


"""simple docstring"""







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


lowerCamelCase_  :  str     		=    get_logger(__name__)
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    if text.startswith(lowercase						):
        return text[len(lowercase						) :]
    return text  # or whatever
def _A		(		lowercase						):







    """simple docstring"""
    return {remove_prefix(lowercase			,       '''model.'''						): v for k, v in sd.items()}
def _A		(		lowercase						):







    """simple docstring"""
    a											={}
    for k in state_dicts[0].keys():
        a											=[sd[k] for sd in state_dicts]
        a											=sum(lowercase						) / len(lowercase						)
        assert isinstance(lowercase			,       torch.Tensor						)
        a											=new_t
    return new_sd
def _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    a											=AutoModelForSeqaSeqLM.from_pretrained(lowercase						)
    if os.path.isfile(lowercase						):
        a											=[pl_ckpt_path]
    else:
        assert os.path.isdir(lowercase						)
        a											=list(Path(lowercase						).glob('''*.ckpt'''						)						)
        assert ckpt_files, f'''could not find any ckpt files inside the {pl_ckpt_path} directory'''

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

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

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


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



"""simple docstring"""







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class  __A							(			_SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE     ):






 """simple docstring"""




 @register_to_config
 def __init__(						self					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A					,       __A = False					,       )   ->	Optional[Any]:
     super().__init__()

     a											=nn.Linear(__A					,       __A					,       bias=__A		)

     a											=nn.Embedding(__A					,       __A		)
     a											=False

     a											=nn.Dropout(p=__A		)

     a											=TaConfig(
         d_model=__A					,       num_heads=__A					,       d_kv=__A					,       d_ff=__A					,       feed_forward_proj=__A					,       dropout_rate=__A					,       is_decoder=__A					,       is_encoder_decoder=__A					,       )
     a											=nn.ModuleList()
     for lyr_num in range(__A		):
         a											=TaBlock(__A		)
         self.encoders.append(__A		)

     a											=TaLayerNorm(__A		)
     a											=nn.Dropout(p=__A		)





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A		)   ->	str:
     a											=self.input_proj(__A		)

     # terminal relative positional encodings
     a											=encoder_inputs.shape[1]
     a											=torch.arange(__A					,       device=encoder_inputs.device		)

     a											=encoder_inputs_mask.sum(-1		)
     a											=torch.roll(input_positions.unsqueeze(0		)					,       tuple(seq_lens.tolist()		)					,       dims=0		)
     x += self.position_encoding(__A		)

     a											=self.dropout_pre(__A		)

     # inverted the attention mask
     a											=encoder_inputs.size()
     a											=self.get_extended_attention_mask(__A					,       __A		)

     for lyr in self.encoders:
         a											=lyr(__A					,       __A		)[0]
     a											=self.layer_norm(__A		)

     return self.dropout_post(__A		), encoder_inputs_mask