


'''simple docstring'''

import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
_lowerCAmelCase     :							int    =				logging.get_logger(__name__)






def 					_A			(			snake_case__    :			List[Any]				):
	snake_case__			:			int             =		torch.load(__A						,						map_location='''cpu'''				)
	if "model" in sd.keys():
		snake_case__			:			List[str]             =		torch.load(__A						,						map_location='''cpu'''				)['''model''']

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

	snake_case__			:			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:
			snake_case__			:			Any             =		sd.pop(__A				)

	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__			:			List[str]             =		key.replace('''.qkv_proj.'''						,						'''.q_proj.'''				)
			snake_case__			:			Any             =		key.replace('''.qkv_proj.'''						,						'''.k_proj.'''				)
			snake_case__			:			Optional[Any]             =		key.replace('''.qkv_proj.'''						,						'''.v_proj.'''				)

			snake_case__			:			List[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__			:			Optional[Any]             =		torch.split(__A						,						depth // 3						,						dim=0				)

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

	return sd






@torch.no_grad()
def 					_A			(			snake_case__    :			Tuple						,						snake_case__    :			Tuple						,						snake_case__    :			Union[str, Any]=None				):
	snake_case__			:			Any             =		load_checkpoint(__A				)

	if config is not None:
		snake_case__			:			Optional[Any]             =		OPTConfig.from_pretrained(__A				)
	else:
		snake_case__			:			Optional[Any]             =		OPTConfig()

	snake_case__			:			Tuple             =		OPTModel(__A				).half().eval()
	model.load_state_dict(__A				)

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


if __name__ == "__main__":
		_lowerCAmelCase     :							Optional[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.")
		_lowerCAmelCase     :							int    =				parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










'''simple docstring'''





from __future__ import annotations




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     if len(__A) == 0:
          return False
     _a           =    len(__A) // 2
     if a_list[midpoint] == item:
          return True
     if item < a_list[midpoint]:
          return binary_search(a_list[:midpoint]    ,      __A)
     else:
          return binary_search(a_list[midpoint + 1 :]    ,      __A)


if __name__ == "__main__":
  lowercase_									=    input("Enter numbers separated by comma:\n").strip()
  lowercase_									=    [int(item.strip()) for item in user_input.split(",")]
  lowercase_									=    int(input("Enter the number to be found in the list:\n").strip())
  lowercase_									=    "" if binary_search(sequence, target) else "not "
  print(F"""{target} was {not_str}found in {sequence}""")



