



import re






def 			lowerCAmelCase__	(					lowerCamelCase_	:	str):





				'''simple docstring'''

				lowerCAmelCase__  :       List[Any]				=  re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''')
				if match := re.search(lowerCamelCase_ ,lowerCamelCase_):
								return match.string == phone
				return False


if __name__ == "__main__":
							print(indian_phone_validator('+918827897895'))






# 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       :	List[str]            ='tiny-wmt19-en-ru'

# Build

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

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =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       :	Union[str, Any]            =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       :	Dict            =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       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =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

