






"""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 ,				snake_case_ ,				snake_case_=7 ,				snake_case_=3 ,				snake_case_=30 ,				snake_case_=400 ,				snake_case_=True ,				snake_case_=None ,				snake_case_=True ,				snake_case_=[0.5, 0.5, 0.5] ,				snake_case_=[0.5, 0.5, 0.5] ,				snake_case_=True ,				snake_case_=1 / 255 ,				snake_case_=True ,				)		->       Any:
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       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 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_=False      )		->       Dict:
       if not batched:
           UpperCamelCase__         =    image_inputs[0]
           if isinstance(snake_case_ ,				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(snake_case_ ,				key=lambda snake_case_      : item[0]      )[0]
           UpperCamelCase__         =    max(snake_case_ ,				key=lambda snake_case_      : item[1]      )[1]

       return expected_height, expected_width







@require_torch
@require_vision
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				Tuple 						=YolosImageProcessor if is_vision_available() else None

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    YolosImageProcessingTester(self      )

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[str]:
       return self.image_processor_tester.prepare_image_processor_dict()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       self.assertTrue(hasattr(snake_case_ ,				'image_mean'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'image_std'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'do_normalize'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'do_resize'      )      )
       self.assertTrue(hasattr(snake_case_ ,				'size'      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       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 ,				snake_case_      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       pass

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       # Initialize image_processing
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random PIL images
       UpperCamelCase__         =    prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case_      )
       for image in image_inputs:
           self.assertIsInstance(snake_case_ ,				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(snake_case_      )

       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(snake_case_ ,				batched=snake_case_      )

       UpperCamelCase__         =    image_processing(snake_case_ ,				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 						SCREAMING_SNAKE_CASE__ (					self      )		->       Tuple:
       # Initialize image_processing
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random numpy tensors
       UpperCamelCase__         =    prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case_ ,				numpify=snake_case_      )
       for image in image_inputs:
           self.assertIsInstance(snake_case_ ,				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(snake_case_      )

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

       # Test batched
       UpperCamelCase__         =    image_processing(snake_case_ ,				return_tensors='pt'      ).pixel_values

       UpperCamelCase__       ,						UpperCamelCase__         =    self.image_processor_tester.get_expected_values(snake_case_ ,				batched=snake_case_      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:
       # Initialize image_processing
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       # create random PyTorch tensors
       UpperCamelCase__         =    prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case_ ,				torchify=snake_case_      )
       for image in image_inputs:
           self.assertIsInstance(snake_case_ ,				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(snake_case_      )

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

       # Test batched
       UpperCamelCase__         =    image_processing(snake_case_ ,				return_tensors='pt'      ).pixel_values

       UpperCamelCase__       ,						UpperCamelCase__         =    self.image_processor_tester.get_expected_values(snake_case_ ,				batched=snake_case_      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       # Initialize image_processings
       UpperCamelCase__         =    self.image_processing_class(**self.image_processor_dict      )
       UpperCamelCase__         =    self.image_processing_class(do_resize=snake_case_ ,				do_normalize=snake_case_ ,				do_rescale=snake_case_      )
       # create random PyTorch tensors
       UpperCamelCase__         =    prepare_image_inputs(self.image_processor_tester ,				equal_resolution=snake_case_ ,				torchify=snake_case_      )
       for image in image_inputs:
           self.assertIsInstance(snake_case_ ,				torch.Tensor      )

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

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

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       # prepare image and target
       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=snake_case_ ,				annotations=snake_case_ ,				return_tensors='pt'      )

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

       UpperCamelCase__         =    torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,				snake_case_ ,				atol=1E-4      )      )

       # verify area
       UpperCamelCase__         =    torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,				snake_case_      )      )
       # verify boxes
       UpperCamelCase__         =    torch.Size([6, 4]      )
       self.assertEqual(encoding['labels'][0]['boxes'].shape ,				snake_case_      )
       UpperCamelCase__         =    torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,				snake_case_ ,				atol=1E-3      )      )
       # verify image_id
       UpperCamelCase__         =    torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,				snake_case_      )      )
       # verify is_crowd
       UpperCamelCase__         =    torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,				snake_case_      )      )
       # verify class_labels
       UpperCamelCase__         =    torch.tensor([75, 75, 63, 65, 17, 17]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,				snake_case_      )      )
       # verify orig_size
       UpperCamelCase__         =    torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,				snake_case_      )      )
       # verify size
       UpperCamelCase__         =    torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,				snake_case_      )      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       # prepare image, target and masks_path
       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=snake_case_ ,				annotations=snake_case_ ,				masks_path=snake_case_ ,				return_tensors='pt'      )

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

       UpperCamelCase__         =    torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,				snake_case_ ,				atol=1E-4      )      )

       # verify area
       UpperCamelCase__         =    torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,				snake_case_      )      )
       # verify boxes
       UpperCamelCase__         =    torch.Size([6, 4]      )
       self.assertEqual(encoding['labels'][0]['boxes'].shape ,				snake_case_      )
       UpperCamelCase__         =    torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,				snake_case_ ,				atol=1E-3      )      )
       # verify image_id
       UpperCamelCase__         =    torch.tensor([3_9769]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,				snake_case_      )      )
       # verify is_crowd
       UpperCamelCase__         =    torch.tensor([0, 0, 0, 0, 0, 0]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,				snake_case_      )      )
       # verify class_labels
       UpperCamelCase__         =    torch.tensor([17, 17, 63, 75, 75, 93]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,				snake_case_      )      )
       # verify masks
       UpperCamelCase__         =    82_2873
       self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,				snake_case_      )
       # verify orig_size
       UpperCamelCase__         =    torch.tensor([480, 640]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,				snake_case_      )      )
       # verify size
       UpperCamelCase__         =    torch.tensor([800, 1066]      )
       self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,				snake_case_      )      )












"""simple docstring"""





from argparse import ArgumentParser

from . import BaseTransformersCLICommand







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    return DownloadCommand(args.model ,	args.cache_dir ,	args.force ,	args.trust_remote_code					)





class    __lowerCamelCase    (  _a  ):

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_      )		->       Union[str, Any]:
       UpperCamelCase__         =    parser.add_parser('download'      )
       download_parser.add_argument(
           '--cache-dir' ,				type=snake_case_ ,				default=snake_case_ ,				help='Path to location to store the models'      )
       download_parser.add_argument(
           '--force' ,				action='store_true' ,				help='Force the model to be download even if already in cache-dir'      )
       download_parser.add_argument(
           '--trust-remote-code' ,				action='store_true' ,				help='Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine' ,				)
       download_parser.add_argument('model' ,				type=snake_case_ ,				help='Name of the model to download'      )
       download_parser.set_defaults(func=snake_case_      )

   def __init__(					self ,				snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       str:
       UpperCamelCase__         =    model
       UpperCamelCase__         =    cache
       UpperCamelCase__         =    force
       UpperCamelCase__         =    trust_remote_code







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       from ..models.auto import AutoModel, AutoTokenizer

       AutoModel.from_pretrained(
           self._model ,				cache_dir=self._cache ,				force_download=self._force ,				trust_remote_code=self._trust_remote_code      )
       AutoTokenizer.from_pretrained(
           self._model ,				cache_dir=self._cache ,				force_download=self._force ,				trust_remote_code=self._trust_remote_code      )




