





"""simple docstring"""



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 		__lowercase					(       snake_case_  :  Optional[int]						)       ->Union[str, Any]:



    '''simple docstring'''







    __A   :						Optional[int]									=					torch.load(snake_case_    ,map_location='''cpu'''						)
    if "model" in sd.keys():
        __A   :						List[str]									=					torch.load(snake_case_    ,map_location='''cpu'''						)['''model''']

    # pop unnecessary weights
    __A   :						Optional[int]									=					[
        '''decoder.version''',
        '''decoder.output_projection.weight''',
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(snake_case_						)

    __A   :						List[Any]									=					{
        '''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   :						str									=					sd.pop(snake_case_						)

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

            __A   :						Optional[Any]									=					key.replace('''.qkv_proj.'''    ,'''.q_proj.'''						)
            __A   :						Dict									=					key.replace('''.qkv_proj.'''    ,'''.k_proj.'''						)
            __A   :						Tuple									=					key.replace('''.qkv_proj.'''    ,'''.v_proj.'''						)

            __A   :						Union[str, 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
            __A			,	__A			,	__A   :						Optional[Any]									=					torch.split(snake_case_    ,depth // 3    ,dim=0						)

            __A   :						str									=					q
            __A   :						Union[str, Any]									=					k
            __A   :						str									=					v
            del sd[key]

    return sd







@torch.no_grad()
def 		__lowercase					(       snake_case_  :  str    ,snake_case_  :  Tuple    ,snake_case_  :  Optional[int]=None						)       ->str:



    '''simple docstring'''







    __A   :						int									=					load_checkpoint(snake_case_						)

    if config is not None:
        __A   :						Dict									=					OPTConfig.from_pretrained(snake_case_						)
    else:
        __A   :						str									=					OPTConfig()

    __A   :						Optional[int]									=					OPTModel(snake_case_						).half().eval()
    model.load_state_dict(snake_case_						)

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


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)











"""simple docstring"""



import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a_			=      {
    """E""": 12.70,
    """T""": 9.06,
    """A""": 8.17,
    """O""": 7.51,
    """I""": 6.97,
    """N""": 6.75,
    """S""": 6.33,
    """H""": 6.09,
    """R""": 5.99,
    """D""": 4.25,
    """L""": 4.03,
    """C""": 2.78,
    """U""": 2.76,
    """M""": 2.41,
    """W""": 2.36,
    """F""": 2.23,
    """G""": 2.02,
    """Y""": 1.97,
    """P""": 1.93,
    """B""": 1.29,
    """V""": 0.98,
    """K""": 0.77,
    """J""": 0.15,
    """X""": 0.15,
    """Q""": 0.10,
    """Z""": 0.07,
}
a_			=      """ETAOINSHRDLCUMWFGYPBVKJXQZ"""
a_			=      """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""







def 		__lowercase					(       snake_case_  :  str						)       ->dict[str, int]:



    '''simple docstring'''







    __A   :						Any									=					{letter: 0 for letter in string.ascii_uppercase}
    for letter in message.upper():
        if letter in LETTERS:
            letter_count[letter] += 1

    return letter_count







def 		__lowercase					(       snake_case_  :  tuple						)       ->str:



    '''simple docstring'''







    return x[0]







def 		__lowercase					(       snake_case_  :  str						)       ->str:



    '''simple docstring'''







    __A   :						Union[str, Any]									=					get_letter_count(snake_case_						)
    __A   :						dict[int, list[str]]									=					{
        freq: [] for letter, freq in letter_to_freq.items()
    }
    for letter in LETTERS:
        freq_to_letter[letter_to_freq[letter]].append(snake_case_						)

    __A   :						dict[int, str]									=					{}

    for freq in freq_to_letter:
        freq_to_letter[freq].sort(key=ETAOIN.find    ,reverse=snake_case_						)
        __A   :						Optional[int]									=					''''''.join(freq_to_letter[freq]						)

    __A   :						str									=					list(freq_to_letter_str.items()						)
    freq_pairs.sort(key=snake_case_    ,reverse=snake_case_						)

    __A   :						list[str]									=					[freq_pair[1] for freq_pair in freq_pairs]

    return "".join(snake_case_						)







def 		__lowercase					(       snake_case_  :  str						)       ->int:



    '''simple docstring'''







    __A   :						Any									=					get_frequency_order(snake_case_						)
    __A   :						str									=					0
    for common_letter in ETAOIN[:6]:
        if common_letter in freq_order[:6]:
            match_score += 1

    for uncommon_letter in ETAOIN[-6:]:
        if uncommon_letter in freq_order[-6:]:
            match_score += 1

    return match_score


if __name__ == "__main__":
       import doctest

       doctest.testmod()




