

'''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 DetrImageProcessor


class 	lowerCAmelCase  (   unittest.TestCase ):







       def __init__(				self      :			Union[str, Any]    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			str=7    ,   __lowercase      :			Union[str, Any]=3    ,   __lowercase      :			Dict=30    ,   __lowercase      :			List[Any]=400    ,   __lowercase      :			List[Any]=True    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Union[str, Any]=True    ,   __lowercase      :			Optional[int]=1 / 255    ,   __lowercase      :			Union[str, Any]=True    ,   __lowercase      :			List[str]=[0.5, 0.5, 0.5]    ,   __lowercase      :			List[str]=[0.5, 0.5, 0.5]    ,   __lowercase      :			List[str]=True    ,   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Optional[Any]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			int    ,   __lowercase      :			List[str]    ,   __lowercase      :			str=False   ):







            """simple docstring"""







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

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

            return expected_height, expected_width






@require_torch
@require_vision
class 	lowerCAmelCase  (   A ,      unittest.TestCase ):
       lowerCAmelCase_												=			DetrImageProcessor if is_vision_available() else None







       def       snake_case (				self      :			int   ):







            """simple docstring"""







            __lowercase      		=DetrImageProcessingTester(self   )







       @property
       def       snake_case (				self      :			int   ):







            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=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_rescale'   )   )
            self.assertTrue(hasattr(__lowercase    ,   'rescale_factor'   )   )
            self.assertTrue(hasattr(__lowercase    ,   'do_resize'   )   )
            self.assertTrue(hasattr(__lowercase    ,   'size'   )   )
            self.assertTrue(hasattr(__lowercase    ,   'do_pad'   )   )







       def       snake_case (				self      :			str   ):







            """simple docstring"""







            __lowercase      		=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    ,   __lowercase   )

            __lowercase      		=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       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            pass







       def       snake_case (				self      :			str   ):







            """simple docstring"""







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

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

            __lowercase		,  __lowercase      		=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
            __lowercase		,  __lowercase      		=self.image_processor_tester.get_expected_values(__lowercase    ,   batched=__lowercase   )

            __lowercase      		=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       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







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

            __lowercase		,  __lowercase      		=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
            __lowercase      		=image_processing(__lowercase    ,   return_tensors='pt'   ).pixel_values

            __lowercase		,  __lowercase      		=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       snake_case (				self      :			int   ):







            """simple docstring"""







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

            __lowercase		,  __lowercase      		=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
            __lowercase      		=image_processing(__lowercase    ,   return_tensors='pt'   ).pixel_values

            __lowercase		,  __lowercase      		=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       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







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

            __lowercase      		={'image_id': 39769, 'annotations': target}

            # encode them
            __lowercase      		=DetrImageProcessor.from_pretrained('facebook/detr-resnet-50'   )
            __lowercase      		=image_processing(images=__lowercase    ,   annotations=__lowercase    ,   return_tensors='pt'   )

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

            __lowercase      		=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]    ,   __lowercase    ,   atol=1E-4   )   )

            # verify area
            __lowercase      		=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']    ,   __lowercase   )   )
            # verify boxes
            __lowercase      		=torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape    ,   __lowercase   )
            __lowercase      		=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]    ,   __lowercase    ,   atol=1E-3   )   )
            # verify image_id
            __lowercase      		=torch.tensor([39769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,   __lowercase   )   )
            # verify is_crowd
            __lowercase      		=torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,   __lowercase   )   )
            # verify class_labels
            __lowercase      		=torch.tensor([75, 75, 63, 65, 17, 17]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,   __lowercase   )   )
            # verify orig_size
            __lowercase      		=torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,   __lowercase   )   )
            # verify size
            __lowercase      		=torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,   __lowercase   )   )







       @slow
       def       snake_case (				self      :			int   ):







            """simple docstring"""







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

            __lowercase      		={'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

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

            # encode them
            __lowercase      		=DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic'   )
            __lowercase      		=image_processing(images=__lowercase    ,   annotations=__lowercase    ,   masks_path=__lowercase    ,   return_tensors='pt'   )

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

            __lowercase      		=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]    ,   __lowercase    ,   atol=1E-4   )   )

            # verify area
            __lowercase      		=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']    ,   __lowercase   )   )
            # verify boxes
            __lowercase      		=torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape    ,   __lowercase   )
            __lowercase      		=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]    ,   __lowercase    ,   atol=1E-3   )   )
            # verify image_id
            __lowercase      		=torch.tensor([39769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,   __lowercase   )   )
            # verify is_crowd
            __lowercase      		=torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,   __lowercase   )   )
            # verify class_labels
            __lowercase      		=torch.tensor([17, 17, 63, 75, 75, 93]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,   __lowercase   )   )
            # verify masks
            __lowercase      		=822873
            self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,   __lowercase   )
            # verify orig_size
            __lowercase      		=torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,   __lowercase   )   )
            # verify size
            __lowercase      		=torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,   __lowercase   )   )





'''simple docstring'''

import os
from collections.abc import Iterator

def 					__UpperCamelCase				(						lowercase__    :	str = "."			):






     '''simple docstring'''





     for dir_path, dir_names, filenames in os.walk(lowercase__			):
          __lowercase      		=[d for d in dir_names if d != 'scripts' and d[0] not in '._']
          for filename in filenames:
               if filename == "__init__.py":
                    continue
               if os.path.splitext(lowercase__			)[1] in (".py", ".ipynb"):
                    yield os.path.join(lowercase__,       lowercase__			).lstrip('./'			)

def 					__UpperCamelCase				(						lowercase__    :	Optional[Any]			):






     '''simple docstring'''





     return F'''{i * "  "}*''' if i else "\n##"

def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=old_path.split(os.sep			)
     for i, new_part in enumerate(new_path.split(os.sep			)			):
          if (i + 1 > len(lowercase__			) or old_parts[i] != new_part) and new_part:
               print(F'''{md_prefix(lowercase__			)} {new_part.replace("_",       " "			).title()}'''			)
     return new_path

def 					__UpperCamelCase				(						lowercase__    :	str = "."			):






     '''simple docstring'''





     __lowercase      		=''
     for filepath in sorted(good_file_paths(lowercase__			)			):
          __lowercase		,  __lowercase      		=os.path.split(lowercase__			)
          if filepath != old_path:
               __lowercase      		=print_path(lowercase__,       lowercase__			)
          __lowercase      		=(filepath.count(os.sep			) + 1) if filepath else 0
          __lowercase      		=F'''{filepath}/{filename}'''.replace(' ',       '%20'			)
          __lowercase      		=os.path.splitext(filename.replace('_',       ' '			).title()			)[0]
          print(F'''{md_prefix(lowercase__			)} [{filename}]({url})'''			)


if __name__ == "__main__":
       print_directory_md('''.''')


