'''simple docstring'''




import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    NezhaForMaskedLM,
						    NezhaForMultipleChoice,
						    NezhaForNextSentencePrediction,
						    NezhaForPreTraining,
						    NezhaForQuestionAnswering,
						    NezhaForSequenceClassification,
						    NezhaForTokenClassification,
						    NezhaModel,
						)
						from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST





class     __magic_name__						:







			def __init__(	self				:  Dict				,			lowercase_				:  List[str]				,			lowercase_				:  List[Any]=13				,			lowercase_				:  Optional[int]=7				,			lowercase_				:  Optional[Any]=True				,			lowercase_				:  List[str]=True				,			lowercase_				:  List[str]=True				,			lowercase_				:  List[Any]=True				,			lowercase_				:  List[str]=99				,			lowercase_				:  Union[str, Any]=32				,			lowercase_				:  Optional[int]=5				,			lowercase_				:  str=4				,			lowercase_				:  int=37				,			lowercase_				:  Any="gelu"				,			lowercase_				:  str=0.1				,			lowercase_				:  Dict=0.1				,			lowercase_				:  Optional[int]=128				,			lowercase_				:  str=32				,			lowercase_				:  str=16				,			lowercase_				:  List[str]=2				,			lowercase_				:  int=0.02				,			lowercase_				:  int=3				,			lowercase_				:  str=4				,			lowercase_				:  int=None				,			):
										lowercase_					:			Union[str, Any]										=       parent
										lowercase_					:			List[str]										=       batch_size
										lowercase_					:			List[str]										=       seq_length
										lowercase_					:			int										=       is_training
										lowercase_					:			Optional[Any]										=       use_input_mask
										lowercase_					:			str										=       use_token_type_ids
										lowercase_					:			int										=       use_labels
										lowercase_					:			List[str]										=       vocab_size
										lowercase_					:			Optional[int]										=       hidden_size
										lowercase_					:			List[Any]										=       num_hidden_layers
										lowercase_					:			str										=       num_attention_heads
										lowercase_					:			List[Any]										=       intermediate_size
										lowercase_					:			Optional[int]										=       hidden_act
										lowercase_					:			int										=       hidden_dropout_prob
										lowercase_					:			Optional[int]										=       attention_probs_dropout_prob
										lowercase_					:			Union[str, Any]										=       max_position_embeddings
										lowercase_					:			Union[str, Any]										=       type_vocab_size
										lowercase_					:			str										=       type_sequence_label_size
										lowercase_					:			Optional[Any]										=       initializer_range
										lowercase_					:			Tuple										=       num_labels
										lowercase_					:			Union[str, Any]										=       num_choices
										lowercase_					:			Any										=       scope







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			int										=       ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size    )

										lowercase_					:			int										=       None
										if self.use_input_mask:
																	lowercase_					:			Tuple										=       random_attention_mask([self.batch_size, self.seq_length]    )

										lowercase_					:			List[Any]										=       None
										if self.use_token_type_ids:
																	lowercase_					:			List[str]										=       ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size    )

										lowercase_					:			List[str]										=       None
										lowercase_					:			Union[str, Any]										=       None
										lowercase_					:			Tuple										=       None
										if self.use_labels:
																	lowercase_					:			Any										=       ids_tensor([self.batch_size]				,			self.type_sequence_label_size    )
																	lowercase_					:			Tuple										=       ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels    )
																	lowercase_					:			int										=       ids_tensor([self.batch_size]				,			self.num_choices    )

										lowercase_					:			Dict										=       self.get_config()

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										return NezhaConfig(
										    vocab_size=self.vocab_size				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			max_position_embeddings=self.max_position_embeddings				,			type_vocab_size=self.type_vocab_size				,			is_decoder=lowercase_				,			initializer_range=self.initializer_range				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										(
										    (
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						
										)					:			Any										=       self.prepare_config_and_inputs()

										lowercase_					:			List[Any]										=       True
										lowercase_					:			List[str]										=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
										lowercase_					:			List[Any]										=       ids_tensor([self.batch_size, self.seq_length]				,			vocab_size=2    )

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  int				,			lowercase_				:  Dict				,			lowercase_				:  int				,			lowercase_				:  Dict				,			lowercase_				:  str				,			lowercase_				:  List[Any]				,			lowercase_				:  Dict    ):
										lowercase_					:			int										=       NezhaModel(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			List[str]										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_    )
										lowercase_					:			Optional[Any]										=       model(lowercase_				,			token_type_ids=lowercase_    )
										lowercase_					:			str										=       model(lowercase_    )
										self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)    )
										self.parent.assertEqual(result.pooler_output.shape				,			(self.batch_size, self.hidden_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  Optional[int]				,			lowercase_				:  int				,			lowercase_				:  List[str]				,			lowercase_				:  List[str]				,			lowercase_				:  Optional[int]				,			lowercase_				:  Tuple				,			lowercase_				:  int				,			lowercase_				:  Tuple				,			lowercase_				:  Optional[int]				,			):
										lowercase_					:			Optional[int]										=       True
										lowercase_					:			Optional[int]										=       NezhaModel(lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			str										=       model(
										    lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			encoder_hidden_states=lowercase_				,			encoder_attention_mask=lowercase_				,			)
										lowercase_					:			List[Any]										=       model(
										    lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			encoder_hidden_states=lowercase_				,			)
										lowercase_					:			str										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_    )
										self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)    )
										self.parent.assertEqual(result.pooler_output.shape				,			(self.batch_size, self.hidden_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  int				,			lowercase_				:  Any				,			lowercase_				:  Tuple				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Optional[int]				,			lowercase_				:  Tuple				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			Any										=       NezhaForMaskedLM(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Any										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  Dict				,			lowercase_				:  Optional[int]				,			lowercase_				:  Optional[int]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Any				,			lowercase_				:  Tuple    ):
										lowercase_					:			Any										=       NezhaForNextSentencePrediction(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Optional[Any]										=       model(
										    lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_				,			)
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, 2)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Dict				,			lowercase_				:  str				,			lowercase_				:  Optional[Any]				,			lowercase_				:  int				,			lowercase_				:  Optional[Any]    ):
										lowercase_					:			str										=       NezhaForPreTraining(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Union[str, Any]										=       model(
										    lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_				,			next_sentence_label=lowercase_				,			)
										self.parent.assertEqual(result.prediction_logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)    )
										self.parent.assertEqual(result.seq_relationship_logits.shape				,			(self.batch_size, 2)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  int				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Optional[int]				,			lowercase_				:  int				,			lowercase_				:  Tuple				,			lowercase_				:  int				,			lowercase_				:  int    ):
										lowercase_					:			List[str]										=       NezhaForQuestionAnswering(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Optional[int]										=       model(
										    lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			start_positions=lowercase_				,			end_positions=lowercase_				,			)
										self.parent.assertEqual(result.start_logits.shape				,			(self.batch_size, self.seq_length)    )
										self.parent.assertEqual(result.end_logits.shape				,			(self.batch_size, self.seq_length)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Any				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Tuple				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Optional[int]    ):
										lowercase_					:			int										=       self.num_labels
										lowercase_					:			Optional[Any]										=       NezhaForSequenceClassification(lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			Optional[Any]										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Any				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  int				,			lowercase_				:  List[str]				,			lowercase_				:  Tuple				,			lowercase_				:  List[Any]				,			lowercase_				:  Tuple    ):
										lowercase_					:			int										=       self.num_labels
										lowercase_					:			Optional[int]										=       NezhaForTokenClassification(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			List[Any]										=       model(lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.num_labels)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  List[Any]				,			lowercase_				:  str				,			lowercase_				:  List[Any]				,			lowercase_				:  Optional[int]				,			lowercase_				:  Dict				,			lowercase_				:  Tuple				,			lowercase_				:  str    ):
										lowercase_					:			str										=       self.num_choices
										lowercase_					:			str										=       NezhaForMultipleChoice(config=lowercase_    )
										model.to(lowercase_    )
										model.eval()
										lowercase_					:			List[str]										=       input_ids.unsqueeze(1    ).expand(-1				,			self.num_choices				,			-1    ).contiguous()
										lowercase_					:			List[Any]										=       token_type_ids.unsqueeze(1    ).expand(-1				,			self.num_choices				,			-1    ).contiguous()
										lowercase_					:			Tuple										=       input_mask.unsqueeze(1    ).expand(-1				,			self.num_choices				,			-1    ).contiguous()
										lowercase_					:			str										=       model(
										    lowercase_				,			attention_mask=lowercase_				,			token_type_ids=lowercase_				,			labels=lowercase_				,			)
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_choices)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			str										=       self.prepare_config_and_inputs()
										(
										    (
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						
										)					:			Optional[Any]										=       config_and_inputs
										lowercase_					:			str										=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
										return config, inputs_dict





@require_torch
class     __magic_name__						(    _UpperCAmelCase,     _UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       (
			    (
			        NezhaModel,
			        NezhaForMaskedLM,
			        NezhaForMultipleChoice,
			        NezhaForNextSentencePrediction,
			        NezhaForPreTraining,
			        NezhaForQuestionAnswering,
			        NezhaForSequenceClassification,
			        NezhaForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			UpperCamelCase__				=       (
			    {
			        '''feature-extraction''': NezhaModel,
			        '''fill-mask''': NezhaForMaskedLM,
			        '''question-answering''': NezhaForQuestionAnswering,
			        '''text-classification''': NezhaForSequenceClassification,
			        '''token-classification''': NezhaForTokenClassification,
			        '''zero-shot''': NezhaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__				=       True







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  int				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  str=False    ):
										lowercase_					:			Any										=       super()._prepare_for_class(lowercase_				,			lowercase_				,			return_labels=lowercase_    )

										if return_labels:
																	if model_class in get_values(lowercase_    ):
																								lowercase_					:			Tuple										=       torch.zeros(
																								    (self.model_tester.batch_size, self.model_tester.seq_length)				,			dtype=torch.long				,			device=lowercase_    )
																								lowercase_					:			Optional[int]										=       torch.zeros(
																								    self.model_tester.batch_size				,			dtype=torch.long				,			device=lowercase_    )
										return inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			str										=       NezhaModelTester(self    )
										lowercase_					:			List[Any]										=       ConfigTester(self				,			config_class=lowercase_				,			hidden_size=37    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										self.config_tester.run_common_tests()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			List[Any]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_					:			Tuple										=       self.model_tester.prepare_config_and_inputs_for_decoder()
										self.model_tester.create_and_check_model_as_decoder(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										# This regression test was failing with PyTorch < 1.3
										(
										    (
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						
										)					:			Optional[int]										=       self.model_tester.prepare_config_and_inputs_for_decoder()

										lowercase_					:			List[str]										=       None

										self.model_tester.create_and_check_model_as_decoder(
										    lowercase_				,			lowercase_				,			lowercase_				,			lowercase_				,			lowercase_				,			lowercase_				,			lowercase_				,			lowercase_				,			lowercase_				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			List[Any]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			str										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			Dict										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_next_sequence_prediction(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			Dict										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										lowercase_					:			str										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			Optional[int]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Any										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowercase_    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_					:			str										=       NezhaModel.from_pretrained(lowercase_    )
																	self.assertIsNotNone(lowercase_    )







			@slow
			@require_torch_gpu
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_						,						lowercase_					:			List[Any]										=       self.model_tester.prepare_config_and_inputs_for_common()
										for model_class in self.all_model_classes:
																	# NezhaForMultipleChoice behaves incorrectly in JIT environments.
																	if model_class == NezhaForMultipleChoice:
																								return

																	lowercase_					:			Any										=       True
																	lowercase_					:			Dict										=       model_class(config=lowercase_    )

																	lowercase_					:			str										=       self._prepare_for_class(lowercase_				,			lowercase_    )
																	lowercase_					:			Any										=       torch.jit.trace(
																	    lowercase_				,			(inputs_dict["""input_ids"""].to("""cpu"""    ), inputs_dict["""attention_mask"""].to("""cpu"""    ))    )

																	with tempfile.TemporaryDirectory() as tmp:
																								torch.jit.save(lowercase_				,			os.path.join(lowercase_				,			"""bert.pt"""    )    )
																								lowercase_					:			Union[str, Any]										=       torch.jit.load(os.path.join(lowercase_				,			"""bert.pt"""    )				,			map_location=lowercase_    )
																								loaded(inputs_dict["""input_ids"""].to(lowercase_    )				,			inputs_dict["""attention_mask"""].to(lowercase_    )    )





@require_torch
class     __magic_name__						(    unittest.TestCase):







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			List[Any]										=       NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""    )
										lowercase_					:			Dict										=       torch.tensor([[0, 1, 2, 3, 4, 5]]    )
										lowercase_					:			Any										=       torch.tensor([[0, 1, 1, 1, 1, 1]]    )
										with torch.no_grad():
																	lowercase_					:			int										=       model(lowercase_				,			attention_mask=lowercase_    )[0]
										lowercase_					:			str										=       torch.Size((1, 6, 768)    )
										self.assertEqual(output.shape				,			lowercase_    )
										lowercase_					:			str										=       torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]]    )

										self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,			lowercase_				,			atol=1E-4    )    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			List[str]										=       NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""    )
										lowercase_					:			Tuple										=       torch.tensor([[0, 1, 2, 3, 4, 5]]    )
										lowercase_					:			str										=       torch.tensor([[1, 1, 1, 1, 1, 1]]    )
										with torch.no_grad():
																	lowercase_					:			int										=       model(lowercase_				,			attention_mask=lowercase_    )[0]
										lowercase_					:			List[str]										=       torch.Size((1, 6, 21128)    )
										self.assertEqual(output.shape				,			lowercase_    )
										lowercase_					:			Optional[Any]										=       torch.tensor(
										    [[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]]    )

										self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,			lowercase_				,			atol=1E-4    )    )






'''simple docstring'''




from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url




@pytest.mark.parametrize("""repo_id"""      ,      ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path"""      ,      ["""filename.csv""", """filename with blanks.csv"""] )
@pytest.mark.parametrize("""revision"""      ,      [None, """v2"""] )
def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					Optional[int]      ,      UpperCAmelCase__       :					Optional[int] )			->      Optional[int]:
							lowercase_					:			Union[str, Any]										=       hf_hub_url(repo_id=UpperCAmelCase__      ,      path=UpperCAmelCase__      ,      revision=UpperCAmelCase__ )
							assert url == F'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(UpperCAmelCase__ )}'''





