




import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]    ,    __UpperCAmelCase		:			Dict							)      ->				Any:
      SCREAMING_SNAKE_CASE_					=    copy.deepcopy(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    sorted(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    [bisect.bisect_right(__UpperCAmelCase    ,    __UpperCAmelCase							) for y in x]
      return quantized

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			int=0							)      ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE_					=    np.arange(start=3    ,    stop=__UpperCAmelCase    ,    step=4							).tolist() if k > 0 else [10]
      SCREAMING_SNAKE_CASE_					=    _quantize(__UpperCAmelCase    ,    __UpperCAmelCase							)
      # count number of elements per group
      SCREAMING_SNAKE_CASE_					=    np.unique(__UpperCAmelCase    ,    return_counts=__UpperCAmelCase							)[1]
      SCREAMING_SNAKE_CASE_					=    [0] + bins + [np.inf]
      logger.info('Using {} as bins for aspect lengths quantization'.format(__UpperCAmelCase							)							)
      logger.info('Count of instances per bin: {}'.format(__UpperCAmelCase							)							)
      return groups





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 def __init__(							self						:	List[Any]       ,       _lowerCAmelCase						:	Any       ,       _lowerCAmelCase						:	str       ,       _lowerCAmelCase						:	Any		):
       if not isinstance(_lowerCAmelCase       ,       _lowerCAmelCase		):
             raise ValueError(
                 'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(_lowerCAmelCase		)		)
       SCREAMING_SNAKE_CASE_					=    sampler
       SCREAMING_SNAKE_CASE_					=    group_ids
       SCREAMING_SNAKE_CASE_					=    batch_size



 def __iter__(							self						:	str		):
       SCREAMING_SNAKE_CASE_					=    defaultdict(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_					=    defaultdict(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_					=    0
       for idx in self.sampler:
             SCREAMING_SNAKE_CASE_					=    self.group_ids[idx]
             buffer_per_group[group_id].append(_lowerCAmelCase		)
             samples_per_group[group_id].append(_lowerCAmelCase		)
             if len(buffer_per_group[group_id]		) == self.batch_size:
                   yield buffer_per_group[group_id]  # TODO
                   num_batches += 1
                   del buffer_per_group[group_id]
             assert len(buffer_per_group[group_id]		) < self.batch_size

       # now we have run out of elements that satisfy
       # the group criteria, let's return the remaining
       # elements so that the size of the sampler is
       # deterministic
       SCREAMING_SNAKE_CASE_					=    len(self		)
       SCREAMING_SNAKE_CASE_					=    expected_num_batches - num_batches
       if num_remaining > 0:
             # for the remaining batches, group the batches by similar lengths
             SCREAMING_SNAKE_CASE_					=    []
             for group_id, idxs in sorted(buffer_per_group.items()       ,       key=lambda _lowerCAmelCase		: x[0]		):
                   batch_idx.extend(_lowerCAmelCase		)
                   if len(_lowerCAmelCase		) >= self.batch_size:
                         yield batch_idx[: self.batch_size]
                         SCREAMING_SNAKE_CASE_					=    batch_idx[self.batch_size :]
                         num_remaining -= 1
             if len(_lowerCAmelCase		) > 0:
                   yield batch_idx
                   num_remaining -= 1
       assert num_remaining == 0




 def __len__(							self						:	Tuple		):
       return (len(self.sampler		) + self.batch_size - 1) // self.batch_size





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__				:     Optional[Any]			       =							get_logger(__name__)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				List[str]:
      if text.startswith(__UpperCAmelCase							):
            return text[len(__UpperCAmelCase							) :]
      return text  # or whatever

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

def       UpperCAmelCase_							(   __UpperCAmelCase		:			List[Dict[str, torch.Tensor]]							)      ->				Dict:
      SCREAMING_SNAKE_CASE_					=    {}
      for k in state_dicts[0].keys():
            SCREAMING_SNAKE_CASE_					=    [sd[k] for sd in state_dicts]
            SCREAMING_SNAKE_CASE_					=    sum(__UpperCAmelCase							) / len(__UpperCAmelCase							)
            assert isinstance(__UpperCAmelCase    ,    torch.Tensor							)
            SCREAMING_SNAKE_CASE_					=    new_t
      return new_sd

def       UpperCAmelCase_							(   __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			str							)      ->				None:
      SCREAMING_SNAKE_CASE_					=    AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase							)
      if os.path.isfile(__UpperCAmelCase							):
            SCREAMING_SNAKE_CASE_					=    [pl_ckpt_path]
      else:
            assert os.path.isdir(__UpperCAmelCase							)
            SCREAMING_SNAKE_CASE_					=    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}"							)

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

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