
"""simple docstring"""







import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     Optional[Any]:







							"""simple docstring"""


							__snake_case     			=       torch.load(SCREAMING_SNAKE_CASE							,    map_location="cpu" )
							if "model" in sd.keys():
														__snake_case     			=       torch.load(SCREAMING_SNAKE_CASE							,    map_location="cpu" )["model"]

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

							__snake_case     			=       {
							    "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     			=       sd.pop(SCREAMING_SNAKE_CASE )

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

																					__snake_case     			=       key.replace(".qkv_proj."							,    ".q_proj." )
																					__snake_case     			=       key.replace(".qkv_proj."							,    ".k_proj." )
																					__snake_case     			=       key.replace(".qkv_proj."							,    ".v_proj." )

																					__snake_case     			=       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     			=       torch.split(SCREAMING_SNAKE_CASE							,    depth // 3							,    dim=0 )

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

							return sd
@torch.no_grad()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=None )  ->     Dict:







							"""simple docstring"""


							__snake_case     			=       load_checkpoint(SCREAMING_SNAKE_CASE )

							if config is not None:
														__snake_case     			=       OPTConfig.from_pretrained(SCREAMING_SNAKE_CASE )
							else:
														__snake_case     			=       OPTConfig()

							__snake_case     			=       OPTModel(SCREAMING_SNAKE_CASE ).half().eval()
							model.load_state_dict(SCREAMING_SNAKE_CASE )

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


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


"""simple docstring"""







from __future__ import annotations

from typing import Any
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     int:







							"""simple docstring"""


							if not postfix_notation:
														return 0

							__snake_case     			=       {"+", "-", "*", "/"}
							__snake_case     			=       []

							for token in postfix_notation:
														if token in operations:
																					__snake_case			,   __snake_case     			=       stack.pop(), stack.pop()
																					if token == "+":
																												stack.append(a + b )
																					elif token == "-":
																												stack.append(a - b )
																					elif token == "*":
																												stack.append(a * b )
																					else:
																												if a * b < 0 and a % b != 0:
																																			stack.append(a // b + 1 )
																												else:
																																			stack.append(a // b )
														else:
																					stack.append(int(SCREAMING_SNAKE_CASE ) )

							return stack.pop()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
