




from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__SCREAMING_SNAKE_CASE     : Any      =logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     : Dict      ={
    """apple/mobilevit-small""": """https://huggingface.co/apple/mobilevit-small/resolve/main/config.json""",
    """apple/mobilevit-x-small""": """https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json""",
    """apple/mobilevit-xx-small""": """https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json""",
    """apple/deeplabv3-mobilevit-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-x-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"""
    ),
    """apple/deeplabv3-mobilevit-xx-small""": (
        """https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"""
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}






class       A      (		_a		):
     """simple docstring"""

     _snake_case:						int  ='mobilevit'




     def __init__(     self	: str						,	A__	: int=3						,	A__	: List[str]=2_5_6						,	A__	: Dict=2						,	A__	: Optional[int]=[1_4_4, 1_9_2, 2_4_0]						,	A__	: Optional[int]=[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]						,	A__	: Optional[Any]=4						,	A__	: str=2.0						,	A__	: List[Any]=4.0						,	A__	: Dict="silu"						,	A__	: Union[str, Any]=3						,	A__	: Optional[int]=3_2						,	A__	: List[str]=0.1						,	A__	: List[str]=0.0						,	A__	: Optional[int]=0.1						,	A__	: Dict=0.02						,	A__	: Optional[Any]=1E-5						,	A__	: str=True						,	A__	: Optional[int]=2_5_6						,	A__	: Dict=[6, 1_2, 1_8]						,	A__	: Any=0.1						,	A__	: Optional[int]=2_5_5						,	**A__	: Tuple						,	)					->							Optional[int]:
           super().__init__(**A__      )

           lowerCamelCase__   	=					num_channels
           lowerCamelCase__   	=					image_size
           lowerCamelCase__   	=					patch_size
           lowerCamelCase__   	=					hidden_sizes
           lowerCamelCase__   	=					neck_hidden_sizes
           lowerCamelCase__   	=					num_attention_heads
           lowerCamelCase__   	=					mlp_ratio
           lowerCamelCase__   	=					expand_ratio
           lowerCamelCase__   	=					hidden_act
           lowerCamelCase__   	=					conv_kernel_size
           lowerCamelCase__   	=					output_stride
           lowerCamelCase__   	=					hidden_dropout_prob
           lowerCamelCase__   	=					attention_probs_dropout_prob
           lowerCamelCase__   	=					classifier_dropout_prob
           lowerCamelCase__   	=					initializer_range
           lowerCamelCase__   	=					layer_norm_eps
           lowerCamelCase__   	=					qkv_bias

           # decode head attributes for semantic segmentation
           lowerCamelCase__   	=					aspp_out_channels
           lowerCamelCase__   	=					atrous_rates
           lowerCamelCase__   	=					aspp_dropout_prob
           lowerCamelCase__   	=					semantic_loss_ignore_index






class       A      (		_a		):
     """simple docstring"""

     _snake_case:						str  =version.parse("1.11"		)




     @property
     def 			__snake_case (     self	: List[Any]      )					->							Mapping[str, Mapping[int, str]]:
           return OrderedDict([('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'})]      )




     @property
     def 			__snake_case (     self	: Optional[Any]      )					->							Mapping[str, Mapping[int, str]]:
           if self.task == "image-classification":
                 return OrderedDict([('logits', {0: 'batch'})]      )
           else:
                 return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]      )




     @property
     def 			__snake_case (     self	: int      )					->							float:
           return 1E-4
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)

