




import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BridgeTowerImageProcessor
class 						_A (  unittest.TestCase				):






  def __init__(  self		:     Tuple				,						_A		:     Optional[Any]				,						_A		:     bool = True				,						_A		:     Dict[str, int] = None				,						_A		:     int = 32				,						_A		:     bool = True				,						_A		:     Union[int, float] = 1 / 255				,						_A		:     bool = True				,						_A		:     bool = True				,						_A		:     Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]				,						_A		:     Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]				,						_A		:     bool = True				,						_A		:     int=7				,						_A		:     Optional[int]=30				,						_A		:     Union[str, Any]=400				,						_A		:     Optional[int]=3				,						)		->   Tuple:






       """simple docstring"""







       lowercase :       str       =			parent
       lowercase :       Union[str, Any]       =			do_resize
       lowercase :       List[str]       =			size if size is not None else {'''shortest_edge''': 288}
       lowercase :       Optional[Any]       =			size_divisor
       lowercase :       List[Any]       =			do_rescale
       lowercase :       Any       =			rescale_factor
       lowercase :       Union[str, Any]       =			do_normalize
       lowercase :       List[str]       =			do_center_crop
       lowercase :       Tuple       =			image_mean
       lowercase :       Dict       =			image_std
       lowercase :       Optional[int]       =			do_pad
       lowercase :       int       =			batch_size
       lowercase :       str       =			num_channels
       lowercase :       Optional[Any]       =			min_resolution
       lowercase :       Any       =			max_resolution






  def  __a      (  self		:     List[Any]				)		->   List[Any]:






       """simple docstring"""







       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "size_divisor": self.size_divisor,
       }






  def  __a      (  self		:     int				,						_A		:     List[Any]				,						_A		:     List[Any]=False				)		->   Tuple:






       """simple docstring"""







       if not batched:
            lowercase :       List[str]       =			self.size['''shortest_edge''']
            lowercase :       Union[str, Any]       =			image_inputs[0]
            if isinstance(_A				,						Image.Image				):
                 lowercase      ,	lowercase :       Optional[int]       =			image.size
            else:
                 lowercase      ,	lowercase :       Optional[Any]       =			image.shape[1], image.shape[2]
            lowercase :       Dict       =			size / min(_A				,						_A				)
            if h < w:
                 lowercase      ,	lowercase :       Optional[Any]       =			size, scale * w
            else:
                 lowercase      ,	lowercase :       List[Any]       =			scale * h, size

            lowercase :       Any       =			int((1_333 / 800) * size				)
            if max(_A				,						_A				) > max_size:
                 lowercase :       str       =			max_size / max(_A				,						_A				)
                 lowercase :       int       =			newh * scale
                 lowercase :       Any       =			neww * scale

            lowercase      ,	lowercase :       str       =			int(newh + 0.5				), int(neww + 0.5				)
            lowercase      ,	lowercase :       int       =			(
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

       else:
            lowercase :       Tuple       =			[]
            for image in image_inputs:
                 lowercase      ,	lowercase :       str       =			self.get_expected_values([image]				)
                 expected_values.append((expected_height, expected_width)				)
            lowercase :       Tuple       =			max(_A				,						key=lambda _A				: item[0]				)[0]
            lowercase :       Union[str, Any]       =			max(_A				,						key=lambda _A				: item[1]				)[1]

       return expected_height, expected_width


@require_torch
@require_vision
class 						_A (  _lowerCamelCase      ,       unittest.TestCase				):
  _UpperCamelCase       :      List[str]			=      BridgeTowerImageProcessor if is_vision_available() else None






  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







       lowercase :       str       =			BridgeTowerImageProcessingTester(self				)






  @property
  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







       return self.image_processor_tester.prepare_image_processor_dict()






  def  __a      (  self		:     str				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Optional[int]       =			self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(_A				,						'''image_mean'''				)				)
       self.assertTrue(hasattr(_A				,						'''image_std'''				)				)
       self.assertTrue(hasattr(_A				,						'''do_normalize'''				)				)
       self.assertTrue(hasattr(_A				,						'''do_resize'''				)				)
       self.assertTrue(hasattr(_A				,						'''size'''				)				)
       self.assertTrue(hasattr(_A				,						'''size_divisor'''				)				)






  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







       pass






  def  __a      (  self		:     Union[str, Any]				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       lowercase :       Optional[Any]       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_A				)
       for image in image_inputs:
            self.assertIsInstance(_A				,						Image.Image				)

       # Test not batched input
       lowercase :       int       =			image_processing(image_inputs[0]				,						return_tensors='''pt'''				).pixel_values

       lowercase      ,	lowercase :       Dict       =			self.image_processor_tester.get_expected_values(_A				)
       self.assertEqual(
           encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

       # Test batched
       lowercase :       str       =			image_processing(_A				,						return_tensors='''pt'''				).pixel_values

       lowercase      ,	lowercase :       Tuple       =			self.image_processor_tester.get_expected_values(_A				,						batched=_A				)
       self.assertEqual(
           encoded_images.shape				,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )				,						)






  def  __a      (  self		:     Any				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Optional[Any]       =			self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       lowercase :       List[Any]       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_A				,						numpify=_A				)
       for image in image_inputs:
            self.assertIsInstance(_A				,						np.ndarray				)

       # Test not batched input
       lowercase :       Union[str, Any]       =			image_processing(image_inputs[0]				,						return_tensors='''pt'''				).pixel_values

       lowercase      ,	lowercase :       Tuple       =			self.image_processor_tester.get_expected_values(_A				)
       self.assertEqual(
           encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

       # Test batched
       lowercase :       Tuple       =			image_processing(_A				,						return_tensors='''pt'''				).pixel_values

       lowercase      ,	lowercase :       str       =			self.image_processor_tester.get_expected_values(_A				,						batched=_A				)
       self.assertEqual(
           encoded_images.shape				,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )				,						)






  def  __a      (  self		:     str				)		->   Dict:






       """simple docstring"""







       lowercase :       str       =			self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       lowercase :       Union[str, Any]       =			prepare_image_inputs(self.image_processor_tester				,						equal_resolution=_A				,						torchify=_A				)
       for image in image_inputs:
            self.assertIsInstance(_A				,						torch.Tensor				)

       # Test not batched input
       lowercase :       Optional[int]       =			image_processing(image_inputs[0]				,						return_tensors='''pt'''				).pixel_values

       lowercase      ,	lowercase :       Optional[Any]       =			self.image_processor_tester.get_expected_values(_A				)
       self.assertEqual(
           encoded_images.shape				,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,						)

       # Test batched
       lowercase :       str       =			image_processing(_A				,						return_tensors='''pt'''				).pixel_values

       lowercase      ,	lowercase :       int       =			self.image_processor_tester.get_expected_values(_A				,						batched=_A				)
       self.assertEqual(
           encoded_images.shape				,						(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )				,						)





from typing import Union

import fire
import torch
from tqdm import tqdm






def  snake_case(    __magic_name__			,		__magic_name__ = "cpu"			,		__magic_name__ = None   )			->							None:





     '''simple docstring'''


     lowercase :       int       =			torch.load(__magic_name__			,		map_location=__magic_name__   )
     for k, v in tqdm(state_dict.items()   ):
          if not isinstance(__magic_name__			,		torch.Tensor   ):
               raise TypeError('''FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'''   )
          lowercase :       Any       =			v.half()
     if save_path is None:  # overwrite src_path
          lowercase :       str       =			src_path
     torch.save(__magic_name__			,		__magic_name__   )


if __name__ == "__main__":
 fire.Fire(convert)