


import jax.numpy as jnp

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


_snake_case					:		Optional[Any]      =    logging.get_logger(__name__)

_snake_case					:		Union[str, Any]      =    "T5Config"







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      str										=       jnp.zeros_like(__lowerCamelCase     )
							__snake_case		:      int										=       shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]     )
							__snake_case		:      Optional[Any]										=       shifted_input_ids.at[:, 0].set(__lowerCamelCase     )

							__snake_case		:      Optional[int]										=       jnp.where(shifted_input_ids == -1_0_0							,     __lowerCamelCase							,     __lowerCamelCase     )
							return shifted_input_ids

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Any					  =  "mt5"
							__UpperCAmelCase   :							Tuple					  =  MTaConfig

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[Any]					  =  "mt5"
							__UpperCAmelCase   :							List[str]					  =  MTaConfig

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]					  =  "mt5"
							__UpperCAmelCase   :							Any					  =  MTaConfig





import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_snake_case					:		Union[str, Any]      =    ["small", "medium", "large"]

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







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Tuple										=       torch.load(__lowerCamelCase     )
							__snake_case		:      Dict										=       d.pop(__lowerCamelCase     )
							os.makedirs(__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							torch.save(__lowerCamelCase							,     os.path.join(__lowerCamelCase							,     __lowerCamelCase     )     )


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

