from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE__		:       int       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       Any       						=       {
    "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    snake_case       (   UpperCamelCase_    ):
  lowercase_								=  'luke'




  def __init__(   self  :						Optional[int]  ,			a_  :						Tuple=5_0267  ,			a_  :						Any=50_0000  ,			a_  :						Dict=768  ,			a_  :						Union[str, Any]=256  ,			a_  :						Dict=12  ,			a_  :						Any=12  ,			a_  :						List[str]=3072  ,			a_  :						List[Any]="gelu"  ,			a_  :						int=0.1  ,			a_  :						int=0.1  ,			a_  :						Tuple=512  ,			a_  :						Dict=2  ,			a_  :						List[Any]=0.02  ,			a_  :						List[str]=1e-1_2  ,			a_  :						Any=True  ,			a_  :						List[Any]=None  ,			a_  :						Any=1  ,			a_  :						Optional[Any]=0  ,			a_  :						Optional[Any]=2  ,			**a_  :						Union[str, Any]  ,			)->							Tuple:




       """simple docstring"""
       super().__init__(pad_token_id=a_  ,			bos_token_id=a_  ,			eos_token_id=a_  ,			**a_					)

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








# 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

lowerCamelCase		       =      """facebook/wmt19-en-de"""
lowerCamelCase		       =      FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
lowerCamelCase		       =      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,
    )
)

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
lowerCamelCase		       =      """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


