import json,hashlib,re,unicodedata,collections,time
from pathlib import Path
from datasets import load_dataset,Dataset
from huggingface_hub import HfApi
api=HfApi();source='Helsinki-NLP/opus-100';revision=api.dataset_info(source).sha
out=Path('/tmp/AstheralDataset');(out/'data').mkdir(parents=True,exist_ok=True)
langs={'de':'German','fr':'French','es':'Spanish','it':'Italian','pt':'Portuguese'}
seen_values=set();ids=set();splits={'train':[],'validation':[],'test':[]};rejected=collections.Counter()
def normalize(s):
 s=unicodedata.normalize('NFKC',s).casefold()
 return ' '.join(''.join(c if c.isalnum() else ' ' for c in s).split())
for split,n in [('test',50),('validation',50),('train',900)]:
 for lang,name in langs.items():
  config='-'.join(sorted(['en',lang]));ds=load_dataset(source,config,split=split,revision=revision).shuffle(seed=20261113)
  added=0
  for index,row in enumerate(ds):
   a=row['translation']['en'].strip();b=row['translation'][lang].strip()
   if not(20<=len(a)<=500 and 12<=len(b)<=600) or len(a.split())<4 or len(b.split())<3:
    rejected['length']+=1;continue
   if any(re.search(r'lorem ipsum|placeholder|\bTODO\b|https?://|<[^>]+>|\{\{|\ufffd',s,re.I) for s in [a,b]):
    rejected['non_sentence_content']+=1;continue
   x,y=normalize(a),normalize(b)
   if x==y or x in seen_values or y in seen_values:
    rejected['duplicate_or_identical_text']+=1;continue
   uid=hashlib.sha256((lang+'\\0'+x+'\\0'+y).encode()).hexdigest()
   assert uid not in ids
   ids.add(uid);seen_values.update([x,y])
   record={'id':uid,'source_language':'en','target_language':lang,'source_text':a,'target_text':b,'instruction':'Translate from English to '+name+'. Return only the translation.','source_dataset':source,'source_config':config,'source_split':split,'source_revision':revision,'source_row_index':int(ds._indices.column(0)[index].as_py()) if ds._indices is not None else index}
   splits[split].append(record);added+=1
   if added==n:break
  assert added==n,(lang,split,added)
  print('COLLECTED',split,lang,added,flush=True)
for split,rows in splits.items():
 Dataset.from_list(rows).shuffle(seed=42).to_parquet(str(out/'data'/f'{split}.parquet'))
total=sum(map(len,splits.values()));assert total==5000 and len(ids)==5000 and len(seen_values)==10000
counter={split:dict(collections.Counter(r['target_language'] for r in rows)) for split,rows in splits.items()}
manifest={'name':'AstheralDataset','version':'1.0','total_examples':total,'unique_example_ids':len(ids),'unique_normalized_text_values':len(seen_values),'split_counts':{k:len(v) for k,v in splits.items()},'language_counts':counter,'source_dataset':source,'source_revision':revision,'normalization':'Unicode NFKC, casefold, punctuation removed, whitespace collapsed','deduplication':'Global source/target text uniqueness across all languages and splits; identical source-target pairs excluded','rejected':dict(rejected),'file_sha256':{p.name:hashlib.sha256(p.read_bytes()).hexdigest() for p in (out/'data').glob('*.parquet')}}
(out/'manifest.json').write_text(json.dumps(manifest,indent=2))
(out/'build_dataset.py').write_text(Path(__file__).read_text())
readme='''---
pretty_name: AstheralDataset
language:
- en
- de
- fr
- es
- it
- pt
task_categories:
- translation
size_categories:
- 1K<n<10K
license: other
license_name: source-corpus-terms
license_link: https://huggingface.co/datasets/Helsinki-NLP/opus-100
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train.parquet
  - split: validation
    path: data/validation.parquet
  - split: test
    path: data/test.parquet
---
# AstheralDataset

**5,000 real English translation pairs**, curated by SEE7 for German, French, Spanish, Italian and Portuguese.

## Contents

| Target | Train | Validation | Test | Total |
| --- | ---: | ---: | ---: | ---: |
| German | 900 | 50 | 50 | 1,000 |
| French | 900 | 50 | 50 | 1,000 |
| Spanish | 900 | 50 | 50 | 1,000 |
| Italian | 900 | 50 | 50 | 1,000 |
| Portuguese | 900 | 50 | 50 | 1,000 |
| **Total** | **4,500** | **250** | **250** | **5,000** |

## Record fields

Each row contains a unique SHA-256 ID, English source text, translated target text, source and target language codes, a translation instruction, and the original dataset/config/split/revision/row index.

## Curation

Text is drawn from real parallel sentences in [OPUS-100](https://huggingface.co/datasets/Helsinki-NLP/opus-100). Selection preserves the original train, validation and test split membership. All 10,000 text values are globally unique after Unicode normalization, case folding, punctuation removal and whitespace normalization. Source-target identity, short fragments, markup and placeholder patterns are filtered. No model-generated translations are included.

The complete build script, exact source revision, split counts, rejection counts and Parquet checksums are published alongside the data.

## Load

```python
from datasets import load_dataset

dataset = load_dataset("SEE7/AstheralDataset")
example = dataset["train"][0]
print(example["source_text"])
print(example["target_text"])
```

## Provenance and attribution

Source: Helsinki-NLP/opus-100, revision '''+revision+'''.
OPUS-100: Zhang et al. (2020), “Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation.” Original parallel text comes from the OPUS collection. The original source corpus terms remain applicable.

Curated dataset repository: SEE7/AstheralDataset.
'''
(out/'README.md').write_text(readme)
api.upload_folder(repo_id='SEE7/AstheralDataset',repo_type='dataset',folder_path=str(out),commit_message='Publish 5000 real globally deduplicated translation pairs with provenance and checksums')
# Download the published splits through the public dataset interface and validate the real release.
published=load_dataset('SEE7/AstheralDataset')
assert {k:len(v) for k,v in published.items()}=={'train':4500,'validation':250,'test':250}
values=[];row_ids=[]
for split in published:
 for row in published[split]:
  values.extend([normalize(row['source_text']),normalize(row['target_text'])]);row_ids.append(row['id'])
assert len(set(values))==10000 and len(set(row_ids))==5000
print('PUBLISHED_VERIFIED',json.dumps(manifest),flush=True)