



'''simple docstring'''





import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__a          =		logging.get_logger(__name__)





def 						__snake_case(				_lowerCAmelCase		)		-> Any:
      snake_case__  :    int    						=      torch.load(_lowerCAmelCase     ,   map_location="""cpu"""		)
      if "model" in sd.keys():
            snake_case__  :    Union[str, Any]    						=      torch.load(_lowerCAmelCase     ,   map_location="""cpu"""		)["""model"""]

      # pop unnecessary weights
      snake_case__  :    Dict    						=      [
          """decoder.version""",
          """decoder.output_projection.weight""",
      ]
      for key in keys_to_delete:
            if key in sd:
                  sd.pop(_lowerCAmelCase		)

      snake_case__  :    Optional[int]    						=      {
          """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:
                  snake_case__  :    Tuple    						=      sd.pop(_lowerCAmelCase		)

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

                  snake_case__  :    str    						=      key.replace(""".qkv_proj."""     ,   """.q_proj."""		)
                  snake_case__  :    Any    						=      key.replace(""".qkv_proj."""     ,   """.k_proj."""		)
                  snake_case__  :    int    						=      key.replace(""".qkv_proj."""     ,   """.v_proj."""		)

                  snake_case__  :    str    						=      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
                  snake_case__						,				snake_case__						,				snake_case__  :    Dict    						=      torch.split(_lowerCAmelCase     ,   depth // 3     ,   dim=0		)

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

      return sd





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=None		)		-> Any:
      snake_case__  :    Any    						=      load_checkpoint(_lowerCAmelCase		)

      if config is not None:
            snake_case__  :    Any    						=      OPTConfig.from_pretrained(_lowerCAmelCase		)
      else:
            snake_case__  :    Union[str, Any]    						=      OPTConfig()

      snake_case__  :    List[Any]    						=      OPTModel(_lowerCAmelCase		).half().eval()
      model.load_state_dict(_lowerCAmelCase		)

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


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




from PIL import Image

def       lowercase_		(		_A			:    Image						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.size
      lowerCamelCase__		:				Dict    =  0
      lowerCamelCase__		:				Dict    =  image.load()
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				List[str]    =  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(_A						):
            for i in range(_A						):
                  lowerCamelCase__		:				Union[str, Any]    =  255 if pixels[i, j] > mean else 0
      return image


if __name__ == "__main__":
    A      :    int		=					mean_threshold(Image.open("path_to_image").convert("L"))
    image.save("output_image_path")


