'''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 ConditionalDetrImageProcessor







class __snake_case       (		unittest.TestCase	):




   def __init__(					self    :       int ,			_lowercase    :       Optional[int] ,			_lowercase    :       Any=7 ,			_lowercase    :       List[str]=3 ,			_lowercase    :       Union[str, Any]=30 ,			_lowercase    :       str=4_00 ,			_lowercase    :       Any=True ,			_lowercase    :       Dict=None ,			_lowercase    :       Union[str, Any]=True ,			_lowercase    :       Any=[0.5, 0.5, 0.5] ,			_lowercase    :       Any=[0.5, 0.5, 0.5] ,			_lowercase    :       List[Any]=True ,			_lowercase    :       Any=1 / 2_55 ,			_lowercase    :       List[str]=True ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 13_33}
     SCREAMING_SNAKE_CASE__    							=   parent
     SCREAMING_SNAKE_CASE__    							=   batch_size
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   min_resolution
     SCREAMING_SNAKE_CASE__    							=   max_resolution
     SCREAMING_SNAKE_CASE__    							=   do_resize
     SCREAMING_SNAKE_CASE__    							=   size
     SCREAMING_SNAKE_CASE__    							=   do_normalize
     SCREAMING_SNAKE_CASE__    							=   image_mean
     SCREAMING_SNAKE_CASE__    							=   image_std
     SCREAMING_SNAKE_CASE__    							=   do_rescale
     SCREAMING_SNAKE_CASE__    							=   rescale_factor
     SCREAMING_SNAKE_CASE__    							=   do_pad




   def 			__a		(					self    :       Tuple   ):



     """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 			__a		(					self    :       Optional[Any] ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       List[Any]=False   ):



     """simple docstring"""






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

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

     return expected_height, expected_width







@require_torch
@require_vision
class __snake_case       (		lowerCamelCase_     ,	unittest.TestCase	):
   lowerCAmelCase_														=							ConditionalDetrImageProcessor if is_vision_available() else None




   def 			__a		(					self    :       Dict   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   ConditionalDetrImageProcessingTester(self   )




   @property
   def 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(_lowercase ,			"""image_mean"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""image_std"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""do_normalize"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""do_resize"""   )   )
     self.assertTrue(hasattr(_lowercase ,			"""size"""   )   )




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.image_processing_class.from_dict(self.image_processor_dict   )
     self.assertEqual(image_processor.size ,			{"""shortest_edge""": 18, """longest_edge""": 13_33}   )
     self.assertEqual(image_processor.do_pad ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   self.image_processing_class.from_dict(
         self.image_processor_dict ,			size=42 ,			max_size=84 ,			pad_and_return_pixel_mask=_lowercase   )
     self.assertEqual(image_processor.size ,			{"""shortest_edge""": 42, """longest_edge""": 84}   )
     self.assertEqual(image_processor.do_pad ,			_lowercase   )




   def 			__a		(					self    :       str   ):



     """simple docstring"""






     pass




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






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

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

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase   )

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

     # Test batched
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase ,			batched=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   image_processing(_lowercase ,			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 			__a		(					self    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__    							=   prepare_image_inputs(self.image_processor_tester ,			equal_resolution=_lowercase ,			numpify=_lowercase   )
     for image in image_inputs:
       self.assertIsInstance(_lowercase ,			np.ndarray   )

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

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase   )

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

     # Test batched
     SCREAMING_SNAKE_CASE__    							=   image_processing(_lowercase ,			return_tensors="""pt"""   ).pixel_values

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase ,			batched=_lowercase   )

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




   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.image_processing_class(**self.image_processor_dict   )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__    							=   prepare_image_inputs(self.image_processor_tester ,			equal_resolution=_lowercase ,			torchify=_lowercase   )
     for image in image_inputs:
       self.assertIsInstance(_lowercase ,			torch.Tensor   )

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

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase   )

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

     # Test batched
     SCREAMING_SNAKE_CASE__    							=   image_processing(_lowercase ,			return_tensors="""pt"""   ).pixel_values

     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   self.image_processor_tester.get_expected_values(_lowercase ,			batched=_lowercase   )

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




   @slow
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   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__    							=   json.loads(f.read()   )

     SCREAMING_SNAKE_CASE__    							=   {"""image_id""": 3_97_69, """annotations""": target}

     # encode them
     SCREAMING_SNAKE_CASE__    							=   ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""   )
     SCREAMING_SNAKE_CASE__    							=   image_processing(images=_lowercase ,			annotations=_lowercase ,			return_tensors="""pt"""   )

     # verify pixel values
     SCREAMING_SNAKE_CASE__    							=   torch.Size([1, 3, 8_00, 10_66]   )
     self.assertEqual(encoding["""pixel_values"""].shape ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,			_lowercase ,			atol=1E-4   )   )

     # verify area
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] ,			_lowercase   )   )
     # verify boxes
     SCREAMING_SNAKE_CASE__    							=   torch.Size([6, 4]   )
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] ,			_lowercase ,			atol=1E-3   )   )
     # verify image_id
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([3_97_69]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] ,			_lowercase   )   )
     # verify is_crowd
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] ,			_lowercase   )   )
     # verify class_labels
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([75, 75, 63, 65, 17, 17]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] ,			_lowercase   )   )
     # verify orig_size
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([4_80, 6_40]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] ,			_lowercase   )   )
     # verify size
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([8_00, 10_66]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] ,			_lowercase   )   )




   @slow
   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   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__    							=   json.loads(f.read()   )

     SCREAMING_SNAKE_CASE__    							=   {"""file_name""": """000000039769.png""", """image_id""": 3_97_69, """segments_info""": target}

     SCREAMING_SNAKE_CASE__    							=   pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""   )

     # encode them
     SCREAMING_SNAKE_CASE__    							=   ConditionalDetrImageProcessor(format="""coco_panoptic"""   )
     SCREAMING_SNAKE_CASE__    							=   image_processing(images=_lowercase ,			annotations=_lowercase ,			masks_path=_lowercase ,			return_tensors="""pt"""   )

     # verify pixel values
     SCREAMING_SNAKE_CASE__    							=   torch.Size([1, 3, 8_00, 10_66]   )
     self.assertEqual(encoding["""pixel_values"""].shape ,			_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.27_96, 0.31_38, 0.34_81]   )
     self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] ,			_lowercase ,			atol=1E-4   )   )

     # verify area
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] ,			_lowercase   )   )
     # verify boxes
     SCREAMING_SNAKE_CASE__    							=   torch.Size([6, 4]   )
     self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape ,			_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] ,			_lowercase ,			atol=1E-3   )   )
     # verify image_id
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([3_97_69]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] ,			_lowercase   )   )
     # verify is_crowd
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([0, 0, 0, 0, 0, 0]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] ,			_lowercase   )   )
     # verify class_labels
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([17, 17, 63, 75, 75, 93]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] ,			_lowercase   )   )
     # verify masks
     SCREAMING_SNAKE_CASE__    							=   82_28_73
     self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() ,			_lowercase   )
     # verify orig_size
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([4_80, 6_40]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] ,			_lowercase   )   )
     # verify size
     SCREAMING_SNAKE_CASE__    							=   torch.tensor([8_00, 10_66]   )
     self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] ,			_lowercase   )   )

import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowerCamelCase_	)
class __snake_case       (		lowerCamelCase_	):
   # `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
   lowerCAmelCase_														=							field(default="text-classification"     ,	metadata={"include_in_asdict_even_if_is_default": True}	)
   lowerCAmelCase_														=							Features({"text": Value("string"	)}	)
   lowerCAmelCase_														=							Features({"labels": ClassLabel}	)
   lowerCAmelCase_														=							"text"
   lowerCAmelCase_														=							"labels"




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     if self.label_column not in features:
       raise ValueError(f"""Column {self.label_column} is not present in features."""   )
     if not isinstance(features[self.label_column] ,			_lowercase   ):
       raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""   )
     SCREAMING_SNAKE_CASE__    							=   copy.deepcopy(self   )
     SCREAMING_SNAKE_CASE__    							=   self.label_schema.copy()
     SCREAMING_SNAKE_CASE__    							=   features[self.label_column]
     SCREAMING_SNAKE_CASE__    							=   label_schema
     return task_template




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     return {
         self.text_column: "text",
         self.label_column: "labels",
     }
