





import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
					from transformers import (
					    FlaxBertModel,
					    FlaxCLIPVisionModel,
					    FlaxVisionTextDualEncoderModel,
					    FlaxViTModel,
					    VisionTextDualEncoderConfig,
					    VisionTextDualEncoderProcessor,
					)
					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)


if is_torch_available():
					import torch

					from transformers import VisionTextDualEncoderModel

if is_vision_available():
					from PIL import Image





def     _UpperCamelCase     (a__   :Any       ):






							"""simple docstring"""





							if isinstance(a__   ,			collections.abc.Iterable       ):
														return x
							return (x, x)

@require_flax
class        __SCREAMING_SNAKE_CASE					:






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														pass






							def 				_lowerCamelCase		(						self  ):
														pass






							def 				_lowerCamelCase		(						self  ):
														pass






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			np.abs((a - b)  ).max()
														self.assertLessEqual(__lowerCAmelCase  , __lowerCAmelCase  , f"""Difference between torch and flax is {diff} (>= {tol})."""  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase=None  , **__lowerCAmelCase  ):
														UpperCamelCase__				     =			VisionTextDualEncoderConfig.from_vision_text_configs(__lowerCAmelCase  , __lowerCAmelCase  )

														UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel(__lowerCAmelCase  )

														UpperCamelCase__				     =			model(input_ids=__lowerCAmelCase  , pixel_values=__lowerCAmelCase  , attention_mask=__lowerCAmelCase  )

														self.assertEqual(output["""text_embeds"""].shape  , (input_ids.shape[0], config.projection_dim)  )
														self.assertEqual(output["""image_embeds"""].shape  , (pixel_values.shape[0], config.projection_dim)  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase=None  , **__lowerCAmelCase  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			self.get_vision_text_model(__lowerCAmelCase  , __lowerCAmelCase  )
														UpperCamelCase__				     =			{"""vision_model""": vision_model, """text_model""": text_model}
														UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__lowerCAmelCase  )

														UpperCamelCase__				     =			model(input_ids=__lowerCAmelCase  , pixel_values=__lowerCAmelCase  , attention_mask=__lowerCAmelCase  )

														self.assertEqual(output["""text_embeds"""].shape  , (input_ids.shape[0], model.config.projection_dim)  )
														self.assertEqual(output["""image_embeds"""].shape  , (pixel_values.shape[0], model.config.projection_dim)  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase=None  , **__lowerCAmelCase  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			self.get_vision_text_model(__lowerCAmelCase  , __lowerCAmelCase  )
														UpperCamelCase__				     =			{"""vision_model""": vision_model, """text_model""": text_model}
														UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__lowerCAmelCase  )

														UpperCamelCase__				     =			model(input_ids=__lowerCAmelCase  , pixel_values=__lowerCAmelCase  , attention_mask=__lowerCAmelCase  )
														UpperCamelCase__				     =			output[0]

														with tempfile.TemporaryDirectory() as tmpdirname:
																					model.save_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel.from_pretrained(__lowerCAmelCase  )

																					UpperCamelCase__				     =			model(input_ids=__lowerCAmelCase  , pixel_values=__lowerCAmelCase  , attention_mask=__lowerCAmelCase  )
																					UpperCamelCase__				     =			after_output[0]
																					UpperCamelCase__				     =			np.amax(np.abs(out_a - out_a  )  )
																					self.assertLessEqual(__lowerCAmelCase  , 1E-3  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase=None  , **__lowerCAmelCase  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			self.get_vision_text_model(__lowerCAmelCase  , __lowerCAmelCase  )
														UpperCamelCase__				     =			{"""vision_model""": vision_model, """text_model""": text_model}
														UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__lowerCAmelCase  )

														UpperCamelCase__				     =			model(
														    input_ids=__lowerCAmelCase  , pixel_values=__lowerCAmelCase  , attention_mask=__lowerCAmelCase  , output_attentions=__lowerCAmelCase  )

														UpperCamelCase__				     =			output.vision_model_output.attentions
														self.assertEqual(len(__lowerCAmelCase  )  , vision_config.num_hidden_layers  )

														# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
														UpperCamelCase__				     =			to_atuple(vision_model.config.image_size  )
														UpperCamelCase__				     =			to_atuple(vision_model.config.patch_size  )
														UpperCamelCase__				     =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
														UpperCamelCase__				     =			num_patches + 1
														self.assertEqual(vision_attentions[0].shape[-3:]  , (vision_config.num_attention_heads, seq_len, seq_len)  )

														UpperCamelCase__				     =			output.text_model_output.attentions
														self.assertEqual(len(__lowerCAmelCase  )  , text_config.num_hidden_layers  )

														self.assertEqual(
														    text_attentions[0].shape[-3:]  , (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])  , )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														pt_model.to(__lowerCAmelCase  )
														pt_model.eval()

														# prepare inputs
														UpperCamelCase__				     =			inputs_dict
														UpperCamelCase__				     =			{k: torch.tensor(v.tolist()  ) for k, v in flax_inputs.items()}

														with torch.no_grad():
																					UpperCamelCase__				     =			pt_model(**__lowerCAmelCase  ).to_tuple()

														UpperCamelCase__				     =			fx_model(**__lowerCAmelCase  ).to_tuple()
														self.assertEqual(len(__lowerCAmelCase  )  , len(__lowerCAmelCase  )  , """Output lengths differ between Flax and PyTorch"""  )
														for fx_output, pt_output in zip(fx_outputs[:4]  , pt_outputs[:4]  ):
																					self.assert_almost_equals(__lowerCAmelCase  , pt_output.numpy()  , 4E-2  )

														# PT -> Flax
														with tempfile.TemporaryDirectory() as tmpdirname:
																					pt_model.save_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel.from_pretrained(__lowerCAmelCase  , from_pt=__lowerCAmelCase  )

														UpperCamelCase__				     =			fx_model_loaded(**__lowerCAmelCase  ).to_tuple()
														self.assertEqual(len(__lowerCAmelCase  )  , len(__lowerCAmelCase  )  , """Output lengths differ between Flax and PyTorch"""  )
														for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]  , pt_outputs[:4]  ):
																					self.assert_almost_equals(__lowerCAmelCase  , pt_output.numpy()  , 4E-2  )

														# Flax -> PT
														with tempfile.TemporaryDirectory() as tmpdirname:
																					fx_model.save_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			VisionTextDualEncoderModel.from_pretrained(__lowerCAmelCase  , from_flax=__lowerCAmelCase  )

														pt_model_loaded.to(__lowerCAmelCase  )
														pt_model_loaded.eval()

														with torch.no_grad():
																					UpperCamelCase__				     =			pt_model_loaded(**__lowerCAmelCase  ).to_tuple()

														self.assertEqual(len(__lowerCAmelCase  )  , len(__lowerCAmelCase  )  , """Output lengths differ between Flax and PyTorch"""  )
														for fx_output, pt_output_loaded in zip(fx_outputs[:4]  , pt_outputs_loaded[:4]  ):
																					self.assert_almost_equals(__lowerCAmelCase  , pt_output_loaded.numpy()  , 4E-2  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			VisionTextDualEncoderConfig.from_vision_text_configs(__lowerCAmelCase  , __lowerCAmelCase  )

														UpperCamelCase__				     =			VisionTextDualEncoderModel(__lowerCAmelCase  )
														UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel(__lowerCAmelCase  )

														UpperCamelCase__				     =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()  , __lowerCAmelCase  )
														UpperCamelCase__				     =			fx_state

														self.check_pt_flax_equivalence(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			VisionTextDualEncoderConfig.from_vision_text_configs(__lowerCAmelCase  , __lowerCAmelCase  )

														UpperCamelCase__				     =			VisionTextDualEncoderModel(__lowerCAmelCase  )
														UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel(__lowerCAmelCase  )

														UpperCamelCase__				     =			load_flax_weights_in_pytorch_model(__lowerCAmelCase  , fx_model.params  )

														self.check_pt_flax_equivalence(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.prepare_config_and_inputs()
														self.check_model_from_pretrained_configs(**__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.prepare_config_and_inputs()
														self.check_vision_text_dual_encoder_from_pretrained(**__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.prepare_config_and_inputs()
														self.check_save_load(**__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.prepare_config_and_inputs()
														self.check_vision_text_output_attention(**__lowerCAmelCase  )






							@is_pt_flax_cross_test
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.prepare_config_and_inputs()
														UpperCamelCase__				     =			config_inputs_dict.pop("""vision_config"""  )
														UpperCamelCase__				     =			config_inputs_dict.pop("""text_config"""  )

														UpperCamelCase__				     =			config_inputs_dict

														self.check_equivalence_pt_to_flax(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )
														self.check_equivalence_flax_to_pt(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )







							@slow
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			self.get_pretrained_model_and_inputs()

														UpperCamelCase__				     =			model_a(**__lowerCAmelCase  )
														UpperCamelCase__				     =			outputs[0]

														with tempfile.TemporaryDirectory() as tmp_dirname:
																					model_a.save_pretrained(__lowerCAmelCase  )
																					UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel.from_pretrained(__lowerCAmelCase  )

																					UpperCamelCase__				     =			model_a(**__lowerCAmelCase  )
																					UpperCamelCase__				     =			after_outputs[0]
																					UpperCamelCase__				     =			np.amax(np.abs(out_a - out_a  )  )
																					self.assertLessEqual(__lowerCAmelCase  , 1E-5  )






@require_flax
class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
														    """hf-internal-testing/tiny-random-vit"""  , """hf-internal-testing/tiny-bert"""  , vision_from_pt=__lowerCAmelCase  , text_from_pt=__lowerCAmelCase  , )
														UpperCamelCase__				     =			13
														UpperCamelCase__				     =			floats_tensor(
														    [
														        batch_size,
														        model.config.vision_config.num_channels,
														        model.config.vision_config.image_size,
														        model.config.vision_config.image_size,
														    ]  )
														UpperCamelCase__				     =			ids_tensor([batch_size, 4]  , model.config.text_config.vocab_size  )
														UpperCamelCase__				     =			random_attention_mask([batch_size, 4]  )
														UpperCamelCase__				     =			{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

														return model, inputs






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			FlaxViTModel(__lowerCAmelCase  )
														UpperCamelCase__				     =			FlaxBertModel(__lowerCAmelCase  )
														return vision_model, text_model







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxViTModelTester(self  )
														UpperCamelCase__				     =			FlaxBertModelTester(self  )
														UpperCamelCase__				     =			vit_model_tester.prepare_config_and_inputs()
														UpperCamelCase__				     =			bert_model_tester.prepare_config_and_inputs()

														UpperCamelCase__     ,     UpperCamelCase__				     =			vision_config_and_inputs

														UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__				     =			text_config_and_inputs

														# make sure that cross attention layers are added
														return {
														    "text_config": text_config,
														    "vision_config": vision_config,
														    "pixel_values": pixel_values,
														    "attention_mask": attention_mask,
														    "input_ids": input_ids,
														    "token_type_ids": token_type_ids,
														}






@require_torch
class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
														    """hf-internal-testing/tiny-random-clip"""  , """hf-internal-testing/tiny-bert"""  , vision_from_pt=__lowerCAmelCase  , text_from_pt=__lowerCAmelCase  , )
														UpperCamelCase__				     =			13
														UpperCamelCase__				     =			floats_tensor(
														    [
														        batch_size,
														        model.config.vision_config.num_channels,
														        model.config.vision_config.image_size,
														        model.config.vision_config.image_size,
														    ]  )
														UpperCamelCase__				     =			ids_tensor([batch_size, 4]  , model.config.text_config.vocab_size  )
														UpperCamelCase__				     =			random_attention_mask([batch_size, 4]  )
														UpperCamelCase__				     =			{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

														return model, inputs






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			FlaxCLIPVisionModel(__lowerCAmelCase  )
														UpperCamelCase__				     =			FlaxBertModel(__lowerCAmelCase  )
														return vision_model, text_model







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxCLIPVisionModelTester(self  )
														UpperCamelCase__				     =			FlaxBertModelTester(self  )
														UpperCamelCase__				     =			clip_model_tester.prepare_config_and_inputs()
														UpperCamelCase__				     =			bert_model_tester.prepare_config_and_inputs()

														UpperCamelCase__     ,     UpperCamelCase__				     =			vision_config_and_inputs

														UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__     ,     UpperCamelCase__				     =			text_config_and_inputs

														# make sure that cross attention layers are added
														return {
														    "text_config": text_config,
														    "vision_config": vision_config,
														    "pixel_values": pixel_values,
														    "attention_mask": attention_mask,
														    "input_ids": input_ids,
														    "token_type_ids": token_type_ids,
														}






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






							@slow
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian"""  , logit_scale_init_value=1.0  )
														UpperCamelCase__				     =			VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""  )

														UpperCamelCase__				     =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
														UpperCamelCase__				     =			processor(
														    text=["""una foto di un gatto""", """una foto di un cane"""]  , images=__lowerCAmelCase  , padding=__lowerCAmelCase  , return_tensors="""np"""  )

														UpperCamelCase__				     =			model(**__lowerCAmelCase  )

														# verify the logits
														self.assertEqual(outputs.logits_per_image.shape  , (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])  )
														self.assertEqual(
														    outputs.logits_per_text.shape  , (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])  , )

														UpperCamelCase__				     =			np.array([[1.228_4727, 0.310_4122]]  )

														self.assertTrue(np.allclose(outputs.logits_per_image  , __lowerCAmelCase  , atol=1E-3  )  )











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     _UpperCamelCase     (a__   :Any   ,			a__   :Union[str, Any]       ):






							"""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(a__       )
							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     _UpperCamelCase     (a__   :Optional[int]   ,			a__   :DatasetInfo       ):






							"""simple docstring"""





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





def     _UpperCamelCase     ():






							"""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=1337   ,			post_processing_size=442   ,			dataset_size=1234   ,			size_in_bytes=1337 + 442 + 1234   ,			)
							UpperCamelCase__				     =			dataset_info._to_yaml_dict()
							assert sorted(a__       ) == 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(a__       )
							UpperCamelCase__				     =			yaml.safe_load(a__       )
							assert dataset_info_yaml_dict == reloaded





def     _UpperCamelCase     ():






							"""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=1337       ),
            }       ),
    ]   ,			)
def     _UpperCamelCase     (a__   :int   ,			a__   :DatasetInfosDict       ):






							"""simple docstring"""





							UpperCamelCase__				     =			str(a__       )
							dataset_infos_dict.write_to_directory(a__       )
							UpperCamelCase__				     =			DatasetInfosDict.from_directory(a__       )

							# 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(a__   ,			"""README.md"""       )       )




