


"""simple docstring"""


from __future__ import annotations

from typing import Any
class   _UpperCamelCase  (   A_		):
							'''simple docstring'''







							pass
class   _UpperCamelCase  :
							'''simple docstring'''







							def __init__(     self    ,   __a	):
										__lowerCAmelCase				    =			data
										__lowerCAmelCase				    =			None

							def __iter__(     self	):
										__lowerCAmelCase				    =			self
										__lowerCAmelCase				    =			[]
										while node:
													if node in visited:
																raise ContainsLoopError
													visited.append(_lowerCamelCase	)
													yield node.data
													__lowerCAmelCase				    =			node.next_node



							@property
							def 			snake_case				(     self	):
										try:
													list(self	)
													return False
										except ContainsLoopError:
													return True


if __name__ == "__main__":
	A     :   int			    =			Node(1)
	A     :   Union[str, Any]			    =			Node(2)
	A     :   Union[str, Any]			    =			Node(3)
	A     :   Tuple			    =			Node(4)
	print(root_node.has_loop)  # False
	A     :   str			    =			root_node.next_node
	print(root_node.has_loop)  # True

	A     :   List[str]			    =			Node(5)
	A     :   Dict			    =			Node(6)
	A     :   List[Any]			    =			Node(5)
	A     :   Any			    =			Node(6)
	print(root_node.has_loop)  # False

	A     :   Dict			    =			Node(1)
	print(root_node.has_loop)  # False






"""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

UpperCAmelCase__          = 'tiny-wmt19-en-ru'

# Build

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

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

 UpperCAmelCase__          = 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,
 )

UpperCAmelCase__          = FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    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,
)

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

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



