




'''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 				_a							(       unittest.TestCase      ):





       '''simple docstring'''






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







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	int   =		size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		parent
             SCREAMING_SNAKE_CASE					:	List[Any]   =		batch_size
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		num_channels
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		min_resolution
             SCREAMING_SNAKE_CASE					:	Dict   =		max_resolution
             SCREAMING_SNAKE_CASE					:	Any   =		do_resize
             SCREAMING_SNAKE_CASE					:	List[str]   =		size
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		do_normalize
             SCREAMING_SNAKE_CASE					:	Tuple   =		image_mean
             SCREAMING_SNAKE_CASE					:	List[Any]   =		image_std
             SCREAMING_SNAKE_CASE					:	List[Any]   =		do_rescale
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		rescale_factor
             SCREAMING_SNAKE_CASE					:	List[str]   =		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,				A,				A=False   ):







             '''simple docstring'''







             if not batched:
                   SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		image_inputs[0]
                   if isinstance(A,				Image.Image   ):
                         SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	Any   =		image.size
                   else:
                         SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	Tuple   =		image.shape[1], image.shape[2]
                   if w < h:
                         SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		int(self.size['shortest_edge'] * h / w   )
                         SCREAMING_SNAKE_CASE					:	List[str]   =		self.size['shortest_edge']
                   elif w > h:
                         SCREAMING_SNAKE_CASE					:	Tuple   =		self.size['shortest_edge']
                         SCREAMING_SNAKE_CASE					:	str   =		int(self.size['shortest_edge'] * w / h   )
                   else:
                         SCREAMING_SNAKE_CASE					:	Tuple   =		self.size['shortest_edge']
                         SCREAMING_SNAKE_CASE					:	int   =		self.size['shortest_edge']

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

             return expected_height, expected_width






@require_torch
@require_vision
class 				_a							(       SCREAMING_SNAKE_CASE__      ,	unittest.TestCase      ):





       '''simple docstring'''






       A		:							List[str]								=		YolosImageProcessor if is_vision_available() else None







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Any   =		YolosImageProcessingTester(self   )







       @property
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             return self.image_processor_tester.prepare_image_processor_dict()







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		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'   )   )







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		self.image_processing_class.from_dict(self.image_processor_dict   )
             self.assertEqual(image_processor.size,				{'shortest_edge': 18, 'longest_edge': 1_333}   )
             self.assertEqual(image_processor.do_pad,				A   )

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







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             pass







       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







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

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

             SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	str   =		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
             SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	str   =		self.image_processor_tester.get_expected_values(A,				batched=A   )

             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		image_processing(A,				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'''







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

             SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	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
             SCREAMING_SNAKE_CASE					:	Tuple   =		image_processing(A,				return_tensors='pt'   ).pixel_values

             SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		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     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Dict   =		self.image_processing_class(**self.image_processor_dict   )
             # create random PyTorch tensors
             SCREAMING_SNAKE_CASE					:	List[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
             SCREAMING_SNAKE_CASE					:	List[str]   =		image_processing(image_inputs[0],				return_tensors='pt'   ).pixel_values

             SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	Optional[int]   =		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
             SCREAMING_SNAKE_CASE					:	Dict   =		image_processing(A,				return_tensors='pt'   ).pixel_values

             SCREAMING_SNAKE_CASE ,  SCREAMING_SNAKE_CASE					:	List[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     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	Optional[int]   =		self.image_processing_class(**self.image_processor_dict   )
             SCREAMING_SNAKE_CASE					:	List[str]   =		self.image_processing_class(do_resize=A,				do_normalize=A,				do_rescale=A   )
             # create random PyTorch tensors
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		prepare_image_inputs(self.image_processor_tester,				equal_resolution=A,				torchify=A   )
             for image in image_inputs:
                   self.assertIsInstance(A,				torch.Tensor   )

             # Test whether the method "pad" and calling the image processor return the same tensors
             SCREAMING_SNAKE_CASE					:	List[Any]   =		image_processing_a.pad(A,				return_tensors='pt'   )
             SCREAMING_SNAKE_CASE					:	str   =		image_processing_a(A,				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'''







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

             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		{'image_id': 39_769, 'annotations': target}

             # encode them
             SCREAMING_SNAKE_CASE					:	str   =		YolosImageProcessor.from_pretrained('hustvl/yolos-small'   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		image_processing(images=A,				annotations=A,				return_tensors='pt'   )

             # verify pixel values
             SCREAMING_SNAKE_CASE					:	List[str]   =		torch.Size([1, 3, 800, 1_066]   )
             self.assertEqual(encoding['pixel_values'].shape,				A   )

             SCREAMING_SNAKE_CASE					:	List[Any]   =		torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],				A,				atol=1E-4   )   )

             # verify area
             SCREAMING_SNAKE_CASE					:	Dict   =		torch.tensor([58_87.96_00, 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'],				A   )   )
             # verify boxes
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.Size([6, 4]   )
             self.assertEqual(encoding['labels'][0]['boxes'].shape,				A   )
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],				A,				atol=1E-3   )   )
             # verify image_id
             SCREAMING_SNAKE_CASE					:	Any   =		torch.tensor([39_769]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],				A   )   )
             # verify is_crowd
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.tensor([0, 0, 0, 0, 0, 0]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],				A   )   )
             # verify class_labels
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		torch.tensor([75, 75, 63, 65, 17, 17]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],				A   )   )
             # verify orig_size
             SCREAMING_SNAKE_CASE					:	List[Any]   =		torch.tensor([480, 640]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],				A   )   )
             # verify size
             SCREAMING_SNAKE_CASE					:	str   =		torch.tensor([800, 1_066]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['size'],				A   )   )







       @slow
       def     UpperCamelCase_				(	self   ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					:	List[Any]   =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
             with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt',				'r'   ) as f:
                   SCREAMING_SNAKE_CASE					:	Optional[Any]   =		json.loads(f.read()   )

             SCREAMING_SNAKE_CASE					:	str   =		{'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

             SCREAMING_SNAKE_CASE					:	Tuple   =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'   )

             # encode them
             SCREAMING_SNAKE_CASE					:	Dict   =		YolosImageProcessor(format='coco_panoptic'   )
             SCREAMING_SNAKE_CASE					:	List[str]   =		image_processing(images=A,				annotations=A,				masks_path=A,				return_tensors='pt'   )

             # verify pixel values
             SCREAMING_SNAKE_CASE					:	Tuple   =		torch.Size([1, 3, 800, 1_066]   )
             self.assertEqual(encoding['pixel_values'].shape,				A   )

             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
             self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3],				A,				atol=1E-4   )   )

             # verify area
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 58_79.65_62, 76_34.11_47]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['area'],				A   )   )
             # verify boxes
             SCREAMING_SNAKE_CASE					:	Dict   =		torch.Size([6, 4]   )
             self.assertEqual(encoding['labels'][0]['boxes'].shape,				A   )
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0],				A,				atol=1E-3   )   )
             # verify image_id
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.tensor([39_769]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'],				A   )   )
             # verify is_crowd
             SCREAMING_SNAKE_CASE					:	str   =		torch.tensor([0, 0, 0, 0, 0, 0]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'],				A   )   )
             # verify class_labels
             SCREAMING_SNAKE_CASE					:	List[Any]   =		torch.tensor([17, 17, 63, 75, 75, 93]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'],				A   )   )
             # verify masks
             SCREAMING_SNAKE_CASE					:	Optional[int]   =		822_873
             self.assertEqual(encoding['labels'][0]['masks'].sum().item(),				A   )
             # verify orig_size
             SCREAMING_SNAKE_CASE					:	Optional[Any]   =		torch.tensor([480, 640]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'],				A   )   )
             # verify size
             SCREAMING_SNAKE_CASE					:	Union[str, Any]   =		torch.tensor([800, 1_066]   )
             self.assertTrue(torch.allclose(encoding['labels'][0]['size'],				A   )   )



from ....configuration_utils import PretrainedConfig
from ....utils import logging


A   :     str       =				logging.get_logger(__name__)

# TODO: upload to AWS
A   :     Dict       =				{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''retribert'''







  def __init__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[Any]=30_522     ,   __magic_name__       :				int=768     ,   __magic_name__       :				Dict=8     ,   __magic_name__       :				List[Any]=12     ,   __magic_name__       :				Tuple=3_072     ,   __magic_name__       :				List[Any]="gelu"     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Tuple=512     ,   __magic_name__       :				Dict=2     ,   __magic_name__       :				int=0.02     ,   __magic_name__       :				List[Any]=1e-12     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Dict=128     ,   __magic_name__       :				Union[str, Any]=0     ,   **__magic_name__       :				List[Any]     ,   )     ->		Dict:
        super().__init__(pad_token_id=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     vocab_size
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     hidden_act
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_      =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_      =     max_position_embeddings
        SCREAMING_SNAKE_CASE_      =     type_vocab_size
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     layer_norm_eps
        SCREAMING_SNAKE_CASE_      =     share_encoders
        SCREAMING_SNAKE_CASE_      =     projection_dim


