

import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
     import optax
     from flax.training.common_utils import onehot

     from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
     from transformers.models.ta.modeling_flax_ta import shift_tokens_right


@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @slow
    def 						snake_case__     (      self :       List[Any]		)->    Optional[int]:





        '''simple docstring'''







        A__          =   FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small'		)
        A__          =   AutoTokenizer.from_pretrained('google/mt5-small'		)

        A__          =   tokenizer('Hello there',return_tensors='np'		).input_ids
        A__          =   tokenizer('Hi I am',return_tensors='np'		).input_ids

        A__          =   shift_tokens_right(lowercase_,model.config.pad_token_id,model.config.decoder_start_token_id		)

        A__          =   model(lowercase_,decoder_input_ids=lowercase_		).logits
        A__          =   optax.softmax_cross_entropy(lowercase_,onehot(lowercase_,logits.shape[-1]		)		).mean()

        A__          =   -(labels.shape[-1] * loss.item())

        A__          =   -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)




from typing import Union

import fire
import torch
from tqdm import tqdm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str = "cpu"    ,			SCREAMING_SNAKE_CASE__			:	Union[str, None] = None      )							->      None:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=SCREAMING_SNAKE_CASE__      )
    for k, v in tqdm(state_dict.items()      ):
        if not isinstance(SCREAMING_SNAKE_CASE__    ,			torch.Tensor      ):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'      )
        A__          =   v.half()
    if save_path is None:  # overwrite src_path
        A__          =   src_path
    torch.save(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     fire.Fire(convert)

