
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowercase  :					List[Any]													=       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
lowercase  :					List[Any]													=       '    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:str						)		-> List[Any]:
								__UpperCamelCase	:   str			   =					tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir     ,   "models/bert/"						)						)
								__UpperCamelCase	:   str			   =					self.transformer_dir
								shutil.copy(
								    os.path.join(a     ,   "src/transformers/models/bert/modeling_bert.py"						)     ,   os.path.join(self.transformer_dir     ,   "models/bert/modeling_bert.py"						)     ,   )







	def    _lowerCamelCase				(					self		:List[str]						)		-> Optional[Any]:
								__UpperCamelCase	:   List[str]			   =					"src/transformers"
								shutil.rmtree(self.transformer_dir						)







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:str     ,   a		:Optional[Any]     ,   a		:Optional[Any]     ,   a		:List[Any]=None						)		-> Optional[int]:
								__UpperCamelCase	:   str			   =					comment + f'\nclass {class_name}(nn.Module):\n' + class_code
								if overwrite_result is not None:
															__UpperCamelCase	:   Any			   =					comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
								__UpperCamelCase	:   Optional[int]			   =					black.Mode(target_versions={black.TargetVersion.PYaa}     ,   line_length=1_1_9						)
								__UpperCamelCase	:   Optional[Any]			   =					black.format_str(a     ,   mode=a						)
								__UpperCamelCase	:   int			   =					os.path.join(self.transformer_dir     ,   "new_code.py"						)
								with open(a     ,   "w"     ,   newline="\n"						) as f:
															f.write(a						)
								if overwrite_result is None:
															self.assertTrue(len(check_copies.is_copy_consistent(a						)						) == 0						)
								else:
															check_copies.is_copy_consistent(f.name     ,   overwrite=a						)
															with open(a     ,   "r"						) as f:
																						self.assertTrue(f.read()     ,   a						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[int]:
								__UpperCamelCase	:   List[Any]			   =					check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"						)
								self.assertEqual(a     ,   a						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[int]:
								# Base copy consistency
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"     ,   "BertLMPredictionHead"     ,   REFERENCE_CODE + "\n"     ,   )

								# With no empty line at the end
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"     ,   "BertLMPredictionHead"     ,   a     ,   )

								# Copy consistency with rename
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"     ,   "TestModelLMPredictionHead"     ,   re.sub("Bert"     ,   "TestModel"     ,   a						)     ,   )

								# Copy consistency with a really long name
								__UpperCamelCase	:   Optional[int]			   =					"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
								self.check_copy_consistency(
								    f'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'     ,   f'{long_class_name}LMPredictionHead'     ,   re.sub("Bert"     ,   a     ,   a						)     ,   )

								# Copy consistency with overwrite
								self.check_copy_consistency(
								    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"     ,   "TestModelLMPredictionHead"     ,   a     ,   overwrite_result=re.sub("Bert"     ,   "TestModel"     ,   a						)     ,   )







	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> List[Any]:
								__UpperCamelCase	:   List[Any]			   =					check_copies.LOCALIZED_READMES["README_zh-hans.md"]

								__UpperCamelCase	:   Tuple			   =					(
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
								    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
								    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
								    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
								    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
								    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
								    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
								    " method has been applied to compress GPT2 into"
								    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
								    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
								    " Multilingual BERT into"
								    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
								    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
								    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
								    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
								    " Luong, Quoc V. Le, Christopher D. Manning."
								)
								__UpperCamelCase	:   Optional[Any]			   =					(
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)
								__UpperCamelCase	:   Tuple			   =					(
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
								    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
								    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
								    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
								    " method has been applied to compress GPT2 into"
								    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
								    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
								    " Multilingual BERT into"
								    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
								    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
								    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
								    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
								    " Christopher D. Manning 发布。\n"
								)

								__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					check_copies.convert_to_localized_md(
								    a     ,   a     ,   localized_readme["format_model_list"]						)

								self.assertFalse(a						)
								self.assertEqual(a     ,   a						)

								__UpperCamelCase   ,						__UpperCamelCase	:   List[Any]			   =					check_copies.convert_to_localized_md(
								    a     ,   a     ,   localized_readme["format_model_list"]						)

								# Check whether the number of models is equal to README.md after conversion.
								self.assertTrue(a						)

								__UpperCamelCase	:   int			   =					(
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
								    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
								    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
								    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
								)
								__UpperCamelCase	:   int			   =					(
								    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
								    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)
								__UpperCamelCase	:   str			   =					(
								    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
								    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
								    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
								    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
								)

								__UpperCamelCase   ,						__UpperCamelCase	:   Union[str, Any]			   =					check_copies.convert_to_localized_md(
								    a     ,   a     ,   localized_readme["format_model_list"]						)

								# Check if the model link is synchronized.
								self.assertEqual(a     ,   a						)

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

import os
from pathlib import Path




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Any						,  _lowerCamelCase :			List[str]						,  _lowerCamelCase :			Any)					->    Dict:






							'''simple docstring'''





							__UpperCamelCase	:   Optional[Any]			   =					{
							    "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]
							__UpperCamelCase	:   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"],
							}
							__UpperCamelCase	:   str			   =					F'{src_lang}-{tgt_lang}'

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


# make sure we are under the root of the project
lowercase  :					List[str]													=       Path(__file__).resolve().parent.parent.parent
lowercase  :					Union[str, Any]													=       repo_dir / 'model_cards'

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