





from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_   :      List[str]			=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_   :      List[Any]			=   {
    '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json''',
    '''google/bigbird-roberta-large''': '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json''',
    '''google/bigbird-base-trivia-itc''': '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json''',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''
	__UpperCamelCase   =		'''big_bird'''




	def __init__(						self							:     Tuple					,      __lowerCamelCase							:     Dict=50_358					,      __lowerCamelCase							:     str=768					,      __lowerCamelCase							:     str=12					,      __lowerCamelCase							:     Optional[Any]=12					,      __lowerCamelCase							:     Any=3_072					,      __lowerCamelCase							:     List[Any]="gelu_new"					,      __lowerCamelCase							:     Optional[int]=0.1					,      __lowerCamelCase							:     Any=0.1					,      __lowerCamelCase							:     Any=4_096					,      __lowerCamelCase							:     Tuple=2					,      __lowerCamelCase							:     Union[str, Any]=0.02					,      __lowerCamelCase							:     Optional[Any]=1E-1_2					,      __lowerCamelCase							:     Any=True					,      __lowerCamelCase							:     Union[str, Any]=0					,      __lowerCamelCase							:     Tuple=1					,      __lowerCamelCase							:     Dict=2					,      __lowerCamelCase							:     List[Any]=66					,      __lowerCamelCase							:     int="block_sparse"					,      __lowerCamelCase							:     List[str]=True					,      __lowerCamelCase							:     Union[str, Any]=False					,      __lowerCamelCase							:     Any=64					,      __lowerCamelCase							:     Union[str, Any]=3					,      __lowerCamelCase							:     List[str]=None					,      **__lowerCamelCase							:     Dict					,      )       -> Tuple:

			'''simple docstring'''




			super().__init__(
			    pad_token_id=__lowerCamelCase					,      bos_token_id=__lowerCamelCase					,      eos_token_id=__lowerCamelCase					,      sep_token_id=__lowerCamelCase					,      **__lowerCamelCase					,      )

			__lowercase											=     vocab_size
			__lowercase											=     max_position_embeddings
			__lowercase											=     hidden_size
			__lowercase											=     num_hidden_layers
			__lowercase											=     num_attention_heads
			__lowercase											=     intermediate_size
			__lowercase											=     hidden_act
			__lowercase											=     hidden_dropout_prob
			__lowercase											=     attention_probs_dropout_prob
			__lowercase											=     initializer_range
			__lowercase											=     type_vocab_size
			__lowercase											=     layer_norm_eps
			__lowercase											=     use_cache

			__lowercase											=     rescale_embeddings
			__lowercase											=     attention_type
			__lowercase											=     use_bias
			__lowercase											=     block_size
			__lowercase											=     num_random_blocks
			__lowercase											=     classifier_dropout





class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	@property
	def    UpperCAmelCase			(						self							:     str   )       -> Mapping[str, Mapping[int, str]]:

			'''simple docstring'''




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











import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Optional[int]:
		return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     _TestCommandArgs(dataset=snake_case						,   all_configs=snake_case						,   save_infos=snake_case							)
		__lowercase											=     TestCommand(*snake_case							)
		test_command.run()
		__lowercase											=     os.path.join(snake_case						,   'README.md'							)
		assert os.path.exists(snake_case							)
		__lowercase											=     DatasetInfosDict.from_directory(snake_case							)
		__lowercase											=     DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'							)							),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']							)							),
		                    'langs': Sequence(Value('string'							)							),
		                    'spans': Sequence(Value('string'							)							),
		                }							)						,   splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_351_563,
		                    'num_examples': 10_000,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 238_418,
		                    'num_examples': 1_000,
		                },
		            ]						,   download_size=3_940_680						,   dataset_size=2_589_981						,   )
		    }							)
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				__lowercase						,      __lowercase											=     getattr(dataset_infos['default']						,   snake_case							), getattr(expected_dataset_infos['default']						,   snake_case							)
				if key == "num_bytes":
						assert is_apercent_close(snake_case						,   snake_case							)
				elif key == "splits":
						assert list(snake_case							) == list(snake_case							)
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,   expected[split].num_bytes							)
				else:
						result == expected




