




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 				_lowerCAmelCase		(  unittest.TestCase						):




				@slow
				def      __a		(			self				)     ->   Dict:
											SCREAMING_SNAKE_CASE      :			int						  =FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''				)
											SCREAMING_SNAKE_CASE      :			str						  =AutoTokenizer.from_pretrained('''google/mt5-small'''				)

											SCREAMING_SNAKE_CASE      :			Any						  =tokenizer('''Hello there'''		,		return_tensors='''np'''				).input_ids
											SCREAMING_SNAKE_CASE      :			Optional[Any]						  =tokenizer('''Hi I am'''		,		return_tensors='''np'''				).input_ids

											SCREAMING_SNAKE_CASE      :			Any						  =shift_tokens_right(lowerCAmelCase__		,		model.config.pad_token_id		,		model.config.decoder_start_token_id				)

											SCREAMING_SNAKE_CASE      :			Optional[Any]						  =model(lowerCAmelCase__		,		decoder_input_ids=lowerCAmelCase__				).logits
											SCREAMING_SNAKE_CASE      :			int						  =optax.softmax_cross_entropy(lowerCAmelCase__		,		onehot(lowerCAmelCase__		,		logits.shape[-1]				)				).mean()

											SCREAMING_SNAKE_CASE      :			Union[str, Any]						  =-(labels.shape[-1] * loss.item())

											SCREAMING_SNAKE_CASE      :			Union[str, Any]						  =-84.9127
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)













"""simple docstring"""




import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a		:					Optional[int]	      =     logging.get_logger(__name__)






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       str   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							RobertaPreLayerNormConfig.from_pretrained(
	    _lowercase       ,			architectures=["RobertaPreLayerNormForMaskedLM"]   )

	# convert state_dict
	a :						Any      =							torch.load(hf_hub_download(repo_id=_lowercase       ,			filename="pytorch_model.bin"   )   )
	a :						int      =							{}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("roberta."   ):
			a :						List[Any]      =							"roberta_prelayernorm." + tensor_key[len("roberta."   ) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(".self.LayerNorm.weight"   ) or tensor_key.endswith(".self.LayerNorm.bias"   ):
			continue

		a :						Dict      =							tensor_value

	a :						Optional[Any]      =							RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=_lowercase       ,			config=_lowercase       ,			state_dict=_lowercase   )
	model.save_pretrained(_lowercase   )

	# convert tokenizer
	a :						List[Any]      =							AutoTokenizer.from_pretrained(_lowercase   )
	tokenizer.save_pretrained(_lowercase   )


if __name__ == "__main__":
		a		:					Any	      =     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint-repo''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		a		:					Tuple	      =     parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)


