






'''simple docstring'''







import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


__lowerCAmelCase									=   ['small', 'medium', 'large']

__lowerCAmelCase									=   'lm_head.decoder.weight'
__lowerCAmelCase									=   'lm_head.weight'





def 		_UpperCAmelCase (				__A				: str    ,		__A				: str   ):
				a_		:	Any									=   torch.load(__A   )
				a_		:	int									=   d.pop(__A   )
				os.makedirs(__A    ,		exist_ok=__A   )
				torch.save(__A    ,		os.path.join(__A    ,		__A   )   )


if __name__ == "__main__":
		__lowerCAmelCase									=   argparse.ArgumentParser()
		parser.add_argument('--dialogpt_path', default='.', type=str)
		__lowerCAmelCase									=   parser.parse_args()
		for MODEL in DIALOGPT_MODELS:
				__lowerCAmelCase									=   os.path.join(args.dialogpt_path, F"""{MODEL}_ft.pkl""")
				__lowerCAmelCase									=   F"""./DialoGPT-{MODEL}"""
				convert_dialogpt_checkpoint(
				    checkpoint_path,
				    pytorch_dump_folder_path,
				)












'''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

__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




