





'''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


snake_case_     			=						get_logger(__name__)
def 				_lowerCamelCase(	UpperCamelCase__     :			str     ,							UpperCamelCase__     :			str		)		->	Optional[Any]:
    if text.startswith(UpperCamelCase__		):
        return text[len(UpperCamelCase__		) :]
    return text  # or whatever
def 				_lowerCamelCase(	UpperCamelCase__     :			Optional[int]		)		->	Optional[Any]:
    return {remove_prefix(UpperCamelCase__     ,							'''model.'''		): v for k, v in sd.items()}
def 				_lowerCamelCase(	UpperCamelCase__     :			List[Dict[str, torch.Tensor]]		)		->	int:
    A							:							int				=    {}
    for k in state_dicts[0].keys():
        A							:							Tuple				=    [sd[k] for sd in state_dicts]
        A							:							str				=    sum(UpperCamelCase__		) / len(UpperCamelCase__		)
        assert isinstance(UpperCamelCase__     ,							torch.Tensor		)
        A							:							Tuple				=    new_t
    return new_sd
def 				_lowerCamelCase(	UpperCamelCase__     :			str     ,							UpperCamelCase__     :			str     ,							UpperCamelCase__     :			str		)		->	None:
    A							:							Tuple				=    AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__		)
    if os.path.isfile(UpperCamelCase__		):
        A							:							int				=    [pl_ckpt_path]
    else:
        assert os.path.isdir(UpperCamelCase__		)
        A							:							Optional[Any]				=    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							:							Optional[int]				=    [sanitize(torch.load(UpperCamelCase__     ,							map_location='''cpu'''		)['''state_dict''']		) for x in ckpt_files]
    A							:							List[str]				=    average_state_dicts(UpperCamelCase__		)

    A,						A							:							Optional[int]				=    hf_model.load_state_dict(UpperCamelCase__     ,							strict=UpperCamelCase__		)
    assert not missing, F'''missing keys: {missing}'''
    hf_model.save_pretrained(UpperCamelCase__		)
    try:
        A							:							List[str]				=    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)







'''simple docstring'''






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


snake_case_     			=						logging.get_logger(__name__)

snake_case_     			=						{
    """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 	_lowercase    (				a					):
   _UpperCamelCase									= """mobilevit"""


   def __init__(			self     , _UpperCAmelCase=3     , _UpperCAmelCase=256     , _UpperCAmelCase=2     , _UpperCAmelCase=[144, 192, 240]     , _UpperCAmelCase=[16, 32, 64, 96, 128, 160, 640]     , _UpperCAmelCase=4     , _UpperCAmelCase=2.0     , _UpperCAmelCase=4.0     , _UpperCAmelCase="silu"     , _UpperCAmelCase=3     , _UpperCAmelCase=32     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.0     , _UpperCAmelCase=0.1     , _UpperCAmelCase=0.02     , _UpperCAmelCase=1E-5     , _UpperCAmelCase=True     , _UpperCAmelCase=256     , _UpperCAmelCase=[6, 12, 18]     , _UpperCAmelCase=0.1     , _UpperCAmelCase=255     , **_UpperCAmelCase     , ):
       super().__init__(**_UpperCAmelCase       )

       A							:							Union[str, Any]				=    num_channels
       A							:							Union[str, Any]				=    image_size
       A							:							List[str]				=    patch_size
       A							:							Any				=    hidden_sizes
       A							:							Dict				=    neck_hidden_sizes
       A							:							int				=    num_attention_heads
       A							:							List[str]				=    mlp_ratio
       A							:							List[Any]				=    expand_ratio
       A							:							Dict				=    hidden_act
       A							:							List[Any]				=    conv_kernel_size
       A							:							List[str]				=    output_stride
       A							:							Dict				=    hidden_dropout_prob
       A							:							List[str]				=    attention_probs_dropout_prob
       A							:							Optional[Any]				=    classifier_dropout_prob
       A							:							Any				=    initializer_range
       A							:							Union[str, Any]				=    layer_norm_eps
       A							:							Union[str, Any]				=    qkv_bias

       # decode head attributes for semantic segmentation
       A							:							int				=    aspp_out_channels
       A							:							Any				=    atrous_rates
       A							:							Tuple				=    aspp_dropout_prob
       A							:							List[Any]				=    semantic_loss_ignore_index







class 	_lowercase    (				a					):
   _UpperCamelCase									= version.parse("""1.11"""					)


   @property
   def        snake_case					(			self       ):
       return OrderedDict([('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''})]       )


   @property
   def        snake_case					(			self       ):
       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       ):
       return 1E-4
