
"""simple docstring"""

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 YolosImageProcessor

class __lowerCAmelCase		(				unittest.TestCase      ):





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=7     ,					__UpperCAmelCase=3     ,					__UpperCAmelCase=30     ,					__UpperCAmelCase=400     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=None     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=[0.5, 0.5, 0.5]     ,					__UpperCAmelCase=[0.5, 0.5, 0.5]     ,					__UpperCAmelCase=True     ,					__UpperCAmelCase=1 / 255     ,					__UpperCAmelCase=True     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
      __UpperCamelCase      =	parent
      __UpperCamelCase      =	batch_size
      __UpperCamelCase      =	num_channels
      __UpperCamelCase      =	min_resolution
      __UpperCamelCase      =	max_resolution
      __UpperCamelCase      =	do_resize
      __UpperCamelCase      =	size
      __UpperCamelCase      =	do_normalize
      __UpperCamelCase      =	image_mean
      __UpperCamelCase      =	image_std
      __UpperCamelCase      =	do_rescale
      __UpperCamelCase      =	rescale_factor
      __UpperCamelCase      =	do_pad





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
      }







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=False				):

      '''simple docstring'''




      if not batched:
       __UpperCamelCase      =	image_inputs[0]
       if isinstance(__UpperCAmelCase     ,					Image.Image				):
        __UpperCamelCase  ,     __UpperCamelCase      =	image.size
       else:
        __UpperCamelCase  ,     __UpperCamelCase      =	image.shape[1], image.shape[2]
       if w < h:
        __UpperCamelCase      =	int(self.size['shortest_edge'] * h / w				)
        __UpperCamelCase      =	self.size['shortest_edge']
       elif w > h:
        __UpperCamelCase      =	self.size['shortest_edge']
        __UpperCamelCase      =	int(self.size['shortest_edge'] * w / h				)
       else:
        __UpperCamelCase      =	self.size['shortest_edge']
        __UpperCamelCase      =	self.size['shortest_edge']

      else:
       __UpperCamelCase      =	[]
       for image in image_inputs:
        __UpperCamelCase  ,     __UpperCamelCase      =	self.get_expected_values([image]				)
        expected_values.append((expected_height, expected_width)				)
       __UpperCamelCase      =	max(__UpperCAmelCase     ,					key=lambda __UpperCAmelCase				: item[0]				)[0]
       __UpperCamelCase      =	max(__UpperCAmelCase     ,					key=lambda __UpperCAmelCase				: item[1]				)[1]

      return expected_height, expected_width


@require_torch
@require_vision
class __lowerCAmelCase		(				__SCREAMING_SNAKE_CASE      ,  unittest.TestCase      ):
     lowercase						       =						YolosImageProcessor if is_vision_available() else None





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	YolosImageProcessingTester(self				)





     @property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	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     ,					'size'				)				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.image_processing_class.from_dict(self.image_processor_dict				)
      self.assertEqual(image_processor.size     ,					{'shortest_edge': 18, 'longest_edge': 1333}				)
      self.assertEqual(image_processor.do_pad     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	self.image_processing_class.from_dict(
          self.image_processor_dict     ,					size=42     ,					max_size=84     ,					pad_and_return_pixel_mask=__UpperCAmelCase				)
      self.assertEqual(image_processor.size     ,					{'shortest_edge': 42, 'longest_edge': 84}				)
      self.assertEqual(image_processor.do_pad     ,					__UpperCAmelCase				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      __UpperCamelCase      =	prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase				)
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase     ,					Image.Image				)

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

      __UpperCamelCase  ,     __UpperCamelCase      =	self.image_processor_tester.get_expected_values(__UpperCAmelCase				)

      self.assertEqual(
          encoded_images.shape     ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,					)

      # Test batched
      __UpperCamelCase  ,     __UpperCamelCase      =	self.image_processor_tester.get_expected_values(__UpperCAmelCase     ,					batched=__UpperCAmelCase				)

      __UpperCamelCase      =	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,
              expected_height,
              expected_width,
          )     ,					)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      __UpperCamelCase      =	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
      __UpperCamelCase      =	image_processing(image_inputs[0]     ,					return_tensors='pt'				).pixel_values

      __UpperCamelCase  ,     __UpperCamelCase      =	self.image_processor_tester.get_expected_values(__UpperCAmelCase				)

      self.assertEqual(
          encoded_images.shape     ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,					)

      # Test batched
      __UpperCamelCase      =	image_processing(__UpperCAmelCase     ,					return_tensors='pt'				).pixel_values

      __UpperCamelCase  ,     __UpperCamelCase      =	self.image_processor_tester.get_expected_values(__UpperCAmelCase     ,					batched=__UpperCAmelCase				)

      self.assertEqual(
          encoded_images.shape     ,					(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,					)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      __UpperCamelCase      =	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
      __UpperCamelCase      =	image_processing(image_inputs[0]     ,					return_tensors='pt'				).pixel_values

      __UpperCamelCase  ,     __UpperCamelCase      =	self.image_processor_tester.get_expected_values(__UpperCAmelCase				)

      self.assertEqual(
          encoded_images.shape     ,					(1, self.image_processor_tester.num_channels, expected_height, expected_width)     ,					)

      # Test batched
      __UpperCamelCase      =	image_processing(__UpperCAmelCase     ,					return_tensors='pt'				).pixel_values

      __UpperCamelCase  ,     __UpperCamelCase      =	self.image_processor_tester.get_expected_values(__UpperCAmelCase     ,					batched=__UpperCAmelCase				)

      self.assertEqual(
          encoded_images.shape     ,					(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )     ,					)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.image_processing_class(**self.image_processor_dict				)
      __UpperCamelCase      =	self.image_processing_class(do_resize=__UpperCAmelCase     ,					do_normalize=__UpperCAmelCase     ,					do_rescale=__UpperCAmelCase				)
      # create random PyTorch tensors
      __UpperCamelCase      =	prepare_image_inputs(self.image_processor_tester     ,					equal_resolution=__UpperCAmelCase     ,					torchify=__UpperCAmelCase				)
      for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase     ,					torch.Tensor				)

      # Test whether the method "pad" and calling the image processor return the same tensors
      __UpperCamelCase      =	image_processing_a.pad(__UpperCAmelCase     ,					return_tensors='pt'				)
      __UpperCamelCase      =	image_processing_a(__UpperCAmelCase     ,					return_tensors='pt'				)

      self.assertTrue(
          torch.allclose(encoded_images_with_method['pixel_values']     ,					encoded_images['pixel_values']     ,					atol=1E-4				)				)





     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'     ,					'r'				) as f:
       __UpperCamelCase      =	json.loads(f.read()				)

      __UpperCamelCase      =	{'image_id': 3_9769, 'annotations': target}

      # encode them
      __UpperCamelCase      =	YolosImageProcessor.from_pretrained('hustvl/yolos-small'				)
      __UpperCamelCase      =	image_processing(images=__UpperCAmelCase     ,					annotations=__UpperCAmelCase     ,					return_tensors='pt'				)

      # verify pixel values
      __UpperCamelCase      =	torch.Size([1, 3, 800, 1066]				)
      self.assertEqual(encoding['pixel_values'].shape     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,					__UpperCAmelCase     ,					atol=1E-4				)				)

      # verify area
      __UpperCamelCase      =	torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,					__UpperCAmelCase				)				)
      # verify boxes
      __UpperCamelCase      =	torch.Size([6, 4]				)
      self.assertEqual(encoding['labels'][0]['boxes'].shape     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,					__UpperCAmelCase     ,					atol=1E-3				)				)
      # verify image_id
      __UpperCamelCase      =	torch.tensor([3_9769]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,					__UpperCAmelCase				)				)
      # verify is_crowd
      __UpperCamelCase      =	torch.tensor([0, 0, 0, 0, 0, 0]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,					__UpperCAmelCase				)				)
      # verify class_labels
      __UpperCamelCase      =	torch.tensor([75, 75, 63, 65, 17, 17]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,					__UpperCAmelCase				)				)
      # verify orig_size
      __UpperCamelCase      =	torch.tensor([480, 640]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,					__UpperCAmelCase				)				)
      # verify size
      __UpperCamelCase      =	torch.tensor([800, 1066]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,					__UpperCAmelCase				)				)







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
      with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'     ,					'r'				) as f:
       __UpperCamelCase      =	json.loads(f.read()				)

      __UpperCamelCase      =	{'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

      __UpperCamelCase      =	pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'				)

      # encode them
      __UpperCamelCase      =	YolosImageProcessor(format='coco_panoptic'				)
      __UpperCamelCase      =	image_processing(images=__UpperCAmelCase     ,					annotations=__UpperCAmelCase     ,					masks_path=__UpperCAmelCase     ,					return_tensors='pt'				)

      # verify pixel values
      __UpperCamelCase      =	torch.Size([1, 3, 800, 1066]				)
      self.assertEqual(encoding['pixel_values'].shape     ,					__UpperCAmelCase				)

      __UpperCamelCase      =	torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
      self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]     ,					__UpperCAmelCase     ,					atol=1E-4				)				)

      # verify area
      __UpperCamelCase      =	torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['area']     ,					__UpperCAmelCase				)				)
      # verify boxes
      __UpperCamelCase      =	torch.Size([6, 4]				)
      self.assertEqual(encoding['labels'][0]['boxes'].shape     ,					__UpperCAmelCase				)
      __UpperCamelCase      =	torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]     ,					__UpperCAmelCase     ,					atol=1E-3				)				)
      # verify image_id
      __UpperCamelCase      =	torch.tensor([3_9769]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']     ,					__UpperCAmelCase				)				)
      # verify is_crowd
      __UpperCamelCase      =	torch.tensor([0, 0, 0, 0, 0, 0]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']     ,					__UpperCAmelCase				)				)
      # verify class_labels
      __UpperCamelCase      =	torch.tensor([17, 17, 63, 75, 75, 93]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']     ,					__UpperCAmelCase				)				)
      # verify masks
      __UpperCamelCase      =	82_2873
      self.assertEqual(encoding['labels'][0]['masks'].sum().item()     ,					__UpperCAmelCase				)
      # verify orig_size
      __UpperCamelCase      =	torch.tensor([480, 640]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']     ,					__UpperCAmelCase				)				)
      # verify size
      __UpperCamelCase      =	torch.tensor([800, 1066]				)
      self.assertTrue(torch.allclose(encoding['labels'][0]['size']     ,					__UpperCAmelCase				)				)





"""simple docstring"""

from typing import Union

import fire
import torch
from tqdm import tqdm






def 							A						(    snake_case				:str    , snake_case				:str = "cpu"    , snake_case				:Union[str, None] = None		)				->     None:
 __UpperCamelCase      =	torch.load(snake_case    , map_location=snake_case		)
 for k, v in tqdm(state_dict.items()		):
  if not isinstance(snake_case    , torch.Tensor		):
   raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'		)
  __UpperCamelCase      =	v.half()
 if save_path is None:  # overwrite src_path
  __UpperCamelCase      =	src_path
 torch.save(snake_case    , snake_case		)


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



