






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
    '''studio-ousia/luke-large''': '''https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json''',
}







class     UpperCamelCase_ (__A  ):
	__magic_name__			   =       '''luke'''





	def __init__(   self      :				List[str]			,			lowerCAmelCase_      :				Tuple=50_267			,			lowerCAmelCase_      :				Dict=500_000			,			lowerCAmelCase_      :				int=768			,			lowerCAmelCase_      :				Optional[int]=256			,			lowerCAmelCase_      :				int=12			,			lowerCAmelCase_      :				Optional[Any]=12			,			lowerCAmelCase_      :				Optional[int]=3_072			,			lowerCAmelCase_      :				List[Any]="gelu"			,			lowerCAmelCase_      :				Optional[int]=0.1			,			lowerCAmelCase_      :				Tuple=0.1			,			lowerCAmelCase_      :				List[str]=512			,			lowerCAmelCase_      :				str=2			,			lowerCAmelCase_      :				List[Any]=0.0_2			,			lowerCAmelCase_      :				List[str]=1e-12			,			lowerCAmelCase_      :				Any=True			,			lowerCAmelCase_      :				Optional[Any]=None			,			lowerCAmelCase_      :				Tuple=1			,			lowerCAmelCase_      :				str=0			,			lowerCAmelCase_      :				Any=2			,			**lowerCAmelCase_      :				Dict			,			)		->    Optional[Any]:
				super().__init__(pad_token_id=lowerCAmelCase_			,			bos_token_id=lowerCAmelCase_			,			eos_token_id=lowerCAmelCase_			,			**lowerCAmelCase_		)

				UpperCAmelCase_  :				Tuple      =      vocab_size
				UpperCAmelCase_  :				str      =      entity_vocab_size
				UpperCAmelCase_  :				str      =      hidden_size
				UpperCAmelCase_  :				Any      =      entity_emb_size
				UpperCAmelCase_  :				Tuple      =      num_hidden_layers
				UpperCAmelCase_  :				Dict      =      num_attention_heads
				UpperCAmelCase_  :				Optional[Any]      =      hidden_act
				UpperCAmelCase_  :				Tuple      =      intermediate_size
				UpperCAmelCase_  :				Optional[int]      =      hidden_dropout_prob
				UpperCAmelCase_  :				Tuple      =      attention_probs_dropout_prob
				UpperCAmelCase_  :				Union[str, Any]      =      max_position_embeddings
				UpperCAmelCase_  :				Dict      =      type_vocab_size
				UpperCAmelCase_  :				str      =      initializer_range
				UpperCAmelCase_  :				Tuple      =      layer_norm_eps
				UpperCAmelCase_  :				Optional[Any]      =      use_entity_aware_attention
				UpperCAmelCase_  :				Optional[Any]      =      classifier_dropout











"""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

lowerCamelCase_       			=   '''tiny-wmt19-en-ru'''

# Build

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

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

			lowerCamelCase_       			=   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,
			)

lowerCamelCase_       			=   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,
)

lowerCamelCase_       			=   FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

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



