






import argparse
from pathlib import Path

import torch

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


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

def    UpperCamelCase						(	_A			):






						"""simple docstring"""







						__magic_name__      :       Any       						= torch.load(_A,   map_location="""cpu"""			)
						if "model" in sd.keys():
												__magic_name__      :       List[str]       						= torch.load(_A,   map_location="""cpu"""			)["""model"""]

						# pop unnecessary weights
						__magic_name__      :       List[str]       						= [
						    """decoder.version""",
						    """decoder.output_projection.weight""",
						]
						for key in keys_to_delete:
												if key in sd:
																		sd.pop(_A			)

						__magic_name__      :       int       						= {
						    """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:
																		__magic_name__      :       List[str]       						= sd.pop(_A			)

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

																		__magic_name__      :       Optional[Any]       						= key.replace(""".qkv_proj.""",   """.q_proj."""			)
																		__magic_name__      :       Dict       						= key.replace(""".qkv_proj.""",   """.k_proj."""			)
																		__magic_name__      :       str       						= key.replace(""".qkv_proj.""",   """.v_proj."""			)

																		__magic_name__      :       Optional[Any]       						= 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
																		__magic_name__      :       List[Any]       						= torch.split(_A,   depth // 3,   dim=0			)

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

						return sd

@torch.no_grad()
def    UpperCamelCase						(	_A,   _A,   _A=None			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= load_checkpoint(_A			)

						if config is not None:
												__magic_name__      :       Optional[Any]       						= OPTConfig.from_pretrained(_A			)
						else:
												__magic_name__      :       int       						= OPTConfig()

						__magic_name__      :       str       						= OPTModel(_A			).half().eval()
						model.load_state_dict(_A			)

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


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








import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer





class 	snake_case__ (							tf.keras.layers.Layer				):
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__ = None			)							->				int:
							super().__init__()
							__magic_name__      :       Any       						= pad_token_id
							__magic_name__      :       Any       						= max_length
							__magic_name__      :       List[str]       						= vocab
							__magic_name__      :       List[Any]       						= merges
							__magic_name__      :       int       						= BytePairTokenizer(lowerCAmelCase__      ,       lowerCAmelCase__      ,       sequence_length=lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= [""" """.join(lowerCAmelCase__			) for m in tokenizer.bpe_ranks.keys()]
							__magic_name__      :       Union[str, Any]       						= tokenizer.get_vocab()
							return cls(lowerCAmelCase__      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= GPTaTokenizer.from_pretrained(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
							return cls.from_tokenizer(lowerCAmelCase__      ,       *lowerCAmelCase__      ,       **lowerCAmelCase__			)
	@classmethod
	def       __magic_name__    (    cls      ,       lowerCAmelCase__			)							->				List[Any]:
							return cls(**lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				int:
							return {
							    "vocab": self.vocab,
							    "merges": self.merges,
							    "max_length": self.max_length,
							    "pad_token_id": self.pad_token_id,
							}







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				int:
							__magic_name__      :       Dict       						= self.tf_tokenizer(lowerCAmelCase__			)
							__magic_name__      :       Dict       						= tf.ones_like(lowerCAmelCase__			)

							if self.pad_token_id is not None:
													# pad the tokens up to max length
													__magic_name__      :       List[Any]       						= max_length if max_length is not None else self.max_length

													if max_length is not None:
																			__magic_name__						,__magic_name__      :       List[Any]       						= pad_model_inputs(
																			    lowerCAmelCase__      ,       max_seq_length=lowerCAmelCase__      ,       pad_value=self.pad_token_id			)

							return {"attention_mask": attention_mask, "input_ids": input_ids}
