




"""simple docstring"""


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  filter(lambda snake_case__    : p.requires_grad		,   model.parameters()    )
  _snake_case							:		str								=  sum([np.prod(p.size()    ) for p in model_parameters]    )
  return params


A_						=				logging.getLogger(__name__)


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					Dict    ):
  """simple docstring"""

  if metric == "rouge2":
    _snake_case							:		Any								=  """{val_avg_rouge2:.4f}-{step_count}"""
  elif metric == "bleu":
    _snake_case							:		Tuple								=  """{val_avg_bleu:.4f}-{step_count}"""
  elif metric == "em":
    _snake_case							:		str								=  """{val_avg_em:.4f}-{step_count}"""
  else:
    raise NotImplementedError(
        F"seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"
        """ function."""    )

  _snake_case							:		str								=  ModelCheckpoint(
      dirpath=snake_case__		,   filename=snake_case__		,   monitor=F"val_{metric}"		,   mode="""max"""		,   save_top_k=3		,   every_n_epochs=1		,   )
  return checkpoint_callback


def 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					Union[str, Any]    ):
  """simple docstring"""

  return EarlyStopping(
      monitor=F"val_{metric}"		,   mode="""min""" if """loss""" in metric else """max"""		,   patience=snake_case__		,   verbose=snake_case__		,   )






class 					lowercase(     pl.Callback ):







 '''simple docstring'''





 def  UpperCamelCase_   (				self:		Optional[int],      a_:		Optional[int],      a_:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  {f"lr_group_{i}": param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups		)}
   pl_module.logger.log_metrics(a_		)


 @rank_zero_only
 def  UpperCamelCase_   (				self:		int,      a_:		pl.Trainer,      a_:		pl.LightningModule,      a_:		str,      a_:		Tuple=True		):




   '''simple docstring'''



   logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"		)
   _snake_case							:		int								=  trainer.callback_metrics
   trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}		)
   # Log results
   _snake_case							:		int								=  Path(pl_module.hparams.output_dir		)
   if type_path == "test":
     _snake_case							:		Tuple								=  od / """test_results.txt"""
     _snake_case							:		str								=  od / """test_generations.txt"""
   else:
     # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
     # If people want this it will be easy enough to add back.
     _snake_case							:		Optional[int]								=  od / f"{type_path}_results/{trainer.global_step:05d}.txt"
     _snake_case							:		Optional[Any]								=  od / f"{type_path}_generations/{trainer.global_step:05d}.txt"
     results_file.parent.mkdir(exist_ok=a_		)
     generations_file.parent.mkdir(exist_ok=a_		)
   with open(a_,      """a+"""		) as writer:
     for key in sorted(a_		):
       if key in ["log", "progress_bar", "preds"]:
         continue
       _snake_case							:		str								=  metrics[key]
       if isinstance(a_,      torch.Tensor		):
         _snake_case							:		int								=  val.item()
       _snake_case							:		int								=  f"{key}: {val:.6f}\n"
       writer.write(a_		)

   if not save_generations:
     return

   if "preds" in metrics:
     _snake_case							:		str								=  """\n""".join(metrics["""preds"""]		)
     generations_file.open("""w+"""		).write(a_		)


 @rank_zero_only
 def  UpperCamelCase_   (				self:		Tuple,      a_:		Optional[Any],      a_:		Any		):




   '''simple docstring'''



   try:
     _snake_case							:		Dict								=  pl_module.model.model.num_parameters()
   except AttributeError:
     _snake_case							:		Tuple								=  pl_module.model.num_parameters()

   _snake_case							:		int								=  count_trainable_parameters(a_		)
   # mp stands for million parameters
   trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1E6, """grad_mp""": n_trainable_pars / 1E6}		)


 @rank_zero_only
 def  UpperCamelCase_   (				self:		List[str],      a_:		pl.Trainer,      a_:		pl.LightningModule		):




   '''simple docstring'''



   save_json(pl_module.metrics,      pl_module.metrics_save_path		)
   return self._write_logs(a_,      a_,      """test"""		)


 @rank_zero_only
 def  UpperCamelCase_   (				self:		Tuple,      a_:		pl.Trainer,      a_:		Any		):




   '''simple docstring'''



   save_json(pl_module.metrics,      pl_module.metrics_save_path		)
   # Uncommenting this will save val generations
   # return self._write_logs(trainer, pl_module, "valid")








"""simple docstring"""


import argparse
import hashlib  # hashlib is only used inside the Test class
import struct






class 					lowercase:







 '''simple docstring'''





 def __init__(				self:		List[Any],      a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		int								=  data
   _snake_case							:		Dict								=  [0X67452301, 0Xefcdab89, 0X98badcfe, 0X10325476, 0Xc3d2e1f0]


 @staticmethod
 def  UpperCamelCase_   (				a_:		Optional[Any],      a_:		Dict		):




   '''simple docstring'''



   return ((n << b) | (n >> (32 - b))) & 0Xffffffff


 def  UpperCamelCase_   (				self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  B"""\x80""" + B"""\x00""" * (63 - (len(self.data		) + 8) % 64)
   _snake_case							:		Optional[int]								=  self.data + padding + struct.pack(""">Q""",      8 * len(self.data		)		)
   return padded_data


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   return [
       self.padded_data[i : i + 64] for i in range(0,      len(self.padded_data		),      64		)
   ]


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		List[str]								=  list(struct.unpack(""">16L""",      a_		)		) + [0] * 64
   for i in range(16,      80		):
     _snake_case							:		List[Any]								=  self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]),      1		)
   return w


 def  UpperCamelCase_   (				self:		int		):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  self.padding()
   _snake_case							:		str								=  self.split_blocks()
   for block in self.blocks:
     _snake_case							:		Any								=  self.expand_block(a_		)
     _snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Optional[int]								=  self.h
     for i in range(0,      80		):
       if 0 <= i < 20:
         _snake_case							:		int								=  (b & c) | ((~b) & d)
         _snake_case							:		str								=  0X5a827999
       elif 20 <= i < 40:
         _snake_case							:		Optional[int]								=  b ^ c ^ d
         _snake_case							:		str								=  0X6ed9eba1
       elif 40 <= i < 60:
         _snake_case							:		List[Any]								=  (b & c) | (b & d) | (c & d)
         _snake_case							:		List[Any]								=  0X8f1bbcdc
       elif 60 <= i < 80:
         _snake_case							:		List[Any]								=  b ^ c ^ d
         _snake_case							:		int								=  0Xca62c1d6
       _snake_case		,					_snake_case		,					_snake_case		,					_snake_case		,					_snake_case							:		Optional[int]								=  (
           self.rotate(a_,      5		) + f + e + k + expanded_block[i] & 0Xffffffff,
           a,
           self.rotate(a_,      30		),
           c,
           d,
       )
     _snake_case							:		Union[str, Any]								=  (
         self.h[0] + a & 0Xffffffff,
         self.h[1] + b & 0Xffffffff,
         self.h[2] + c & 0Xffffffff,
         self.h[3] + d & 0Xffffffff,
         self.h[4] + e & 0Xffffffff,
     )
   return ("{:08x}" * 5).format(*self.h		)







def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Any								=  B"""Test String"""
  assert SHAaHash(snake_case__    ).final_hash() == hashlib.shaa(snake_case__    ).hexdigest()  # noqa: S324


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		List[Any]								=  argparse.ArgumentParser(description="""Process some strings or files"""    )
  parser.add_argument(
      """--string"""		,   dest="""input_string"""		,   default="""Hello World!! Welcome to Cryptography"""		,   help="""Hash the string"""		,   )
  parser.add_argument("""--file"""		,   dest="""input_file"""		,   help="""Hash contents of a file"""    )
  _snake_case							:		Union[str, Any]								=  parser.parse_args()
  _snake_case							:		List[Any]								=  args.input_string
  # In any case hash input should be a bytestring
  if args.input_file:
    with open(args.input_file		,   """rb"""    ) as f:
      _snake_case							:		str								=  f.read()
  else:
    _snake_case							:		int								=  bytes(snake_case__		,   """utf-8"""    )
  print(SHAaHash(snake_case__    ).final_hash()    )


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod()


