






'''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 model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_A	:			str         ='''facebook/wmt19-en-de'''
_A	:			List[Any]         =FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A	:			Any         =FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        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	:			List[Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
_A	:			Tuple         =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A	:			Optional[int]         =tiny_model(**batch)

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

# Save
_A	:			Any         ='''tiny-wmt19-en-de'''
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-de









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


_A	:			Dict         ={'''tokenization_herbert''': ['''HerbertTokenizer''']}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			Optional[int]         =['''HerbertTokenizerFast''']


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

			_A	:			Optional[int]         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

