


"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE					:       Any							   =							logging.get_logger(__name__)

SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		lowerCamelCase__           ='rembert'



		def __init__(self							,			a_=25_03_00							,			a_=11_52							,			a_=32							,			a_=18							,			a_=2_56							,			a_=16_64							,			a_=46_08							,			a_="gelu"							,			a_=0.0							,			a_=0.0							,			a_=0.1							,			a_=5_12							,			a_=2							,			a_=0.02							,			a_=1E-12							,			a_=True							,			a_=0							,			a_=3_12							,			a_=3_13							,			**a_							,			):





			'''simple docstring'''

			super().__init__(pad_token_id=a_							,			bos_token_id=a_							,			eos_token_id=a_							,			**a_   )

			__snake_case      :		List[str]         =							vocab_size
			__snake_case      :		int         =							input_embedding_size
			__snake_case      :		List[Any]         =							output_embedding_size
			__snake_case      :		str         =							max_position_embeddings
			__snake_case      :		Tuple         =							hidden_size
			__snake_case      :		int         =							num_hidden_layers
			__snake_case      :		int         =							num_attention_heads
			__snake_case      :		List[str]         =							intermediate_size
			__snake_case      :		Optional[Any]         =							hidden_act
			__snake_case      :		Optional[Any]         =							hidden_dropout_prob
			__snake_case      :		Union[str, Any]         =							attention_probs_dropout_prob
			__snake_case      :		Any         =							classifier_dropout_prob
			__snake_case      :		List[str]         =							initializer_range
			__snake_case      :		Tuple         =							type_vocab_size
			__snake_case      :		Dict         =							layer_norm_eps
			__snake_case      :		Union[str, Any]         =							use_cache
			__snake_case      :		Any         =							False



class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			if self.task == "multiple-choice":
				__snake_case      :		List[str]         =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
				__snake_case      :		Any         =							{0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ('''input_ids''', dynamic_axis),
			        ('''attention_mask''', dynamic_axis),
			        ('''token_type_ids''', dynamic_axis),
			    ]   )



		@property
		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return 1E-4






'''simple docstring'''


import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def    _lowerCamelCase       ( lowerCamelCase_   :				str  ):



     """simple docstring"""





     UpperCAmelCase_				:  str  = min(re.search('^ +'      ,	lowerCamelCase_  ).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(lowerCamelCase_  )]  )


snake_case__	:     Dict      =    '''\
---
language:
- zh
- en
task_ids:
- sentiment-classification
---
# Begin of markdown

Some cool dataset card
'''

snake_case__	:     Union[str, Any]      =    '''\
---
---
# Begin of markdown

Some cool dataset card
'''


snake_case__	:     int      =    '''\
# Begin of markdown

Some cool dataset card
'''






class 	__SCREAMING_SNAKE_CASE		(			unittest.TestCase			):







  '''simple docstring'''






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

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

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  Union[str, Any]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertDictEqual(snake_case_ ,						{}		)

            with open(snake_case_ ,						'w+'		) as readme_file:
                 readme_file.write(snake_case_		)
            UpperCAmelCase_				:  List[str]  = DatasetMetadata.from_readme(snake_case_		)
            self.assertEqual(snake_case_ ,						{}		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  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            - question-answering\n            task_ids:\n            - open-domain-qa\n            '		)
       assert DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  int  = _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(snake_case_		):
            DatasetMetadata.from_yaml_string(snake_case_		)

       UpperCAmelCase_				:  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            - 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(snake_case_		)






