

'''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


SCREAMING_SNAKE_CASE_    =   get_logger(__name__)

def   lowerCamelCase__       (      a__			,      a__)		->    Tuple:



    """simple docstring"""







    if text.startswith(a__):
        return text[len(a__) :]
    return text  # or whatever

def   lowerCamelCase__       (      a__)		->    Tuple:



    """simple docstring"""







    return {remove_prefix(a__			,      'model.'): v for k, v in sd.items()}

def   lowerCamelCase__       (      a__)		->    Optional[int]:



    """simple docstring"""







    _snake_case : Tuple												=		{}
    for k in state_dicts[0].keys():
        _snake_case : Any												=		[sd[k] for sd in state_dicts]
        _snake_case : Union[str, Any]												=		sum(a__) / len(a__)
        assert isinstance(a__			,      torch.Tensor)
        _snake_case : Union[str, Any]												=		new_t
    return new_sd

def   lowerCamelCase__       (      a__			,      a__			,      a__)		->    None:



    """simple docstring"""







    _snake_case : List[str]												=		AutoModelForSeqaSeqLM.from_pretrained(a__)
    if os.path.isfile(a__):
        _snake_case : Any												=		[pl_ckpt_path]
    else:
        assert os.path.isdir(a__)
        _snake_case : List[Any]												=		list(Path(a__).glob('*.ckpt'))
        assert ckpt_files, F"""could not find any ckpt files inside the {pl_ckpt_path} directory"""

    if len(a__) > 1:
        logger.info(F"""averaging the weights of {ckpt_files}""")

    _snake_case : List[str]												=		[sanitize(torch.load(a__			,      map_location='cpu')['state_dict']) for x in ckpt_files]
    _snake_case : Optional[int]												=		average_state_dicts(a__)

    _snake_case					,							_snake_case : Optional[Any]												=		hf_model.load_state_dict(a__			,      strict=a__)
    assert not missing, F"""missing keys: {missing}"""
    hf_model.save_pretrained(a__)
    try:
        _snake_case : Tuple												=		AutoTokenizer.from_pretrained(a__)
        tok.save_pretrained(a__)
    except Exception:
        pass
        # dont copy tokenizer if cant


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






'''simple docstring'''



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_    =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_    =   {}

class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				'''fsmt_decoder'''




      def __init__(					self :		Any     ,					snake_case :		int=0     ,					snake_case :		Dict=0   ):



          """simple docstring"""





          super().__init__()
          _snake_case : int												=		vocab_size
          _snake_case : Dict												=		bos_token_id



class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				'''fsmt'''
      SCREAMING_SNAKE_CASE__ :				Union[str, Any]    =				{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




      def __init__(					self :		Optional[int]     ,					snake_case :		Tuple=["en", "de"]     ,					snake_case :		Optional[int]=42024     ,					snake_case :		List[str]=42024     ,					snake_case :		Union[str, Any]="relu"     ,					snake_case :		Optional[Any]=1024     ,					snake_case :		Union[str, Any]=200     ,					snake_case :		int=1024     ,					snake_case :		str=4096     ,					snake_case :		Dict=12     ,					snake_case :		str=16     ,					snake_case :		List[str]=0.0     ,					snake_case :		List[Any]=4096     ,					snake_case :		str=12     ,					snake_case :		str=16     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		Optional[int]=0.0     ,					snake_case :		str=0.1     ,					snake_case :		Tuple=0.0     ,					snake_case :		Optional[int]=0.02     ,					snake_case :		List[str]=2     ,					snake_case :		int=True     ,					snake_case :		Any=True     ,					snake_case :		str=False     ,					snake_case :		Union[str, Any]=5     ,					snake_case :		Dict=1.0     ,					snake_case :		Union[str, Any]=False     ,					snake_case :		Optional[Any]=True     ,					snake_case :		Optional[int]=1     ,					snake_case :		int=0     ,					snake_case :		Optional[int]=2     ,					snake_case :		Optional[Any]=2     ,					**snake_case :		Any     ,					):



          """simple docstring"""





          _snake_case : int												=		langs
          _snake_case : str												=		src_vocab_size
          _snake_case : List[Any]												=		tgt_vocab_size
          _snake_case : Optional[int]												=		d_model  # encoder_embed_dim and decoder_embed_dim

          _snake_case : Optional[Any]												=		encoder_ffn_dim
          _snake_case : Dict												=		encoder_layers
          _snake_case : List[Any]												=		encoder_attention_heads
          _snake_case : Any												=		encoder_layerdrop
          _snake_case : List[str]												=		decoder_layerdrop
          _snake_case : str												=		decoder_ffn_dim
          _snake_case : Union[str, Any]												=		decoder_layers
          _snake_case : Tuple												=		decoder_attention_heads
          _snake_case : str												=		max_position_embeddings
          _snake_case : Dict												=		init_std  # Normal(0, this parameter)
          _snake_case : List[str]												=		activation_function

          _snake_case : Any												=		DecoderConfig(vocab_size=snake_case     ,					bos_token_id=snake_case   )
          if "decoder" in common_kwargs:
              del common_kwargs["decoder"]

          _snake_case : str												=		scale_embedding  # scale factor will be sqrt(d_model) if True

          # 3 Types of Dropout
          _snake_case : int												=		attention_dropout
          _snake_case : List[Any]												=		activation_dropout
          _snake_case : Optional[int]												=		dropout

          _snake_case : Union[str, Any]												=		use_cache
          super().__init__(
              pad_token_id=snake_case     ,					bos_token_id=snake_case     ,					eos_token_id=snake_case     ,					decoder_start_token_id=snake_case     ,					is_encoder_decoder=snake_case     ,					tie_word_embeddings=snake_case     ,					forced_eos_token_id=snake_case     ,					max_length=snake_case     ,					num_beams=snake_case     ,					length_penalty=snake_case     ,					early_stopping=snake_case     ,					**snake_case     ,					)




      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          _snake_case : Any												=		copy.deepcopy(self.__dict__   )
          _snake_case : str												=		self.decoder.to_dict()
          _snake_case : Dict												=		self.__class__.model_type
          return output



