




import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


a_   		=     get_logger(__name__)

class  _lowerCamelCase						(			enum.Enum   ):

      """simple docstring"""

      lowerCAmelCase__			: Union[str, Any]    =					"""all_checks"""
      lowerCAmelCase__			: Optional[Any]    =					"""basic_checks"""
      lowerCAmelCase__			: Optional[int]    =					"""no_checks"""







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""








class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""








class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""








class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""








def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	Dict:

    """simple docstring"""


    if expected_checksums is None:
        logger.info('''Unable to verify checksums.'''      )
        return
    if len(set(_UpperCamelCase      ) - set(_UpperCamelCase      )      ) > 0:
        raise ExpectedMoreDownloadedFiles(str(set(_UpperCamelCase      ) - set(_UpperCamelCase      )      )      )
    if len(set(_UpperCamelCase      ) - set(_UpperCamelCase      )      ) > 0:
        raise UnexpectedDownloadedFile(str(set(_UpperCamelCase      ) - set(_UpperCamelCase      )      )      )
    __UpperCamelCase  		=						[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
    __UpperCamelCase  		=						''' for ''' + verification_name if verification_name is not None else ''''''
    if len(_UpperCamelCase      ) > 0:
        raise NonMatchingChecksumError(
            F'''Checksums didn\'t match{for_verification_name}:\n'''
            F'''{bad_urls}\n'''
            '''Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'''      )
    logger.info('''All the checksums matched successfully''' + for_verification_name      )

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""








class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""








class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""








class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""








def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple:

    """simple docstring"""


    if expected_splits is None:
        logger.info('''Unable to verify splits sizes.'''      )
        return
    if len(set(_UpperCamelCase      ) - set(_UpperCamelCase      )      ) > 0:
        raise ExpectedMoreSplits(str(set(_UpperCamelCase      ) - set(_UpperCamelCase      )      )      )
    if len(set(_UpperCamelCase      ) - set(_UpperCamelCase      )      ) > 0:
        raise UnexpectedSplits(str(set(_UpperCamelCase      ) - set(_UpperCamelCase      )      )      )
    __UpperCamelCase  		=						[
        {'''expected''': expected_splits[name], '''recorded''': recorded_splits[name]}
        for name in expected_splits
        if expected_splits[name].num_examples != recorded_splits[name].num_examples
    ]
    if len(_UpperCamelCase      ) > 0:
        raise NonMatchingSplitsSizesError(str(_UpperCamelCase      )      )
    logger.info('''All the splits matched successfully.'''      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_ = True      )       ->	dict:

    """simple docstring"""


    if record_checksum:
        __UpperCamelCase  		=						shaaaa()
        with open(_UpperCamelCase					,     '''rb'''      ) as f:
            for chunk in iter(lambda: f.read(1 << 20      )					,     b''''''      ):
                m.update(_UpperCamelCase      )
            __UpperCamelCase  		=						m.hexdigest()
    else:
        __UpperCamelCase  		=						None
    return {"num_bytes": os.path.getsize(_UpperCamelCase      ), "checksum": checksum}




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	int:

    """simple docstring"""


    if dataset_size and config.IN_MEMORY_MAX_SIZE:
        return dataset_size < config.IN_MEMORY_MAX_SIZE
    else:
        return False












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)






