




import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
lowerCamelCase       :       Any        =    logging.get_logger(__name__)




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   Any:
   snake_case      :				Optional[int]				=       torch.load(lowercase						,map_location="""cpu"""     )
   if "model" in sd.keys():
      snake_case      :				Any				=       torch.load(lowercase						,map_location="""cpu"""     )["""model"""]

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

   snake_case      :				Dict				=       {
       """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      :				Optional[Any]				=       sd.pop(lowercase     )

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

         snake_case      :				List[str]				=       key.replace(""".qkv_proj."""						,""".q_proj."""     )
         snake_case      :				Optional[int]				=       key.replace(""".qkv_proj."""						,""".k_proj."""     )
         snake_case      :				Any				=       key.replace(""".qkv_proj."""						,""".v_proj."""     )

         snake_case      :				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      :				Any				=       torch.split(lowercase						,depth // 3						,dim=0     )

         snake_case      :				List[Any]				=       q
         snake_case      :				Tuple				=       k
         snake_case      :				Optional[Any]				=       v
         del sd[key]

   return sd




@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase						,lowercase=None     )  ->   Any:
   snake_case      :				Optional[Any]				=       load_checkpoint(lowercase     )

   if config is not None:
      snake_case      :				Optional[Any]				=       OPTConfig.from_pretrained(lowercase     )
   else:
      snake_case      :				List[Any]				=       OPTConfig()

   snake_case      :				Union[str, Any]				=       OPTModel(lowercase     ).half().eval()
   model.load_state_dict(lowercase     )

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


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













from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Callable ,  __lowerCamelCase				:					Optional[Features] = None ,  __lowerCamelCase				:					str = None ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional[dict] = None ,  __lowerCamelCase				:					Optional[int] = None ,  **__lowerCamelCase				:					List[Any] ,  ):
		super().__init__(
		    features=__lowerCamelCase ,  cache_dir=__lowerCamelCase ,  keep_in_memory=__lowerCamelCase ,  streaming=__lowerCamelCase ,  num_proc=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :Union[str, Any]          =	Generator(
		    cache_dir=__lowerCamelCase ,  features=__lowerCamelCase ,  generator=__lowerCamelCase ,  gen_kwargs=__lowerCamelCase ,  **__lowerCamelCase ,  )





	def 						_A (		self				:					List[str]	):
		# Build iterable dataset
		if self.streaming:
			UpperCamelCase     :Any          =	self.builder.as_streaming_dataset(split="""train"""	)
		# Build regular (map-style) dataset
		else:
			UpperCamelCase     :Tuple          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :Dict          =	None
			UpperCamelCase     :List[str]          =	None

			self.builder.download_and_prepare(
			    download_config=__lowerCamelCase ,  download_mode=__lowerCamelCase ,  verification_mode=__lowerCamelCase ,  base_path=__lowerCamelCase ,  num_proc=self.num_proc ,  )
			UpperCamelCase     :Tuple          =	self.builder.as_dataset(
			    split="""train""" ,  verification_mode=__lowerCamelCase ,  in_memory=self.keep_in_memory	)
		return dataset
