




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 			_UpperCamelCase      (				unittest.TestCase		):
      @slow
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			List[Any]:
       UpperCAmelCase__							 =						FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"      )
       UpperCAmelCase__							 =						AutoTokenizer.from_pretrained("google/mt5-small"      )

       UpperCAmelCase__							 =						tokenizer("Hello there" ,      return_tensors="np"      ).input_ids
       UpperCAmelCase__							 =						tokenizer("Hi I am" ,      return_tensors="np"      ).input_ids

       UpperCAmelCase__							 =						shift_tokens_right(lowerCamelCase ,      model.config.pad_token_id ,      model.config.decoder_start_token_id      )

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

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

       UpperCAmelCase__							 =						-84.91_27
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )











import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase						:   List[Any]		=   ["small", "medium", "large"]

_lowerCAmelCase						:   List[Any]		=   "lm_head.decoder.weight"
_lowerCAmelCase						:   Optional[int]		=   "lm_head.weight"



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						torch.load(_lowerCAmelCase	)
 UpperCAmelCase__							 =						d.pop(_lowerCAmelCase	)
 os.makedirs(_lowerCAmelCase						,							exist_ok=_lowerCAmelCase	)
 torch.save(_lowerCAmelCase						,							os.path.join(_lowerCAmelCase						,							_lowerCAmelCase	)	)


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





