





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	torch.Tensor:
    # The first operation in a checkpoint can't be in-place, but it's
    # nice to have in-place addition during inference. Thus...
    if not inplace:
        SCREAMING_SNAKE_CASE__       							= ma + ma
    else:
        ma += ma

    return ma


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	torch.Tensor:
    SCREAMING_SNAKE_CASE__       							= -1 * len(lowerCAmelCase_  )
    SCREAMING_SNAKE_CASE__       							= list(range(len(tensor.shape[:zero_index]  )  )  )
    return tensor.permute(first_inds + [zero_index + i for i in inds]  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	torch.Tensor:
    return t.reshape(t.shape[:-no_dims] + (-1,)  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_ = 1e-4  )							->	torch.Tensor:
    SCREAMING_SNAKE_CASE__       							= mask.expand(*value.shape  )
    return torch.sum(mask * value   ,       dim=lowerCAmelCase_  ) / (eps + torch.sum(lowerCAmelCase_   ,       dim=lowerCAmelCase_  ))


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_ = 2.31_25   ,       lowerCAmelCase_ = 21.68_75   ,       lowerCAmelCase_ = 6_4  )							->	torch.Tensor:
    SCREAMING_SNAKE_CASE__       							= torch.linspace(lowerCAmelCase_   ,       lowerCAmelCase_   ,       no_bins - 1   ,       device=pts.device  )
    SCREAMING_SNAKE_CASE__       							= torch.sqrt(torch.sum((pts.unsqueeze(-2  ) - pts.unsqueeze(-3  )) ** 2   ,       dim=-1  )  )
    return torch.bucketize(lowerCAmelCase_   ,       lowerCAmelCase_  )


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	dict:
    SCREAMING_SNAKE_CASE__       							= dicts[0]
    SCREAMING_SNAKE_CASE__       							= {}
    for k, v in first.items():
        SCREAMING_SNAKE_CASE__       							= [d[k] for d in dicts]
        if isinstance(lowerCAmelCase_   ,       lowerCAmelCase_  ):
            SCREAMING_SNAKE_CASE__       							= dict_multimap(lowerCAmelCase_   ,       lowerCAmelCase_  )
        else:
            SCREAMING_SNAKE_CASE__       							= fn(lowerCAmelCase_  )

    return new_dict


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	torch.Tensor:
    SCREAMING_SNAKE_CASE__       							= v_bins.view(((1,) * len(x.shape  )) + (len(lowerCAmelCase_  ),)  )
    SCREAMING_SNAKE_CASE__       							= x[..., None] - reshaped_bins
    SCREAMING_SNAKE_CASE__       							= torch.argmin(torch.abs(lowerCAmelCase_  )   ,       dim=-1  )
    return nn.functional.one_hot(lowerCAmelCase_   ,       num_classes=len(lowerCAmelCase_  )  ).float()


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_ = 0   ,       lowerCAmelCase_ = 0  )							->	torch.Tensor:
    SCREAMING_SNAKE_CASE__       							= []
    for i, s in enumerate(data.shape[:no_batch_dims]  ):
        SCREAMING_SNAKE_CASE__       							= torch.arange(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape  ) - i - 1)))  )
        ranges.append(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__       							= [slice(lowerCAmelCase_  ) for _ in range(len(data.shape  ) - no_batch_dims  )]
    SCREAMING_SNAKE_CASE__       							= inds
    ranges.extend(lowerCAmelCase_  )
    # Matt note: Editing this to get around the behaviour of using a list as an array index changing
    # in recent Numpy versions
    return data[tuple(lowerCAmelCase_  )]


_A				:							List[str]							= TypeVar("""T""")


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Dict[Any, Union[dict, list, tuple, Any]]:
    SCREAMING_SNAKE_CASE__       							= {}
    for k, v in dic.items():
        if isinstance(lowerCAmelCase_   ,       lowerCAmelCase_  ):
            SCREAMING_SNAKE_CASE__       							= dict_map(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
        else:
            SCREAMING_SNAKE_CASE__       							= tree_map(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )

    return new_dict


@overload
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Any:
    ...


@overload
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	dict:
    ...


@overload
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	list:
    ...


@overload
def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	tuple:
    ...


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	List[str]:
    if isinstance(lowerCAmelCase_   ,       lowerCAmelCase_  ):
        return dict_map(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )
    elif isinstance(lowerCAmelCase_   ,       lowerCAmelCase_  ):
        return [tree_map(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  ) for x in tree]
    elif isinstance(lowerCAmelCase_   ,       lowerCAmelCase_  ):
        return tuple(tree_map(lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  ) for x in tree  )
    elif isinstance(lowerCAmelCase_   ,       lowerCAmelCase_  ):
        return fn(lowerCAmelCase_  )
    else:
        print(type(lowerCAmelCase_  )  )
        raise ValueError('''Not supported'''  )



_A				:							Optional[int]							= partial(tree_map, leaf_type=torch.Tensor)







import os
from pathlib import Path
from typing import Dict, List

import fire
import torch

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.utils.logging import get_logger


_A				:							Any							= get_logger(__name__)


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	str:
    if text.startswith(lowerCAmelCase_  ):
        return text[len(lowerCAmelCase_  ) :]
    return text  # or whatever


def        __snake_case       (						lowerCAmelCase_  )							->	Union[str, Any]:
    return {remove_prefix(lowerCAmelCase_   ,       '''model.'''  ): v for k, v in sd.items()}


def        __snake_case       (						lowerCAmelCase_  )							->	Optional[int]:
    SCREAMING_SNAKE_CASE__       							= {}
    for k in state_dicts[0].keys():
        SCREAMING_SNAKE_CASE__       							= [sd[k] for sd in state_dicts]
        SCREAMING_SNAKE_CASE__       							= sum(lowerCAmelCase_  ) / len(lowerCAmelCase_  )
        assert isinstance(lowerCAmelCase_   ,       torch.Tensor  )
        SCREAMING_SNAKE_CASE__       							= new_t
    return new_sd


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_   ,       lowerCAmelCase_  )							->	None:
    SCREAMING_SNAKE_CASE__       							= AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase_  )
    if os.path.isfile(lowerCAmelCase_  ):
        SCREAMING_SNAKE_CASE__       							= [pl_ckpt_path]
    else:
        assert os.path.isdir(lowerCAmelCase_  )
        SCREAMING_SNAKE_CASE__       							= list(Path(lowerCAmelCase_  ).glob('''*.ckpt'''  )  )
        assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

    if len(lowerCAmelCase_  ) > 1:
        logger.info(f'''averaging the weights of {ckpt_files}'''  )

    SCREAMING_SNAKE_CASE__       							= [sanitize(torch.load(lowerCAmelCase_   ,       map_location='''cpu'''  )['''state_dict''']  ) for x in ckpt_files]
    SCREAMING_SNAKE_CASE__       							= average_state_dicts(lowerCAmelCase_  )

    SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__       							= hf_model.load_state_dict(lowerCAmelCase_   ,       strict=lowerCAmelCase_  )
    assert not missing, f'''missing keys: {missing}'''
    hf_model.save_pretrained(lowerCAmelCase_  )
    try:
        SCREAMING_SNAKE_CASE__       							= AutoTokenizer.from_pretrained(lowerCAmelCase_  )
        tok.save_pretrained(lowerCAmelCase_  )
    except Exception:
        pass
        # dont copy tokenizer if cant


if __name__ == "__main__":
  fire.Fire(convert_pl_to_hf)
