from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase      			=     logging.get_logger(__name__)

UpperCamelCase      			=     {
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}


class _a       (	lowerCAmelCase__ ):

	'''simple docstring'''



	lowerCamelCase_   :      str       =	"""led"""
	lowerCamelCase_   :      Dict       =	{
	    """num_attention_heads""": """encoder_attention_heads""",
	    """hidden_size""": """d_model""",
	    """attention_probs_dropout_prob""": """attention_dropout""",
	    """initializer_range""": """init_std""",
	}

	def __init__(       self							,			__UpperCAmelCase=50_265							,			__UpperCAmelCase=16_384							,			__UpperCAmelCase=1_024							,			__UpperCAmelCase=12							,			__UpperCAmelCase=4_096							,			__UpperCAmelCase=16							,			__UpperCAmelCase=12							,			__UpperCAmelCase=4_096							,			__UpperCAmelCase=16							,			__UpperCAmelCase=0.0							,			__UpperCAmelCase=0.0							,			__UpperCAmelCase=True							,			__UpperCAmelCase=True							,			__UpperCAmelCase="gelu"							,			__UpperCAmelCase=1_024							,			__UpperCAmelCase=0.1							,			__UpperCAmelCase=0.0							,			__UpperCAmelCase=0.0							,			__UpperCAmelCase=0.02							,			__UpperCAmelCase=2							,			__UpperCAmelCase=0.0							,			__UpperCAmelCase=1							,			__UpperCAmelCase=0							,			__UpperCAmelCase=2							,			__UpperCAmelCase = 512							,			**__UpperCAmelCase							,			):
			__A							:   List[str]	  =				vocab_size
			__A							:   List[Any]	  =				max_encoder_position_embeddings
			__A							:   Tuple	  =				max_decoder_position_embeddings
			__A							:   List[Any]	  =				d_model
			__A							:   Optional[int]	  =				encoder_ffn_dim
			__A							:   Optional[Any]	  =				encoder_layers
			__A							:   List[str]	  =				encoder_attention_heads
			__A							:   Optional[Any]	  =				decoder_ffn_dim
			__A							:   Tuple	  =				decoder_layers
			__A							:   int	  =				decoder_attention_heads
			__A							:   Tuple	  =				dropout
			__A							:   int	  =				attention_dropout
			__A							:   int	  =				activation_dropout
			__A							:   int	  =				activation_function
			__A							:   Dict	  =				init_std
			__A							:   List[str]	  =				encoder_layerdrop
			__A							:   Union[str, Any]	  =				decoder_layerdrop
			__A							:   List[Any]	  =				classifier_dropout
			__A							:   Dict	  =				use_cache
			__A							:   str	  =				encoder_layers
			__A							:   int	  =				attention_window

			super().__init__(
			    pad_token_id=__UpperCAmelCase							,			bos_token_id=__UpperCAmelCase							,			eos_token_id=__UpperCAmelCase							,			is_encoder_decoder=__UpperCAmelCase							,			decoder_start_token_id=__UpperCAmelCase							,			**__UpperCAmelCase							,			)






import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata



def    lowerCamelCase_   (  _lowercase    )							->	str:
		__A							:   Optional[int]	  =				min(re.search("^ +" ,     _lowercase    ).end() if t.startswith(" "    ) else 0 for t in string.splitlines()    )
		return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_lowercase    )]    )


UpperCamelCase      			=     '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

UpperCamelCase      			=     '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


UpperCamelCase      			=     '\\n# Begin of markdown\n\nSome cool dataset card\n'





class _a       (	unittest.TestCase ):

	'''simple docstring'''



	def 					__UpperCAmelCase(       self   ):
			with tempfile.TemporaryDirectory() as tmp_dir:
					__A							:   List[Any]	  =				Path(__UpperCAmelCase   ) / "README.md"
					with open(__UpperCAmelCase							,			"w+"   ) as readme_file:
							readme_file.write(__UpperCAmelCase   )
					__A							:   Tuple	  =				DatasetMetadata.from_readme(__UpperCAmelCase   )
					self.assertDictEqual(__UpperCAmelCase							,			{"language": ["zh", "en"], "task_ids": ["sentiment-classification"]}   )

					with open(__UpperCAmelCase							,			"w+"   ) as readme_file:
							readme_file.write(__UpperCAmelCase   )
					__A							:   Any	  =				DatasetMetadata.from_readme(__UpperCAmelCase   )
					self.assertDictEqual(__UpperCAmelCase							,			{}   )

					with open(__UpperCAmelCase							,			"w+"   ) as readme_file:
							readme_file.write(__UpperCAmelCase   )
					__A							:   Union[str, Any]	  =				DatasetMetadata.from_readme(__UpperCAmelCase   )
					self.assertEqual(__UpperCAmelCase							,			{}   )







	def 					__UpperCAmelCase(       self   ):
			__A							:   List[str]	  =				_dedent(
			    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            "   )
			assert DatasetMetadata.from_yaml_string(__UpperCAmelCase   )

			__A							:   List[Any]	  =				_dedent(
			    "\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            "   )
			with self.assertRaises(__UpperCAmelCase   ):
					DatasetMetadata.from_yaml_string(__UpperCAmelCase   )

			__A							:   Optional[int]	  =				_dedent(
			    "\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the \"Researcher\") has requested permission to use the ImageNet database (the \"Database\") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            "   )
			assert DatasetMetadata.from_yaml_string(__UpperCAmelCase   )





