




from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_torch_dist_unique_port,
    require_torch_multi_gpu,
    require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging


a_   		=     logging.get_logger(__name__)


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset

 from transformers import Trainer

 class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

       """simple docstring"""

       def __init__(     self					: List[Any] ,       snake_case					: int = 101				):
           __UpperCamelCase  		=						length







       def __len__(     self					: List[Any]				):
           return self.length







       def __getitem__(     self					: Dict ,       snake_case					: List[str]				):
           return i







 class  _lowerCamelCase						:

       """simple docstring"""

       def __call__(     self					: Optional[Any] ,       snake_case					: Union[str, Any]				):
           return {"input_ids": torch.tensor(lowercase_				), "labels": torch.tensor(lowercase_				)}







 class  _lowerCamelCase						(			nn.Module   ):

       """simple docstring"""

       def __init__(     self					: Any				):
           super().__init__()
           # Add some (unused) params otherwise DDP will complain.
           __UpperCamelCase  		=						nn.Linear(120 ,       80				)







       def       snake_case	(     self					: Any ,       snake_case					: Optional[int] ,       snake_case					: Any=None				):







           if labels is not None:
               return torch.tensor(0.0 ,       device=input_ids.device				), input_ids
           else:
               return input_ids

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @require_torch_neuroncore
      def       snake_case	(     self					: Optional[Any]				):
          __UpperCamelCase  		=						F'''--nproc_per_node=2\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '''.split()
          __UpperCamelCase  		=						self.get_auto_remove_tmp_dir()
          __UpperCamelCase  		=						F'''--output_dir {output_dir}'''.split()
          __UpperCamelCase  		=						['''torchrun'''] + distributed_args + args
          execute_subprocess_async(lowercase_ ,       env=self.get_env()				)
          # successful return here == success - any errors would have caused an error in the sub-call







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @require_torch_multi_gpu
      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						F'''--nproc_per_node={torch.cuda.device_count()}\n            --master_port={get_torch_dist_unique_port()}\n            {self.test_file_dir}/test_trainer_distributed.py\n        '''.split()
          __UpperCamelCase  		=						self.get_auto_remove_tmp_dir()
          __UpperCamelCase  		=						F'''--output_dir {output_dir}'''.split()
          __UpperCamelCase  		=						['''torchrun'''] + distributed_args + args
          execute_subprocess_async(lowercase_ ,       env=self.get_env()				)
          # successful return here == success - any errors would have caused an error in the sub-call


if __name__ == "__main__":
 # The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
 #
 # PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py

 a_   		=     HfArgumentParser((TrainingArguments,))
 a_   		=     parser.parse_args_into_dataclasses()[0]

 logger.warning(
     f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, """
     f"""distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}"""
 )

 # Essentially, what we want to verify in the distributed case is that we get all samples back,
 # in the right order. (this is crucial for prediction for instance)
 for dataset_length in [101, 40, 7]:
  a_   		=     DummyDataset(dataset_length)




  def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Dict:

      """simple docstring"""


      __UpperCamelCase  		=						list(range(len(a__      )      )      )
      __UpperCamelCase  		=						p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
      if not success and training_args.local_rank == 0:
          logger.warning(
              '''Predictions and/or labels do not match expected results:\n  - predictions: '''
              F'''{p.predictions.tolist()}\n  - labels: {p.label_ids.tolist()}\n  - expected: {sequential}'''      )
      return {"success": success}

  a_   		=     Trainer(
      model=DummyModel(),
      args=training_args,
      data_collator=DummyDataCollator(),
      eval_dataset=dataset,
      compute_metrics=compute_metrics,
  )
  a_   		=     trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  a_   		=     trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)

  a_   		=     2

  a_   		=     trainer.evaluate()
  logger.info(metrics)
  if metrics["eval_success"] is not True:
   logger.error(metrics)
   exit(1)

  a_   		=     trainer.predict(dataset)
  logger.info(p.metrics)
  if p.metrics["test_success"] is not True:
   logger.error(p.metrics)
   exit(1)


  a_   		=     None












import argparse
from pathlib import Path

import torch

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


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    if "model" in sd.keys():
        __UpperCamelCase  		=						torch.load(lowercase_					,     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(lowercase_      )

    __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(lowercase_      )

    __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		,      __UpperCamelCase		,      __UpperCamelCase  		=						torch.split(lowercase_					,     depth // 3					,     dim=0      )

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

    return sd




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						load_checkpoint(lowercase_      )

    if config is not None:
        __UpperCamelCase  		=						OPTConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						OPTConfig()

    __UpperCamelCase  		=						OPTModel(lowercase_      ).half().eval()
    model.load_state_dict(lowercase_      )

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


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






