





from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Optional[int]         =  ['''image_processor''', '''tokenizer''']
   __A   :				Dict         =  '''BlipImageProcessor'''
   __A   :				Optional[Any]         =  ('''BertTokenizer''', '''BertTokenizerFast''')

   def __init__(					self					,  lowercase					,  lowercase)			->						Any:







          '''simple docstring'''

          a__						:	List[str] =				False
          super().__init__(lowercase					,  lowercase)
          a__						:	List[Any] =				self.image_processor

   def __call__(					self					,  lowercase = None					,  lowercase = None					,  lowercase = True					,  lowercase = False					,  lowercase = None					,  lowercase = None					,  lowercase = 0					,  lowercase = None					,  lowercase = None					,  lowercase = False					,  lowercase = False					,  lowercase = False					,  lowercase = False					,  lowercase = False					,  lowercase = True					,  lowercase = None					,  **lowercase					,  )			->						BatchEncoding:







          '''simple docstring'''

          if images is None and text is None:
                 raise ValueError('You have to specify either images or text.')

          # Get only text
          if images is None:
                 a__						:	Tuple =				self.tokenizer
                 a__						:	Tuple =				self.tokenizer(
                     text=lowercase					,  add_special_tokens=lowercase					,  padding=lowercase					,  truncation=lowercase					,  max_length=lowercase					,  stride=lowercase					,  pad_to_multiple_of=lowercase					,  return_attention_mask=lowercase					,  return_overflowing_tokens=lowercase					,  return_special_tokens_mask=lowercase					,  return_offsets_mapping=lowercase					,  return_token_type_ids=lowercase					,  return_length=lowercase					,  verbose=lowercase					,  return_tensors=lowercase					,  **lowercase					,  )
                 return text_encoding

          # add pixel_values
          a__						:	Optional[int] =				self.image_processor(lowercase					,  return_tensors=lowercase)

          if text is not None:
                 a__						:	List[str] =				self.tokenizer(
                     text=lowercase					,  add_special_tokens=lowercase					,  padding=lowercase					,  truncation=lowercase					,  max_length=lowercase					,  stride=lowercase					,  pad_to_multiple_of=lowercase					,  return_attention_mask=lowercase					,  return_overflowing_tokens=lowercase					,  return_special_tokens_mask=lowercase					,  return_offsets_mapping=lowercase					,  return_token_type_ids=lowercase					,  return_length=lowercase					,  verbose=lowercase					,  return_tensors=lowercase					,  **lowercase					,  )
          else:
                 a__						:	Any =				None

          if text_encoding is not None:
                 encoding_image_processor.update(lowercase)

          return encoding_image_processor

   def 							__lowercase		(					self					,  *lowercase					,  **lowercase)			->						Union[str, Any]:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  *lowercase					,  **lowercase)			->						List[Any]:







          '''simple docstring'''

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

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







          '''simple docstring'''

          a__						:	Tuple =				self.tokenizer.model_input_names
          a__						:	Tuple =				self.image_processor.model_input_names
          return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))









import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
lowercase      :       Any									=  logging.get_logger(__name__)



def A_	(		A__				)  ->		List[str]:
       a__						:	Union[str, Any] =				torch.load(A__		,      map_location='cpu'				)
       if "model" in sd.keys():
              a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)['model']

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

       a__						:	Union[str, Any] =				{
           '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:
                     a__						:	Any =				sd.pop(A__				)

       a__						:	Optional[int] =				list(sd.keys()				)
       for key in keys:
              if ".qkv_proj." in key:
                     a__						:	str =				sd[key]
                     # We split QKV in separate Q,K,V

                     a__						:	Union[str, Any] =				key.replace('.qkv_proj.'		,      '.q_proj.'				)
                     a__						:	int =				key.replace('.qkv_proj.'		,      '.k_proj.'				)
                     a__						:	Optional[int] =				key.replace('.qkv_proj.'		,      '.v_proj.'				)

                     a__						:	List[Any] =				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
                     a__ ,							a__ ,							a__						:	Any =				torch.split(A__		,      depth // 3		,      dim=0				)

                     a__						:	int =				q
                     a__						:	List[Any] =				k
                     a__						:	int =				v
                     del sd[key]

       return sd



@torch.no_grad()
def A_	(		A__		,      A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				load_checkpoint(A__				)

       if config is not None:
              a__						:	List[Any] =				OPTConfig.from_pretrained(A__				)
       else:
              a__						:	int =				OPTConfig()

       a__						:	Optional[int] =				OPTModel(A__				).half().eval()
       model.load_state_dict(A__				)

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


if __name__ == "__main__":
    lowercase      :       str									=  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.""")
    lowercase      :       Any									=  parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


