





'''simple docstring'''

from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__lowerCAmelCase							:     List[Any]		     =logging.get_logger(__name__)  # pylint: disable=invalid-name





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    if not path:
        return "pipe"

    for ext in PipelineDataFormat.SUPPORTED_FORMATS:
        if path.endswith(_lowerCamelCase	):
            return ext

    raise Exception(
        F"Unable to determine file format from file extension {path}. "
        F"Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"	)





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      pipeline(
        task=args.task						,	model=args.model if args.model else None						,	config=args.config						,	tokenizer=args.tokenizer						,	device=args.device						,	)
    A__    =      try_infer_format_from_ext(args.input	) if args.format == "infer" else args.format
    A__    =      PipelineDataFormat.from_str(
        format=_lowerCamelCase						,	output_path=args.output						,	input_path=args.input						,	column=args.column if args.column else nlp.default_input_names						,	overwrite=args.overwrite						,	)
    return RunCommand(_lowerCamelCase						,	_lowerCamelCase	)






class        UpperCAmelCase     (	UpperCamelCase__    ):
 def __init__(    self				:Union[str, Any]						,	lowercase_				:Pipeline						,	lowercase_				:PipelineDataFormat							)->							str:
     A__    =      nlp
     A__    =      reader
 @staticmethod
 def 					UpperCAmelCase_							(    lowercase_				:ArgumentParser							)->							Optional[Any]:
     A__    =      parser.add_parser("run"						,	help="Run a pipeline through the CLI"							)
     run_parser.add_argument("--task"						,	choices=get_supported_tasks()						,	help="Task to run"							)
     run_parser.add_argument("--input"						,	type=lowercase_						,	help="Path to the file to use for inference"							)
     run_parser.add_argument("--output"						,	type=lowercase_						,	help="Path to the file that will be used post to write results."							)
     run_parser.add_argument("--model"						,	type=lowercase_						,	help="Name or path to the model to instantiate."							)
     run_parser.add_argument("--config"						,	type=lowercase_						,	help="Name or path to the model's config to instantiate."							)
     run_parser.add_argument(
         "--tokenizer"						,	type=lowercase_						,	help="Name of the tokenizer to use. (default: same as the model name)"							)
     run_parser.add_argument(
         "--column"						,	type=lowercase_						,	help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"						,	)
     run_parser.add_argument(
         "--format"						,	type=lowercase_						,	default="infer"						,	choices=PipelineDataFormat.SUPPORTED_FORMATS						,	help="Input format to read from"						,	)
     run_parser.add_argument(
         "--device"						,	type=lowercase_						,	default=-1						,	help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"						,	)
     run_parser.add_argument("--overwrite"						,	action="store_true"						,	help="Allow overwriting the output file."							)
     run_parser.set_defaults(func=lowercase_							)



 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[Any]:
     A__,     A__    =      self._nlp, []

     for entry in self._reader:
         A__    =      nlp(**lowercase_							) if self._reader.is_multi_columns else nlp(lowercase_							)
         if isinstance(lowercase_						,	lowercase_							):
             outputs.append(lowercase_							)
         else:
             outputs += output

        # Saving data
     if self._nlp.binary_output:
         A__    =      self._reader.save_binary(lowercase_							)
         logger.warning(F"Current pipeline requires output to be in binary format, saving at {binary_path}"							)
     else:
         self._reader.save(lowercase_							)






'''simple docstring'''

import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__lowerCAmelCase							:     List[str]		     =logging.get_logger(__name__)





def 						UpperCamelCase    (							_lowerCamelCase : str	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    if "model" in sd.keys():
        A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)["model"]

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

    A__    =      {
        "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:
            A__    =      sd.pop(_lowerCamelCase	)

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

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

            A__    =      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
            A__,     A__,     A__    =      torch.split(_lowerCamelCase						,	depth // 3						,	dim=0	)

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

    return sd





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : List[Any]						,	_lowerCamelCase : Any						,	_lowerCamelCase : Dict=None	):
    A__    =      load_checkpoint(_lowerCamelCase	)

    if config is not None:
        A__    =      OPTConfig.from_pretrained(_lowerCamelCase	)
    else:
        A__    =      OPTConfig()

    A__    =      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)



