


"""simple docstring"""




import json
import os
from datetime import date
from pathlib import Path

from tabulate import DataRow, TableFormat, tabulate


__UpperCamelCase						=      TableFormat(
    lineabove=None,
    linebelowheader=None,
    linebetweenrows=None,
    linebelow=None,
    headerrow=DataRow('''''', '''|''', '''|'''),
    datarow=DataRow('''''', '''|''', '''|'''),
    padding=1,
    with_header_hide=None,
)


__UpperCamelCase						=      []
__UpperCamelCase						=      []

__UpperCamelCase						=      {'''type''': '''section''', '''text''': {'''type''': '''plain_text''', '''text''': '''No failed tests! 🤗''', '''emoji''': True}}

__UpperCamelCase						=      [
    {
        '''type''': '''header''',
        '''text''': {
            '''type''': '''plain_text''',
            '''text''': f'''🤗 Accelerate nightly {os.environ.get('TEST_TYPE', '')} test results''',
            '''emoji''': True,
        },
    }
]

__UpperCamelCase						=      0
for log in Path().glob('''*.log'''):
	__UpperCamelCase						=      0
	with open(log, '''r''') as f:
		for line in f:
			__UpperCamelCase						=      json.loads(line)
			if line.get('''nodeid''', '''''') != "":
				__UpperCamelCase						=      line['''nodeid''']
				if line.get('''duration''', None) is not None:
					__UpperCamelCase						=      f'''{line['duration']:.4f}'''
					if line.get('''outcome''', '''''') == "failed":
						section_num_failed += 1
						failed.append([test, duration, log.name.split('''_''')[0]])
						total_num_failed += 1
	group_info.append([str(log), section_num_failed, failed])
	__UpperCamelCase						=      []
	log.unlink()

__UpperCamelCase						=      ''''''
__UpperCamelCase						=      []
if total_num_failed > 0:
	for name, num_failed, failed_tests in group_info:
		if num_failed > 0:
			if num_failed == 1:
				message += f"*{name[1:]}: {num_failed} failed test*\n"
			else:
				message += f"*{name[1:]}: {num_failed} failed tests*\n"
			__UpperCamelCase						=      []
			__UpperCamelCase						=      {}
			for test in failed_tests:
				__UpperCamelCase						=      test[0].split('''::''')
				__UpperCamelCase						=      data[0].split('''/''')[-1]
				if data[0] not in filesafailed:
					__UpperCamelCase						=      [data[1:]]
				else:
					filesafailed[data[0]] += [data[1:]]
				failed_table.append(data)

			__UpperCamelCase						=      [test[0] for test in failed_table]
			__UpperCamelCase						=      list(set(files))
			# Count number of instances in failed_tests
			__UpperCamelCase						=      []
			for file in individual_files:
				table.append([file, len(filesafailed[file])])

			__UpperCamelCase						=      tabulate(
			    table,
			    headers=['''Test Location''', '''Num Failed'''],
			    tablefmt=hf_table_format,
			    stralign='''right''',
			)
			message += f"\n```\n{failed_table}\n```"
			all_filesafailed.append(filesafailed)
	if len(message) > 3000:
		__UpperCamelCase						=      '''Too many failed tests, please see the full report in the Action results.'''
		__UpperCamelCase						=      len(err) + 10
		__UpperCamelCase						=      message[: 3000 - offset] + f'''\n...\n```\n{err}'''
	print(f'''### {message}''')
else:
	__UpperCamelCase						=      '''No failed tests! 🤗'''
	print(f'''## {message}''')
	payload.append(no_error_payload)

if os.environ.get('''TEST_TYPE''', '''''') != "":
	from slack_sdk import WebClient

	__UpperCamelCase						=      WebClient(token=os.environ['''SLACK_API_TOKEN'''])
	if message != "No failed tests! 🤗":
		__UpperCamelCase						=      {
		    '''type''': '''section''',
		    '''text''': {
		        '''type''': '''mrkdwn''',
		        '''text''': message,
		    },
		}
		payload.append(md_report)
		__UpperCamelCase						=      {
		    '''type''': '''section''',
		    '''text''': {
		        '''type''': '''mrkdwn''',
		        '''text''': '''*For more details:*''',
		    },
		    '''accessory''': {
		        '''type''': '''button''',
		        '''text''': {
		            '''type''': '''plain_text''',
		            '''text''': '''Check Action results''',
		            '''emoji''': True,
		        },
		        '''url''': f'''https://github.com/{os.environ['GITHUB_REPOSITORY']}/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
		    },
		}
		payload.append(action_button)
		__UpperCamelCase						=      {
		    '''type''': '''context''',
		    '''elements''': [
		        {
		            '''type''': '''plain_text''',
		            '''text''': f'''Nightly {os.environ.get('TEST_TYPE')} test results for {date.today()}''',
		        }
		    ],
		}
		payload.append(date_report)
	__UpperCamelCase						=      client.chat_postMessage(channel='''#accelerate-ci-daily''', text=message, blocks=payload)
	__UpperCamelCase						=      response.data['''ts''']
	for failed_file in all_filesafailed:
		for test_location, test_failures in failed_file.items():
			# Keep only the first instance of the test name
			__UpperCamelCase						=      ''''''
			for i, row in enumerate(test_failures):
				if row[0] != test_class:
					__UpperCamelCase						=      row[0]
				else:
					__UpperCamelCase						=      ''''''

			__UpperCamelCase						=      {
			    '''type''': '''section''',
			    '''text''': {
			        '''type''': '''mrkdwn''',
			        '''text''': f'''Test location: {test_location}\n```\n{tabulate(test_failures, headers=['Class', 'Test'], tablefmt=hf_table_format, stralign='right')}\n```''',
			    },
			}

			client.chat_postMessage(
			    channel='''#accelerate-ci-daily''',
			    thread_ts=ts,
			    blocks=[payload],
			)






"""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)


