



'''simple docstring'''




import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType






class 		_UpperCamelCase    :


			'''simple docstring'''

			def __init__(				self	:					Union[str, Any]				,	lowerCAmelCase__	:					int=2				,	lowerCAmelCase__	:					List[Any]=3				,	lowerCAmelCase__	:					Union[str, Any]=6_4				,	lowerCAmelCase__	:					List[str]=None		):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE			:						str       			=						np.random.default_rng(_lowercase		)
					__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						length
					__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						rng.normal(size=(length,)		).astype(np.floataa		)
					__SCREAMING_SNAKE_CASE			:						Union[str, Any]       			=						a * self.x + b + rng.normal(scale=0.1				,	size=(length,)		).astype(np.floataa		)

			def __len__(				self	:					Tuple		):





					"""simple docstring"""






					return self.length



			def __getitem__(				self	:					Dict				,	lowerCAmelCase__	:					Dict		):





					"""simple docstring"""






					return {"x": self.x[i], "y": self.y[i]}






class 		_UpperCamelCase    (				torch.nn.Module       ):


			'''simple docstring'''

			def __init__(				self	:					int				,	lowerCAmelCase__	:					Optional[int]=0				,	lowerCAmelCase__	:					List[str]=0				,	lowerCAmelCase__	:					Dict=False		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE			:						Tuple       			=						torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
					__SCREAMING_SNAKE_CASE			:						int       			=						torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
					__SCREAMING_SNAKE_CASE			:						Any       			=						True



			def UpperCamelCase__   (				self	:					List[Any]				,	lowerCAmelCase__	:					Dict=None		):





					"""simple docstring"""






					if self.first_batch:
							print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
							__SCREAMING_SNAKE_CASE			:						Tuple       			=						False
					return x * self.a[0] + self.b[0]






class 		_UpperCamelCase    (				torch.nn.Module       ):


			'''simple docstring'''

			def __init__(				self	:					Union[str, Any]				,	lowerCAmelCase__	:					Dict=0				,	lowerCAmelCase__	:					List[Any]=0				,	lowerCAmelCase__	:					Tuple=False		):





					"""simple docstring"""






					super().__init__()
					__SCREAMING_SNAKE_CASE			:						List[str]       			=						torch.nn.Parameter(torch.tensor(_lowercase		).float()		)
					__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						torch.nn.Parameter(torch.tensor(_lowercase		).float()		)
					__SCREAMING_SNAKE_CASE			:						Optional[Any]       			=						True



			def UpperCamelCase__   (				self	:					Union[str, Any]				,	lowerCAmelCase__	:					List[Any]=None		):





					"""simple docstring"""






					if self.first_batch:
							print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
							__SCREAMING_SNAKE_CASE			:						str       			=						False
					return x * self.a + self.b


def 							lowerCAmelCase_  (      _lowerCamelCase:  Dict			, _lowerCamelCase:  int = 16   ):
		from datasets import load_dataset
		from transformers import AutoTokenizer

		__SCREAMING_SNAKE_CASE			:						Tuple       			=						AutoTokenizer.from_pretrained("""bert-base-cased"""   )
		__SCREAMING_SNAKE_CASE			:						List[str]       			=						{'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						load_dataset("""csv"""			, data_files=_lowerCamelCase   )
		__SCREAMING_SNAKE_CASE			:						str       			=						datasets['''train'''].unique("""label"""   )

		__SCREAMING_SNAKE_CASE			:						int       			=						{v: i for i, v in enumerate(_lowerCamelCase   )}

		def tokenize_function(_lowerCamelCase:  List[str]   ):
				# max_length=None => use the model max length (it's actually the default)
				__SCREAMING_SNAKE_CASE			:						int       			=						tokenizer(
				    examples["""sentence1"""]			, examples["""sentence2"""]			, truncation=_lowerCamelCase			, max_length=_lowerCamelCase			, padding="""max_length"""   )
				if "label" in examples:
						__SCREAMING_SNAKE_CASE			:						Dict       			=						[label_to_id[l] for l in examples['''label''']]
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		__SCREAMING_SNAKE_CASE			:						Dict       			=						datasets.map(
		    _lowerCamelCase			, batched=_lowerCamelCase			, remove_columns=["""sentence1""", """sentence2""", """label"""]			, )

		def collate_fn(_lowerCamelCase:  Dict   ):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(_lowerCamelCase			, padding="""max_length"""			, max_length=1_28			, return_tensors="""pt"""   )
				return tokenizer.pad(_lowerCamelCase			, padding="""longest"""			, return_tensors="""pt"""   )

		# Instantiate dataloaders.
		__SCREAMING_SNAKE_CASE			:						List[Any]       			=						DataLoader(tokenized_datasets["""train"""]			, shuffle=_lowerCamelCase			, collate_fn=_lowerCamelCase			, batch_size=2   )
		__SCREAMING_SNAKE_CASE			:						int       			=						DataLoader(tokenized_datasets["""validation"""]			, shuffle=_lowerCamelCase			, collate_fn=_lowerCamelCase			, batch_size=1   )

		return train_dataloader, eval_dataloader
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,
 )