


"""simple docstring"""




import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__UpperCamelCase						=      '''src/transformers'''
__UpperCamelCase						=      '''docs/source/en'''
__UpperCamelCase						=      '''.'''


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Dict  )   ->						List[str]:
						with open(SCREAMING_SNAKE_CASE_ ,			'r' ,			encoding='utf-8' ,			newline='\n'  ) as f:
												SCREAMING_SNAKE_CASE													=					f.readlines()
						# Find the start prompt.
						SCREAMING_SNAKE_CASE													=					0
						while not lines[start_index].startswith(SCREAMING_SNAKE_CASE_  ):
												start_index += 1
						start_index += 1

						SCREAMING_SNAKE_CASE													=					start_index
						while not lines[end_index].startswith(SCREAMING_SNAKE_CASE_  ):
												end_index += 1
						end_index -= 1

						while len(lines[start_index]  ) <= 1:
												start_index += 1
						while len(lines[end_index]  ) <= 1:
												end_index -= 1
						end_index += 1
						return "".join(lines[start_index:end_index]  ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__UpperCamelCase						=      '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
__UpperCamelCase						=      re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
__UpperCamelCase						=      re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__UpperCamelCase						=      re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
__UpperCamelCase						=      direct_transformers_import(TRANSFORMERS_PATH)


def     lowercase			(SCREAMING_SNAKE_CASE_		:				List[Any]  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)' ,			SCREAMING_SNAKE_CASE_  )
						return [m.group(0  ) for m in matches]


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				Any  )   ->						Optional[Any]:
						SCREAMING_SNAKE_CASE													=					2 if text == '✅' or text == '❌' else len(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					(width - text_length) // 2
						SCREAMING_SNAKE_CASE													=					width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent


def     lowercase			()   ->						Optional[Any]:
						SCREAMING_SNAKE_CASE													=					transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						SCREAMING_SNAKE_CASE													=					{
						    name: config_maping_names[code]
						    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_maping_names
						}
						SCREAMING_SNAKE_CASE													=					{name: config.replace('Config' ,			''  ) for name, config in model_name_to_config.items()}

						# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
						SCREAMING_SNAKE_CASE													=					collections.defaultdict(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					collections.defaultdict(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					collections.defaultdict(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					collections.defaultdict(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					collections.defaultdict(SCREAMING_SNAKE_CASE_  )

						# Let's lookup through all transformers object (once).
						for attr_name in dir(SCREAMING_SNAKE_CASE_  ):
												SCREAMING_SNAKE_CASE													=					None
												if attr_name.endswith('Tokenizer'  ):
																		SCREAMING_SNAKE_CASE													=					slow_tokenizers
																		SCREAMING_SNAKE_CASE													=					attr_name[:-9]
												elif attr_name.endswith('TokenizerFast'  ):
																		SCREAMING_SNAKE_CASE													=					fast_tokenizers
																		SCREAMING_SNAKE_CASE													=					attr_name[:-13]
												elif _re_tf_models.match(SCREAMING_SNAKE_CASE_  ) is not None:
																		SCREAMING_SNAKE_CASE													=					tf_models
																		SCREAMING_SNAKE_CASE													=					_re_tf_models.match(SCREAMING_SNAKE_CASE_  ).groups()[0]
												elif _re_flax_models.match(SCREAMING_SNAKE_CASE_  ) is not None:
																		SCREAMING_SNAKE_CASE													=					flax_models
																		SCREAMING_SNAKE_CASE													=					_re_flax_models.match(SCREAMING_SNAKE_CASE_  ).groups()[0]
												elif _re_pt_models.match(SCREAMING_SNAKE_CASE_  ) is not None:
																		SCREAMING_SNAKE_CASE													=					pt_models
																		SCREAMING_SNAKE_CASE													=					_re_pt_models.match(SCREAMING_SNAKE_CASE_  ).groups()[0]

												if lookup_dict is not None:
																		while len(SCREAMING_SNAKE_CASE_  ) > 0:
																								if attr_name in model_name_to_prefix.values():
																														SCREAMING_SNAKE_CASE													=					True
																														break
																								# Try again after removing the last word in the name
																								SCREAMING_SNAKE_CASE													=					''.join(camel_case_split(SCREAMING_SNAKE_CASE_  )[:-1]  )

    # Let's build that table!
						SCREAMING_SNAKE_CASE													=					list(model_name_to_config.keys()  )
						model_names.sort(key=str.lower  )
						SCREAMING_SNAKE_CASE													=					['Model', 'Tokenizer slow', 'Tokenizer fast', 'PyTorch support', 'TensorFlow support', 'Flax Support']
						# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
						SCREAMING_SNAKE_CASE													=					[len(SCREAMING_SNAKE_CASE_  ) + 2 for c in columns]
						SCREAMING_SNAKE_CASE													=					max([len(SCREAMING_SNAKE_CASE_  ) for name in model_names]  ) + 2

						# Build the table per se
						SCREAMING_SNAKE_CASE													=					'|' + '|'.join([_center_text(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) for c, w in zip(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )]  ) + '|\n'
						# Use ":-----:" format to center-aligned table cell texts
						table += "|" + "|".join([':' + '-' * (w - 2) + ':' for w in widths]  ) + "|\n"

						SCREAMING_SNAKE_CASE													=					{True: '✅', False: '❌'}
						for name in model_names:
												SCREAMING_SNAKE_CASE													=					model_name_to_prefix[name]
												SCREAMING_SNAKE_CASE													=					[
												    name,
												    check[slow_tokenizers[prefix]],
												    check[fast_tokenizers[prefix]],
												    check[pt_models[prefix]],
												    check[tf_models[prefix]],
												    check[flax_models[prefix]],
												]
												table += "|" + "|".join([_center_text(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  ) for l, w in zip(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )]  ) + "|\n"
						return table


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=False  )   ->						Dict:
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					_find_text_in_file(
						    filename=os.path.join(SCREAMING_SNAKE_CASE_ ,			'index.md'  ) ,			start_prompt='<!--This table is updated automatically from the auto modules' ,			end_prompt='<!-- End table-->' ,			)
						SCREAMING_SNAKE_CASE													=					get_model_table_from_auto_modules()

						if current_table != new_table:
												if overwrite:
																		with open(os.path.join(SCREAMING_SNAKE_CASE_ ,			'index.md'  ) ,			'w' ,			encoding='utf-8' ,			newline='\n'  ) as f:
																								f.writelines(lines[:start_index] + [new_table] + lines[end_index:]  )
												else:
																		raise ValueError(
																		    'The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this.'  )


if __name__ == "__main__":
	__UpperCamelCase						=      argparse.ArgumentParser()
	parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
	__UpperCamelCase						=      parser.parse_args()

	check_model_table(args.fix_and_overwrite)






"""simple docstring"""




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

import os
from pathlib import Path


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Tuple ,			SCREAMING_SNAKE_CASE_		:				List[str] ,			SCREAMING_SNAKE_CASE_		:				int  )   ->						List[str]:
						SCREAMING_SNAKE_CASE													=					{
						    '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]
						SCREAMING_SNAKE_CASE													=					{
						    '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'],
						}
						SCREAMING_SNAKE_CASE													=					F'{src_lang}-{tgt_lang}'

						SCREAMING_SNAKE_CASE													=					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(SCREAMING_SNAKE_CASE_ ,			exist_ok=SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE													=					os.path.join(SCREAMING_SNAKE_CASE_ ,			'README.md'  )
						print(F'Generating {path}'  )
						with open(SCREAMING_SNAKE_CASE_ ,			'w' ,			encoding='utf-8'  ) as f:
												f.write(SCREAMING_SNAKE_CASE_  )


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

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


