Train language if it is different from the evaluation language."""} )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,    )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""}			,    )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}			,    )
     lowerCAmelCase					=			field(
         default="""main"""			,    metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}			,    )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={
             """help""": (
                 """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                 """with private models)."""
             )
         }			,    )
     lowerCAmelCase					=			field(
         default=UpperCamelCase			,    metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}			,    )






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	parser.parse_args_into_dataclasses()

 # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
 # information sent is the one passed as arguments along with your Python/PyTorch versions.
 send_example_telemetry("run_xnli"		,	__lowerCamelCase     )

 # Setup logging
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"		,	datefmt="%m/%d/%Y %H:%M:%S"		,	handlers=[logging.StreamHandler(sys.stdout     )]		,	)

 if training_args.should_log:
  # The default of training_args.log_level is passive, so we set log level at info here to have that default.
  transformers.utils.logging.set_verbosity_info()

 __SCREAMING_SNAKE_CASE       :    Any       =	training_args.get_process_log_level()
 logger.setLevel(__lowerCamelCase     )
 datasets.utils.logging.set_verbosity(__lowerCamelCase     )
 transformers.utils.logging.set_verbosity(__lowerCamelCase     )
 transformers.utils.logging.enable_default_handler()
 transformers.utils.logging.enable_explicit_format()

 # Log on each process the small summary:
 logger.warning(
     F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
     + F"""distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}"""     )
 logger.info(F"""Training/evaluation parameters {training_args}"""     )

 # Detecting last checkpoint.
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	None
 if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
  __SCREAMING_SNAKE_CASE       :    Dict       =	get_last_checkpoint(training_args.output_dir     )
  if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
   raise ValueError(
       F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
       "Use --overwrite_output_dir to overcome."     )
  elif last_checkpoint is not None:
   logger.info(
       F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
       "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."     )

    # Set seed before initializing model.
 set_seed(training_args.seed     )

 # In distributed training, the load_dataset function guarantees that only one local process can concurrently
 # download the dataset.
 # Downloading and loading xnli dataset from the hub.
 if training_args.do_train:
  if model_args.train_language is None:
   __SCREAMING_SNAKE_CASE       :    int       =	load_dataset(
       "xnli"		,	model_args.language		,	split="train"		,	cache_dir=model_args.cache_dir		,	use_auth_token=True if model_args.use_auth_token else None		,	)
  else:
   __SCREAMING_SNAKE_CASE       :    Any       =	load_dataset(
       "xnli"		,	model_args.train_language		,	split="train"		,	cache_dir=model_args.cache_dir		,	use_auth_token=True if model_args.use_auth_token else None		,	)
  __SCREAMING_SNAKE_CASE       :    Any       =	train_dataset.features["label"].names

 if training_args.do_eval:
  __SCREAMING_SNAKE_CASE       :    str       =	load_dataset(
      "xnli"		,	model_args.language		,	split="validation"		,	cache_dir=model_args.cache_dir		,	use_auth_token=True if model_args.use_auth_token else None		,	)
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	eval_dataset.features["label"].names

 if training_args.do_predict:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	load_dataset(
      "xnli"		,	model_args.language		,	split="test"		,	cache_dir=model_args.cache_dir		,	use_auth_token=True if model_args.use_auth_token else None		,	)
  __SCREAMING_SNAKE_CASE       :    int       =	predict_dataset.features["label"].names

 # Labels
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	len(__lowerCamelCase     )

 # Load pretrained model and tokenizer
 # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path		,	num_labels=__lowerCamelCase		,	idalabel={str(__lowerCamelCase     ): label for i, label in enumerate(__lowerCamelCase     )}		,	labelaid={label: i for i, label in enumerate(__lowerCamelCase     )}		,	finetuning_task="xnli"		,	cache_dir=model_args.cache_dir		,	revision=model_args.model_revision		,	use_auth_token=True if model_args.use_auth_token else None		,	)
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path		,	do_lower_case=model_args.do_lower_case		,	cache_dir=model_args.cache_dir		,	use_fast=model_args.use_fast_tokenizer		,	revision=model_args.model_revision		,	use_auth_token=True if model_args.use_auth_token else None		,	)
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	AutoModelForSequenceClassification.from_pretrained(
     model_args.model_name_or_path		,	from_tf=bool(".ckpt" in model_args.model_name_or_path     )		,	config=__lowerCamelCase		,	cache_dir=model_args.cache_dir		,	revision=model_args.model_revision		,	use_auth_token=True if model_args.use_auth_token else None		,	ignore_mismatched_sizes=model_args.ignore_mismatched_sizes		,	)

 # Preprocessing the datasets
 # Padding strategy
 if data_args.pad_to_max_length:
  __SCREAMING_SNAKE_CASE       :    Dict       =	"max_length"
 else:
  # We will pad later, dynamically at batch creation, to the max sequence length in each batch
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	False

 def preprocess_function(__lowerCamelCase   :     Optional[int]     ):
  # Tokenize the texts
  return tokenizer(
      examples["premise"]		,	examples["hypothesis"]		,	padding=__lowerCamelCase		,	max_length=data_args.max_seq_length		,	truncation=__lowerCamelCase		,	)

 if training_args.do_train:
  if data_args.max_train_samples is not None:
   __SCREAMING_SNAKE_CASE       :    Dict       =	min(len(__lowerCamelCase     )		,	data_args.max_train_samples     )
   __SCREAMING_SNAKE_CASE       :    Any       =	train_dataset.select(range(__lowerCamelCase     )     )
  with training_args.main_process_first(desc="train dataset map pre-processing"     ):
   __SCREAMING_SNAKE_CASE       :    List[str]       =	train_dataset.map(
       __lowerCamelCase		,	batched=__lowerCamelCase		,	load_from_cache_file=not data_args.overwrite_cache		,	desc="Running tokenizer on train dataset"		,	)
  # Log a few random samples from the training set:
  for index in random.sample(range(len(__lowerCamelCase     )     )		,	3     ):
   logger.info(F"""Sample {index} of the training set: {train_dataset[index]}."""     )

 if training_args.do_eval:
  if data_args.max_eval_samples is not None:
   __SCREAMING_SNAKE_CASE       :    int       =	min(len(__lowerCamelCase     )		,	data_args.max_eval_samples     )
   __SCREAMING_SNAKE_CASE       :    Any       =	eval_dataset.select(range(__lowerCamelCase     )     )
  with training_args.main_process_first(desc="validation dataset map pre-processing"     ):
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	eval_dataset.map(
       __lowerCamelCase		,	batched=__lowerCamelCase		,	load_from_cache_file=not data_args.overwrite_cache		,	desc="Running tokenizer on validation dataset"		,	)

 if training_args.do_predict:
  if data_args.max_predict_samples is not None:
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	min(len(__lowerCamelCase     )		,	data_args.max_predict_samples     )
   __SCREAMING_SNAKE_CASE       :    int       =	predict_dataset.select(range(__lowerCamelCase     )     )
  with training_args.main_process_first(desc="prediction dataset map pre-processing"     ):
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	predict_dataset.map(
       __lowerCamelCase		,	batched=__lowerCamelCase		,	load_from_cache_file=not data_args.overwrite_cache		,	desc="Running tokenizer on prediction dataset"		,	)

    # Get the metric function
 __SCREAMING_SNAKE_CASE       :    int       =	evaluate.load("xnli"     )

 # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
 # predictions and label_ids field) and has to return a dictionary string to float.
 def compute_metrics(__lowerCamelCase   :     EvalPrediction     ):
  __SCREAMING_SNAKE_CASE       :    str       =	p.predictions[0] if isinstance(p.predictions		,	__lowerCamelCase     ) else p.predictions
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	np.argmax(__lowerCamelCase		,	axis=1     )
  return metric.compute(predictions=__lowerCamelCase		,	references=p.label_ids     )

 # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
 if data_args.pad_to_max_length:
  __SCREAMING_SNAKE_CASE       :    str       =	default_data_collator
 elif training_args.fpaa:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	DataCollatorWithPadding(__lowerCamelCase		,	pad_to_multiple_of=8     )
 else:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	None

 # Initialize our Trainer
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	Trainer(
     model=__lowerCamelCase		,	args=__lowerCamelCase		,	train_dataset=train_dataset if training_args.do_train else None		,	eval_dataset=eval_dataset if training_args.do_eval else None		,	compute_metrics=__lowerCamelCase		,	tokenizer=__lowerCamelCase		,	data_collator=__lowerCamelCase		,	)

 # Training
 if training_args.do_train:
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	None
  if training_args.resume_from_checkpoint is not None:
   __SCREAMING_SNAKE_CASE       :    Any       =	training_args.resume_from_checkpoint
  elif last_checkpoint is not None:
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	last_checkpoint
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	trainer.train(resume_from_checkpoint=__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	train_result.metrics
  __SCREAMING_SNAKE_CASE       :    Dict       =	(
      data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCamelCase     )
  )
  __SCREAMING_SNAKE_CASE       :    Dict       =	min(__lowerCamelCase		,	len(__lowerCamelCase     )     )

  trainer.save_model()  # Saves the tokenizer too for easy upload

  trainer.log_metrics("train"		,	__lowerCamelCase     )
  trainer.save_metrics("train"		,	__lowerCamelCase     )
  trainer.save_state()

 # Evaluation
 if training_args.do_eval:
  logger.info("*** Evaluate ***"     )
  __SCREAMING_SNAKE_CASE       :    Dict       =	trainer.evaluate(eval_dataset=__lowerCamelCase     )

  __SCREAMING_SNAKE_CASE       :    str       =	data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    List[str]       =	min(__lowerCamelCase		,	len(__lowerCamelCase     )     )

  trainer.log_metrics("eval"		,	__lowerCamelCase     )
  trainer.save_metrics("eval"		,	__lowerCamelCase     )

 # Prediction
 if training_args.do_predict:
  logger.info("*** Predict ***"     )
  __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[int]       =	trainer.predict(__lowerCamelCase		,	metric_key_prefix="predict"     )

  __SCREAMING_SNAKE_CASE       :    Dict       =	(
      data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCamelCase     )
  )
  __SCREAMING_SNAKE_CASE       :    Tuple       =	min(__lowerCamelCase		,	len(__lowerCamelCase     )     )

  trainer.log_metrics("predict"		,	__lowerCamelCase     )
  trainer.save_metrics("predict"		,	__lowerCamelCase     )

  __SCREAMING_SNAKE_CASE       :    List[Any]       =	np.argmax(__lowerCamelCase		,	axis=1     )
  __SCREAMING_SNAKE_CASE       :    List[str]       =	os.path.join(training_args.output_dir		,	"predictions.txt"     )
  if trainer.is_world_process_zero():
   with open(__lowerCamelCase		,	"w"     ) as writer:
    writer.write("index\tprediction\n"     )
    for index, item in enumerate(__lowerCamelCase     ):
     __SCREAMING_SNAKE_CASE       :    int       =	label_list[item]
     writer.write(F"""{index}\t{item}\n"""     )


if __name__ == "__main__":
      main()











from collections.abc import Generator
from math import sin






def 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





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

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	b""
 for i in [3, 2, 1, 0]:
  little_endian += string_aa[8 * i : 8 * i + 8]
 return little_endian






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





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

 __SCREAMING_SNAKE_CASE       :    Any       =	format(__lowerCamelCase		,	"08x"     )[-8:]
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	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 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	b""
 for char in message:
  bit_string += format(__lowerCamelCase		,	"08b"     ).encode("utf-8"     )
 __SCREAMING_SNAKE_CASE       :    List[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 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





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

 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	format(__lowerCamelCase		,	"032b"     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	""
 for c in i_str:
  new_str += "1" if c == "0" else "0"
 return int(__lowerCamelCase		,	2     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 return (a + b) % 2**32






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     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 	_lowerCAmelCase						(				__lowerCamelCase   :     bytes     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Tuple       =	preprocess(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	[int(2**32 * abs(sin(i + 1     )     )     ) for i in range(64     )]

 # Starting states
 __SCREAMING_SNAKE_CASE       :    Tuple       =	0X67452301
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	0Xefcdab89
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	0X98badcfe
 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	0X10325476

 __SCREAMING_SNAKE_CASE       :    List[Any]       =	[
     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     ):
  __SCREAMING_SNAKE_CASE       :    Any       =	aa
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	ba
  __SCREAMING_SNAKE_CASE       :    str       =	ca
  __SCREAMING_SNAKE_CASE       :    Dict       =	da

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

  # Add hashed chunk to running total
  __SCREAMING_SNAKE_CASE       :    Dict       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )
  __SCREAMING_SNAKE_CASE       :    str       =	sum_aa(__lowerCamelCase		,	__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    List[str]       =	reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     ) + reformat_hex(__lowerCamelCase     )
 return digest


if __name__ == "__main__":
      import doctest

      doctest.testmod()






