'''simple docstring'''





import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


_lowerCAmelCase  :Any  				=    trt.Logger(trt.Logger.WARNING)
_lowerCAmelCase  :List[str]  				=    absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_lowerCAmelCase  :str  				=    logging.getLogger(__name__)

_lowerCAmelCase  :Tuple  				=    argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    """--onnx_model_path""",
    default=None,
    type=str,
    required=True,
    help="""Path to ONNX model: """,
)

parser.add_argument(
    """--output_dir""",
    default=None,
    type=str,
    required=True,
    help="""The output directory where the model checkpoints and predictions will be written.""",
)

# Other parameters

parser.add_argument(
    """--tokenizer_name""",
    default="""""",
    type=str,
    required=True,
    help="""Pretrained tokenizer name or path if not the same as model_name""",
)

parser.add_argument(
    """--version_2_with_negative""",
    action="""store_true""",
    help="""If true, the SQuAD examples contain some that do not have an answer.""",
)
parser.add_argument(
    """--null_score_diff_threshold""",
    type=float,
    default=0.0,
    help="""If null_score - best_non_null is greater than the threshold predict null.""",
)

parser.add_argument(
    """--max_seq_length""",
    default=384,
    type=int,
    help=(
        """The maximum total input sequence length after WordPiece tokenization. Sequences """
        """longer than this will be truncated, and sequences shorter than this will be padded."""
    ),
)
parser.add_argument(
    """--doc_stride""",
    default=128,
    type=int,
    help="""When splitting up a long document into chunks, how much stride to take between chunks.""",
)

parser.add_argument("""--per_device_eval_batch_size""", default=8, type=int, help="""Batch size per GPU/CPU for evaluation.""")

parser.add_argument(
    """--n_best_size""",
    default=20,
    type=int,
    help="""The total number of n-best predictions to generate in the nbest_predictions.json output file.""",
)
parser.add_argument(
    """--max_answer_length""",
    default=30,
    type=int,
    help=(
        """The maximum length of an answer that can be generated. This is needed because the start """
        """and end predictions are not conditioned on one another."""
    ),
)

parser.add_argument("""--seed""", type=int, default=42, help="""random seed for initialization""")

parser.add_argument(
    """--dataset_name""",
    type=str,
    default=None,
    required=True,
    help="""The name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
    """--dataset_config_name""",
    type=str,
    default=None,
    help="""The configuration name of the dataset to use (via the datasets library).""",
)
parser.add_argument(
    """--preprocessing_num_workers""", type=int, default=4, help="""A csv or a json file containing the training data."""
)
parser.add_argument("""--overwrite_cache""", action="""store_true""", help="""Overwrite the cached training and evaluation sets""")
parser.add_argument(
    """--fp16""",
    action="""store_true""",
    help="""Whether to use 16-bit (mixed) precision instead of 32-bit""",
)
parser.add_argument(
    """--int8""",
    action="""store_true""",
    help="""Whether to use INT8""",
)

_lowerCAmelCase  :Tuple  				=    parser.parse_args()

if args.tokenizer_name:
 _lowerCAmelCase  :str  				=    AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
 raise ValueError(
     """You are instantiating a new tokenizer from scratch. This is not supported by this script."""
     """You can do it from another script, save it, and load it from here, using --tokenizer_name."""
 )

logger.info("""Training/evaluation parameters %s""", args)

_lowerCAmelCase  :List[Any]  				=    args.per_device_eval_batch_size

_lowerCAmelCase  :Union[str, Any]  				=    (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_lowerCAmelCase  :Optional[Any]  				=    True

_lowerCAmelCase  :Union[str, Any]  				=    """temp_engine/bert-fp32.engine"""
if args.fpaa:
 _lowerCAmelCase  :Dict  				=    """temp_engine/bert-fp16.engine"""
if args.inta:
 _lowerCAmelCase  :Optional[Any]  				=    """temp_engine/bert-int8.engine"""

# import ONNX file
if not os.path.exists("""temp_engine"""):
 os.makedirs("""temp_engine""")

_lowerCAmelCase  :List[str]  				=    1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
 with open(args.onnx_model_path, """rb""") as model:
  if not parser.parse(model.read()):
   for error in range(parser.num_errors):
    print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
 _lowerCAmelCase  :Dict  				=    [network.get_input(i) for i in range(network.num_inputs)]
 _lowerCAmelCase  :Dict  				=    [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

 with builder.create_builder_config() as config:
  _lowerCAmelCase  :Any  				=    1 << 50
  if STRICT_TYPES:
   config.set_flag(trt.BuilderFlag.STRICT_TYPES)
  if args.fpaa:
   config.set_flag(trt.BuilderFlag.FPaa)
  if args.inta:
   config.set_flag(trt.BuilderFlag.INTa)
  _lowerCAmelCase  :List[Any]  				=    builder.create_optimization_profile()
  config.add_optimization_profile(profile)
  for i in range(len(input_names)):
   profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
  _lowerCAmelCase  :Optional[Any]  				=    builder.build_engine(network, config)

  # serialize_engine and store in file (can be directly loaded and deserialized):
  with open(engine_name, """wb""") as f:
   f.write(engine.serialize())


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_       ,      a_       ,      a_       ,      a_       ,      a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			str    	=       np.asarray(inputs['input_ids']       ,      dtype=np.intaa		)
     SCREAMING_SNAKE_CASE					:			int    	=       np.asarray(inputs['attention_mask']       ,      dtype=np.intaa		)
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       np.asarray(inputs['token_type_ids']       ,      dtype=np.intaa		)

     # Copy inputs
     cuda.memcpy_htod_async(d_inputs[0]       ,      input_ids.ravel()       ,      a_		)
     cuda.memcpy_htod_async(d_inputs[1]       ,      attention_mask.ravel()       ,      a_		)
     cuda.memcpy_htod_async(d_inputs[2]       ,      token_type_ids.ravel()       ,      a_		)
     # start time
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       time.time()
     # Run inference
     context.execute_async(
         bindings=[int(a_		) for d_inp in d_inputs] + [int(a_		), int(a_		)]       ,      stream_handle=stream.handle		)
     # Transfer predictions back from GPU
     cuda.memcpy_dtoh_async(a_       ,      a_       ,      a_		)
     cuda.memcpy_dtoh_async(a_       ,      a_       ,      a_		)
     # Synchronize the stream and take time
     stream.synchronize()
     # end time
     SCREAMING_SNAKE_CASE					:			str    	=       time.time()
     SCREAMING_SNAKE_CASE					:			Tuple    	=       end_time - start_time
     SCREAMING_SNAKE_CASE					:			Any    	=       (h_outputa, h_outputa)
     # print(outputs)
     return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_lowerCAmelCase  :Tuple  				=    Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
 datasets.utils.logging.set_verbosity_warning()
 transformers.utils.logging.set_verbosity_info()
else:
 datasets.utils.logging.set_verbosity_error()
 transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
 set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
 # Downloading and loading a dataset from the hub.
 _lowerCAmelCase  :List[Any]  				=    load_dataset(args.dataset_name, args.dataset_config_name)
else:
 raise ValueError("""Evaluation requires a dataset name""")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

_lowerCAmelCase  :Tuple  				=    raw_datasets["""validation"""].column_names

_lowerCAmelCase  :Any  				=    """question""" if """question""" in column_names else column_names[0]
_lowerCAmelCase  :int  				=    """context""" if """context""" in column_names else column_names[1]
_lowerCAmelCase  :Any  				=    """answers""" if """answers""" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_lowerCAmelCase  :List[str]  				=    tokenizer.padding_side == """right"""

if args.max_seq_length > tokenizer.model_max_length:
 logger.warning(
     f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
     f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
 )

_lowerCAmelCase  :List[Any]  				=    min(args.max_seq_length, tokenizer.model_max_length)


def     __lowerCAmelCase						(       a_		)      ->	Optional[Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [q.lstrip() for q in examples[question_column_name]]

     # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
     # in one example possible giving several features when a context is long, each of those features having a
     # context that overlaps a bit the context of the previous feature.
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       tokenizer(
         examples[question_column_name if pad_on_right else context_column_name]       ,      examples[context_column_name if pad_on_right else question_column_name]       ,      truncation='only_second' if pad_on_right else 'only_first'       ,      max_length=a_       ,      stride=args.doc_stride       ,      return_overflowing_tokens=a_       ,      return_offsets_mapping=a_       ,      padding='max_length'       ,      )

     # Since one example might give us several features if it has a long context, we need a map from a feature to
     # its corresponding example. This key gives us just that.
     SCREAMING_SNAKE_CASE					:			List[str]    	=       tokenized_examples.pop('overflow_to_sample_mapping'		)

     # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
     # corresponding example_id and we will store the offset mappings.
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       []

     for i in range(len(tokenized_examples['input_ids']		)		):
          # Grab the sequence corresponding to that example (to know what is the context and what is the question).
          SCREAMING_SNAKE_CASE					:			Any    	=       tokenized_examples.sequence_ids(a_		)
          SCREAMING_SNAKE_CASE					:			Tuple    	=       1 if pad_on_right else 0

          # One example can give several spans, this is the index of the example containing this span of text.
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       sample_mapping[i]
          tokenized_examples["example_id"].append(examples['id'][sample_index]		)

          # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
          # position is part of the context or not.
          SCREAMING_SNAKE_CASE					:			Dict    	=       [
              (o if sequence_ids[k] == context_index else None)
              for k, o in enumerate(tokenized_examples['offset_mapping'][i]		)
          ]

     return tokenized_examples


_lowerCAmelCase  :Union[str, Any]  				=    raw_datasets["""validation"""]
# Validation Feature Creation
_lowerCAmelCase  :str  				=    eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="""Running tokenizer on validation dataset""",
)

_lowerCAmelCase  :List[str]  				=    default_data_collator

_lowerCAmelCase  :int  				=    eval_dataset.remove_columns(["""example_id""", """offset_mapping"""])
_lowerCAmelCase  :int  				=    DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_       ,      a_="eval"		)      ->	Dict:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       postprocess_qa_predictions(
         examples=a_       ,      features=a_       ,      predictions=a_       ,      version_2_with_negative=args.version_2_with_negative       ,      n_best_size=args.n_best_size       ,      max_answer_length=args.max_answer_length       ,      null_score_diff_threshold=args.null_score_diff_threshold       ,      output_dir=args.output_dir       ,      prefix=a_       ,      )
     # Format the result to the format the metric expects.
     if args.version_2_with_negative:
          SCREAMING_SNAKE_CASE					:			Dict    	=       [
              {"""id""": k, """prediction_text""": v, """no_answer_probability""": 0.0} for k, v in predictions.items()
          ]
     else:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [{"""id""": k, """prediction_text""": v} for k, v in predictions.items()]

     SCREAMING_SNAKE_CASE					:			Any    	=       [{"""id""": ex["""id"""], """answers""": ex[answer_column_name]} for ex in examples]
     return EvalPrediction(predictions=a_       ,      label_ids=a_		)


_lowerCAmelCase  :List[Any]  				=    load_metric("""squad_v2""" if args.version_2_with_negative else """squad""")

# Evaluation!
logger.info("""Loading ONNX model %s for evaluation""", args.onnx_model_path)
with open(engine_name, """rb""") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
 # setup for TRT inferrence
 for i in range(len(input_names)):
  context.set_binding_shape(i, INPUT_SHAPE)
 assert context.all_binding_shapes_specified


 def     __lowerCAmelCase						(       a_		)      ->	Union[str, Any]:





      '''simple docstring'''





      return trt.volume(engine.get_binding_shape(a_		)		) * engine.get_binding_dtype(a_		).itemsize

 # Allocate device memory for inputs and outputs.
 _lowerCAmelCase  :Optional[Any]  				=    [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

 # Allocate output buffer
 _lowerCAmelCase  :Optional[Any]  				=    cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
 _lowerCAmelCase  :Tuple  				=    cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
 _lowerCAmelCase  :str  				=    cuda.mem_alloc(h_outputa.nbytes)
 _lowerCAmelCase  :List[Any]  				=    cuda.mem_alloc(h_outputa.nbytes)

 # Create a stream in which to copy inputs/outputs and run inference.
 _lowerCAmelCase  :Optional[Any]  				=    cuda.Stream()

 # Evaluation
 logger.info("""***** Running Evaluation *****""")
 logger.info(f"""  Num examples = {len(eval_dataset)}""")
 logger.info(f"""  Batch size = {args.per_device_eval_batch_size}""")

 _lowerCAmelCase  :List[Any]  				=    0.0
 _lowerCAmelCase  :str  				=    0
 _lowerCAmelCase  :Union[str, Any]  				=    timeit.default_timer()

 _lowerCAmelCase  :Any  				=    None
 for step, batch in enumerate(eval_dataloader):
  _lowerCAmelCase				,    _lowerCAmelCase  :Dict  				=    model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
  total_time += infer_time
  niter += 1

  _lowerCAmelCase				,    _lowerCAmelCase  :Optional[Any]  				=    outputs
  _lowerCAmelCase  :str  				=    torch.tensor(start_logits)
  _lowerCAmelCase  :Any  				=    torch.tensor(end_logits)

  # necessary to pad predictions and labels for being gathered
  _lowerCAmelCase  :str  				=    accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
  _lowerCAmelCase  :Tuple  				=    accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

  _lowerCAmelCase  :str  				=    (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
  _lowerCAmelCase  :Dict  				=    logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

 if all_preds is not None:
  _lowerCAmelCase  :List[str]  				=    nested_truncate(all_preds, len(eval_dataset))

 _lowerCAmelCase  :Dict  				=    timeit.default_timer() - start_time
 logger.info("""  Evaluation done in total %f secs (%f sec per example)""", evalTime, evalTime / len(eval_dataset))
 # Inference time from TRT
 logger.info("""Average Inference Time = {:.3f} ms""".format(total_time * 1000 / niter))
 logger.info("""Total Inference Time =  {:.3f} ms""".format(total_time * 1000))


 logger.info("""Total Number of Inference =  %d""", niter)

_lowerCAmelCase  :Dict  				=    post_processing_function(eval_examples, eval_dataset, all_preds)
_lowerCAmelCase  :List[str]  				=    metric.compute(predictions=prediction.predictions, references=prediction.label_ids)


logger.info(f"""Evaluation metrics: {eval_metric}""")




'''simple docstring'''





import argparse
import struct
import unittest






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__ )							-> None:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       data

         # Initialize hash values
         SCREAMING_SNAKE_CASE					:			str    	=       [
             0X6A_09_E6_67,
             0XBB_67_AE_85,
             0X3C_6E_F3_72,
             0XA5_4F_F5_3A,
             0X51_0E_52_7F,
             0X9B_05_68_8C,
             0X1F_83_D9_AB,
             0X5B_E0_CD_19,
         ]

         # Initialize round constants
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       [
             0X42_8A_2F_98,
             0X71_37_44_91,
             0XB5_C0_FB_CF,
             0XE9_B5_DB_A5,
             0X39_56_C2_5B,
             0X59_F1_11_F1,
             0X92_3F_82_A4,
             0XAB_1C_5E_D5,
             0XD8_07_AA_98,
             0X12_83_5B_01,
             0X24_31_85_BE,
             0X55_0C_7D_C3,
             0X72_BE_5D_74,
             0X80_DE_B1_FE,
             0X9B_DC_06_A7,
             0XC1_9B_F1_74,
             0XE4_9B_69_C1,
             0XEF_BE_47_86,
             0X0F_C1_9D_C6,
             0X24_0C_A1_CC,
             0X2D_E9_2C_6F,
             0X4A_74_84_AA,
             0X5C_B0_A9_DC,
             0X76_F9_88_DA,
             0X98_3E_51_52,
             0XA8_31_C6_6D,
             0XB0_03_27_C8,
             0XBF_59_7F_C7,
             0XC6_E0_0B_F3,
             0XD5_A7_91_47,
             0X06_CA_63_51,
             0X14_29_29_67,
             0X27_B7_0A_85,
             0X2E_1B_21_38,
             0X4D_2C_6D_FC,
             0X53_38_0D_13,
             0X65_0A_73_54,
             0X76_6A_0A_BB,
             0X81_C2_C9_2E,
             0X92_72_2C_85,
             0XA2_BF_E8_A1,
             0XA8_1A_66_4B,
             0XC2_4B_8B_70,
             0XC7_6C_51_A3,
             0XD1_92_E8_19,
             0XD6_99_06_24,
             0XF4_0E_35_85,
             0X10_6A_A0_70,
             0X19_A4_C1_16,
             0X1E_37_6C_08,
             0X27_48_77_4C,
             0X34_B0_BC_B5,
             0X39_1C_0C_B3,
             0X4E_D8_AA_4A,
             0X5B_9C_CA_4F,
             0X68_2E_6F_F3,
             0X74_8F_82_EE,
             0X78_A5_63_6F,
             0X84_C8_78_14,
             0X8C_C7_02_08,
             0X90_BE_FF_FA,
             0XA4_50_6C_EB,
             0XBE_F9_A3_F7,
             0XC6_71_78_F2,
         ]

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.preprocessing(self.data )
         self.final_hash()






    @staticmethod
    def       _UpperCamelCase    (       lowercase__ )							-> bytes:
         SCREAMING_SNAKE_CASE					:			str    	=       B'\x80' + (B'\x00' * (63 - (len(lowercase__ ) + 8) % 64))
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       struct.pack('>Q'    ,							(len(lowercase__ ) * 8) )
         return data + padding + big_endian_integer






    def       _UpperCamelCase    (       self )							-> None:
         # Convert into blocks of 64 bytes
         SCREAMING_SNAKE_CASE					:			Dict    	=       [
             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
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       list(struct.unpack('>16L'    ,							lowercase__ ) )
              # add 48 0-ed integers
              words += [0] * 48

              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Dict    	=       self.hashes

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

                        SCREAMING_SNAKE_CASE					:			Tuple    	=       (
                            words[index - 16] + sa + words[index - 7] + sa
                        ) % 0X1_00_00_00_00

                   # Compression
                   SCREAMING_SNAKE_CASE					:			Any    	=       self.ror(lowercase__    ,							6 ) ^ self.ror(lowercase__    ,							11 ) ^ self.ror(lowercase__    ,							25 )
                   SCREAMING_SNAKE_CASE					:			Any    	=       (e & f) ^ ((~e & 0XFF_FF_FF_FF) & g)
                   SCREAMING_SNAKE_CASE					:			str    	=       (
                       h + sa + ch + self.round_constants[index] + words[index]
                   ) % 0X1_00_00_00_00
                   SCREAMING_SNAKE_CASE					:			Any    	=       self.ror(lowercase__    ,							2 ) ^ self.ror(lowercase__    ,							13 ) ^ self.ror(lowercase__    ,							22 )
                   SCREAMING_SNAKE_CASE					:			str    	=       (a & b) ^ (a & c) ^ (b & c)
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       (sa + maj) % 0X1_00_00_00_00

                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       (
                       g,
                       f,
                       e,
                       ((d + tempa) % 0X1_00_00_00_00),
                       c,
                       b,
                       a,
                       ((tempa + tempa) % 0X1_00_00_00_00),
                   )

              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       [a, b, c, d, e, f, g, h]

              # Modify final values
              SCREAMING_SNAKE_CASE					:			Tuple    	=       [
                  ((element + mutated_hash_values[index]) % 0X1_00_00_00_00)
                  for index, element in enumerate(self.hashes )
              ]

         SCREAMING_SNAKE_CASE					:			Tuple    	=       ''.join([hex(lowercase__ )[2:].zfill(8 ) for value in self.hashes] )






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__ )							-> int:
         return 0XFF_FF_FF_FF & (value << (32 - rotations)) | (value >> rotations)






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> None:
         import hashlib

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       bytes('Test String'    ,							'utf-8' )
         self.assertEqual(SHAaaa(lowercase__ ).hash    ,							hashlib.shaaaa(lowercase__ ).hexdigest() )






def     __lowerCAmelCase						(       )      ->	None:





     '''simple docstring'''





     import doctest

     doctest.testmod()

     SCREAMING_SNAKE_CASE					:			List[str]    	=       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'		)

     SCREAMING_SNAKE_CASE					:			Tuple    	=       parser.parse_args()

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       args.input_string

     # hash input should be a bytestring
     if args.input_file:
          with open(args.input_file       ,      'rb'		) as f:
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       f.read()
     else:
          SCREAMING_SNAKE_CASE					:			Any    	=       bytes(a_       ,      'utf-8'		)

     print(SHAaaa(a_		).hash		)


if __name__ == "__main__":
 main()



