

'''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 		__SCREAMING_SNAKE_CASE							(			unittest.TestCase      ):







				def __init__(       self     :		int		,					UpperCAmelCase__     :		Optional[Any]		,					UpperCAmelCase__     :		Any=7		,					UpperCAmelCase__     :		Optional[int]=3		,					UpperCAmelCase__     :		Dict=30		,					UpperCAmelCase__     :		Optional[int]=400		,					UpperCAmelCase__     :		Tuple=True		,					UpperCAmelCase__     :		str=None		,					UpperCAmelCase__     :		str=True		,					UpperCAmelCase__     :		Union[str, Any]=[0.5, 0.5, 0.5]		,					UpperCAmelCase__     :		str=[0.5, 0.5, 0.5]		,					UpperCAmelCase__     :		Optional[int]=True		,					UpperCAmelCase__     :		int=1 / 255		,					UpperCAmelCase__     :		str=True		,					):
										'''simple docstring'''
										# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
										lowercase :							Any    		=size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
										lowercase :							Dict    		=parent
										lowercase :							Optional[Any]    		=batch_size
										lowercase :							List[Any]    		=num_channels
										lowercase :							Optional[int]    		=min_resolution
										lowercase :							str    		=max_resolution
										lowercase :							int    		=do_resize
										lowercase :							Optional[int]    		=size
										lowercase :							str    		=do_normalize
										lowercase :							Optional[int]    		=image_mean
										lowercase :							Any    		=image_std
										lowercase :							Optional[int]    		=do_rescale
										lowercase :							Union[str, Any]    		=rescale_factor
										lowercase :							str    		=do_pad







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''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  lowerCamelCase_	(       self     :		Dict		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Union[str, Any]=False   ):
										'''simple docstring'''
										if not batched:
																lowercase :							str    		=image_inputs[0]
																if isinstance(__A		,					Image.Image   ):
																						lowercase :							Optional[Any]    		=image.size
																else:
																						lowercase :							str    		=image.shape[1], image.shape[2]
																if w < h:
																						lowercase :							List[Any]    		=int(self.size['''shortest_edge'''] * h / w   )
																						lowercase :							Optional[int]    		=self.size['''shortest_edge''']
																elif w > h:
																						lowercase :							List[Any]    		=self.size['''shortest_edge''']
																						lowercase :							Dict    		=int(self.size['''shortest_edge'''] * w / h   )
																else:
																						lowercase :							str    		=self.size['''shortest_edge''']
																						lowercase :							str    		=self.size['''shortest_edge''']

										else:
																lowercase :							Any    		=[]
																for image in image_inputs:
																						lowercase :							str    		=self.get_expected_values([image]   )
																						expected_values.append((expected_height, expected_width)   )
																lowercase :							str    		=max(__A		,					key=lambda UpperCAmelCase__   : item[0]   )[0]
																lowercase :							List[Any]    		=max(__A		,					key=lambda UpperCAmelCase__   : item[1]   )[1]

										return expected_height, expected_width





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







				def  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										lowercase :							str    		=YolosImageProcessingTester(self   )







				@property
				def  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										return self.image_processor_tester.prepare_image_processor_dict()







				def  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase :							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  lowerCamelCase_	(       self     :		Optional[int]   ):
										'''simple docstring'''
										lowercase :							str    		=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		,					__A   )

										lowercase :							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  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										pass







				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							str    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowercase :							Union[str, Any]    		=prepare_image_inputs(self.image_processor_tester		,					equal_resolution=__A   )
										for image in image_inputs:
																self.assertIsInstance(__A		,					Image.Image   )

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

										lowercase :							List[Any]    		=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
										lowercase :							List[str]    		=self.image_processor_tester.get_expected_values(__A		,					batched=__A   )

										lowercase :							str    		=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  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							Tuple    		=self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowercase :							Union[str, 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
										lowercase :							Dict    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values

										lowercase :							List[Any]    		=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
										lowercase :							Dict    		=image_processing(__A		,					return_tensors='''pt'''   ).pixel_values

										lowercase :							List[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  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										# Initialize image_processing
										lowercase :							str    		=self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowercase :							Dict    		=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
										lowercase :							Any    		=image_processing(image_inputs[0]		,					return_tensors='''pt'''   ).pixel_values

										lowercase :							Union[str, Any]    		=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
										lowercase :							str    		=image_processing(__A		,					return_tensors='''pt'''   ).pixel_values

										lowercase :							Optional[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  lowerCamelCase_	(       self     :		Union[str, Any]   ):
										'''simple docstring'''
										# Initialize image_processings
										lowercase :							List[str]    		=self.image_processing_class(**self.image_processor_dict   )
										lowercase :							str    		=self.image_processing_class(do_resize=__A		,					do_normalize=__A		,					do_rescale=__A   )
										# create random PyTorch tensors
										lowercase :							Dict    		=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
										lowercase :							List[Any]    		=image_processing_a.pad(__A		,					return_tensors='''pt'''   )
										lowercase :							Union[str, Any]    		=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  lowerCamelCase_	(       self     :		Dict   ):
										'''simple docstring'''
										# prepare image and target
										lowercase :							Dict    		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
										with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''		,					'''r'''   ) as f:
																lowercase :							Optional[Any]    		=json.loads(f.read()   )

										lowercase :							Dict    		={'''image_id''': 39769, '''annotations''': target}

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

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

										lowercase :							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
										lowercase :							Tuple    		=torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,					__A   )   )
										# verify boxes
										lowercase :							Dict    		=torch.Size([6, 4]   )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,					__A   )
										lowercase :							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
										lowercase :							Union[str, Any]    		=torch.tensor([39769]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,					__A   )   )
										# verify is_crowd
										lowercase :							Optional[int]    		=torch.tensor([0, 0, 0, 0, 0, 0]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,					__A   )   )
										# verify class_labels
										lowercase :							str    		=torch.tensor([75, 75, 63, 65, 17, 17]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,					__A   )   )
										# verify orig_size
										lowercase :							str    		=torch.tensor([480, 640]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,					__A   )   )
										# verify size
										lowercase :							Dict    		=torch.tensor([800, 1066]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,					__A   )   )







				@slow
				def  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										# prepare image, target and masks_path
										lowercase :							Union[str, Any]    		=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
										with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''		,					'''r'''   ) as f:
																lowercase :							Optional[Any]    		=json.loads(f.read()   )

										lowercase :							Union[str, Any]    		={'''file_name''': '''000000039769.png''', '''image_id''': 39769, '''segments_info''': target}

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

										# encode them
										lowercase :							Optional[Any]    		=YolosImageProcessor(format='''coco_panoptic'''   )
										lowercase :							Optional[int]    		=image_processing(images=__A		,					annotations=__A		,					masks_path=__A		,					return_tensors='''pt'''   )

										# verify pixel values
										lowercase :							Optional[int]    		=torch.Size([1, 3, 800, 1066]   )
										self.assertEqual(encoding['''pixel_values'''].shape		,					__A   )

										lowercase :							Optional[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
										lowercase :							List[Any]    		=torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']		,					__A   )   )
										# verify boxes
										lowercase :							Tuple    		=torch.Size([6, 4]   )
										self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,					__A   )
										lowercase :							List[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
										lowercase :							Any    		=torch.tensor([39769]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,					__A   )   )
										# verify is_crowd
										lowercase :							Tuple    		=torch.tensor([0, 0, 0, 0, 0, 0]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,					__A   )   )
										# verify class_labels
										lowercase :							Optional[Any]    		=torch.tensor([17, 17, 63, 75, 75, 93]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,					__A   )   )
										# verify masks
										lowercase :							List[Any]    		=822873
										self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		,					__A   )
										# verify orig_size
										lowercase :							List[str]    		=torch.tensor([480, 640]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,					__A   )   )
										# verify size
										lowercase :							Any    		=torch.tensor([800, 1066]   )
										self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,					__A   )   )







import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[Any]				)			->						Dict:
       assert "mock" in _fsspec_registry
       assert "bz2" in _fsspec_registry
def 							SCREAMING_SNAKE_CASE_	(					)			->						Union[str, Any]:
       assert "mock" not in _fsspec_registry
       assert "bz2" in _fsspec_registry
def 							SCREAMING_SNAKE_CASE_	(					)			->						Dict:
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		='''mock-s3-bucket'''
       SCREAMING_SNAKE_CASE_							:				Any		=f's3://{mock_bucket}'
       SCREAMING_SNAKE_CASE_							:				Dict		=extract_path_from_uri(UpperCAmelCase_				)
       assert dataset_path.startswith('''s3://'''				) is False

       SCREAMING_SNAKE_CASE_							:				Dict		='''./local/path'''
       SCREAMING_SNAKE_CASE_							:				str		=extract_path_from_uri(UpperCAmelCase_				)
       assert dataset_path == new_dataset_path
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Union[str, Any]				)			->						List[str]:
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=is_remote_filesystem(UpperCAmelCase_				)
       assert is_remote is True

       SCREAMING_SNAKE_CASE_							:				Dict		=fsspec.filesystem('''file'''				)

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=is_remote_filesystem(UpperCAmelCase_				)
       assert is_remote is False
@pytest.mark.parametrize('''compression_fs_class'''			,							UpperCAmelCase_				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     Any			,							UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     Optional[Any]			,							UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     str			,							UpperCAmelCase_	:     Any				)			->						List[Any]:
       SCREAMING_SNAKE_CASE_							:				str		={'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
       SCREAMING_SNAKE_CASE_							:				List[Any]		=input_paths[compression_fs_class.protocol]
       if input_path is None:
              SCREAMING_SNAKE_CASE_							:				List[str]		=f'for \'{compression_fs_class.protocol}\' compression protocol, '
              if compression_fs_class.protocol == "lz4":
                     reason += require_lza.kwargs["reason"]
              elif compression_fs_class.protocol == "zstd":
                     reason += require_zstandard.kwargs["reason"]
              pytest.skip(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=fsspec.filesystem(compression_fs_class.protocol			,							fo=UpperCAmelCase_				)
       assert isinstance(UpperCAmelCase_			,							UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				str		=os.path.basename(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Optional[Any]		=expected_filename[: expected_filename.rindex('''.'''				)]
       assert fs.glob('''*'''				) == [expected_filename]
       with fs.open(UpperCAmelCase_			,							'''r'''			,							encoding='''utf-8'''				) as f, open(UpperCAmelCase_			,							encoding='''utf-8'''				) as expected_file:
              assert f.read() == expected_file.read()
@pytest.mark.parametrize('''protocol'''			,							['''zip''', '''gzip''']				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Any			,							UpperCAmelCase_	:     Union[str, Any]			,							UpperCAmelCase_	:     str				)			->						Any:
       SCREAMING_SNAKE_CASE_							:				Tuple		={'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
       SCREAMING_SNAKE_CASE_							:				List[Any]		=compressed_file_paths[protocol]
       SCREAMING_SNAKE_CASE_							:				str		='''dataset.jsonl'''
       SCREAMING_SNAKE_CASE_							:				Dict		=f'{protocol}://{member_file_path}::{compressed_file_path}'
       SCREAMING_SNAKE_CASE_	,     *SCREAMING_SNAKE_CASE_							:				Dict		=fsspec.get_fs_token_paths(UpperCAmelCase_				)
       assert fs.isfile(UpperCAmelCase_				)
       assert not fs.isfile('''non_existing_''' + member_file_path				)
@pytest.mark.integration
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[str]			,							UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     int				)			->						int:
       SCREAMING_SNAKE_CASE_							:				Dict		=hf_api.dataset_info(UpperCAmelCase_			,							token=UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=HfFileSystem(repo_info=UpperCAmelCase_			,							token=UpperCAmelCase_				)
       assert sorted(hffs.glob('''*'''				)				) == [".gitattributes", "data"]
       assert hffs.isdir('''data'''				)
       assert hffs.isfile('''.gitattributes'''				) and hffs.isfile('''data/text_data.txt'''				)
       with open(UpperCAmelCase_				) as f:
              assert hffs.open('''data/text_data.txt'''			,							'''r'''				).read() == f.read()

def 							SCREAMING_SNAKE_CASE_	(					)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				Tuple		='''bz2'''

       # Import module
       import datasets.filesystems

       # Overwrite protocol and reload
       register_implementation(UpperCAmelCase_			,							UpperCAmelCase_			,							clobber=UpperCAmelCase_				)
       with pytest.warns(UpperCAmelCase_				) as warning_info:
              importlib.reload(datasets.filesystems				)

       assert len(UpperCAmelCase_				) == 1
       assert (
           str(warning_info[0].message				)
           == f'A filesystem protocol was already set for {protocol} and will be overwritten.'
       )
