



import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__lowerCamelCase     :							List[Any]  							=				logging.get_logger(__name__)

def    lowerCamelCase_(lowerCamelCase_    ) ->       Optional[int]:
						UpperCAmelCase         =     torch.load(lowerCamelCase_    ,   map_location="cpu"    )
						if "model" in sd.keys():
												UpperCAmelCase         =     torch.load(lowerCamelCase_    ,   map_location="cpu"    )["""model"""]

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

						UpperCAmelCase         =     {
						    """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:
																		UpperCAmelCase         =     sd.pop(lowerCamelCase_    )

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

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

																		UpperCAmelCase         =     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
																		UpperCAmelCase         =     torch.split(lowerCamelCase_    ,   depth // 3    ,   dim=0    )

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

						return sd

@torch.no_grad()
def    lowerCamelCase_(lowerCamelCase_    ,   lowerCamelCase_    ,   lowerCamelCase_=None    ) ->       Any:
						UpperCAmelCase         =     load_checkpoint(lowerCamelCase_    )

						if config is not None:
												UpperCAmelCase         =     OPTConfig.from_pretrained(lowerCamelCase_    )
						else:
												UpperCAmelCase         =     OPTConfig()

						UpperCAmelCase         =     OPTModel(lowerCamelCase_    ).half().eval()
						model.load_state_dict(lowerCamelCase_    )

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


if __name__ == "__main__":
		__lowerCamelCase     :							List[str]  							=				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     :							List[Any]  							=				parser.parse_args()
		convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)








import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin





@require_jieba
class  __magic_name__   (							A__,  unittest.TestCase						):
		lowercase      :       Optional[Any]          =CpmAntTokenizer
		lowercase      :       Dict          =False






		def SCREAMING_SNAKE_CASE_			(						self      :     Optional[Any]      )				->					Dict:
								'''simple docstring'''







								super().setUp()

								UpperCAmelCase         =     [
								    "<d>",
								    "</d>",
								    "<s>",
								    "</s>",
								    "</_>",
								    "<unk>",
								    "<pad>",
								    "</n>",
								    "我",
								    "是",
								    "C",
								    "P",
								    "M",
								    "A",
								    "n",
								    "t",
								]
								UpperCAmelCase         =     os.path.join(self.tmpdirname				,		VOCAB_FILES_NAMES["vocab_file"]      )
								with open(self.vocab_file				,		"w"				,		encoding="utf-8"      ) as vocab_writer:
														vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )







		@tooslow
		def SCREAMING_SNAKE_CASE_			(						self      :     List[str]      )				->					Dict:
								'''simple docstring'''







								UpperCAmelCase         =     CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     ["今天", "天气", "真", "好", "！"]
								UpperCAmelCase         =     tokenizer.tokenize(UpperCamelCase__      )
								self.assertListEqual(UpperCamelCase__				,		UpperCamelCase__      )
								UpperCAmelCase         =     "今天天气真好！"
								UpperCAmelCase         =     [tokenizer.bos_token] + tokens

								UpperCAmelCase         =     [6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__      )				,		UpperCamelCase__      )

								UpperCAmelCase         =     tokenizer.decode(UpperCamelCase__      )
								self.assertEqual(UpperCamelCase__				,		UpperCamelCase__      )



