






'''simple docstring'''







import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
							import torch




class 		UpperCamelCase__    (_snake_case			):




		'''simple docstring'''





		_UpperCamelCase				  =					'char'
		_UpperCamelCase				  =					'bpe'
		_UpperCamelCase				  =					'wp'



UpperCamelCase    :      int  			=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)




class 		UpperCamelCase__    (_snake_case			):




		'''simple docstring'''





		_UpperCamelCase				  =					['image_processor', 'char_tokenizer']
		_UpperCamelCase				  =					'ViTImageProcessor'
		_UpperCamelCase				  =					'MgpstrTokenizer'






		def __init__(      self   ,_lowerCAmelCase=None   ,_lowerCAmelCase=None   ,**_lowerCAmelCase       ):
								lowerCamelCase__         =  None
								if "feature_extractor" in kwargs:
														warnings.warn(
														    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
														    """ instead."""   ,_lowerCAmelCase   ,)
														lowerCamelCase__         =  kwargs.pop("""feature_extractor"""       )

								lowerCamelCase__         =  image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
														raise ValueError("""You need to specify an `image_processor`."""       )
								if tokenizer is None:
														raise ValueError("""You need to specify a `tokenizer`."""       )

								lowerCamelCase__         =  tokenizer
								lowerCamelCase__         =  AutoTokenizer.from_pretrained("""gpt2"""       )
								lowerCamelCase__         =  AutoTokenizer.from_pretrained("""bert-base-uncased"""       )

								super().__init__(_lowerCAmelCase   ,_lowerCAmelCase       )






		def __call__(      self   ,_lowerCAmelCase=None   ,_lowerCAmelCase=None   ,_lowerCAmelCase=None   ,**_lowerCAmelCase       ):

								if images is None and text is None:
														raise ValueError("""You need to specify either an `images` or `text` input to process."""       )

								if images is not None:
														lowerCamelCase__         =  self.image_processor(_lowerCAmelCase   ,return_tensors=_lowerCAmelCase   ,**_lowerCAmelCase       )
								if text is not None:
														lowerCamelCase__         =  self.char_tokenizer(_lowerCAmelCase   ,return_tensors=_lowerCAmelCase   ,**_lowerCAmelCase       )

								if text is None:
														return inputs
								elif images is None:
														return encodings
								else:
														lowerCamelCase__         =  encodings["""input_ids"""]
														return inputs






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__       ,			lowerCamelCase__       ,			lowerCamelCase__         =  sequences
								lowerCamelCase__         =  char_preds.size(0       )

								lowerCamelCase__       ,			lowerCamelCase__         =  self._decode_helper(_lowerCAmelCase   ,"""char"""       )
								lowerCamelCase__       ,			lowerCamelCase__         =  self._decode_helper(_lowerCAmelCase   ,"""bpe"""       )
								lowerCamelCase__       ,			lowerCamelCase__         =  self._decode_helper(_lowerCAmelCase   ,"""wp"""       )

								lowerCamelCase__         =  []
								lowerCamelCase__         =  []
								for i in range(_lowerCAmelCase       ):
														lowerCamelCase__         =  [char_scores[i], bpe_scores[i], wp_scores[i]]
														lowerCamelCase__         =  [char_strs[i], bpe_strs[i], wp_strs[i]]
														lowerCamelCase__         =  scores.index(max(_lowerCAmelCase       )       )
														final_strs.append(strs[max_score_index]       )
														final_scores.append(scores[max_score_index]       )

								lowerCamelCase__         =  {}
								lowerCamelCase__         =  final_strs
								lowerCamelCase__         =  final_scores
								lowerCamelCase__         =  char_strs
								lowerCamelCase__         =  bpe_strs
								lowerCamelCase__         =  wp_strs
								return out






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase       ):

								if format == DecodeType.CHARACTER:
														lowerCamelCase__         =  self.char_decode
														lowerCamelCase__         =  1
														lowerCamelCase__         =  """[s]"""
								elif format == DecodeType.BPE:
														lowerCamelCase__         =  self.bpe_decode
														lowerCamelCase__         =  2
														lowerCamelCase__         =  """#"""
								elif format == DecodeType.WORDPIECE:
														lowerCamelCase__         =  self.wp_decode
														lowerCamelCase__         =  1_02
														lowerCamelCase__         =  """[SEP]"""
								else:
														raise ValueError(F'''Format {format} is not supported.'''       )

								lowerCamelCase__       ,			lowerCamelCase__         =  [], []
								lowerCamelCase__         =  pred_logits.size(0       )
								lowerCamelCase__         =  pred_logits.size(1       )
								lowerCamelCase__       ,			lowerCamelCase__         =  pred_logits.topk(1   ,dim=-1   ,largest=_lowerCAmelCase   ,sorted=_lowerCAmelCase       )
								lowerCamelCase__         =  preds_index.view(-1   ,_lowerCAmelCase       )[:, 1:]
								lowerCamelCase__         =  decoder(_lowerCAmelCase       )
								lowerCamelCase__       ,			lowerCamelCase__         =  torch.nn.functional.softmax(_lowerCAmelCase   ,dim=2       ).max(dim=2       )
								lowerCamelCase__         =  preds_max_prob[:, 1:]

								for index in range(_lowerCAmelCase       ):
														lowerCamelCase__         =  preds_str[index].find(_lowerCAmelCase       )
														lowerCamelCase__         =  preds_str[index][:pred_eos]
														lowerCamelCase__         =  preds_index[index].cpu().tolist()
														lowerCamelCase__         =  pred_index.index(_lowerCAmelCase       ) if eos_token in pred_index else -1
														lowerCamelCase__         =  preds_max_prob[index][: pred_eos_index + 1]
														lowerCamelCase__         =  pred_max_prob.cumprod(dim=0       )[-1] if pred_max_prob.nelement() != 0 else 0.0
														dec_strs.append(_lowerCAmelCase       )
														conf_scores.append(_lowerCAmelCase       )

								return dec_strs, conf_scores






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  [seq.replace(""" """   ,""""""       ) for seq in self.char_tokenizer.batch_decode(_lowerCAmelCase       )]
								return decode_strs






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								return self.bpe_tokenizer.batch_decode(_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  [seq.replace(""" """   ,""""""       ) for seq in self.wp_tokenizer.batch_decode(_lowerCAmelCase       )]
								return decode_strs









'''simple docstring'''







import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


UpperCamelCase    :      Optional[Any]  			=		['small', 'medium', 'large']

UpperCamelCase    :      Dict  			=		'lm_head.decoder.weight'
UpperCamelCase    :      int  			=		'lm_head.weight'
def 						A__			(				__lowerCAmelCase		:			str   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase						)
						lowerCamelCase__         =  d.pop(__lowerCAmelCase						)
						os.makedirs(__lowerCAmelCase   ,    exist_ok=__lowerCAmelCase						)
						torch.save(__lowerCAmelCase   ,    os.path.join(__lowerCAmelCase   ,    __lowerCAmelCase						)						)


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

