




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


lowercase__   :    List[Any] 					=      get_logger(__name__)

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase	,      __UpperCamelCase)    ->  Optional[Any]:
     if text.startswith(__UpperCamelCase):
          return text[len(__UpperCamelCase) :]
     return text  # or whatever

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  List[Any]:
     return {remove_prefix(__UpperCamelCase	,      "model."): v for k, v in sd.items()}

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  Any:
     a											=   {}
     for k in state_dicts[0].keys():
          a											=   [sd[k] for sd in state_dicts]
          a											=   sum(__UpperCamelCase) / len(__UpperCamelCase)
          assert isinstance(__UpperCamelCase	,      torch.Tensor)
          a											=   new_t
     return new_sd

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

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

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

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


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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__   :    str 					=      logging.get_logger(__name__)

lowercase__   :    List[str] 					=      {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}




class     a__ (      UpperCamelCase__   ):
     a	:    Union[str, Any]	      =			MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
     a	:    Optional[int]	      =			"""mobilebert"""







     def __init__(			self						,	A=30522						,	A=512						,	A=24						,	A=4						,	A=512						,	A="relu"						,	A=0.0						,	A=0.1						,	A=512						,	A=2						,	A=0.0_2						,	A=1e-12						,	A=0						,	A=128						,	A=True						,	A=True						,	A=128						,	A=False						,	A=True						,	A=4						,	A="no_norm"						,	A=True						,	A=None						,	**A						,	)						->					str:



          '''simple docstring'''



          super().__init__(pad_token_id=A						,	**A				)

          a											=   vocab_size
          a											=   hidden_size
          a											=   num_hidden_layers
          a											=   num_attention_heads
          a											=   hidden_act
          a											=   intermediate_size
          a											=   hidden_dropout_prob
          a											=   attention_probs_dropout_prob
          a											=   max_position_embeddings
          a											=   type_vocab_size
          a											=   initializer_range
          a											=   layer_norm_eps
          a											=   embedding_size
          a											=   trigram_input
          a											=   use_bottleneck
          a											=   intra_bottleneck_size
          a											=   use_bottleneck_attention
          a											=   key_query_shared_bottleneck
          a											=   num_feedforward_networks
          a											=   normalization_type
          a											=   classifier_activation

          if self.use_bottleneck:
               a											=   intra_bottleneck_size
          else:
               a											=   hidden_size

          a											=   classifier_dropout







class     a__ (      UpperCamelCase__   ):







     @property
     def     lowerCAmelCase_       (			self				)						->					Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          if self.task == "multiple-choice":
               a											=   {0: "batch", 1: "choice", 2: "sequence"}
          else:
               a											=   {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ]				)

