'''simple docstring'''

import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__     :	Union[str, Any]				=			logging.get_logger(__name__)







def __UpperCamelCase(   _A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  torch.load(_A  ,			map_location='''cpu'''    )
   if "model" in sd.keys():
      UpperCAmelCase__							:				int   				=  torch.load(_A  ,			map_location='''cpu'''    )['''model''']

   # pop unnecessary weights
   UpperCAmelCase__							:				Any   				=  [
       '''decoder.version''',
       '''decoder.output_projection.weight''',
   ]
   for key in keys_to_delete:
      if key in sd:
         sd.pop(_A    )

   UpperCAmelCase__							:				List[str]   				=  {
       '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
       '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
       '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
       '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
   }
   for old_key, new_key in keys_to_rename.items():
      if old_key in sd:
         UpperCAmelCase__							:				Dict   				=  sd.pop(_A    )

   UpperCAmelCase__							:				List[Any]   				=  list(sd.keys()    )
   for key in keys:
      if ".qkv_proj." in key:
         UpperCAmelCase__							:				Any   				=  sd[key]
         # We split QKV in separate Q,K,V

         UpperCAmelCase__							:				Dict   				=  key.replace('''.qkv_proj.'''  ,			'''.q_proj.'''    )
         UpperCAmelCase__							:				str   				=  key.replace('''.qkv_proj.'''  ,			'''.k_proj.'''    )
         UpperCAmelCase__							:				Dict   				=  key.replace('''.qkv_proj.'''  ,			'''.v_proj.'''    )

         UpperCAmelCase__							:				Dict   				=  value.shape[0]
         assert depth % 3 == 0
         # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
         # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
         UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				str   				=  torch.split(_A  ,			depth // 3  ,			dim=0    )

         UpperCAmelCase__							:				Optional[Any]   				=  q
         UpperCAmelCase__							:				List[str]   				=  k
         UpperCAmelCase__							:				int   				=  v
         del sd[key]

   return sd







@torch.no_grad()
def __UpperCamelCase(   _A					:				int  ,			_A					:				Any  ,			_A					:				List[str]=None    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[Any]   				=  load_checkpoint(_A    )

   if config is not None:
      UpperCAmelCase__							:				Optional[Any]   				=  OPTConfig.from_pretrained(_A    )
   else:
      UpperCAmelCase__							:				Union[str, Any]   				=  OPTConfig()

   UpperCAmelCase__							:				Dict   				=  OPTModel(_A    ).half().eval()
   model.load_state_dict(_A    )

   # Check results
   Path(_A    ).mkdir(exist_ok=_A    )
   model.save_pretrained(_A    )


if __name__ == "__main__":
  UpperCamelCase__     :	int				=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--fairseq_path',
      type=str,
      help=(
          'path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'
          ' https://huggingface.co/models?other=opt_metasq'
      ),
  )
  parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
  parser.add_argument('--hf_config', default=None, type=str, help='Define HF config.')
  UpperCamelCase__     :	int				=			parser.parse_args()
  convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)





'''simple docstring'''

import warnings

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


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   ['''image_processor''', '''tokenizer''']
      UpperCAmelCase_      :      str      							=   '''CLIPImageProcessor'''
      UpperCAmelCase_      :      Dict      							=   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')




      def __init__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				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_					,)
            UpperCAmelCase__							:				str   				=  kwargs.pop('''feature_extractor'''							)

         UpperCAmelCase__							:				Optional[Any]   				=  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_							)




      def __call__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         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:
            UpperCAmelCase__							:				Tuple   				=  self.tokenizer(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

         if images is not None:
            UpperCAmelCase__							:				Optional[int]   				=  self.image_processor(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

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




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         return self.tokenizer.batch_decode(*lowerCamelCase_					,**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         return self.tokenizer.decode(*lowerCamelCase_					,**lowerCamelCase_							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.tokenizer.model_input_names
         UpperCAmelCase__							:				Optional[Any]   				=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         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 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,lowerCamelCase_					,)
         return self.image_processor




