



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

import os
from pathlib import Path







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[Any]					     =					{
						    """en""": """Machine learning is great, isn't it?""",
						    """ru""": """Машинное обучение - это здорово, не так ли?""",
						    """de""": """Maschinelles Lernen ist großartig, nicht wahr?""",
						}

						# BLUE scores as follows:
						# "pair": [fairseq, transformers]
						lowerCAmelCase	:   Optional[Any]					     =					{
						    """wmt19-de-en-6-6-base""": [0, 38.37],
						    """wmt19-de-en-6-6-big""": [0, 39.90],
						}
						lowerCAmelCase	:   Dict					     =					F"""{src_lang}-{tgt_lang}"""

						lowerCAmelCase	:   List[Any]					     =					F"""\n---\n\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- allenai\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-based [wmt19 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\n2 models are available:\n\n* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)\n* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"allenai/{model_name}\"\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\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   |  transformers\n-------|---------\n{model_name}  |  {scores[model_name][1]}\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=5\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 allenai/{model_name} $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```\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```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n"""
						model_card_dir.mkdir(parents=_lowerCAmelCase			,exist_ok=_lowerCAmelCase  )
						lowerCAmelCase	:   int					     =					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
lowerCAmelCase					:				Dict   	=Path(__file__).resolve().parent.parent.parent
lowerCAmelCase					:				Union[str, Any]   	=repo_dir / 'model_cards'

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
		lowerCAmelCase					:				Any   	=model_cards_dir / 'allenai' / model_name
		write_model_card(model_card_dir, src_lang='de', tgt_lang='en', model_name=model_name)


from urllib.error import HTTPError

from bsa import BeautifulSoup
from requests import exceptions, get

__lowerCAmelCase    :  Optional[int]										= 'https://www.wellrx.com/prescriptions/{0}/{1}/?freshSearch=true'

def   a_					(_lowerCAmelCase  :   str			,	_lowerCAmelCase  :   str       )->				list | None:

    try:
        # Has user provided both inputs?
        if not drug_name or not zip_code:
            return None

        snake_case:						List[str]         =   BASE_URL.format(_lowerCAmelCase			,	_lowerCAmelCase       )
        snake_case:						Optional[Any]         =   get(_lowerCAmelCase       )

        # Is the response ok?
        response.raise_for_status()

        # Scrape the data using bs4
        snake_case:						Tuple         =   BeautifulSoup(response.text			,	"""html.parser"""       )

        # This list will store the name and price.
        snake_case:						List[Any]         =   []

        # Fetch all the grids that contains the items.
        snake_case:						Any         =   soup.find_all("""div"""			,	{"""class""": """grid-x pharmCard"""}       )
        if grid_list and len(_lowerCAmelCase       ) > 0:
            for grid in grid_list:
                # Get the pharmacy price.
                snake_case:						Union[str, Any]         =   grid.find("""p"""			,	{"""class""": """list-title"""}       ).text

                # Get price of the drug.
                snake_case:						Optional[Any]         =   grid.find("""span"""			,	{"""p""", """price price-large"""}       ).text

                pharmacy_price_list.append(
                    {
                        """pharmacy_name""": pharmacy_name,
                        """price""": price,
                    }       )

        return pharmacy_price_list

    except (HTTPError, exceptions.RequestException, ValueError):
        return None


if __name__ == "__main__":
    # Enter a drug name and a zip code
    __lowerCAmelCase    :  Union[str, Any]										= input('Enter drug name: ').strip()
    __lowerCAmelCase    :  Tuple										= input('Enter zip code: ').strip()

    __lowerCAmelCase    :  list | None										= fetch_pharmacy_and_price_list(
        drug_name, zip_code
    )

    if pharmacy_price_list:
        print(F"""\nSearch results for {drug_name} at location {zip_code}:""")
        for pharmacy_price in pharmacy_price_list:
            __lowerCAmelCase    :  int										= pharmacy_price['pharmacy_name']
            __lowerCAmelCase    :  List[str]										= pharmacy_price['price']

            print(F"""Pharmacy: {name} Price: {price}""")
    else:
        print(F"""No results found for {drug_name}""")


