


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


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

a__				:							Tuple						=     {
    "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json",
    "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json",
    "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json",
    "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json",
    "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json",
    "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json",
    "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json",
    "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json",
    "bert-large-uncased-whole-word-masking": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking": (
        "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"
    ),
    "bert-large-uncased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-large-cased-whole-word-masking-finetuned-squad": (
        "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"
    ),
    "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json",
    "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json",
    "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json",
    "cl-tohoku/bert-base-japanese-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"
    ),
    "cl-tohoku/bert-base-japanese-char-whole-word-masking": (
        "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-cased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"
    ),
    "TurkuNLP/bert-base-finnish-uncased-v1": (
        "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"
    ),
    "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json",
    # See all BERT models at https://huggingface.co/models?filter=bert
}




class  UpperCamelCase_	( _SCREAMING_SNAKE_CASE):

	"""simple docstring"""





	snake_case__				:  Dict    					=			"bert"




	def __init__( self			:				Union[str, Any]			,			UpperCAmelCase__			:				Optional[Any]=3_0_5_2_2			,			UpperCAmelCase__			:				int=7_6_8			,			UpperCAmelCase__			:				int=1_2			,			UpperCAmelCase__			:				Optional[int]=1_2			,			UpperCAmelCase__			:				Any=3_0_7_2			,			UpperCAmelCase__			:				List[Any]="gelu"			,			UpperCAmelCase__			:				Union[str, Any]=0.1			,			UpperCAmelCase__			:				Optional[Any]=0.1			,			UpperCAmelCase__			:				Optional[int]=5_1_2			,			UpperCAmelCase__			:				Dict=2			,			UpperCAmelCase__			:				Optional[int]=0.02			,			UpperCAmelCase__			:				List[Any]=1E-12			,			UpperCAmelCase__			:				str=0			,			UpperCAmelCase__			:				Any="absolute"			,			UpperCAmelCase__			:				str=True			,			UpperCAmelCase__			:				Union[str, Any]=None			,			**UpperCAmelCase__			:				Tuple			,			)       ->	int:
						super().__init__(pad_token_id=A_			,			**A_   )

						__SCREAMING_SNAKE_CASE						   =				vocab_size
						__SCREAMING_SNAKE_CASE						   =				hidden_size
						__SCREAMING_SNAKE_CASE						   =				num_hidden_layers
						__SCREAMING_SNAKE_CASE						   =				num_attention_heads
						__SCREAMING_SNAKE_CASE						   =				hidden_act
						__SCREAMING_SNAKE_CASE						   =				intermediate_size
						__SCREAMING_SNAKE_CASE						   =				hidden_dropout_prob
						__SCREAMING_SNAKE_CASE						   =				attention_probs_dropout_prob
						__SCREAMING_SNAKE_CASE						   =				max_position_embeddings
						__SCREAMING_SNAKE_CASE						   =				type_vocab_size
						__SCREAMING_SNAKE_CASE						   =				initializer_range
						__SCREAMING_SNAKE_CASE						   =				layer_norm_eps
						__SCREAMING_SNAKE_CASE						   =				position_embedding_type
						__SCREAMING_SNAKE_CASE						   =				use_cache
						__SCREAMING_SNAKE_CASE						   =				classifier_dropout




class  UpperCamelCase_	( _SCREAMING_SNAKE_CASE):

	"""simple docstring"""





	@property
	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
											__SCREAMING_SNAKE_CASE						   =				{0: "batch", 1: "choice", 2: "sequence"}
						else:
											__SCREAMING_SNAKE_CASE						   =				{0: "batch", 1: "sequence"}
						return OrderedDict(
						    [
						        ("input_ids", dynamic_axis),
						        ("attention_mask", dynamic_axis),
						        ("token_type_ids", dynamic_axis),
						    ]   )


from unittest import TestCase

from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters


def       A       (					)   ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase		=     {
		    'repo_name': ['test_repo1', 'test_repo2', 'test_repo3'],
		    'path': ['test_1.py', 'test_2.py', 'unit_test.py'],
		    'content': ['a ' * 20, 'a ' * 30, 'b ' * 7],
		}
		UpperCamelCase		=     Dataset.from_dict(lowercase )
		return dataset


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     get_dataset()
				UpperCamelCase		=     make_duplicate_clusters(A_				,      0.85       )
				self.assertEqual(len(duplicate_clusters[0]       )				,      2       )



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     get_dataset()
				UpperCamelCase	,  UpperCamelCase		=     deduplicate_dataset(A_       )
				self.assertEqual(len(A_       )				,      2       )
				print(A_       )
				self.assertEqual(duplicate_clusters[0][0]['copies']				,      2       )
				self.assertEqual(duplicate_clusters[0][0]['is_extreme']				,      A_       )
