
"""simple docstring"""







from __future__ import annotations
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase = None	,  _UpperCamelCase = None	,  _UpperCamelCase = False	,  ):
	__lowerCAmelCase      :						Union[str, Any]						      =				cipher_alphabet or [chr(_UpperCamelCase		) 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      :						Optional[int]						      =				{
		    '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      :						Union[str, Any]						      =				frequencies_dict

	if not case_sensitive:
		__lowerCAmelCase      :						int						      =				ciphertext.lower()

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

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

		# decrypt the message with the shift
		for letter in ciphertext:
			try:
				# Try to index the letter in the alphabet
				__lowerCAmelCase      :						int						      =				(alphabet_letters.index(letter.lower()		) - shift) % len(
				    _UpperCamelCase		)
				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      :						Tuple						      =				0.0

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

					# 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      :						List[str]						      =				((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      :						Union[str, Any]						      =				decrypted_with_shift.count(_UpperCamelCase		)

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

					# Complete the chi squared statistic formula
					__lowerCAmelCase      :						Optional[int]						      =				((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      :						List[str]						      =				(
		    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]

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

	# 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,
	)

"""simple docstring"""







# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						str						      =				{
	    'en': 'Machine learning is great, isn\'t it?',
	    'ru': 'Машинное обучение - это здорово, не так ли?',
	    'de': 'Maschinelles Lernen ist großartig, oder?',
	}

	# BLUE scores as follows:
	# "pair": [fairseq, transformers]
	__lowerCAmelCase      :						Union[str, Any]						      =				{
	    'ru-en': ['[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)', '39.20'],
	    'en-ru': ['[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)', '33.47'],
	    'en-de': ['[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)', '42.83'],
	    'de-en': ['[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)', '41.35'],
	}
	__lowerCAmelCase      :						List[str]						      =				F"{src_lang}-{tgt_lang}"

	__lowerCAmelCase      :						Tuple						      =				F"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = \"{texts[src_lang]}\"\ninput_ids = tokenizer.encode(input, return_tensors=\"pt\")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn't seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
	os.makedirs(_UpperCamelCase	,  exist_ok=_UpperCamelCase		)
	__lowerCAmelCase      :						Any						      =				os.path.join(_UpperCamelCase	,  'README.md'		)
	print(F"Generating {path}"		)
	with open(_UpperCamelCase	,  'w'	,  encoding='utf-8'		) as f:
		f.write(_UpperCamelCase		)


# make sure we are under the root of the project
lowerCamelCase__        =		Path(__file__).resolve().parent.parent.parent
lowerCamelCase__        =		repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
							lowerCamelCase__ ,						lowerCamelCase__ ,						lowerCamelCase__        =		model_name.split("""-""")
							lowerCamelCase__        =		model_cards_dir / """facebook""" / model_name
							write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)