




import warnings

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


class  lowerCamelCase     (		_lowerCamelCase    ):






			'''simple docstring'''





			UpperCamelCase__										=['''image_processor''', '''tokenizer''']
			UpperCamelCase__										='''VideoMAEImageProcessor'''
			UpperCamelCase__										=('''CLIPTokenizer''', '''CLIPTokenizerFast''')

			def __init__(			self      : int							,    lowerCamelCase_      : str=None							,    lowerCamelCase_      : str=None							,    **lowerCamelCase_      : List[Any]	)    -> List[str]:
								__magic_name__				:						Any     				=				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_							,    )
													__magic_name__				:						Any     				=				kwargs.pop('''feature_extractor'''	)

								__magic_name__				:						List[str]     				=				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`.'''	)

								super().__init__(lowerCamelCase_							,    lowerCamelCase_	)
								__magic_name__				:						Optional[int]     				=				self.image_processor

			def __call__(			self      : List[str]							,    lowerCamelCase_      : int=None							,    lowerCamelCase_      : List[str]=None							,    lowerCamelCase_      : Tuple=None							,    **lowerCamelCase_      : Union[str, Any]	)    -> str:

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

								if text is not None:
													__magic_name__				:						Tuple     				=				self.tokenizer(lowerCamelCase_							,    return_tensors=lowerCamelCase_							,    **lowerCamelCase_	)

								if videos is not None:
													__magic_name__				:						List[Any]     				=				self.image_processor(lowerCamelCase_							,    return_tensors=lowerCamelCase_							,    **lowerCamelCase_	)

								if text is not None and videos is not None:
													__magic_name__				:						Optional[Any]     				=				image_features.pixel_values
													return encoding
								elif text is not None:
													return encoding
								else:
													return BatchEncoding(data=dict(**lowerCamelCase_	)							,    tensor_type=lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : List[Any]							,    *lowerCamelCase_      : Any							,    **lowerCamelCase_      : str	)    -> str:
								return self.tokenizer.batch_decode(*lowerCamelCase_							,    **lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : str							,    *lowerCamelCase_      : Union[str, Any]							,    **lowerCamelCase_      : int	)    -> List[str]:
								return self.tokenizer.decode(*lowerCamelCase_							,    **lowerCamelCase_	)

			@property
			def 				UpperCAmelCase__							(			self      : Dict	)    -> Any:
								return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

			@property
			def 				UpperCAmelCase__							(			self      : Any	)    -> Union[str, Any]:
								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,    lowerCamelCase_							,    )
								return self.image_processor_class


			@property
			def 				UpperCAmelCase__							(			self      : List[Any]	)    -> List[Any]:
								warnings.warn(
								    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,    lowerCamelCase_							,    )
								return self.image_processor









import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


__lowerCamelCase :    Any      						=      get_logger(__name__)







def 		lowercase__ (		__A:						str				,__A:						str					):





					'''simple docstring'''






					if text.startswith(__A					):
										return text[len(__A					) :]
					return text  # or whatever







def 		lowercase__ (		__A:						List[str]					):





					'''simple docstring'''






					return {remove_prefix(__A				,'''model.'''					): v for k, v in sd.items()}







def 		lowercase__ (		__A:						List[Dict[str, torch.Tensor]]					):





					'''simple docstring'''






					__magic_name__				:						List[Any]     				=				{}
					for k in state_dicts[0].keys():
										__magic_name__				:						str     				=				[sd[k] for sd in state_dicts]
										__magic_name__				:						List[Any]     				=				sum(__A					) / len(__A					)
										assert isinstance(__A				,torch.Tensor					)
										__magic_name__				:						Any     				=				new_t
					return new_sd







def 		lowercase__ (		__A:						str				,__A:						str				,__A:						str					):





					'''simple docstring'''






					__magic_name__				:						List[Any]     				=				AutoModelForSeqaSeqLM.from_pretrained(__A					)
					if os.path.isfile(__A					):
										__magic_name__				:						str     				=				[pl_ckpt_path]
					else:
										assert os.path.isdir(__A					)
										__magic_name__				:						List[str]     				=				list(Path(__A					).glob('''*.ckpt'''					)					)
										assert ckpt_files, F'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

					if len(__A					) > 1:
										logger.info(F'''averaging the weights of {ckpt_files}'''					)

					__magic_name__				:						Union[str, Any]     				=				[sanitize(torch.load(__A				,map_location='''cpu'''					)['''state_dict''']					) for x in ckpt_files]
					__magic_name__				:						Dict     				=				average_state_dicts(__A					)

					__magic_name__				,     __magic_name__				:						Union[str, Any]     				=				hf_model.load_state_dict(__A				,strict=__A					)
					assert not missing, F'''missing keys: {missing}'''
					hf_model.save_pretrained(__A					)
					try:
										__magic_name__				:						Optional[Any]     				=				AutoTokenizer.from_pretrained(__A					)
										tok.save_pretrained(__A					)
					except Exception:
										pass
										# dont copy tokenizer if cant


if __name__ == "__main__":
	fire.Fire(convert_pl_to_hf)



