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        UpperCAmelCase_					(				_a  ,  _a  ,  _a     ):




    @register_to_config
    def __init__(       self	: str   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: float   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: int   ,   lowerCAmelCase	: str   ,   lowerCAmelCase	: bool = False   ,   )						->			List[Any]:
      super().__init__()

      a__  		=  nn.Linear(lowerCAmelCase   ,   lowerCAmelCase   ,   bias=lowerCAmelCase )

      a__  		=  nn.Embedding(lowerCAmelCase   ,   lowerCAmelCase )
      a__  		=  False

      a__  		=  nn.Dropout(p=lowerCAmelCase )

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

      a__  		=  TaLayerNorm(lowerCAmelCase )
      a__  		=  nn.Dropout(p=lowerCAmelCase )




    def     __lowerCamelCase			(       self	: List[Any]   ,   lowerCAmelCase	: Any   ,   lowerCAmelCase	: Dict )						->			Union[str, Any]:
      a__  		=  self.input_proj(lowerCAmelCase )

      # terminal relative positional encodings
      a__  		=  encoder_inputs.shape[1]
      a__  		=  torch.arange(lowerCAmelCase   ,   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(lowerCAmelCase )

      a__  		=  self.dropout_pre(lowerCAmelCase )

      # inverted the attention mask
      a__  		=  encoder_inputs.size()
      a__  		=  self.get_extended_attention_mask(lowerCAmelCase   ,   lowerCAmelCase )

      for lyr in self.encoders:
        a__  		=  lyr(lowerCAmelCase   ,   lowerCAmelCase )[0]
      a__  		=  self.layer_norm(lowerCAmelCase )

      return self.dropout_post(lowerCAmelCase ), encoder_inputs_mask


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 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->Optional[int]:

  '''simple docstring'''




  if text.startswith(lowerCamelCase__       ):
    return text[len(lowerCamelCase__       ) :]
  return text  # or whatever
def 					__lowercase						(    lowerCamelCase__       )					->int:

  '''simple docstring'''




  return {remove_prefix(lowerCamelCase__			,					"model."       ): v for k, v in sd.items()}
def 					__lowercase						(    lowerCamelCase__       )					->Any:

  '''simple docstring'''




  a__  		=  {}
  for k in state_dicts[0].keys():
    a__  		=  [sd[k] for sd in state_dicts]
    a__  		=  sum(lowerCamelCase__       ) / len(lowerCamelCase__       )
    assert isinstance(lowerCamelCase__			,					torch.Tensor       )
    a__  		=  new_t
  return new_sd
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->None:

  '''simple docstring'''




  a__  		=  AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__       )
  if os.path.isfile(lowerCamelCase__       ):
    a__  		=  [pl_ckpt_path]
  else:
    assert os.path.isdir(lowerCamelCase__       )
    a__  		=  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}"       )

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

  a__		,a__  		=  hf_model.load_state_dict(lowerCamelCase__			,					strict=lowerCamelCase__       )
  assert not missing, f"missing keys: {missing}"
  hf_model.save_pretrained(lowerCamelCase__       )
  try:
    a__  		=  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)

