






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 MobileNetVaImageProcessor


class       __lowercase      (	unittest.TestCase  ):







     '''simple docstring'''




     def __init__(				self       :			Any ,	UpperCamelCase_       :			Union[str, Any] ,	UpperCamelCase_       :			Optional[Any]=7 ,	UpperCamelCase_       :			str=3 ,	UpperCamelCase_       :			Any=18 ,	UpperCamelCase_       :			Optional[Any]=30 ,	UpperCamelCase_       :			List[Any]=400 ,	UpperCamelCase_       :			List[Any]=True ,	UpperCamelCase_       :			Tuple=None ,	UpperCamelCase_       :			Tuple=True ,	UpperCamelCase_       :			Tuple=None ,	):


         """simple docstring"""



         __A         =	size if size is not None else {"shortest_edge": 20}
         __A         =	crop_size if crop_size is not None else {"height": 18, "width": 18}
         __A         =	parent
         __A         =	batch_size
         __A         =	num_channels
         __A         =	image_size
         __A         =	min_resolution
         __A         =	max_resolution
         __A         =	do_resize
         __A         =	size
         __A         =	do_center_crop
         __A         =	crop_size





     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }


@require_torch
@require_vision
class       __lowercase      (	_A			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				MobileNetVaImageProcessor if is_vision_available() else None




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	MobileNetVaImageProcessingTester(self				)




     @property
     def 		lowerCAmelCase_     (				self       :			Optional[Any]				):


         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()




     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         __A         =	self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""do_resize"""				)				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""size"""				)				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""do_center_crop"""				)				)
         self.assertTrue(hasattr(UpperCamelCase_ ,	"""crop_size"""				)				)




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	self.image_processing_class.from_dict(self.image_processor_dict				)
         self.assertEqual(image_processor.size ,	{"""shortest_edge""": 20}				)
         self.assertEqual(image_processor.crop_size ,	{"""height""": 18, """width""": 18}				)

         __A         =	self.image_processing_class.from_dict(self.image_processor_dict ,	size=42 ,	crop_size=84				)
         self.assertEqual(image_processor.size ,	{"""shortest_edge""": 42}				)
         self.assertEqual(image_processor.crop_size ,	{"""height""": 84, """width""": 84}				)




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         pass




     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """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=UpperCamelCase_				)
         for image in image_inputs:
             self.assertIsInstance(UpperCamelCase_ ,	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.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,	)

         # Test batched
         __A         =	image_processing(UpperCamelCase_ ,	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.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,	)




     def 		lowerCAmelCase_     (				self       :			Any				):


         """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=UpperCamelCase_ ,	numpify=UpperCamelCase_				)
         for image in image_inputs:
             self.assertIsInstance(UpperCamelCase_ ,	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.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,	)

         # Test batched
         __A         =	image_processing(UpperCamelCase_ ,	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.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,	)





     def 		lowerCAmelCase_     (				self       :			Any				):


         """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=UpperCamelCase_ ,	torchify=UpperCamelCase_				)
         for image in image_inputs:
             self.assertIsInstance(UpperCamelCase_ ,	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.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,	)

         # Test batched
         __A         =	image_processing(UpperCamelCase_ ,	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.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             ) ,	)












import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__a      :       List[str]				  =   pytest.mark.integration



@pytest.mark.parametrize("""path"""			,				["""paws""", """csv"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Optional[int]    )						->      Tuple:





    """simple docstring"""

    inspect_dataset(__lowercase			,				__lowercase    )
    __A         =	path + """.py"""
    assert script_name in os.listdir(__lowercase    )
    assert "__pycache__" not in os.listdir(__lowercase    )



@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning"""    )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""    )
@pytest.mark.parametrize("""path"""			,				["""accuracy"""]    )
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      Any:





    """simple docstring"""

    inspect_metric(__lowercase			,				__lowercase    )
    __A         =	path + """.py"""
    assert script_name in os.listdir(__lowercase    )
    assert "__pycache__" not in os.listdir(__lowercase    )



@pytest.mark.parametrize(
    """path, config_name, expected_splits"""			,				[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			Optional[int]			,				__lowercase   :			List[Any]    )						->      Optional[Any]:





    """simple docstring"""

    __A         =	get_dataset_config_info(__lowercase			,				config_name=__lowercase    )
    assert info.config_name == config_name
    assert list(info.splits.keys()    ) == expected_splits



@pytest.mark.parametrize(
    """path, config_name, expected_exception"""			,				[
        ("""paws""", None, ValueError),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			int			,				__lowercase   :			List[str]    )						->      int:





    """simple docstring"""

    with pytest.raises(__lowercase    ):
        get_dataset_config_info(__lowercase			,				config_name=__lowercase    )



@pytest.mark.parametrize(
    """path, expected"""			,				[
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Tuple			,				__lowercase   :			Optional[Any]    )						->      Any:





    """simple docstring"""

    __A         =	get_dataset_config_names(__lowercase    )
    assert expected in config_names



@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config"""			,				[
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int			,				__lowercase   :			Tuple			,				__lowercase   :			List[Any]    )						->      List[str]:





    """simple docstring"""

    __A         =	get_dataset_infos(__lowercase    )
    assert list(infos.keys()    ) == expected_configs
    __A         =	expected_configs[0]
    assert expected_config in infos
    __A         =	infos[expected_config]
    assert info.config_name == expected_config
    assert list(info.splits.keys()    ) == expected_splits_in_first_config



@pytest.mark.parametrize(
    """path, expected_config, expected_splits"""			,				[
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]			,				__lowercase   :			str			,				__lowercase   :			Union[str, Any]    )						->      Dict:





    """simple docstring"""

    __A         =	get_dataset_infos(__lowercase    )
    assert expected_config in infos
    __A         =	infos[expected_config]
    assert info.config_name == expected_config
    assert list(info.splits.keys()    ) == expected_splits






@pytest.mark.parametrize(
    """path, config_name, expected_exception"""			,				[
        ("""paws""", None, ValueError),
    ]			,				)
def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[str]			,				__lowercase   :			Optional[int]			,				__lowercase   :			Tuple    )						->      Union[str, Any]:





    """simple docstring"""

    with pytest.raises(__lowercase    ):
        get_dataset_split_names(__lowercase			,				config_name=__lowercase    )




