




import argparse
from pathlib import Path

import torch

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


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







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[Any]  ):
					__lowerCAmelCase            = torch.load(SCREAMING_SNAKE_CASE_   ,   map_location="cpu"  )
					if "model" in sd.keys():
										__lowerCAmelCase            = torch.load(SCREAMING_SNAKE_CASE_   ,   map_location="cpu"  )["model"]

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

					__lowerCAmelCase            = {
					    "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:
															__lowerCAmelCase            = sd.pop(SCREAMING_SNAKE_CASE_  )

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

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

															__lowerCAmelCase            = 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
															__lowerCAmelCase     ,							__lowerCAmelCase     ,							__lowerCAmelCase            = torch.split(SCREAMING_SNAKE_CASE_   ,   depth // 3   ,   dim=0  )

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

					return sd







@torch.no_grad()
def 			_a   (		SCREAMING_SNAKE_CASE_							:       Any   ,   SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]=None  ):
					__lowerCAmelCase            = load_checkpoint(SCREAMING_SNAKE_CASE_  )

					if config is not None:
										__lowerCAmelCase            = OPTConfig.from_pretrained(SCREAMING_SNAKE_CASE_  )
					else:
										__lowerCAmelCase            = OPTConfig()

					__lowerCAmelCase            = 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__":
					UpperCamelCase__					    =    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.""")
					UpperCamelCase__					    =    parser.parse_args()
					convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
UpperCamelCase__					    =    """."""


if __name__ == "__main__":
					UpperCamelCase__					    =    os.path.join(REPO_PATH, """utils/documentation_tests.txt""")
					UpperCamelCase__					    =    []
					UpperCamelCase__					    =    []
					with open(doctest_file_path) as fp:
										for line in fp:
															UpperCamelCase__					    =    line.strip()
															UpperCamelCase__					    =    os.path.join(REPO_PATH, line)
															if not (os.path.isfile(path) or os.path.isdir(path)):
																				non_existent_paths.append(line)
															all_paths.append(path)
					if len(non_existent_paths) > 0:
										UpperCamelCase__					    =    """\n""".join(non_existent_paths)
										raise ValueError(f'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
					if all_paths != sorted(all_paths):
										raise ValueError("""Files in `utils/documentation_tests.txt` are not in alphabetical order.""")




