




"""simple docstring"""

import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a         =      get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
       a         =      json.load(f)





@require_torch
class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	int		):
       return FSMTTokenizer.from_pretrained(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	int		):
       _A        =       FSMTForConditionalGeneration.from_pretrained(_UpperCAmelCase		).to(_UpperCAmelCase		)
       if torch_device == "cuda":
             model.half()
       return model



 @parameterized.expand(
     [
         ['en-ru', 26.0],
         ['ru-en', 22.0],
         ['en-de', 22.0],
         ['de-en', 29.0],
     ]		)
 @slow
 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	List[Any]		):
       # note: this test is not testing the best performance since it only evals a small batch
       # but it should be enough to detect a regression in the output quality
       _A        =       F'''facebook/wmt19-{pair}'''
       _A        =       self.get_tokenizer(_UpperCAmelCase		)
       _A        =       self.get_model(_UpperCAmelCase		)

       _A        =       bleu_data[pair]['src']
       _A        =       bleu_data[pair]['tgt']

       _A        =       tokenizer(_UpperCAmelCase   ,							return_tensors='pt'   ,							truncation=_UpperCAmelCase   ,							padding='longest'		).to(_UpperCAmelCase		)
       _A        =       model.generate(
           input_ids=batch.input_ids   ,							num_beams=8   ,							)
       _A        =       tokenizer.batch_decode(
           _UpperCAmelCase   ,							skip_special_tokens=_UpperCAmelCase   ,							clean_up_tokenization_spaces=_UpperCAmelCase		)
       _A        =       calculate_bleu(_UpperCAmelCase   ,							_UpperCAmelCase		)
       print(_UpperCAmelCase		)
       self.assertGreaterEqual(scores['bleu']   ,							_UpperCAmelCase		)







"""simple docstring"""

import argparse
from collections import defaultdict

import yaml


a         =      '''docs/source/en/_toctree.yml'''






def 						_snake_case					( _snake_case							: List[Any]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       defaultdict(_snake_case							)
      _A        =       []
      _A        =       []
      for doc in doc_list:
            if "local" in doc:
                  counts[doc["local"]] += 1

            if doc["title"].lower() == "overview":
                  overview_doc.append({'local': doc['local'], 'title': doc['title']}							)
            else:
                  new_doc_list.append(_snake_case							)

      _A        =       new_doc_list
      _A        =       [key for key, value in counts.items() if value > 1]

      _A        =       []
      for duplicate_key in duplicates:
            _A        =       list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}							)
            if len(_snake_case							) > 1:
                  raise ValueError(
                      F'''{duplicate_key} is present several times in the documentation table of content at '''
                      '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                      'others.'							)
            # Only add this once
            new_doc.append({'local': duplicate_key, 'title': titles[0]}							)

      # Add none duplicate-keys
      new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]							)
      _A        =       sorted(_snake_case    ,  key=lambda _snake_case							: s["title"].lower()							)

      # "overview" gets special treatment and is always first
      if len(_snake_case							) > 1:
            raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'							)

      overview_doc.extend(_snake_case							)

      # Sort
      return overview_doc






def 						_snake_case					( _snake_case							: Tuple=False							)   -> List[Any]:






      '''simple docstring'''


      with open(_snake_case    ,  encoding='utf-8'							) as f:
            _A        =       yaml.safe_load(f.read()							)

      # Get to the API doc
      _A        =       0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      _A        =       content[api_idx]['sections']

      # Then to the model doc
      _A        =       0
      while api_doc[scheduler_idx]["title"] != "Schedulers":
            scheduler_idx += 1

      _A        =       api_doc[scheduler_idx]['sections']
      _A        =       clean_doc_toc(_snake_case							)

      _A        =       False
      if new_scheduler_doc != scheduler_doc:
            _A        =       True
            if overwrite:
                  _A        =       new_scheduler_doc

      if diff:
            if overwrite:
                  _A        =       api_doc
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
                        f.write(yaml.dump(_snake_case    ,  allow_unicode=_snake_case							)							)
            else:
                  raise ValueError(
                      'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)






def 						_snake_case					( _snake_case							: str=False							)   -> Union[str, Any]:






      '''simple docstring'''


      with open(_snake_case    ,  encoding='utf-8'							) as f:
            _A        =       yaml.safe_load(f.read()							)

      # Get to the API doc
      _A        =       0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      _A        =       content[api_idx]['sections']

      # Then to the model doc
      _A        =       0
      while api_doc[pipeline_idx]["title"] != "Pipelines":
            pipeline_idx += 1

      _A        =       False
      _A        =       api_doc[pipeline_idx]['sections']
      _A        =       []

      # sort sub pipeline docs
      for pipeline_doc in pipeline_docs:
            if "section" in pipeline_doc:
                  _A        =       pipeline_doc['section']
                  _A        =       clean_doc_toc(_snake_case							)
                  if overwrite:
                        _A        =       new_sub_pipeline_doc
            new_pipeline_docs.append(_snake_case							)

      # sort overall pipeline doc
      _A        =       clean_doc_toc(_snake_case							)

      if new_pipeline_docs != pipeline_docs:
            _A        =       True
            if overwrite:
                  _A        =       new_pipeline_docs

      if diff:
            if overwrite:
                  _A        =       api_doc
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
                        f.write(yaml.dump(_snake_case    ,  allow_unicode=_snake_case							)							)
            else:
                  raise ValueError(
                      'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)


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

       check_scheduler_doc(args.fix_and_overwrite)
       check_pipeline_doc(args.fix_and_overwrite)

