





'''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()
_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)


def 	__a(SCREAMING_SNAKE_CASE_  :					Dict		,       SCREAMING_SNAKE_CASE_  :					Dict    ):



				'''simple docstring'''





				_lowerCAmelCase				=      RobertaPreLayerNormConfig.from_pretrained(
				    __snake_case		,       architectures=["RobertaPreLayerNormForMaskedLM"]    )

				# convert state_dict
				_lowerCAmelCase				=      torch.load(hf_hub_download(repo_id=__snake_case		,       filename="pytorch_model.bin"    )    )
				_lowerCAmelCase				=      {}
				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."    ):
												_lowerCAmelCase				=      "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

								_lowerCAmelCase				=      tensor_value

				_lowerCAmelCase				=      RobertaPreLayerNormForMaskedLM.from_pretrained(
				    pretrained_model_name_or_path=__snake_case		,       config=__snake_case		,       state_dict=__snake_case    )
				model.save_pretrained(__snake_case    )

				# convert tokenizer
				_lowerCAmelCase				=      AutoTokenizer.from_pretrained(__snake_case    )
				tokenizer.save_pretrained(__snake_case    )


if __name__ == "__main__":
							_SCREAMING_SNAKE_CASE	 =							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."
							)
							_SCREAMING_SNAKE_CASE	 =							parser.parse_args()
							convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)









'''simple docstring'''


import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							"T5Config"


def 	__a(SCREAMING_SNAKE_CASE_  :					jnp.array		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      jnp.zeros_like(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      jnp.where(shifted_input_ids == -100		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    return shifted_input_ids

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Any         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Dict         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[Any]         =					"mt5"
 __lowerCamelCase						:							str         =					MTaConfig


