



from __future__ import annotations






def 			lowerCAmelCase__	(					lowerCamelCase_	:	str ,lowerCamelCase_	:	list[str] | None = None ,lowerCamelCase_	:	dict[str, float] | None = None ,lowerCamelCase_	:	bool = False ,):





				'''simple docstring'''

				lowerCAmelCase__  :       str				=  cipher_alphabet or [chr(lowerCamelCase_) 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)
								lowerCAmelCase__  :       Union[str, 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
								lowerCAmelCase__  :       str				=  frequencies_dict

				if not case_sensitive:
								lowerCAmelCase__  :       Optional[Any]				=  ciphertext.lower()

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

				# cycle through all of the shifts
				for shift in range(len(lowerCamelCase_)):
								lowerCAmelCase__  :       Optional[Any]				=  ''''''

								# decrypt the message with the shift
								for letter in ciphertext:
												try:
																# Try to index the letter in the alphabet
																lowerCAmelCase__  :       Optional[Any]				=  (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

								lowerCAmelCase__  :       str				=  0.0

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

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

																				# Complete the chi squared statistic formula
																				lowerCAmelCase__  :       Optional[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
																				lowerCAmelCase__  :       List[str]				=  decrypted_with_shift.count(lowerCamelCase_)

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

																				# Complete the chi squared statistic formula
																				lowerCAmelCase__  :       List[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
								lowerCAmelCase__  :       Optional[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(lowerCamelCase_	:	int) -> tuple[float, str]:
								return chi_squared_statistic_values[key]

				lowerCAmelCase__  :       int				=  min(
				    lowerCamelCase_ ,key=lowerCamelCase_ ,)

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






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__snake_case       :	List[str]            ='tiny-wmt19-en-ru'

# Build

# borrowed from a test
__snake_case       :	Any            =[
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
__snake_case       :	Union[str, Any]            =dict(zip(vocab, range(len(vocab))))
__snake_case       :	List[Any]            =['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =build_dir / VOCAB_FILES_NAMES['merges_file']
							with open(src_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, 'w') as fp:
														fp.write('\n'.join(merges))

							__snake_case       :	Union[str, Any]            =FSMTTokenizer(
							    langs=['en', 'ru'],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

__snake_case       :	Dict            =FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__snake_case       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru

