




'''simple docstring'''


import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCAmelCase_ 		=      "src/diffusers"

# Matches is_xxx_available()
UpperCAmelCase_ 		=      re.compile(r"is\_([a-z_]*)_available\(\)")
# Matches from xxx import bla
UpperCAmelCase_ 		=      re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")


UpperCAmelCase_ 		=      "\n{0} = None\n"

UpperCAmelCase_ 		=      "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


UpperCAmelCase_ 		=      "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"


def 						SCREAMING_SNAKE_CASE       (      a_	:							Any			):
  __a			=					_re_backend.findall(a_			)
  if len(a_			) == 0:
    return None

  return "_and_".join(a_			)


def 						SCREAMING_SNAKE_CASE       (      ):
  with open(os.path.join(a_				,					'__init__.py'			)				,					'r'				,					encoding='utf-8'				,					newline='\n'			) as f:
    __a			=					f.readlines()

  # Get to the point we do the actual imports for type checking
  __a			=					0
  __a			=					{}
  # Go through the end of the file
  while line_index < len(a_			):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    __a			=					find_backend(lines[line_index]			)
    if backend is not None:
      while not lines[line_index].startswith('else:'			):
        line_index += 1
      line_index += 1
      __a			=					[]
      # Until we unindent, add backend objects to the list
      while line_index < len(a_			) and len(lines[line_index]			) > 1:
        __a			=					lines[line_index]
        __a			=					_re_single_line_import.search(a_			)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(', '			)			)
        elif line.startswith(' ' * 8			):
          objects.append(line[8:-2]			)
        line_index += 1

      if len(a_			) > 0:
        __a			=					objects
    else:
      line_index += 1

  return backend_specific_objects


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[Any]				,					a_	:							str			):
  if name.isupper():
    return DUMMY_CONSTANT.format(a_			)
  elif name.islower():
    return DUMMY_FUNCTION.format(a_				,					a_			)
  else:
    return DUMMY_CLASS.format(a_				,					a_			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							Union[str, Any]=None			):
  if backend_specific_objects is None:
    __a			=					read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  __a			=					{}

  for backend, objects in backend_specific_objects.items():
    __a			=					'[' + ', '.join(f"\"{b}\"" for b in backend.split('_and_'			)			) + ']'
    __a			=					'# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(a_				,					a_			) for o in objects]			)
    __a			=					dummy_file

  return dummy_files


def 						SCREAMING_SNAKE_CASE       (      a_	:							Union[str, Any]=False			):
  __a			=					create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  __a			=					{'torch': 'pt'}

  # Locate actual dummy modules and read their content.
  __a			=					os.path.join(a_				,					'utils'			)
  __a			=					{
      backend: os.path.join(a_				,					f"dummy_{short_names.get(a_				,					a_			)}_objects.py"			)
      for backend in dummy_files.keys()
  }

  __a			=					{}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(a_			):
      with open(a_				,					'r'				,					encoding='utf-8'				,					newline='\n'			) as f:
        __a			=					f.read()
    else:
      __a			=					''

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            f"Updating diffusers.utils.dummy_{short_names.get(a_				,					a_			)}_objects.py as the main "
            '__init__ has new objects.'			)
        with open(dummy_file_paths[backend]				,					'w'				,					encoding='utf-8'				,					newline='\n'			) as f:
          f.write(dummy_files[backend]			)
      else:
        raise ValueError(
            'The main __init__ has objects that are not present in '
            f"diffusers.utils.dummy_{short_names.get(a_				,					a_			)}_objects.py. 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_dummies(args.fix_and_overwrite)









'''simple docstring'''


# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[Any]				,					a_	:							int				,					a_	:							Tuple				,					a_	:							Tuple			):
  __a			=					{
      '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]
  __a			=					{
      'wmt16-en-de-dist-12-1': [28.3, 27.52],
      'wmt16-en-de-dist-6-1': [27.4, 27.11],
      'wmt16-en-de-12-1': [26.9, 25.75],
  }
  __a			=					f"{src_lang}-{tgt_lang}"

  __a			=					f"\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 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\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\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   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.\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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -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/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\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=a_				,					exist_ok=a_			)
  __a			=					os.path.join(a_				,					'README.md'			)
  print(f"Generating {path}"			)
  with open(a_				,					'w'				,					encoding='utf-8'			) as f:
    f.write(a_			)


# 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 ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
  UpperCAmelCase_ 		=      model_cards_dir / "allenai" / model_name
  write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)



