



import unittest

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 LevitImageProcessor

class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def __init__(							self :     str				,       SCREAMING_SNAKE_CASE :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]=7				,       SCREAMING_SNAKE_CASE :     Optional[int]=3				,       SCREAMING_SNAKE_CASE :     Optional[Any]=18				,       SCREAMING_SNAKE_CASE :     List[Any]=30				,       SCREAMING_SNAKE_CASE :     Optional[Any]=400				,       SCREAMING_SNAKE_CASE :     Dict=True				,       SCREAMING_SNAKE_CASE :     Optional[int]=None				,       SCREAMING_SNAKE_CASE :     Tuple=True				,       SCREAMING_SNAKE_CASE :     List[Any]=None				,       SCREAMING_SNAKE_CASE :     Dict=True				,       SCREAMING_SNAKE_CASE :     Tuple=[0.5, 0.5, 0.5]				,       SCREAMING_SNAKE_CASE :     Optional[Any]=[0.5, 0.5, 0.5]				,       ):
									lowercase__					:   List[str]													=					size if size is not None else {'''shortest_edge''': 18}
									lowercase__					:   List[str]													=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									lowercase__					:   Union[str, Any]													=					parent
									lowercase__					:   Optional[Any]													=					batch_size
									lowercase__					:   Optional[int]													=					num_channels
									lowercase__					:   int													=					image_size
									lowercase__					:   Dict													=					min_resolution
									lowercase__					:   List[str]													=					max_resolution
									lowercase__					:   List[Any]													=					do_resize
									lowercase__					:   List[Any]													=					size
									lowercase__					:   Dict													=					do_center_crop
									lowercase__					:   Optional[int]													=					crop_size
									lowercase__					:   int													=					do_normalize
									lowercase__					:   Optional[Any]													=					image_mean
									lowercase__					:   int													=					image_std







					def 			snake_case					(							self :     int					):
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "do_center_crop": self.do_center_crop,
									    "size": self.size,
									    "crop_size": self.crop_size,
									}






@require_torch
@require_vision
class 				snake_case__(__SCREAMING_SNAKE_CASE    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		LevitImageProcessor if is_vision_available() else None







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   List[Any]													=					LevitImageProcessingTester(self					)







					@property
					def 			snake_case					(							self :     List[Any]					):
									return self.image_processor_tester.prepare_image_processor_dict()







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Any													=					self.image_processing_class(**self.image_processor_dict					)
									self.assertTrue(hasattr(UpperCamelCase__				,       "image_mean"					)					)
									self.assertTrue(hasattr(UpperCamelCase__				,       "image_std"					)					)
									self.assertTrue(hasattr(UpperCamelCase__				,       "do_normalize"					)					)
									self.assertTrue(hasattr(UpperCamelCase__				,       "do_resize"					)					)
									self.assertTrue(hasattr(UpperCamelCase__				,       "do_center_crop"					)					)
									self.assertTrue(hasattr(UpperCamelCase__				,       "size"					)					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Optional[Any]													=					self.image_processing_class.from_dict(self.image_processor_dict					)
									self.assertEqual(image_processor.size				,       {"shortest_edge": 18}					)
									self.assertEqual(image_processor.crop_size				,       {"height": 18, "width": 18}					)

									lowercase__					:   str													=					self.image_processing_class.from_dict(self.image_processor_dict				,       size=42				,       crop_size=84					)
									self.assertEqual(image_processor.size				,       {"shortest_edge": 42}					)
									self.assertEqual(image_processor.crop_size				,       {"height": 84, "width": 84}					)







					def 			snake_case					(							self :     List[str]					):
									pass







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   str													=					self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									lowercase__					:   Dict													=					prepare_image_inputs(self.image_processor_tester				,       equal_resolution=UpperCamelCase__					)
									for image in image_inputs:
													self.assertIsInstance(UpperCamelCase__				,       Image.Image					)

									# Test not batched input
									lowercase__					:   Optional[Any]													=					image_processing(image_inputs[0]				,       return_tensors="pt"					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )				,       )

									# Test batched
									lowercase__					:   int													=					image_processing(UpperCamelCase__				,       return_tensors="pt"					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )				,       )







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   List[Any]													=					self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									lowercase__					:   Union[str, Any]													=					prepare_image_inputs(self.image_processor_tester				,       equal_resolution=UpperCamelCase__				,       numpify=UpperCamelCase__					)
									for image in image_inputs:
													self.assertIsInstance(UpperCamelCase__				,       np.ndarray					)

									# Test not batched input
									lowercase__					:   Tuple													=					image_processing(image_inputs[0]				,       return_tensors="pt"					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )				,       )

									# Test batched
									lowercase__					:   Tuple													=					image_processing(UpperCamelCase__				,       return_tensors="pt"					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )				,       )







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Any													=					self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									lowercase__					:   Optional[int]													=					prepare_image_inputs(self.image_processor_tester				,       equal_resolution=UpperCamelCase__				,       torchify=UpperCamelCase__					)
									for image in image_inputs:
													self.assertIsInstance(UpperCamelCase__				,       torch.Tensor					)

									# Test not batched input
									lowercase__					:   int													=					image_processing(image_inputs[0]				,       return_tensors="pt"					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,       (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )				,       )

									# Test batched
									lowercase__					:   List[Any]													=					image_processing(UpperCamelCase__				,       return_tensors="pt"					).pixel_values
									self.assertEqual(
									    encoded_images.shape				,       (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size["height"],
									        self.image_processor_tester.crop_size["width"],
									    )				,       )









from typing import Union

import fire
import torch
from tqdm import tqdm


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__ = "cpu"					,			lowerCamelCase__ = None						):

				"""simple docstring"""


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


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




