





import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
    "files"       ,       [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]       ,       )
def      _A       (		SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   List[str]						):





  """simple docstring"""



  a__	:					Optional[Any]										=tmp_path_factory.mktemp("dset_infos_dir"						)
  if "full:README.md" in files:
    with open(dataset_infos_dir / "README.md"       ,       "w"						) as f:
      f.write("---\ndataset_info:\n  dataset_size: 42\n---"						)
  if "empty:README.md" in files:
    with open(dataset_infos_dir / "README.md"       ,       "w"						) as f:
      f.write(""						)
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / "dataset_infos.json"       ,       "w"						) as f:
      f.write("{\"default\": {\"dataset_size\": 42}}"						)
  a__	:					str										=DatasetInfosDict.from_directory(SCREAMING_SNAKE_CASE						)
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
    "dataset_info"       ,       [
        DatasetInfo(),
        DatasetInfo(
            description="foo"       ,       features=Features({"a": Value("int32"						)}						)       ,       builder_name="builder"       ,       config_name="config"       ,       version="1.0.0"       ,       splits=[{"name": "train"}]       ,       download_size=42       ,       ),
    ]       ,       )
def      _A       (		SCREAMING_SNAKE_CASE     :   Any       ,       SCREAMING_SNAKE_CASE     :   DatasetInfo						):





  """simple docstring"""



  a__	:					List[str]										=str(SCREAMING_SNAKE_CASE						)
  dataset_info.write_to_directory(SCREAMING_SNAKE_CASE						)
  a__	:					Optional[int]										=DatasetInfo.from_directory(SCREAMING_SNAKE_CASE						)
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(SCREAMING_SNAKE_CASE       ,       "dataset_info.json"						)						)
def      _A       (		):





  """simple docstring"""



  a__	:					Tuple										=DatasetInfo(
      description="foo"       ,       citation="bar"       ,       homepage="https://foo.bar"       ,       license="CC0"       ,       features=Features({"a": Value("int32"						)}						)       ,       post_processed={}       ,       supervised_keys=()       ,       task_templates=[]       ,       builder_name="builder"       ,       config_name="config"       ,       version="1.0.0"       ,       splits=[{"name": "train", "num_examples": 42}]       ,       download_checksums={}       ,       download_size=1_337       ,       post_processing_size=442       ,       dataset_size=1_234       ,       size_in_bytes=1_337 + 442 + 1_234       ,       )
  a__	:					Union[str, Any]										=dataset_info._to_yaml_dict()
  assert sorted(SCREAMING_SNAKE_CASE						) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML						)
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key]       ,       (list, dict, int, str)						)
  a__	:					Union[str, Any]										=yaml.safe_dump(SCREAMING_SNAKE_CASE						)
  a__	:					Any										=yaml.safe_load(SCREAMING_SNAKE_CASE						)
  assert dataset_info_yaml_dict == reloaded
def      _A       (		):





  """simple docstring"""



  a__	:					Dict										=DatasetInfo()
  a__	:					Tuple										=dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    "dataset_infos_dict"       ,       [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}						),
        DatasetInfosDict({"my_config_name": DatasetInfo()}						),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"       ,       features=Features({"a": Value("int32"						)}						)       ,       builder_name="builder"       ,       config_name="config"       ,       version="1.0.0"       ,       splits=[{"name": "train"}]       ,       download_size=42       ,       )
            }						),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42						),
                "v2": DatasetInfo(dataset_size=1_337						),
            }						),
    ]       ,       )
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   DatasetInfosDict						):





  """simple docstring"""



  a__	:					str										=str(SCREAMING_SNAKE_CASE						)
  dataset_infos_dict.write_to_directory(SCREAMING_SNAKE_CASE						)
  a__	:					Tuple										=DatasetInfosDict.from_directory(SCREAMING_SNAKE_CASE						)

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    a__	:					Optional[int]										=config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    a__	:					Optional[int]										=DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()						)
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(SCREAMING_SNAKE_CASE       ,       "README.md"						)						)













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 MobileViTImageProcessor







class 				__lowerCAmelCase       (    unittest.TestCase):







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=7		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8		,      lowerCAmelCase__=3_0		,      lowerCAmelCase__=4_0_0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      lowerCAmelCase__=None		,      lowerCAmelCase__=True		,      )   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=size if size is not None else {"shortest_edge": 2_0}
        a__	:					List[str]										=crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
        a__	:					Tuple										=parent
        a__	:					Union[str, Any]										=batch_size
        a__	:					List[str]										=num_channels
        a__	:					List[Any]										=image_size
        a__	:					str										=min_resolution
        a__	:					Optional[int]										=max_resolution
        a__	:					Tuple										=do_resize
        a__	:					Union[str, Any]										=size
        a__	:					List[Any]										=do_center_crop
        a__	:					List[str]										=crop_size
        a__	:					Optional[int]										=do_flip_channel_order







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




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







@require_torch
@require_vision
class 				__lowerCAmelCase       (    UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					int						=			MobileViTImageProcessor if is_vision_available() else None







      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					Tuple										=MobileViTImageProcessingTester(self							)







      @property
      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        return self.image_processor_tester.prepare_image_processor_dict()







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					str										=self.image_processing_class(**self.image_processor_dict							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_resize"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "size"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "center_crop"							)							)
        self.assertTrue(hasattr(lowerCAmelCase__		,      "do_flip_channel_order"							)							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					List[Any]										=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}							)

        a__	:					Union[str, Any]										=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							)   ->						Optional[Any]:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__	:					List[str]										=self.image_processing_class(**self.image_processor_dict							)
        # create random PIL images
        a__	:					Any										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      Image.Image							)

        # Test not batched input
        a__	:					Tuple										=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__	:					List[Any]										=image_processing(lowerCAmelCase__		,      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							)   ->						Any:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.image_processing_class(**self.image_processor_dict							)
        # create random numpy tensors
        a__	:					List[Any]										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      numpify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      np.ndarray							)

        # Test not batched input
        a__	:					Tuple										=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__	:					int										=image_processing(lowerCAmelCase__		,      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							)   ->						Optional[Any]:



        '''simple docstring'''




        a__	:					int										=self.image_processing_class(**self.image_processor_dict							)
        # create random PyTorch tensors
        a__	:					Tuple										=prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase__		,      torchify=lowerCAmelCase__							)
        for image in image_inputs:
          self.assertIsInstance(lowerCAmelCase__		,      torch.Tensor							)

        # Test not batched input
        a__	:					List[str]										=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__	:					List[str]										=image_processing(lowerCAmelCase__		,      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"],
            )		,      )






