






"""simple docstring"""





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   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    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}}'					)
    UpperCamelCase__         =    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   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    str(SCREAMING_SNAKE_CASE					)
    dataset_info.write_to_directory(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    DatasetInfo.from_directory(SCREAMING_SNAKE_CASE					)
    assert dataset_info == reloaded
    assert os.path.exists(os.path.join(SCREAMING_SNAKE_CASE ,	'dataset_info.json'					)					)







def   lowerCAmelCase_( )					->	Optional[int]:
    """simple docstring"""

    UpperCamelCase__         =    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=13_37 ,	post_processing_size=4_42 ,	dataset_size=12_34 ,	size_in_bytes=13_37 + 4_42 + 12_34 ,	)
    UpperCamelCase__         =    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)					)
    UpperCamelCase__         =    yaml.safe_dump(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    yaml.safe_load(SCREAMING_SNAKE_CASE					)
    assert dataset_info_yaml_dict == reloaded







def   lowerCAmelCase_( )					->	Dict:
    """simple docstring"""

    UpperCamelCase__         =    DatasetInfo()
    UpperCamelCase__         =    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=13_37					),
            }					),
    ] ,	)
def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    UpperCamelCase__         =    str(SCREAMING_SNAKE_CASE					)
    dataset_infos_dict.write_to_directory(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    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():
        UpperCamelCase__         =    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
        UpperCamelCase__         =    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'					)					)












"""simple docstring"""





import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
  import jax

  from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
  from PIL import Image

  from transformers import BeitImageProcessor
class    __lowerCamelCase    (  unittest.TestCase  ):

   def __init__(					self ,				snake_case_ ,				snake_case_=100 ,				snake_case_=13 ,				snake_case_=30 ,				snake_case_=2 ,				snake_case_=3 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=32 ,				snake_case_=5 ,				snake_case_=4 ,				snake_case_=37 ,				snake_case_="gelu" ,				snake_case_=0.1 ,				snake_case_=0.1 ,				snake_case_=10 ,				snake_case_=0.02 ,				snake_case_=3 ,				)		->       Optional[int]:
       UpperCamelCase__         =    parent
       UpperCamelCase__         =    vocab_size
       UpperCamelCase__         =    batch_size
       UpperCamelCase__         =    image_size
       UpperCamelCase__         =    patch_size
       UpperCamelCase__         =    num_channels
       UpperCamelCase__         =    is_training
       UpperCamelCase__         =    use_labels
       UpperCamelCase__         =    hidden_size
       UpperCamelCase__         =    num_hidden_layers
       UpperCamelCase__         =    num_attention_heads
       UpperCamelCase__         =    intermediate_size
       UpperCamelCase__         =    hidden_act
       UpperCamelCase__         =    hidden_dropout_prob
       UpperCamelCase__         =    attention_probs_dropout_prob
       UpperCamelCase__         =    type_sequence_label_size
       UpperCamelCase__         =    initializer_range

       # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       UpperCamelCase__         =    (image_size // patch_size) ** 2
       UpperCamelCase__         =    num_patches + 1

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       UpperCamelCase__         =    None
       if self.use_labels:
           UpperCamelCase__         =    ids_tensor([self.batch_size] ,				self.type_sequence_label_size      )

       UpperCamelCase__         =    BeitConfig(
           vocab_size=self.vocab_size ,				image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				intermediate_size=self.intermediate_size ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				is_decoder=snake_case_ ,				initializer_range=self.initializer_range ,				)

       return config, pixel_values, labels

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    FlaxBeitModel(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[Any]:
       UpperCamelCase__         =    FlaxBeitForMaskedImageModeling(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length - 1, self.vocab_size)      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    self.type_sequence_label_size
       UpperCamelCase__         =    FlaxBeitForImageClassification(config=snake_case_      )
       UpperCamelCase__         =    model(snake_case_      )
       self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       UpperCamelCase__         =    1
       UpperCamelCase__         =    FlaxBeitForImageClassification(snake_case_      )

       UpperCamelCase__         =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       UpperCamelCase__         =    model(snake_case_      )







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase__
       )       ,						(
           UpperCamelCase__
       )       ,						(
           UpperCamelCase__
       )       ,						
       )         =    config_and_inputs
       UpperCamelCase__         =    {'pixel_values': pixel_values}
       return config, inputs_dict







@require_flax
class    __lowerCamelCase    (  _a							,	unittest.TestCase  ):
   a      :				int 						=(
       (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
   )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       None:
       UpperCamelCase__         =    FlaxBeitModelTester(self      )
       UpperCamelCase__         =    ConfigTester(self ,				config_class=snake_case_ ,				has_text_modality=snake_case_ ,				hidden_size=37      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       self.config_tester.run_common_tests()

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__         =    model_class(snake_case_      )
           UpperCamelCase__         =    inspect.signature(model.__call__      )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase__         =    [*signature.parameters.keys()]

           UpperCamelCase__         =    ['pixel_values']
           self.assertListEqual(arg_names[:1] ,				snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__       ,						UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__      ):
               UpperCamelCase__         =    self._prepare_for_class(snake_case_ ,				snake_case_      )
               UpperCamelCase__         =    model_class(snake_case_      )

               @jax.jit
               def model_jitted(snake_case_ ,				**snake_case_      ):
                   return model(pixel_values=snake_case_ ,				**snake_case_      )

               with self.subTest('JIT Enabled'      ):
                   UpperCamelCase__         =    model_jitted(**snake_case_      ).to_tuple()

               with self.subTest('JIT Disabled'      ):
                   with jax.disable_jit():
                       UpperCamelCase__         =    model_jitted(**snake_case_      ).to_tuple()

               self.assertEqual(len(snake_case_      ) ,				len(snake_case_      )      )
               for jitted_output, output in zip(snake_case_ ,				snake_case_      ):
                   self.assertEqual(jitted_output.shape ,				output.shape      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Dict:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*snake_case_      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__         =    model_class_name.from_pretrained('microsoft/beit-base-patch16-224'      )
           UpperCamelCase__         =    model(np.ones((1, 3, 224, 224)      )      )
           self.assertIsNotNone(snake_case_      )







def   lowerCAmelCase_( )					->	Optional[int]:
    """simple docstring"""

    UpperCamelCase__         =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
    return image





@require_vision
@require_flax
class    __lowerCamelCase    (  unittest.TestCase  ):

   @cached_property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'      ) if is_vision_available() else None

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:
       UpperCamelCase__         =    FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      ).pixel_values

       # prepare bool_masked_pos
       UpperCamelCase__         =    np.ones((1, 196) ,				dtype=snake_case_      )

       # forward pass
       UpperCamelCase__         =    model(pixel_values=snake_case_ ,				bool_masked_pos=snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 196, 8192)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array(
           [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]      )

       self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3] ,				snake_case_ ,				atol=1E-2      )      )

   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      )

       # forward pass
       UpperCamelCase__         =    model(**snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 1000)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array([-1.2_385, -1.0_987, -1.0_108]      )

       self.assertTrue(np.allclose(logits[0, :3] ,				snake_case_ ,				atol=1E-4      )      )

       UpperCamelCase__         =    281
       self.assertEqual(logits.argmax(-1      ).item() ,				snake_case_      )







   @slow
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       UpperCamelCase__         =    FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'      )

       UpperCamelCase__         =    self.default_image_processor
       UpperCamelCase__         =    prepare_img()
       UpperCamelCase__         =    image_processor(images=snake_case_ ,				return_tensors='np'      )

       # forward pass
       UpperCamelCase__         =    model(**snake_case_      )
       UpperCamelCase__         =    outputs.logits

       # verify the logits
       UpperCamelCase__         =    (1, 2_1841)
       self.assertEqual(logits.shape ,				snake_case_      )

       UpperCamelCase__         =    np.array([1.6_881, -0.2_787, 0.5_901]      )

       self.assertTrue(np.allclose(logits[0, :3] ,				snake_case_ ,				atol=1E-4      )      )

       UpperCamelCase__         =    2396
       self.assertEqual(logits.argmax(-1      ).item() ,				snake_case_      )




