





"""simple docstring"""





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


__A											=		"""bert-base-cased"""
__A											=		"""google/pegasus-xsum"""
__A											=		[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__A											=		["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__A											=		"""patrickvonplaten/t5-tiny-random"""
__A											=		"""sshleifer/bart-tiny-random"""
__A											=		"""sshleifer/tiny-mbart"""
__A											=		"""sshleifer/tiny-marian-en-de"""
def  __A   (_SCREAMING_SNAKE_CASE     ,   _SCREAMING_SNAKE_CASE			)					->str:





  """simple docstring"""


  lowerCAmelCase__			:Tuple											=							'\n'.join(_SCREAMING_SNAKE_CASE			)
  Path(_SCREAMING_SNAKE_CASE			).open('w'			).writelines(_SCREAMING_SNAKE_CASE			)
def  __A   (_SCREAMING_SNAKE_CASE			)					->Any:





  """simple docstring"""


  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(_SCREAMING_SNAKE_CASE     ,   F"{split}.source"			)     ,   _SCREAMING_SNAKE_CASE			)
    _dump_articles(os.path.join(_SCREAMING_SNAKE_CASE     ,   F"{split}.target"			)     ,   _SCREAMING_SNAKE_CASE			)
  return tmp_dir






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       @parameterized.expand(
           [
               MBART_TINY,
               MARIAN_TINY,
               T5_TINY,
               BART_TINY,
               PEGASUS_XSUM,
           ]    , )
       @slow
       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Tuple											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:Optional[Any]											=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
         lowerCAmelCase__			:Optional[Any]											=							max(len(tokenizer.encode(__UpperCAmelCase      )      ) for a in ARTICLES      )
         lowerCAmelCase__			:int											=							max(len(tokenizer.encode(__UpperCAmelCase      )      ) for a in SUMMARIES      )
         lowerCAmelCase__			:Optional[Any]											=							4
         lowerCAmelCase__			:List[str]											=							8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         lowerCAmelCase__							,				lowerCAmelCase__			:Optional[Any]											=							'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
         lowerCAmelCase__			:List[Any]											=							SeqaSeqDataset(
             __UpperCAmelCase    , data_dir=__UpperCAmelCase    , type_path='train'    , max_source_length=__UpperCAmelCase    , max_target_length=__UpperCAmelCase    , src_lang=__UpperCAmelCase    , tgt_lang=__UpperCAmelCase    , )
         lowerCAmelCase__			:Union[str, Any]											=							DataLoader(__UpperCAmelCase    , batch_size=2    , collate_fn=train_dataset.collate_fn      )
         for batch in dataloader:
           assert isinstance(__UpperCAmelCase    , __UpperCAmelCase      )
           assert batch["attention_mask"].shape == batch["input_ids"].shape
           # show that articles were trimmed.
           assert batch["input_ids"].shape[1] == max_src_len
           # show that targets are the same len
           assert batch["labels"].shape[1] == max_tgt_len
           if tok_name != MBART_TINY:
             continue
           # check language codes in correct place
           lowerCAmelCase__			:Dict											=							shift_tokens_right(batch['labels']    , tokenizer.pad_token_id      )
           assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
           assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

           break  # No need to test every batch






       @parameterized.expand([BART_TINY, BERT_BASE_CASED]      )
       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:Tuple											=							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
         lowerCAmelCase__			:Any											=							max(len(tokenizer.encode(__UpperCAmelCase      )      ) for a in ARTICLES      )
         lowerCAmelCase__			:Any											=							max(len(tokenizer.encode(__UpperCAmelCase      )      ) for a in SUMMARIES      )
         lowerCAmelCase__			:Optional[Any]											=							4
         lowerCAmelCase__			:Optional[int]											=							LegacySeqaSeqDataset(
             __UpperCAmelCase    , data_dir=__UpperCAmelCase    , type_path='train'    , max_source_length=2_0    , max_target_length=__UpperCAmelCase    , )
         lowerCAmelCase__			:Dict											=							DataLoader(__UpperCAmelCase    , batch_size=2    , collate_fn=train_dataset.collate_fn      )
         for batch in dataloader:
           assert batch["attention_mask"].shape == batch["input_ids"].shape
           # show that articles were trimmed.
           assert batch["input_ids"].shape[1] == max_len_source
           assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
           # show that targets were truncated
           assert batch["labels"].shape[1] == trunc_target  # Truncated
           assert max_len_target > trunc_target  # Truncated
           break  # No need to test every batch






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'      )

         lowerCAmelCase__			:Tuple											=							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
         lowerCAmelCase__			:List[Any]											=							tmp_dir.joinpath('train.source'      ).open().readlines()
         lowerCAmelCase__			:Union[str, Any]											=							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
         pack_data_dir(__UpperCAmelCase    , __UpperCAmelCase    , 1_2_8    , __UpperCAmelCase      )
         lowerCAmelCase__			:Optional[Any]											=							{x.name for x in tmp_dir.iterdir()}
         lowerCAmelCase__			:Optional[int]											=							{x.name for x in save_dir.iterdir()}
         lowerCAmelCase__			:Tuple											=							save_dir.joinpath('train.source'      ).open().readlines()
         # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
         # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
         assert len(__UpperCAmelCase      ) < len(__UpperCAmelCase      )
         assert len(__UpperCAmelCase      ) == 1
         assert len(packed_examples[0]      ) == sum(len(__UpperCAmelCase      ) for x in orig_examples      )
         assert orig_paths == new_paths






       @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    , reason='This test requires fairseq'      )
       def      snake_case						(    self      ):



         '''simple docstring'''

         if not FAIRSEQ_AVAILABLE:
           return
         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:List[str]											=							self._get_dataset(max_len=6_4      )
         lowerCAmelCase__			:Tuple											=							6_4
         lowerCAmelCase__			:List[str]											=							ds.make_dynamic_sampler(__UpperCAmelCase    , required_batch_size_multiple=__UpperCAmelCase      )
         lowerCAmelCase__			:Union[str, Any]											=							[len(__UpperCAmelCase      ) for x in batch_sampler]
         assert len(set(__UpperCAmelCase      )      ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(__UpperCAmelCase      ) == len(__UpperCAmelCase      )  # no dropped or added examples
         lowerCAmelCase__			:Optional[Any]											=							DataLoader(__UpperCAmelCase    , batch_sampler=__UpperCAmelCase    , collate_fn=ds.collate_fn    , num_workers=2      )
         lowerCAmelCase__			:Optional[int]											=							[]
         lowerCAmelCase__			:Any											=							[]
         for batch in data_loader:
           lowerCAmelCase__			:int											=							batch['input_ids'].shape
           lowerCAmelCase__			:Optional[int]											=							src_shape[0]
           assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
           lowerCAmelCase__			:str											=							np.product(batch['input_ids'].shape      )
           num_src_per_batch.append(__UpperCAmelCase      )
           if num_src_tokens > (max_tokens * 1.1):
             failures.append(__UpperCAmelCase      )
         assert num_src_per_batch[0] == max(__UpperCAmelCase      )
         if failures:
           raise AssertionError(F"too many tokens in {len(__UpperCAmelCase      )} batches"      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:Any											=							self._get_dataset(max_len=5_1_2      )
         lowerCAmelCase__			:int											=							2
         lowerCAmelCase__			:int											=							ds.make_sortish_sampler(__UpperCAmelCase    , shuffle=__UpperCAmelCase      )

         lowerCAmelCase__			:Union[str, Any]											=							DataLoader(__UpperCAmelCase    , batch_size=__UpperCAmelCase    , collate_fn=ds.collate_fn    , num_workers=2      )
         lowerCAmelCase__			:List[Any]											=							DataLoader(__UpperCAmelCase    , batch_size=__UpperCAmelCase    , collate_fn=ds.collate_fn    , num_workers=2    , sampler=__UpperCAmelCase      )

         lowerCAmelCase__			:List[Any]											=							tokenizer.pad_token_id

         def count_pad_tokens(__UpperCAmelCase    , __UpperCAmelCase="input_ids"      ):
           return [batch[k].eq(__UpperCAmelCase      ).sum().item() for batch in data_loader]

         assert sum(count_pad_tokens(__UpperCAmelCase    , k='labels'      )      ) < sum(count_pad_tokens(__UpperCAmelCase    , k='labels'      )      )
         assert sum(count_pad_tokens(__UpperCAmelCase      )      ) < sum(count_pad_tokens(__UpperCAmelCase      )      )
         assert len(__UpperCAmelCase      ) == len(__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase=1_0_0_0    , __UpperCAmelCase=1_2_8      ):



         '''simple docstring'''

         if os.getenv('USE_REAL_DATA'    , __UpperCAmelCase      ):
           lowerCAmelCase__			:Optional[int]											=							'examples/seq2seq/wmt_en_ro'
           lowerCAmelCase__			:Optional[Any]											=							max_len * 2 * 6_4
           if not Path(__UpperCAmelCase      ).joinpath('train.len'      ).exists():
             save_len_file(__UpperCAmelCase    , __UpperCAmelCase      )
         else:
           lowerCAmelCase__			:List[Any]											=							'examples/seq2seq/test_data/wmt_en_ro'
           lowerCAmelCase__			:Optional[int]											=							max_len * 4
           save_len_file(__UpperCAmelCase    , __UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							SeqaSeqDataset(
             __UpperCAmelCase    , data_dir=__UpperCAmelCase    , type_path='train'    , max_source_length=__UpperCAmelCase    , max_target_length=__UpperCAmelCase    , n_obs=__UpperCAmelCase    , )
         return ds, max_tokens, tokenizer






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__			:str											=							self._get_dataset()
         lowerCAmelCase__			:Dict											=							set(DistributedSortishSampler(__UpperCAmelCase    , 2_5_6    , num_replicas=2    , rank=0    , add_extra_examples=__UpperCAmelCase      )      )
         lowerCAmelCase__			:Optional[Any]											=							set(DistributedSortishSampler(__UpperCAmelCase    , 2_5_6    , num_replicas=2    , rank=1    , add_extra_examples=__UpperCAmelCase      )      )
         assert idsa.intersection(__UpperCAmelCase      ) == set()






       @parameterized.expand(
           [
               MBART_TINY,
               MARIAN_TINY,
               T5_TINY,
               BART_TINY,
               PEGASUS_XSUM,
           ]    , )
       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[str]											=							AutoTokenizer.from_pretrained(__UpperCAmelCase    , use_fast=__UpperCAmelCase      )
         if tok_name == MBART_TINY:
           lowerCAmelCase__			:Tuple											=							SeqaSeqDataset(
               __UpperCAmelCase    , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )    , type_path='train'    , max_source_length=4    , max_target_length=8    , src_lang='EN'    , tgt_lang='FR'    , )
           lowerCAmelCase__			:Any											=							train_dataset.dataset_kwargs
           assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
           lowerCAmelCase__			:str											=							SeqaSeqDataset(
               __UpperCAmelCase    , data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )    , type_path='train'    , max_source_length=4    , max_target_length=8    , )
           lowerCAmelCase__			:int											=							train_dataset.dataset_kwargs
           assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
           assert len(__UpperCAmelCase      ) == 1 if tok_name == BART_TINY else len(__UpperCAmelCase      ) == 0







"""simple docstring"""





from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml






class 					_lowerCAmelCase   (     yaml.SafeLoader       ):
       """simple docstring"""




       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							[self.constructed_objects[key_node] for key_node, _ in node.value]
         lowerCAmelCase__			:str											=							[tuple(__UpperCAmelCase      ) if isinstance(__UpperCAmelCase    , __UpperCAmelCase      ) else key for key in keys]
         lowerCAmelCase__			:Optional[int]											=							Counter(__UpperCAmelCase      )
         lowerCAmelCase__			:int											=							[key for key in counter if counter[key] > 1]
         if duplicate_keys:
           raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}"      )






       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase=False      ):



         '''simple docstring'''

         lowerCAmelCase__			:Union[str, Any]											=							super().construct_mapping(__UpperCAmelCase    , deep=__UpperCAmelCase      )
         self._check_no_duplicates_on_constructed_node(__UpperCAmelCase      )
         return mapping






def  __A   (_SCREAMING_SNAKE_CASE			)					->Tuple[Optional[str], str]:





  """simple docstring"""


  lowerCAmelCase__			:Optional[Any]											=							list(readme_content.splitlines()			)
  if full_content and full_content[0] == "---" and "---" in full_content[1:]:
    lowerCAmelCase__			:Optional[int]											=							full_content[1:].index('---'			) + 1
    lowerCAmelCase__			:Union[str, Any]											=							'\n'.join(full_content[1:sep_idx]			)
    return yamlblock, "\n".join(full_content[sep_idx + 1 :]			)

  return None, "\n".join(_SCREAMING_SNAKE_CASE			)






class 					_lowerCAmelCase   (     a       ):
       """simple docstring"""




       __magic_name__     :List[str]		  =					{"""train_eval_index"""}  # train-eval-index in the YAML metadata






       @classmethod
       def      snake_case						(    cls    , __UpperCAmelCase      ):



         '''simple docstring'''

         with open(__UpperCAmelCase    , encoding='utf-8'      ) as readme_file:
           lowerCAmelCase__							,				lowerCAmelCase__			:Union[str, Any]											=							_split_yaml_from_readme(readme_file.read()      )
         if yaml_string is not None:
           return cls.from_yaml_string(__UpperCAmelCase      )
         else:
           return cls()






       def      snake_case						(    self    , __UpperCAmelCase      ):



         '''simple docstring'''

         if path.exists():
           with open(__UpperCAmelCase    , encoding='utf-8'      ) as readme_file:
             lowerCAmelCase__			:Optional[Any]											=							readme_file.read()
         else:
           lowerCAmelCase__			:Union[str, Any]											=							None
         lowerCAmelCase__			:Union[str, Any]											=							self._to_readme(__UpperCAmelCase      )
         with open(__UpperCAmelCase    , 'w'    , encoding='utf-8'      ) as readme_file:
           readme_file.write(__UpperCAmelCase      )






       def      snake_case						(    self    , __UpperCAmelCase = None      ):



         '''simple docstring'''

         if readme_content is not None:
           lowerCAmelCase__							,				lowerCAmelCase__			:Optional[int]											=							_split_yaml_from_readme(__UpperCAmelCase      )
           lowerCAmelCase__			:Optional[Any]											=							'---\n' + self.to_yaml_string() + '---\n' + content
         else:
           lowerCAmelCase__			:str											=							'---\n' + self.to_yaml_string() + '---\n'
         return full_content






       @classmethod
       def      snake_case						(    cls    , __UpperCAmelCase      ):



         '''simple docstring'''

         lowerCAmelCase__			:Dict											=							yaml.load(__UpperCAmelCase    , Loader=_NoDuplicateSafeLoader      ) or {}

         # Convert the YAML keys to DatasetMetadata fields
         lowerCAmelCase__			:int											=							{
             (key.replace('-'    , '_'      ) if key.replace('-'    , '_'      ) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**__UpperCAmelCase      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         return yaml.safe_dump(
             {
                 (key.replace('_'    , '-'      ) if key in self._FIELDS_WITH_DASHES else key): value
                 for key, value in self.items()
             }    , sort_keys=__UpperCAmelCase    , allow_unicode=__UpperCAmelCase    , encoding='utf-8'    , ).decode('utf-8'      )






__A											=		{
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
  from argparse import ArgumentParser

  __A											=		ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
  ap.add_argument("""readme_filepath""")
  __A											=		ap.parse_args()

  __A											=		Path(args.readme_filepath)
  __A											=		DatasetMetadata.from_readme(readme_filepath)
  print(dataset_metadata)
  dataset_metadata.to_readme(readme_filepath)
