"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from .utils.import_utils import is_torchvision_available
from .image_processing_utils import BaseImageProcessor
from dataclasses import dataclass
import functools
if is_torchvision_available(): from torchvision.transforms import Compose
@dataclass(frozen=True)
class SizeDict:
    height: int = None
    width: int = None
    longest_edge: int = None
    shortest_edge: int = None
    max_height: int = None
    max_width: int = None
    def __getitem__(self, key):
        if hasattr(self, key): return getattr(self, key)
        raise KeyError(f"Key {key} not found in SizeDict.")
class BaseImageProcessorFast(BaseImageProcessor):
    _transform_params = None
    def _build_transforms(self, **kwargs) -> "Compose": raise NotImplementedError
    def _validate_params(self, **kwargs) -> None:
        for k, v in kwargs.items():
            if k not in self._transform_params: raise ValueError(f"Invalid transform parameter {k}={v}.")
    @functools.lru_cache(maxsize=1)
    def get_transforms(self, **kwargs) -> "Compose":
        self._validate_params(**kwargs)
        return self._build_transforms(**kwargs)
    def to_dict(self):
        encoder_dict = super().to_dict()
        encoder_dict.pop("_transform_params", None)
        return encoder_dict
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
