

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 ConvNextImageProcessor


class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def __init__( self					:			Optional[int],lowercase_					:			Union[str, Any],lowercase_					:			Any=7,lowercase_					:			Any=3,lowercase_					:			List[Any]=1_8,lowercase_					:			Optional[Any]=3_0,lowercase_					:			int=4_0_0,lowercase_					:			Dict=True,lowercase_					:			Any=None,lowercase_					:			Union[str, Any]=0.875,lowercase_					:			Optional[int]=True,lowercase_					:			int=[0.5, 0.5, 0.5],lowercase_					:			Union[str, Any]=[0.5, 0.5, 0.5],)->       Union[str, Any]:





        '''simple docstring'''







        A__        =  size if size is not None else {'shortest_edge': 2_0}
        A__        =  parent
        A__        =  batch_size
        A__        =  num_channels
        A__        =  image_size
        A__        =  min_resolution
        A__        =  max_resolution
        A__        =  do_resize
        A__        =  size
        A__        =  crop_pct
        A__        =  do_normalize
        A__        =  image_mean
        A__        =  image_std


    def 		snake_case__	( self					:			Tuple						)->       Tuple:





        '''simple docstring'''







        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_pct": self.crop_pct,
        }




@require_torch
@require_vision
class 					A		(				_UpperCAmelCase       ,				unittest.TestCase					):







    """simple docstring"""





    lowerCamelCase						      =				ConvNextImageProcessor if is_vision_available() else None


    def 		snake_case__	( self					:			int						)->       List[Any]:





        '''simple docstring'''







        A__        =  ConvNextImageProcessingTester(self						)


    @property
    def 		snake_case__	( self					:			Optional[Any]						)->       str:





        '''simple docstring'''







        return self.image_processor_tester.prepare_image_processor_dict()


    def 		snake_case__	( self					:			Any						)->       List[str]:





        '''simple docstring'''







        A__        =  self.image_processing_class(**self.image_processor_dict						)
        self.assertTrue(hasattr(lowercase_,'do_resize'						)						)
        self.assertTrue(hasattr(lowercase_,'size'						)						)
        self.assertTrue(hasattr(lowercase_,'crop_pct'						)						)
        self.assertTrue(hasattr(lowercase_,'do_normalize'						)						)
        self.assertTrue(hasattr(lowercase_,'image_mean'						)						)
        self.assertTrue(hasattr(lowercase_,'image_std'						)						)


    def 		snake_case__	( self					:			Optional[int]						)->       Any:





        '''simple docstring'''







        A__        =  self.image_processing_class.from_dict(self.image_processor_dict						)
        self.assertEqual(image_processor.size,{'shortest_edge': 2_0}						)

        A__        =  self.image_processing_class.from_dict(self.image_processor_dict,size=4_2						)
        self.assertEqual(image_processor.size,{'shortest_edge': 4_2}						)


    def 		snake_case__	( self					:			List[Any]						)->       int:





        '''simple docstring'''







        pass


    def 		snake_case__	( self					:			int						)->       Optional[Any]:





        '''simple docstring'''







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

        # Test not batched input
        A__        =  image_processing(image_inputs[0],return_tensors='pt'						).pixel_values
        self.assertEqual(
            encoded_images.shape,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),)

        # Test batched
        A__        =  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,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),)


    def 		snake_case__	( self					:			List[Any]						)->       List[str]:





        '''simple docstring'''







        A__        =  self.image_processing_class(**self.image_processor_dict						)
        # create random numpy tensors
        A__        =  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
        A__        =  image_processing(image_inputs[0],return_tensors='pt'						).pixel_values
        self.assertEqual(
            encoded_images.shape,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),)

        # Test batched
        A__        =  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,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),)


    def 		snake_case__	( self					:			Dict						)->       int:





        '''simple docstring'''







        A__        =  self.image_processing_class(**self.image_processor_dict						)
        # create random PyTorch tensors
        A__        =  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
        A__        =  image_processing(image_inputs[0],return_tensors='pt'						).pixel_values
        self.assertEqual(
            encoded_images.shape,(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),)

        # Test batched
        A__        =  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,
                self.image_processor_tester.size['shortest_edge'],
                self.image_processor_tester.size['shortest_edge'],
            ),)




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      str )				-> str:





    '''simple docstring'''






    A__        =  min(re.search('^ +'       ,     SCREAMING_SNAKE_CASE__ ).end() if t.startswith(' ' ) else 0 for t in string.splitlines() )
    return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(SCREAMING_SNAKE_CASE__ )] )


lowercase_									=    "\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n"

lowercase_									=    "\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n"


lowercase_									=    "\\n# Begin of markdown\n\nSome cool dataset card\n"


class 					A		(				unittest.TestCase					):







    """simple docstring"""





    def 		snake_case__	( self					:			List[Any]						)->       str:





        '''simple docstring'''







        with tempfile.TemporaryDirectory() as tmp_dir:
            A__        =  Path(lowercase_						) / 'README.md'
            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertDictEqual(lowercase_,{}						)

            with open(lowercase_,'w+'						) as readme_file:
                readme_file.write(lowercase_						)
            A__        =  DatasetMetadata.from_readme(lowercase_						)
            self.assertEqual(lowercase_,{}						)


    def 		snake_case__	( self					:			Tuple						)->       List[Any]:





        '''simple docstring'''







        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '						)
        assert DatasetMetadata.from_yaml_string(lowercase_						)

        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '						)
        with self.assertRaises(lowercase_						):
            DatasetMetadata.from_yaml_string(lowercase_						)

        A__        =  _dedent(
            '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '						)
        assert DatasetMetadata.from_yaml_string(lowercase_						)

