





"""simple docstring"""
from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Tuple = "isbn/0140328726"			):
 lowerCAmelCase    				= olid.strip().strip('/'			)  # Remove leading/trailing whitespace & slashes
 if new_olid.count('/'			) != 1:
  lowerCAmelCase    				= F'{olid} is not a valid Open Library olid'
  raise ValueError(lowerCAmelCase_			)
 return requests.get(F'https://openlibrary.org/{new_olid}.json'			).json()

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Union[str, Any]			):
 lowerCAmelCase    				= {
     'title': 'Title',
     'publish_date': 'Publish date',
     'authors': 'Authors',
     'number_of_pages': 'Number of pages:',
     'first_sentence': 'First sentence',
     'isbn_10': 'ISBN (10)',
     'isbn_13': 'ISBN (13)',
 }
 lowerCAmelCase    				= {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
 lowerCAmelCase    				= [
     get_openlibrary_data(author['key']			)['name'] for author in data['Authors']
 ]
 lowerCAmelCase    				= data['First sentence']['value']
 for key, value in data.items():
  if isinstance(lowerCAmelCase_  ,     lowerCAmelCase_			):
   lowerCAmelCase    				= ', '.join(lowerCAmelCase_			)
 return data


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       while True:
              __UpperCamelCase     :						List[Any]						       =  input('''\nEnter the ISBN code to search (or \'quit\' to stop): ''').strip()
              if isbn.lower() in ("", "q", "quit", "exit", "stop"):
                     break

              if len(isbn) not in (10, 13) or not isbn.isdigit():
                     print(f'''Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.''')
                     continue

              print(f'''\nSearching Open Library for ISBN: {isbn}...\n''')

              try:
                     __UpperCamelCase     :						Tuple						       =  summarize_book(get_openlibrary_data(f'''isbn/{isbn}'''))
                     print('''\n'''.join(f'''{key}: {value}''' for key, value in book_summary.items()))
              except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
                     print(f'''Sorry, there are no results for ISBN: {isbn}.''')










"""simple docstring"""






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

snake_case   					=				'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
snake_case   					=				[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
snake_case   					=				dict(zip(vocab, range(len(vocab))))
snake_case   					=				['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
     snake_case   					=				Path(tmpdirname)
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
     snake_case   					=				build_dir / VOCAB_FILES_NAMES['''merges_file''']
     with open(src_vocab_file, '''w''') as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, '''w''') as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, '''w''') as fp:
          fp.write('''\n'''.join(merges))

     snake_case   					=				FSMTTokenizer(
         langs=['''en''', '''ru'''],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

snake_case   					=				FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

snake_case   					=				FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
snake_case   					=				tokenizer(['''Making tiny model'''], return_tensors='''pt''')
snake_case   					=				tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-ru





