


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							Optional[int]					  =  ["image_processor", "tokenizer"]
							__UpperCAmelCase   :							Optional[int]					  =  "AutoImageProcessor"
							__UpperCAmelCase   :							int					  =  "AutoTokenizer"

							def __init__(							self	:  List[Any]   ,     lowerCamelCase	:  List[Any]   ,     lowerCamelCase	:  str    )     ->   List[str]:
														super().__init__(lowerCamelCase   ,     lowerCamelCase    )
														__snake_case		:      Union[str, Any]										=       self.image_processor

							def __call__(							self	:  List[Any]   ,     lowerCamelCase	:  Optional[int]=None   ,     lowerCamelCase	:  int=None   ,     lowerCamelCase	:  int=None   ,     **lowerCamelCase	:  Optional[int]    )     ->   int:

														if text is None and images is None:
																					raise ValueError("You have to specify either text or images. Both cannot be none."    )

														if text is not None:
																					__snake_case		:      Any										=       self.tokenizer(lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )

														if images is not None:
																					__snake_case		:      List[Any]										=       self.image_processor(lowerCamelCase   ,     return_tensors=lowerCamelCase   ,     **lowerCamelCase    )

														if text is not None and images is not None:
																					__snake_case		:      Any										=       image_features.pixel_values
																					return encoding
														elif text is not None:
																					return encoding
														else:
																					return BatchEncoding(data=dict(**lowerCamelCase    )   ,     tensor_type=lowerCamelCase    )

							def    __snake_case (							self	:  int   ,     *lowerCamelCase	:  int   ,     **lowerCamelCase	:  Any    )     ->   List[Any]:
														return self.tokenizer.batch_decode(*lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Union[str, Any]   ,     *lowerCamelCase	:  Dict   ,     **lowerCamelCase	:  List[str]    )     ->   Tuple:
														return self.tokenizer.decode(*lowerCamelCase   ,     **lowerCamelCase    )






							@property
							def    __snake_case (							self	:  List[str]    )     ->   Tuple:
														return ["input_ids", "attention_mask", "pixel_values"]





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,
						)

