




import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_=5						)       ->	str:
       # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
       assert masked_input.count('''<mask>'''						) == 1
       A__							=   torch.tensor(tokenizer.encode(lowercase__			,    add_special_tokens=lowercase__						)						).unsqueeze(0						)  # Batch size 1
       A__							=   model(lowercase__						)[0]  # The last hidden-state is the first element of the output tuple
       A__							=   (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
       A__							=   logits[0, masked_index, :]
       A__							=   logits.softmax(dim=0						)
       A__							=   prob.topk(k=lowercase__			,    dim=0						)
       A__							=   ' '.join(
           [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(lowercase__						)						)]						)
       A__							=   tokenizer.mask_token
       A__							=   []
       for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''						)						):
              A__							=   predicted_token_bpe.replace('''\u2581'''			,    ''' '''						)
              if " {0}".format(lowercase__						) in masked_input:
                     topk_filled_outputs.append(
                         (
                             masked_input.replace(''' {0}'''.format(lowercase__						)			,    lowercase__						),
                             values[index].item(),
                             predicted_token,
                         )						)
              else:
                     topk_filled_outputs.append(
                         (
                             masked_input.replace(lowercase__			,    lowercase__						),
                             values[index].item(),
                             predicted_token,
                         )						)
       return topk_filled_outputs


_lowerCamelCase     :		Optional[Any]								=       CamembertTokenizer.from_pretrained("""camembert-base""")
_lowerCamelCase     :		List[Any]								=       CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

_lowerCamelCase     :		Union[str, Any]								=       """Le camembert est <mask> :)"""
print(fill_mask(masked_input, model, tokenizer, topk=3))








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

# Test
_lowerCamelCase     :		int								=       tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_lowerCamelCase     :		int								=       tiny_model(**batch)

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

# Save
_lowerCamelCase     :		str								=       """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


