



"""simple docstring"""





_UpperCamelCase     :		int											=					{
    "meter": "m",
    "kilometer": "km",
    "megametre": "Mm",
    "gigametre": "Gm",
    "terametre": "Tm",
    "petametre": "Pm",
    "exametre": "Em",
    "zettametre": "Zm",
    "yottametre": "Ym",
}
# Exponent of the factor(meter)
_UpperCamelCase     :		Optional[Any]											=					{
    "m": 0,
    "km": 3,
    "Mm": 6,
    "Gm": 9,
    "Tm": 12,
    "Pm": 15,
    "Em": 18,
    "Zm": 21,
    "Ym": 24,
}





def        a_  (   _lowerCAmelCase						:				float     ,				_lowerCAmelCase						:				str     ,				_lowerCAmelCase						:				str  ):





       '''simple docstring'''

       lowercase__    :	Dict     					=			from_type.lower().strip('s'  )
       lowercase__    :	str     					=			to_type.lower().strip('s'  )

       lowercase__    :	Tuple     					=			UNIT_SYMBOL.get(_lowerCAmelCase     ,				_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			UNIT_SYMBOL.get(_lowerCAmelCase     ,				_lowerCAmelCase  )

       if from_sanitized not in METRIC_CONVERSION:
              lowercase__    :	Optional[Any]     					=			(
                  f"""Invalid 'from_type' value: {from_type!r}.\n"""
                  f"""Conversion abbreviations are: {", ".join(_lowerCAmelCase  )}"""
              )
              raise ValueError(_lowerCAmelCase  )
       if to_sanitized not in METRIC_CONVERSION:
              lowercase__    :	List[str]     					=			(
                  f"""Invalid 'to_type' value: {to_type!r}.\n"""
                  f"""Conversion abbreviations are: {", ".join(_lowerCAmelCase  )}"""
              )
              raise ValueError(_lowerCAmelCase  )
       lowercase__    :	Optional[Any]     					=			METRIC_CONVERSION[from_sanitized]
       lowercase__    :	str     					=			METRIC_CONVERSION[to_sanitized]
       lowercase__    :	Union[str, Any]     					=			1

       if from_exponent > to_exponent:
              lowercase__    :	int     					=			from_exponent - to_exponent
       else:
              lowercase__    :	Dict     					=			-(to_exponent - from_exponent)

       return value * pow(10     ,				_lowerCAmelCase  )


if __name__ == "__main__":
       from doctest import testmod

       testmod()


"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor


@require_torch
@require_vision
class   UpperCAmelCase_			(					unittest.TestCase):


      @slow
      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	Optional[Any]     					=			AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             lowercase__    :	Union[str, Any]     					=			AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'							)
             model.to(a							)

             from datasets import load_dataset

             lowercase__    :	str     					=			load_dataset('nielsr/rvlcdip-demo'							)

             lowercase__    :	Tuple     					=			dataset['train'][0]['image'].convert('RGB'							)

             lowercase__    :	int     					=			image_processor(a	,    return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__    :	List[str]     					=			model(**a							)
                    lowercase__    :	List[Any]     					=			outputs.logits

             lowercase__    :	Union[str, Any]     					=			torch.Size((1, 1_6)							)
             self.assertEqual(logits.shape	,    a							)

             lowercase__    :	Tuple     					=			torch.tensor(
                 [-0.4_158, -0.4_092, -0.4_347]	,    device=a	,    dtype=torch.float	,    )
             self.assertTrue(torch.allclose(logits[0, :3]	,    a	,    atol=1e-4							)							)

