





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      A_	(unittest.TestCase      ):
	def __init__(   self							, _A							, _A=7							, _A=3							, _A=1_8							, _A=3_0							, _A=4_0_0							, _A=True							, _A=None							, _A=True							, _A=None							, ):






				'''simple docstring'''







				UpperCAmelCase       		=    size if size is not None else {"shortest_edge": 2_0}
				UpperCAmelCase       		=    crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
				UpperCAmelCase       		=    parent
				UpperCAmelCase       		=    batch_size
				UpperCAmelCase       		=    num_channels
				UpperCAmelCase       		=    image_size
				UpperCAmelCase       		=    min_resolution
				UpperCAmelCase       		=    max_resolution
				UpperCAmelCase       		=    do_resize
				UpperCAmelCase       		=    size
				UpperCAmelCase       		=    do_center_crop
				UpperCAmelCase       		=    crop_size







	def 					_lowercase    (   self		):






				'''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      A_	(a_					,							unittest.TestCase      ):
	UpperCAmelCase__ =      MobileNetVaImageProcessor if is_vision_available() else None
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    MobileNetVaImageProcessingTester(self		)
	@property
	def 					_lowercase    (   self		):






				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(__A							, '''do_resize'''		)		)
				self.assertTrue(hasattr(__A							, '''size'''		)		)
				self.assertTrue(hasattr(__A							, '''do_center_crop'''		)		)
				self.assertTrue(hasattr(__A							, '''center_crop'''		)		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.image_processing_class.from_dict(self.image_processor_dict		)
				self.assertEqual(image_processor.size							, {'''shortest_edge''': 2_0}		)
				self.assertEqual(image_processor.crop_size							, {'''height''': 1_8, '''width''': 1_8}		)

				UpperCAmelCase       		=    self.image_processing_class.from_dict(self.image_processor_dict							, size=4_2							, crop_size=8_4		)
				self.assertEqual(image_processor.size							, {'''shortest_edge''': 4_2}		)
				self.assertEqual(image_processor.crop_size							, {'''height''': 8_4, '''width''': 8_4}		)
	def 					_lowercase    (   self		):






				'''simple docstring'''







				pass
	def 					_lowercase    (   self		):






				'''simple docstring'''







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

				# Test not batched input
				UpperCAmelCase       		=    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
				UpperCAmelCase       		=    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,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )							, )
	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				UpperCAmelCase       		=    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
				UpperCAmelCase       		=    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
				UpperCAmelCase       		=    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,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )							, )







	def 					_lowercase    (   self		):






				'''simple docstring'''







				UpperCAmelCase       		=    self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				UpperCAmelCase       		=    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
				UpperCAmelCase       		=    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
				UpperCAmelCase       		=    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,
				        self.image_processor_tester.crop_size['''height'''],
				        self.image_processor_tester.crop_size['''width'''],
				    )							, )










import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Value
from datasets.packaged_modules.json.json import Json





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    str:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file.jsonl'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Union[str, Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_utf16_encoded.jsonl'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {"col_1": -1}
        {"col_1": 1, "col_2": 2}
        {"col_1": 10, "col_2": 20}
        '''			)
			with open(UpperCamelCase__      ,      '''w'''      ,      encoding='''utf-16'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    Optional[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_with_list_of_dicts.json'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        [
            {"col_1": -1},
            {"col_1": 1, "col_2": 2},
            {"col_1": 10, "col_2": 20}
        ]
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.fixture
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    int:







			'''simple docstring'''

			UpperCAmelCase       		=    tmp_path / '''file_with_list_of_dicts_field.json'''
			UpperCAmelCase       		=    textwrap.dedent(
			    '''\
        {
            "field1": 1,
            "field2": "aabb",
            "field3": [
                {"col_1": -1},
                {"col_1": 1, "col_2": 2},
                {"col_1": 10, "col_2": 20}
            ]
        }
        '''			)
			with open(UpperCamelCase__      ,      '''w'''			) as f:
						f.write(UpperCamelCase__			)
			return str(UpperCamelCase__			)





@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''      ,      [
        ('''jsonl_file''', {}),
        ('''jsonl_file_utf16_encoded''', {'''encoding''': '''utf-16'''}),
        ('''json_file_with_list_of_dicts''', {}),
        ('''json_file_with_list_of_dicts_field''', {'''field''': '''field3'''}),
    ]      ,      )
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    Dict:







			'''simple docstring'''

			UpperCAmelCase       		=    Json(**UpperCamelCase__			)
			UpperCAmelCase       		=    json._generate_tables([[request.getfixturevalue(UpperCamelCase__			)]]			)
			UpperCAmelCase       		=    pa.concat_tables([table for _, table in generator]			)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20]}






@pytest.mark.parametrize(
    '''file_fixture, config_kwargs'''      ,      [
        (
            '''jsonl_file''',
            {'''features''': Features({'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			)},
        ),
        (
            '''json_file_with_list_of_dicts''',
            {'''features''': Features({'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			)},
        ),
        (
            '''json_file_with_list_of_dicts_field''',
            {
                '''field''': '''field3''',
                '''features''': Features(
                    {'''col_1''': Value('''int64'''			), '''col_2''': Value('''int64'''			), '''missing_col''': Value('''string'''			)}			),
            },
        ),
    ]      ,      )
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[Any]:







			'''simple docstring'''

			UpperCAmelCase       		=    Json(**UpperCamelCase__			)
			UpperCAmelCase       		=    json._generate_tables([[request.getfixturevalue(UpperCamelCase__			)]]			)
			UpperCAmelCase       		=    pa.concat_tables([table for _, table in generator]			)
			assert pa_table.to_pydict() == {"col_1": [-1, 1, 10], "col_2": [None, 2, 20], "missing_col": [None, None, None]}



