






"""simple docstring"""




from __future__ import annotations

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = None     ,   UpperCAmelCase__ = False     ,   )	->    tuple[int, float, str]:
  UpperCamelCase_: Optional[int]   				=    cipher_alphabet or [chr(lowerCAmelCase__  ) for i in range(9_7     ,   1_2_3  )]

  # 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_: Union[str, Any]   				=    {
        'a': 0.0_8497,
        'b': 0.0_1492,
        'c': 0.0_2202,
        'd': 0.0_4253,
        'e': 0.1_1162,
        'f': 0.0_2228,
        'g': 0.0_2015,
        'h': 0.0_6094,
        'i': 0.0_7546,
        'j': 0.0_0153,
        'k': 0.0_1292,
        'l': 0.0_4025,
        'm': 0.0_2406,
        'n': 0.0_6749,
        'o': 0.0_7507,
        'p': 0.0_1929,
        'q': 0.0_0095,
        'r': 0.0_7587,
        's': 0.0_6327,
        't': 0.0_9356,
        'u': 0.0_2758,
        'v': 0.0_0978,
        'w': 0.0_2560,
        'x': 0.0_0150,
        'y': 0.0_1994,
        'z': 0.0_0077,
    }
  else:
    # Custom frequencies dictionary
    UpperCamelCase_: List[Any]   				=    frequencies_dict

  if not case_sensitive:
    UpperCamelCase_: List[str]   				=    ciphertext.lower()

  # Chi squared statistic values
  UpperCamelCase_: int   				=    {}

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

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        UpperCamelCase_: Tuple   				=    (alphabet_letters.index(letter.lower()  ) - shift) % len(
            lowerCAmelCase__  )
        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_: Union[str, Any]   				=    0.0

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

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          UpperCamelCase_: List[str]   				=    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          UpperCamelCase_: Any   				=    ((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_: Union[str, Any]   				=    decrypted_with_shift.count(lowerCAmelCase__  )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          UpperCamelCase_: List[Any]   				=    frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          UpperCamelCase_: Any   				=    ((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_: Union[str, 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(UpperCAmelCase__  ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  UpperCamelCase_: List[Any]   				=    min(
      lowerCAmelCase__     ,   key=lowerCAmelCase__     ,   )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      UpperCamelCase_				
  )				,(
      UpperCamelCase_				
  )				,				
  ): int   				=    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,
  )







import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


A_		:		Tuple		     =			[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['memory_attention', 'encoder_attn'],
    ['attention', 'attn'],
    ['/', '.'],
    ['.LayerNorm.gamma', '_layer_norm.weight'],
    ['.LayerNorm.beta', '_layer_norm.bias'],
    ['r.layer_', 'r.layers.'],
    ['output_proj', 'out_proj'],
    ['ffn.dense_1.', 'fc2.'],
    ['ffn.dense.', 'fc1.'],
    ['ffn_layer_norm', 'final_layer_norm'],
    ['kernel', 'weight'],
    ['encoder_layer_norm.', 'encoder.layer_norm.'],
    ['decoder_layer_norm.', 'decoder.layer_norm.'],
    ['embeddings.weights', 'shared.weight'],
]

def   snake_case (UpperCAmelCase__  )	->    str:
  for pegasus_name, hf_name in PATTERNS:
    UpperCamelCase_: List[str]   				=    k.replace(UpperCAmelCase__     ,   UpperCAmelCase__  )
  return k

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    PegasusForConditionalGeneration:
  UpperCamelCase_: List[str]   				=    DEFAULTS.copy()
  cfg_kwargs.update(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusConfig(**UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    PegasusForConditionalGeneration(UpperCAmelCase__  )
  UpperCamelCase_: List[Any]   				=    torch_model.model.state_dict()
  UpperCamelCase_: str   				=    {}
  for k, v in tf_weights.items():
    UpperCamelCase_: Dict   				=    rename_state_dict_key(UpperCAmelCase__  )
    if new_k not in sd:
      raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''  )

    if "dense" in k or "proj" in new_k:
      UpperCamelCase_: int   				=    v.T
    UpperCamelCase_: Union[str, Any]   				=    torch.tensor(UpperCAmelCase__     ,   dtype=sd[new_k].dtype  )
    assert v.shape == sd[new_k].shape, F'''{new_k}, {k}, {v.shape}, {sd[new_k].shape}'''
  # make sure embedding.padding_idx is respected
  UpperCamelCase_: Tuple   				=    torch.zeros_like(mapping['shared.weight'][cfg.pad_token_id + 1]  )
  UpperCamelCase_: int   				=    mapping['shared.weight']
  UpperCamelCase_: Union[str, Any]   				=    mapping['shared.weight']
  UpperCamelCase_: Dict   				=    {k: torch.zeros_like(UpperCAmelCase__  ) for k, v in sd.items() if k.endswith('bias'  ) and k not in mapping}
  mapping.update(**UpperCAmelCase__  )
  UpperCamelCase_				,UpperCamelCase_: Optional[int]   				=    torch_model.model.load_state_dict(UpperCAmelCase__     ,   strict=UpperCAmelCase__  )
  UpperCamelCase_: List[str]   				=    [
      k for k in missing if k not in ['encoder.embed_positions.weight', 'decoder.embed_positions.weight']
  ]
  assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
  assert extra == [], F'''no matches found for the following tf keys {extra}'''
  return torch_model

def   snake_case (UpperCAmelCase__="./ckpt/aeslc/model.ckpt-32000"  )	->    Dict:
  UpperCamelCase_: Union[str, Any]   				=    tf.train.list_variables(UpperCAmelCase__  )
  UpperCamelCase_: Tuple   				=    {}
  UpperCamelCase_: Dict   				=    ['Adafactor', 'global_step']
  for name, shape in tqdm(UpperCAmelCase__     ,   desc='converting tf checkpoint to dict'  ):
    UpperCamelCase_: Union[str, Any]   				=    any(pat in name for pat in ignore_name  )
    if skip_key:
      continue
    UpperCamelCase_: Dict   				=    tf.train.load_variable(UpperCAmelCase__     ,   UpperCAmelCase__  )
    UpperCamelCase_: Optional[Any]   				=    array
  return tf_weights

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  # save tokenizer first
  UpperCamelCase_: Any   				=    Path(UpperCAmelCase__  ).parent.name
  UpperCamelCase_: Tuple   				=    task_specific_params[F'''summarization_{dataset}''']['max_position_embeddings']
  UpperCamelCase_: Optional[Any]   				=    PegasusTokenizer.from_pretrained('sshleifer/pegasus'     ,   model_max_length=UpperCAmelCase__  )
  assert tok.model_max_length == desired_max_model_length
  tok.save_pretrained(UpperCAmelCase__  )

  # convert model
  UpperCamelCase_: Optional[Any]   				=    get_tf_weights_as_numpy(UpperCAmelCase__  )
  UpperCamelCase_: Any   				=    task_specific_params[F'''summarization_{dataset}''']
  if dataset == "large":
    UpperCamelCase_: Union[str, Any]   				=    task_specific_params
  UpperCamelCase_: Tuple   				=    convert_pegasus(UpperCAmelCase__     ,   UpperCAmelCase__  )
  torch_model.save_pretrained(UpperCAmelCase__  )
  UpperCamelCase_: int   				=    torch_model.state_dict()
  sd.pop('model.decoder.embed_positions.weight'  )
  sd.pop('model.encoder.embed_positions.weight'  )
  torch.save(UpperCAmelCase__     ,   Path(UpperCAmelCase__  ) / 'pytorch_model.bin'  )


if __name__ == "__main__":
   A_		:		str		     =			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   A_		:		Optional[Any]		     =			parser.parse_args()
   if args.save_dir is None:
      A_		:		Union[str, Any]		     =			Path(args.tf_ckpt_path).parent.name
      A_		:		Optional[Any]		     =			os.path.join('pegasus', dataset)
   convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)