





import argparse
from pathlib import Path

import torch

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


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

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

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

		_lowercase							= {
		    '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:
						_lowercase							= sd.pop(_lowerCamelCase     )

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

						_lowercase							= key.replace('.qkv_proj.'			,							'.q_proj.'     )
						_lowercase							= key.replace('.qkv_proj.'			,							'.k_proj.'     )
						_lowercase							= key.replace('.qkv_proj.'			,							'.v_proj.'     )

						_lowercase							= 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
						_lowercase							= torch.split(_lowerCamelCase			,							depth // 3			,							dim=0     )

						_lowercase							= q
						_lowercase							= k
						_lowercase							= v
						del sd[key]

		return sd

@torch.no_grad()
def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[Any]			,							snake_case__  :str			,							snake_case__  :Tuple=None     )      ->						Optional[int]:
		_lowercase							= load_checkpoint(_lowerCamelCase     )

		if config is not None:
				_lowercase							= OPTConfig.from_pretrained(_lowerCamelCase     )
		else:
				_lowercase							= OPTConfig()

		_lowercase							= OPTModel(_lowerCamelCase     ).half().eval()
		model.load_state_dict(_lowerCamelCase     )

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


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






def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Dict:
  _lowercase							= len(snake_case__     )
  while cur > 1:
    # Find the maximum number in arr
    _lowercase							= arr.index(max(arr[0:cur]     )     )
    # Reverse from 0 to mi
    _lowercase							= arr[mi::-1] + arr[mi + 1 : len(snake_case__     )]
    # Reverse whole list
    _lowercase							= arr[cur - 1 :: -1] + arr[cur : len(snake_case__     )]
    cur -= 1
  return arr


if __name__ == "__main__":
      snake_case 				=       input("""Enter numbers separated by a comma:\n""").strip()
      snake_case 				=       [int(item) for item in user_input.split(""",""")]
      print(pancake_sort(unsorted))