



'''simple docstring'''




import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


UpperCamelCase__			:      List[Any]        =					getLogger(__name__)


UpperCamelCase__			:      Optional[int]        =					'''cuda''' if torch.cuda.is_available() else '''cpu'''


def 							lowerCAmelCase_  (      _lowerCamelCase:  List[str]			, _lowerCamelCase:  str			, _lowerCamelCase:  str			, _lowerCamelCase:  int = 8			, _lowerCamelCase:  str = DEFAULT_DEVICE			, _lowerCamelCase:  Optional[Any]=False			, _lowerCamelCase:  Any="summarization"			, _lowerCamelCase:  Any=None			, **_lowerCamelCase:  List[str]			, ):
		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						Path(_lowerCamelCase   ).open("""w"""			, encoding="""utf-8"""   )
		__SCREAMING_SNAKE_CASE			:						List[str]       			=						str(_lowerCamelCase   )
		__SCREAMING_SNAKE_CASE			:						Dict       			=						AutoModelForSeqaSeqLM.from_pretrained(_lowerCamelCase   ).to(_lowerCamelCase   )
		if fpaa:
				__SCREAMING_SNAKE_CASE			:						Tuple       			=						model.half()

		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						AutoTokenizer.from_pretrained(_lowerCamelCase   )
		logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"   )  # if this is wrong, check config.model_type.

		__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						time.time()
		# update config with task specific params
		use_task_specific_params(_lowerCamelCase			, _lowerCamelCase   )
		if prefix is None:
				__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						prefix or getattr(model.config			, """prefix"""			, """"""   ) or ''''''
		for examples_chunk in tqdm(list(chunks(_lowerCamelCase			, _lowerCamelCase   )   )   ):
				__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						[prefix + text for text in examples_chunk]
				__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						tokenizer(_lowerCamelCase			, return_tensors="""pt"""			, truncation=_lowerCamelCase			, padding="""longest"""   ).to(_lowerCamelCase   )
				__SCREAMING_SNAKE_CASE			:						List[str]       			=						model.generate(
				    input_ids=batch.input_ids			, attention_mask=batch.attention_mask			, **_lowerCamelCase			, )
				__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						tokenizer.batch_decode(_lowerCamelCase			, skip_special_tokens=_lowerCamelCase			, clean_up_tokenization_spaces=_lowerCamelCase   )
				for hypothesis in dec:
						fout.write(hypothesis + """\n"""   )
						fout.flush()
		fout.close()
		__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						int(time.time() - start_time   )  # seconds
		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						len(_lowerCamelCase   )
		return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs			, 4   )}


def 							lowerCAmelCase_  (      ):
		return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""   )


def 							lowerCAmelCase_  (      _lowerCamelCase:  List[str]=True   ):
		__SCREAMING_SNAKE_CASE			:						int       			=						argparse.ArgumentParser()
		parser.add_argument("""model_name"""			, type=_lowerCamelCase			, help="""like facebook/bart-large-cnn,t5-base, etc."""   )
		parser.add_argument("""input_path"""			, type=_lowerCamelCase			, help="""like cnn_dm/test.source"""   )
		parser.add_argument("""save_path"""			, type=_lowerCamelCase			, help="""where to save summaries"""   )
		parser.add_argument("""--reference_path"""			, type=_lowerCamelCase			, required=_lowerCamelCase			, help="""like cnn_dm/test.target"""   )
		parser.add_argument("""--score_path"""			, type=_lowerCamelCase			, required=_lowerCamelCase			, default="""metrics.json"""			, help="""where to save metrics"""   )
		parser.add_argument("""--device"""			, type=_lowerCamelCase			, required=_lowerCamelCase			, default=_lowerCamelCase			, help="""cuda, cuda:1, cpu etc."""   )
		parser.add_argument(
		    """--prefix"""			, type=_lowerCamelCase			, required=_lowerCamelCase			, default=_lowerCamelCase			, help="""will be added to the begininng of src examples"""   )
		parser.add_argument("""--task"""			, type=_lowerCamelCase			, default="""summarization"""			, help="""used for task_specific_params + metrics"""   )
		parser.add_argument("""--bs"""			, type=_lowerCamelCase			, default=8			, required=_lowerCamelCase			, help="""batch size"""   )
		parser.add_argument(
		    """--n_obs"""			, type=_lowerCamelCase			, default=-1			, required=_lowerCamelCase			, help="""How many observations. Defaults to all."""   )
		parser.add_argument("""--fp16"""			, action="""store_true"""   )
		parser.add_argument("""--dump-args"""			, action="""store_true"""			, help="""print the custom hparams with the results"""   )
		parser.add_argument(
		    """--info"""			, nargs="""?"""			, type=_lowerCamelCase			, const=datetime_now()			, help=(
		        """use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g."""
		        """ lang=en-ru. If no value is passed, the current datetime string will be used."""
		    )			, )
		# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
		__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						parser.parse_known_args()
		__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						parse_numeric_n_bool_cl_kwargs(_lowerCamelCase   )
		if parsed_args and verbose:
				print(F"parsed the following generate kwargs: {parsed_args}"   )
		__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path   ).readlines()]
		if args.n_obs > 0:
				__SCREAMING_SNAKE_CASE			:						Tuple       			=						examples[: args.n_obs]
		Path(args.save_path   ).parent.mkdir(exist_ok=_lowerCamelCase   )

		if args.reference_path is None and Path(args.score_path   ).exists():
				warnings.warn(F"score_path {args.score_path} will be overwritten unless you type ctrl-c."   )

		if args.device == "cpu" and args.fpaa:
				# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
				raise ValueError("""Can\'t mix --fp16 and --device cpu"""   )

		__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						generate_summaries_or_translations(
		    _lowerCamelCase			, args.save_path			, args.model_name			, batch_size=args.bs			, device=args.device			, fpaa=args.fpaa			, task=args.task			, prefix=args.prefix			, **_lowerCamelCase			, )

		if args.reference_path is None:
				return {}

		# Compute scores
		__SCREAMING_SNAKE_CASE			:						str       			=						calculate_bleu if '''translation''' in args.task else calculate_rouge
		__SCREAMING_SNAKE_CASE			:						Any       			=						[x.rstrip() for x in open(args.save_path   ).readlines()]
		__SCREAMING_SNAKE_CASE			:						Optional[int]       			=						[x.rstrip() for x in open(args.reference_path   ).readlines()][: len(_lowerCamelCase   )]
		__SCREAMING_SNAKE_CASE			:						dict       			=						score_fn(_lowerCamelCase			, _lowerCamelCase   )
		scores.update(_lowerCamelCase   )

		if args.dump_args:
				scores.update(_lowerCamelCase   )
		if args.info:
				__SCREAMING_SNAKE_CASE			:						int       			=						args.info

		if verbose:
				print(_lowerCamelCase   )

		if args.score_path is not None:
				json.dump(_lowerCamelCase			, open(args.score_path			, """w"""   )   )

		return scores


if __name__ == "__main__":
						# Usage for MT:
						# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
						run_generate(verbose=True)
from __future__ import annotations




def    lowerCAmelCase_  (		lowercase:				str	,       lowercase:				list[str] | None = None	,       lowercase:				dict[str, float] | None = None	,       lowercase:				bool = False	,       )     ->					tuple[int, float, str]:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			cipher_alphabet or [chr(lowercase		) for i in range(97	,       123		)]

 # If the argument is None or the user provided an empty dictionary
 if not frequencies_dict:
  # Frequencies of letters in the english language (how much they show up)
  _UpperCamelCase:  Any    						=			{
      '''a''': 0.08497,
      '''b''': 0.01492,
      '''c''': 0.02202,
      '''d''': 0.04253,
      '''e''': 0.11162,
      '''f''': 0.02228,
      '''g''': 0.02015,
      '''h''': 0.06094,
      '''i''': 0.07546,
      '''j''': 0.00153,
      '''k''': 0.01292,
      '''l''': 0.04025,
      '''m''': 0.02406,
      '''n''': 0.06749,
      '''o''': 0.07507,
      '''p''': 0.01929,
      '''q''': 0.00095,
      '''r''': 0.07587,
      '''s''': 0.06327,
      '''t''': 0.09356,
      '''u''': 0.02758,
      '''v''': 0.00978,
      '''w''': 0.02560,
      '''x''': 0.00150,
      '''y''': 0.01994,
      '''z''': 0.00077,
  }
 else:
  # Custom frequencies dictionary
  _UpperCamelCase:  str    						=			frequencies_dict

 if not case_sensitive:
  _UpperCamelCase:  Union[str, Any]    						=			ciphertext.lower()

 # Chi squared statistic values
 _UpperCamelCase:  dict[int, tuple[float, str]]    						=			{}

 # cycle through all of the shifts
 for shift in range(len(lowercase		)		):
  _UpperCamelCase:  Tuple    						=			''''''

  # decrypt the message with the shift
  for letter in ciphertext:
   try:
    # Try to index the letter in the alphabet
    _UpperCamelCase:  Optional[int]    						=			(alphabet_letters.index(letter.lower()		) - shift) % len(
        lowercase		)
    decrypted_with_shift += (
        alphabet_letters[new_key].upper()
        if case_sensitive and letter.isupper()
        else alphabet_letters[new_key]
    )
   except ValueError:
    # Append the character if it isn't in the alphabet
    decrypted_with_shift += letter

  _UpperCamelCase:  Optional[int]    						=			0.0

  # Loop through each letter in the decoded message with the shift
  for letter in decrypted_with_shift:
   if case_sensitive:
    _UpperCamelCase:  Optional[int]    						=			letter.lower()
    if letter in frequencies:
     # Get the amount of times the letter occurs in the message
     _UpperCamelCase:  Any    						=			decrypted_with_shift.lower().count(lowercase		)

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     _UpperCamelCase:  int    						=			frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     _UpperCamelCase:  int    						=			((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value
   else:
    if letter.lower() in frequencies:
     # Get the amount of times the letter occurs in the message
     _UpperCamelCase:  List[Any]    						=			decrypted_with_shift.count(lowercase		)

     # Get the excepcted amount of times the letter should appear based
     # on letter frequencies
     _UpperCamelCase:  Union[str, Any]    						=			frequencies[letter] * occurrences

     # Complete the chi squared statistic formula
     _UpperCamelCase:  List[str]    						=			((occurrences - expected) ** 2) / expected

     # Add the margin of error to the total chi squared statistic
     chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
  _UpperCamelCase:  List[Any]    						=			(
      chi_squared_statistic,
      decrypted_with_shift,
  )

 # Get the most likely cipher by finding the cipher with the smallest chi squared
 # statistic
 def chi_squared_statistic_values_sorting_key(lowercase:				int		) -> tuple[float, str]:
  return chi_squared_statistic_values[key]

 _UpperCamelCase:  int    						=			min(
     lowercase	,       key=lowercase	,       )

 # Get all the data from the most likely cipher (key, decoded message)
 (
     (
     _UpperCamelCase
 )						,				(
     _UpperCamelCase
 )						,				
 ):  str    						=			chi_squared_statistic_values[most_likely_cipher]

 # Return the data on the most likely shift
 return (
     most_likely_cipher,
     most_likely_cipher_chi_squared_value,
     decoded_most_likely_cipher,
 )