




# 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

_A     =			'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A     =			[
    '''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>''',
]
_A     =			dict(zip(vocab, range(len(vocab))))
_A     =			['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
						_A     =			Path(tmpdirname)
						_A     =			build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
						_A     =			build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
						_A     =			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))

						_A     =			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,
						)

_A     =			FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    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,
)

_A     =			FSMTForConditionalGeneration(config)
print(f"num of params {tiny_model.num_parameters()}")

# Test
_A     =			tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A     =			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










import argparse
from collections import defaultdict

import yaml


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





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  defaultdict(_A  )
						for doc in model_doc:
												counts[doc["local"]] += 1
						lowerCAmelCase_      			=  [key for key, value in counts.items() if value > 1]

						lowerCAmelCase_      			=  []
						for duplicate_key in duplicates:
												lowerCAmelCase_      			=  list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}  )
												if len(_A  ) > 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 model_doc if counts[doc['''local''']] == 1]  )

						# Sort
						return sorted(_A ,  key=lambda _A  : s["title"].lower()  )





def __UpperCamelCase			(       _A=False  ):
						with open(_A ,  encoding='''utf-8'''  ) as f:
												lowerCAmelCase_      			=  yaml.safe_load(f.read()  )

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

						# Then to the model doc
						lowerCAmelCase_      			=  0
						while api_doc[model_idx]["title"] != "Models":
												model_idx += 1

						lowerCAmelCase_      			=  api_doc[model_idx]['''sections''']

						lowerCAmelCase_      			=  [(idx, section) for idx, section in enumerate(_A  ) if '''sections''' in section]
						lowerCAmelCase_      			=  False
						for idx, modality_doc in modalities_docs:
												lowerCAmelCase_      			=  modality_doc['''sections''']
												lowerCAmelCase_      			=  clean_model_doc_toc(_A  )

												if old_modality_doc != new_modality_doc:
																		lowerCAmelCase_      			=  True
																		if overwrite:
																								lowerCAmelCase_      			=  new_modality_doc

						if diff:
												if overwrite:
																		lowerCAmelCase_      			=  model_doc
																		lowerCAmelCase_      			=  api_doc
																		with open(_A ,  '''w''' ,  encoding='''utf-8'''  ) as f:
																								f.write(yaml.dump(_A ,  allow_unicode=_A  )  )
												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_model_doc(args.fix_and_overwrite)




