


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


SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: int				   =					{
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      Optional[int]       					=     """visual_bert"""
       def __init__(   self, lowerCamelCase=3_05_22, lowerCamelCase=7_68, lowerCamelCase=5_12, lowerCamelCase=12, lowerCamelCase=12, lowerCamelCase=30_72, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=5_12, lowerCamelCase=2, lowerCamelCase=0.0_2, lowerCamelCase=1E-12, lowerCamelCase=False, lowerCamelCase=True, lowerCamelCase=1, lowerCamelCase=0, lowerCamelCase=2, **lowerCamelCase, )			->  Optional[int]:




           """simple docstring"""

           super().__init__(pad_token_id=lowerCamelCase, bos_token_id=lowerCamelCase, eos_token_id=lowerCamelCase, **lowerCamelCase)

           _lowercase			:				Optional[int] 						=						vocab_size
           _lowercase			:				Dict 						=						max_position_embeddings
           _lowercase			:				Any 						=						hidden_size
           _lowercase			:				Tuple 						=						visual_embedding_dim
           _lowercase			:				Optional[int] 						=						num_hidden_layers
           _lowercase			:				Union[str, Any] 						=						num_attention_heads
           _lowercase			:				Any 						=						intermediate_size
           _lowercase			:				List[str] 						=						hidden_act
           _lowercase			:				int 						=						hidden_dropout_prob
           _lowercase			:				List[str] 						=						attention_probs_dropout_prob
           _lowercase			:				List[str] 						=						initializer_range
           _lowercase			:				str 						=						type_vocab_size
           _lowercase			:				Union[str, Any] 						=						layer_norm_eps
           _lowercase			:				Any 						=						bypass_transformer
           _lowercase			:				Any 						=						special_visual_initialize




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

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

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

# Save
_A     :			List[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


