'''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			(   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      Tuple				)    ->   List[str]:
							lowercase_      :    List[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}}"""				)
							lowercase_      :    List[Any]       		= DatasetInfosDict.from_directory(UpperCAmelCase__				)
							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			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      DatasetInfo				)    ->   Union[str, Any]:
							lowercase_      :    Any       		= str(UpperCAmelCase__				)
							dataset_info.write_to_directory(UpperCAmelCase__				)
							lowercase_      :    Tuple       		= DatasetInfo.from_directory(UpperCAmelCase__				)
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(UpperCAmelCase__   ,   """dataset_info.json"""				)				)




def 							lowerCamelCase			(   )    ->   Any:
							lowercase_      :    List[Any]       		= 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   ,   )
							lowercase_      :    str       		= dataset_info._to_yaml_dict()
							assert sorted(UpperCAmelCase__				) == 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)				)
							lowercase_      :    List[Any]       		= yaml.safe_dump(UpperCAmelCase__				)
							lowercase_      :    List[str]       		= yaml.safe_load(UpperCAmelCase__				)
							assert dataset_info_yaml_dict == reloaded




def 							lowerCamelCase			(   )    ->   Any:
							lowercase_      :    Optional[Any]       		= DatasetInfo()
							lowercase_      :    Optional[int]       		= 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 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      DatasetInfosDict				)    ->   Union[str, Any]:
							lowercase_      :    Optional[int]       		= str(UpperCAmelCase__				)
							dataset_infos_dict.write_to_directory(UpperCAmelCase__				)
							lowercase_      :    List[str]       		= DatasetInfosDict.from_directory(UpperCAmelCase__				)

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														lowercase_      :    Union[str, Any]       		= 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
														lowercase_      :    Dict       		= 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(UpperCAmelCase__   ,   """README.md"""				)				)






'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import tensorflow as tf

						from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""					)
										lowercase_      :    Dict       		= AutoTokenizer.from_pretrained("""google/mt5-small"""					)

										lowercase_      :    Union[str, Any]       		= tokenizer("""Hello there"""       ,    return_tensors="""tf"""					).input_ids
										lowercase_      :    List[str]       		= tokenizer("""Hi I am"""       ,    return_tensors="""tf"""					).input_ids

										lowercase_      :    Optional[Any]       		= model(lowercase_       ,    labels=lowercase_					).loss
										lowercase_      :    Optional[int]       		= -tf.math.reduce_mean(lowercase_					).numpy()

										lowercase_      :    Optional[int]       		= -21.22_81_68
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)





