






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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						List[str]:
       _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(__snake_case )
       assert dataset_infos
       assert dataset_infos["default"].dataset_size == 4_2




@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=4_2   ,      ),
    ]   ,      )
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Optional[int]:
       _UpperCAmelCase							=    str(__snake_case )
       dataset_info.write_to_directory(__snake_case )
       _UpperCAmelCase							=    DatasetInfo.from_directory(__snake_case )
       assert dataset_info == reloaded
       assert os.path.exists(os.path.join(__snake_case   ,      """dataset_info.json""" ) )




def       _SCREAMING_SNAKE_CASE (							)				->						Any:
       _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""": 4_2}]   ,      download_checksums={}   ,      download_size=1_3_3_7   ,      post_processing_size=4_4_2   ,      dataset_size=1_2_3_4   ,      size_in_bytes=1_3_3_7 + 4_4_2 + 1_2_3_4   ,      )
       _UpperCAmelCase							=    dataset_info._to_yaml_dict()
       assert sorted(__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(__snake_case )
       _UpperCAmelCase							=    yaml.safe_load(__snake_case )
       assert dataset_info_yaml_dict == reloaded




def       _SCREAMING_SNAKE_CASE (							)				->						int:
       _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=4_2   ,      )
            } ),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=4_2 ),
                """v2""": DatasetInfo(dataset_size=1_3_3_7 ),
            } ),
    ]   ,      )
def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Union[str, Any]:
       _UpperCAmelCase							=    str(__snake_case )
       dataset_infos_dict.write_to_directory(__snake_case )
       _UpperCAmelCase							=    DatasetInfosDict.from_directory(__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(__snake_case   ,      """README.md""" ) )







import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
  from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def __init__(      self		:			Dict       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[Any]=13       ,				lowerCamelCase		:			Dict=32       ,				lowerCamelCase		:			Optional[int]=3       ,				lowerCamelCase		:			List[Any]=4       ,				lowerCamelCase		:			Tuple=[10, 20, 30, 40]       ,				lowerCamelCase		:			str=[2, 2, 3, 2]       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			Union[str, Any]=37       ,				lowerCamelCase		:			Any="gelu"       ,				lowerCamelCase		:			Tuple=10       ,				lowerCamelCase		:			Optional[Any]=0.02       ,				lowerCamelCase		:			Union[str, Any]=["stage2", "stage3", "stage4"]       ,				lowerCamelCase		:			Any=[2, 3, 4]       ,				lowerCamelCase		:			Tuple=None       ,				)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    num_stages
         _UpperCAmelCase							=    hidden_sizes
         _UpperCAmelCase							=    depths
         _UpperCAmelCase							=    is_training
         _UpperCAmelCase							=    use_labels
         _UpperCAmelCase							=    intermediate_size
         _UpperCAmelCase							=    hidden_act
         _UpperCAmelCase							=    num_labels
         _UpperCAmelCase							=    initializer_range
         _UpperCAmelCase							=    out_features
         _UpperCAmelCase							=    out_indices
         _UpperCAmelCase							=    scope


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Union[str, Any]:


         """simple docstring"""
         _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.num_labels							)

         _UpperCAmelCase							=    self.get_config()
         return config, pixel_values, labels


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


         """simple docstring"""
         return ConvNextConfig(
             num_channels=self.num_channels       ,				hidden_sizes=self.hidden_sizes       ,				depths=self.depths       ,				num_stages=self.num_stages       ,				hidden_act=self.hidden_act       ,				is_decoder=lowerCamelCase       ,				initializer_range=self.initializer_range       ,				out_features=self.out_features       ,				out_indices=self.out_indices       ,				num_labels=self.num_labels       ,				)


  def  lowerCamelCase       (      self		:			Union[str, Any]       ,				lowerCamelCase		:			List[str]       ,				lowerCamelCase		:			Optional[int]       ,				lowerCamelCase		:			Any							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModel(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,				)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextForImageClassification(lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase       ,				labels=lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)							)


  def  lowerCamelCase       (      self		:			Dict       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Dict							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextBackbone(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)

         # verify hidden states
         self.parent.assertEqual(len(result.feature_maps							)       ,				len(config.out_features							)							)
         self.parent.assertListEqual(list(result.feature_maps[0].shape							)       ,				[self.batch_size, self.hidden_sizes[1], 4, 4]							)

         # verify channels
         self.parent.assertEqual(len(model.channels							)       ,				len(config.out_features							)							)
         self.parent.assertListEqual(model.channels       ,				config.hidden_sizes[1:]							)

         # verify backbone works with out_features=None
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    ConvNextBackbone(config=lowerCamelCase							)
         model.to(lowerCamelCase							)
         model.eval()
         _UpperCAmelCase							=    model(lowerCamelCase							)

         # verify feature maps
         self.parent.assertEqual(len(result.feature_maps							)       ,				1							)
         self.parent.assertListEqual(list(result.feature_maps[0].shape							)       ,				[self.batch_size, self.hidden_sizes[-1], 1, 1]							)

         # verify channels
         self.parent.assertEqual(len(model.channels							)       ,				1							)
         self.parent.assertListEqual(model.channels       ,				[config.hidden_sizes[-1]]							)





  def  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.prepare_config_and_inputs()
         _UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase							=    config_and_inputs
         _UpperCAmelCase							=    {"""pixel_values""": pixel_values}
         return config, inputs_dict



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (
      (
          ConvNextModel,
          ConvNextForImageClassification,
          ConvNextBackbone,
      )
      if is_torch_available()
      else ()
  )
  _lowerCamelCase    =  (
      {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
      if is_torch_available()
      else {}
  )

  _lowerCamelCase    =  True
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False
  _lowerCamelCase    =  False


  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModelTester(self							)
         _UpperCAmelCase							=    ConfigTester(self       ,				config_class=lowerCamelCase       ,				has_text_modality=lowerCamelCase       ,				hidden_size=37							)


  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()


  def  lowerCamelCase       (      self		:			str							)						->	Union[str, Any]:


         """simple docstring"""
         return


  @unittest.skip(reason="""ConvNext does not use inputs_embeds"""							)
  def  lowerCamelCase       (      self		:			List[Any]							)						->	Any:


         """simple docstring"""
         pass


  @unittest.skip(reason="""ConvNext does not support input and output embeddings"""							)
  def  lowerCamelCase       (      self		:			int							)						->	int:


         """simple docstring"""
         pass


  @unittest.skip(reason="""ConvNext does not use feedforward chunking"""							)
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	int:


         """simple docstring"""
         pass


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


         """simple docstring"""
         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase							=    ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,				lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_backbone(*lowerCamelCase							)


  def  lowerCamelCase       (      self		:			int							)						->	Any:


         """simple docstring"""
         def check_hidden_states_output(lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Tuple       ,				lowerCamelCase		:			Optional[int]							):
                _UpperCAmelCase							=    model_class(lowerCamelCase							)
                model.to(lowerCamelCase							)
                model.eval()

                with torch.no_grad():
                       _UpperCAmelCase							=    model(**self._prepare_for_class(lowerCamelCase       ,				lowerCamelCase							)							)

                _UpperCAmelCase							=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                _UpperCAmelCase							=    self.model_tester.num_stages
                self.assertEqual(len(lowerCamelCase							)       ,				expected_num_stages + 1							)

                # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]							)       ,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,				)

         _UpperCAmelCase	,			_UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                _UpperCAmelCase							=    True
                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                _UpperCAmelCase							=    True

                check_hidden_states_output(lowerCamelCase       ,				lowerCamelCase       ,				lowerCamelCase							)


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


         """simple docstring"""
         _UpperCAmelCase							=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)





  @slow
  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[Any]:


         """simple docstring"""
         for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase							=    ConvNextModel.from_pretrained(lowerCamelCase							)
                self.assertIsNotNone(lowerCamelCase							)




def       _SCREAMING_SNAKE_CASE (							)				->						Tuple:
       _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
       return image
@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  @cached_property
  def  lowerCamelCase       (      self		:			Dict							)						->	int:


         """simple docstring"""
         return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""							) if is_vision_available() else None





  @slow
  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""							).to(lowerCamelCase							)

         _UpperCAmelCase							=    self.default_image_processor
         _UpperCAmelCase							=    prepare_img()
         _UpperCAmelCase							=    image_processor(images=lowerCamelCase       ,				return_tensors="""pt"""							).to(lowerCamelCase							)

         # forward pass
         with torch.no_grad():
                _UpperCAmelCase							=    model(**lowerCamelCase							)

         # verify the logits
         _UpperCAmelCase							=    torch.Size((1, 1000)							)
         self.assertEqual(outputs.logits.shape       ,				lowerCamelCase							)

         _UpperCAmelCase							=    torch.tensor([-0.0260, -0.4739, 0.1911]							).to(lowerCamelCase							)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				lowerCamelCase       ,				atol=1E-4							)							)



@require_torch
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase					,      UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  (ConvNextBackbone,) if is_torch_available() else ()
  _lowerCamelCase    =  ConvNextConfig

  _lowerCamelCase    =  False


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


         """simple docstring"""
         _UpperCAmelCase							=    ConvNextModelTester(self							)