





'''simple docstring'''






import unittest

from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_torch_available():
						import torch

						from transformers import (
						    MraForMaskedLM,
						    MraForMultipleChoice,
						    MraForQuestionAnswering,
						    MraForSequenceClassification,
						    MraForTokenClassification,
						    MraModel,
						)
						from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  Any	,    __snake_case    :  int	,    __snake_case    :  Dict=2	,    __snake_case    :  Optional[int]=8	,    __snake_case    :  Optional[Any]=True	,    __snake_case    :  Union[str, Any]=True	,    __snake_case    :  Any=True	,    __snake_case    :  Union[str, Any]=True	,    __snake_case    :  Optional[Any]=99	,    __snake_case    :  int=16	,    __snake_case    :  Tuple=5	,    __snake_case    :  int=2	,    __snake_case    :  Optional[int]=36	,    __snake_case    :  Dict="gelu"	,    __snake_case    :  Tuple=0.0	,    __snake_case    :  str=0.0	,    __snake_case    :  Dict=512	,    __snake_case    :  str=16	,    __snake_case    :  Tuple=2	,    __snake_case    :  Any=0.02	,    __snake_case    :  List[str]=3	,    __snake_case    :  List[Any]=4	,    __snake_case    :  List[str]=None	,    )    ->	Dict:
								UpperCAmelCase				: Optional[int]											=	parent
								UpperCAmelCase				: List[str]											=	batch_size
								UpperCAmelCase				: Optional[Any]											=	seq_length
								UpperCAmelCase				: str											=	is_training
								UpperCAmelCase				: Tuple											=	use_input_mask
								UpperCAmelCase				: Any											=	use_token_type_ids
								UpperCAmelCase				: int											=	use_labels
								UpperCAmelCase				: Optional[int]											=	vocab_size
								UpperCAmelCase				: Tuple											=	hidden_size
								UpperCAmelCase				: Any											=	num_hidden_layers
								UpperCAmelCase				: Optional[int]											=	num_attention_heads
								UpperCAmelCase				: str											=	intermediate_size
								UpperCAmelCase				: str											=	hidden_act
								UpperCAmelCase				: Optional[int]											=	hidden_dropout_prob
								UpperCAmelCase				: List[str]											=	attention_probs_dropout_prob
								UpperCAmelCase				: Dict											=	max_position_embeddings
								UpperCAmelCase				: str											=	type_vocab_size
								UpperCAmelCase				: Union[str, Any]											=	type_sequence_label_size
								UpperCAmelCase				: List[str]											=	initializer_range
								UpperCAmelCase				: Tuple											=	num_labels
								UpperCAmelCase				: Dict											=	num_choices
								UpperCAmelCase				: Any											=	scope







	def   A			(		self    :  Optional[int]	)    ->	Tuple:
								UpperCAmelCase				: Union[str, Any]											=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size	)

								UpperCAmelCase				: Optional[Any]											=	None
								if self.use_input_mask:
															UpperCAmelCase				: Optional[Any]											=	random_attention_mask([self.batch_size, self.seq_length]	)

								UpperCAmelCase				: str											=	None
								if self.use_token_type_ids:
															UpperCAmelCase				: int											=	ids_tensor([self.batch_size, self.seq_length]	,    self.type_vocab_size	)

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

								UpperCAmelCase				: List[str]											=	self.get_config()

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







	def   A			(		self    :  Tuple	)    ->	int:
								return MraConfig(
								    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=__snake_case	,    initializer_range=self.initializer_range	,    )







	def   A			(		self    :  Optional[Any]	)    ->	Dict:
								UpperCAmelCase				: Any											=	self.get_config()
								UpperCAmelCase				: Union[str, Any]											=	300
								return config







	def   A			(		self    :  str	)    ->	List[Any]:
								(
								    UpperCAmelCase	
								)				: List[Any]											=	self.prepare_config_and_inputs()

								UpperCAmelCase				: Tuple											=	True
								UpperCAmelCase				: Optional[int]											=	floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
								UpperCAmelCase				: str											=	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   A			(		self    :  List[Any]	,    __snake_case    :  Dict	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Optional[int]	,    __snake_case    :  Dict	,    __snake_case    :  List[Any]	,    __snake_case    :  Optional[int]	,    __snake_case    :  List[str]	)    ->	Dict:
								UpperCAmelCase				: Tuple											=	MraModel(config=__snake_case	)
								model.to(__snake_case	)
								model.eval()
								UpperCAmelCase				: Any											=	model(__snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	)
								UpperCAmelCase				: Union[str, Any]											=	model(__snake_case	,    token_type_ids=__snake_case	)
								UpperCAmelCase				: Dict											=	model(__snake_case	)
								self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)	)







	def   A			(		self    :  str	,    __snake_case    :  Tuple	,    __snake_case    :  Optional[int]	,    __snake_case    :  Any	,    __snake_case    :  Tuple	,    __snake_case    :  List[str]	,    __snake_case    :  int	,    __snake_case    :  Any	,    __snake_case    :  Optional[int]	,    __snake_case    :  List[str]	,    )    ->	List[str]:
								UpperCAmelCase				: Union[str, Any]											=	True
								UpperCAmelCase				: List[Any]											=	MraModel(__snake_case	)
								model.to(__snake_case	)
								model.eval()
								UpperCAmelCase				: str											=	model(
								    __snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	,    encoder_hidden_states=__snake_case	,    encoder_attention_mask=__snake_case	,    )
								UpperCAmelCase				: List[Any]											=	model(
								    __snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	,    encoder_hidden_states=__snake_case	,    )
								UpperCAmelCase				: Dict											=	model(__snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	)
								self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)	)







	def   A			(		self    :  Dict	,    __snake_case    :  Optional[int]	,    __snake_case    :  Optional[int]	,    __snake_case    :  Dict	,    __snake_case    :  Tuple	,    __snake_case    :  int	,    __snake_case    :  Optional[int]	,    __snake_case    :  Optional[Any]	)    ->	List[str]:
								UpperCAmelCase				: Tuple											=	MraForMaskedLM(config=__snake_case	)
								model.to(__snake_case	)
								model.eval()
								UpperCAmelCase				: Tuple											=	model(__snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	,    labels=__snake_case	)
								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)	)







	def   A			(		self    :  Optional[int]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  int	,    __snake_case    :  int	,    __snake_case    :  List[str]	,    __snake_case    :  List[str]	,    __snake_case    :  Dict	,    __snake_case    :  Union[str, Any]	)    ->	Union[str, Any]:
								UpperCAmelCase				: Dict											=	MraForQuestionAnswering(config=__snake_case	)
								model.to(__snake_case	)
								model.eval()
								UpperCAmelCase				: Optional[int]											=	model(
								    __snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	,    start_positions=__snake_case	,    end_positions=__snake_case	,    )
								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   A			(		self    :  Union[str, Any]	,    __snake_case    :  Optional[Any]	,    __snake_case    :  int	,    __snake_case    :  Any	,    __snake_case    :  Tuple	,    __snake_case    :  str	,    __snake_case    :  Tuple	,    __snake_case    :  int	)    ->	List[Any]:
								UpperCAmelCase				: Union[str, Any]											=	self.num_labels
								UpperCAmelCase				: Tuple											=	MraForSequenceClassification(__snake_case	)
								model.to(__snake_case	)
								model.eval()
								UpperCAmelCase				: List[Any]											=	model(__snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	,    labels=__snake_case	)
								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)	)







	def   A			(		self    :  Tuple	,    __snake_case    :  Dict	,    __snake_case    :  Any	,    __snake_case    :  int	,    __snake_case    :  List[Any]	,    __snake_case    :  Optional[int]	,    __snake_case    :  Dict	,    __snake_case    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: str											=	self.num_labels
								UpperCAmelCase				: int											=	MraForTokenClassification(config=__snake_case	)
								model.to(__snake_case	)
								model.eval()
								UpperCAmelCase				: Optional[int]											=	model(__snake_case	,    attention_mask=__snake_case	,    token_type_ids=__snake_case	,    labels=__snake_case	)
								self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.num_labels)	)







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







	def   A			(		self    :  List[Any]	)    ->	int:
								UpperCAmelCase				: List[Any]											=	self.prepare_config_and_inputs()
								(
								    UpperCAmelCase	
								)				: Dict											=	config_and_inputs
								UpperCAmelCase				: Optional[Any]											=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict

@require_torch
class        SCREAMING_SNAKE_CASE(							A__							,	unittest.TestCase    ):







	"""simple docstring"""






	lowerCamelCase__    				= (
	    (
	        MraModel,
	        MraForMaskedLM,
	        MraForMultipleChoice,
	        MraForQuestionAnswering,
	        MraForSequenceClassification,
	        MraForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	lowerCamelCase__    				= False
	lowerCamelCase__    				= False
	lowerCamelCase__    				= False
	lowerCamelCase__    				= False

	lowerCamelCase__    				= ()







	def   A			(		self    :  Any	)    ->	Tuple:
								UpperCAmelCase				: Dict											=	MraModelTester(self	)
								UpperCAmelCase				: str											=	ConfigTester(self	,    config_class=__snake_case	,    hidden_size=37	)







	def   A			(		self    :  Tuple	)    ->	Any:
								self.config_tester.run_common_tests()







	def   A			(		self    :  Optional[int]	)    ->	Dict:
								UpperCAmelCase				: Tuple											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__snake_case	)







	def   A			(		self    :  Union[str, Any]	)    ->	List[str]:
								UpperCAmelCase				: Any											=	self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
															UpperCAmelCase				: List[str]											=	type
															self.model_tester.create_and_check_model(*__snake_case	)







	def   A			(		self    :  int	)    ->	Any:
								UpperCAmelCase				: int											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*__snake_case	)







	def   A			(		self    :  Tuple	)    ->	Optional[int]:
								UpperCAmelCase				: str											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*__snake_case	)







	def   A			(		self    :  Dict	)    ->	Optional[Any]:
								UpperCAmelCase				: List[Any]											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*__snake_case	)







	def   A			(		self    :  Optional[int]	)    ->	str:
								UpperCAmelCase				: Optional[int]											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*__snake_case	)







	def   A			(		self    :  Union[str, Any]	)    ->	Optional[int]:
								UpperCAmelCase				: Any											=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*__snake_case	)







	@slow
	def   A			(		self    :  str	)    ->	Tuple:
								for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															UpperCAmelCase				: Any											=	MraModel.from_pretrained(__snake_case	)
															self.assertIsNotNone(__snake_case	)







	@unittest.skip(reason='''MRA does not output attentions'''	)
	def   A			(		self    :  Any	)    ->	Any:
								return

@require_torch
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	@slow
	def   A			(		self    :  Optional[int]	)    ->	Tuple:
								UpperCAmelCase				: int											=	MraModel.from_pretrained('''uw-madison/mra-base-512-4'''	)
								UpperCAmelCase				: str											=	torch.arange(256	).unsqueeze(0	)

								with torch.no_grad():
															UpperCAmelCase				: Dict											=	model(__snake_case	)[0]

								UpperCAmelCase				: List[Any]											=	torch.Size((1, 256, 768)	)
								self.assertEqual(output.shape	,    __snake_case	)

								UpperCAmelCase				: List[Any]											=	torch.tensor(
								    [[[-0.01_40, 0.08_30, -0.03_81], [0.15_46, 0.14_02, 0.02_20], [0.11_62, 0.08_51, 0.01_65]]]	)

								self.assertTrue(torch.allclose(output[:, :3, :3]	,    __snake_case	,    atol=1E-4	)	)







	@slow
	def   A			(		self    :  str	)    ->	Optional[int]:
								UpperCAmelCase				: List[Any]											=	MraForMaskedLM.from_pretrained('''uw-madison/mra-base-512-4'''	)
								UpperCAmelCase				: int											=	torch.arange(256	).unsqueeze(0	)

								with torch.no_grad():
															UpperCAmelCase				: Optional[Any]											=	model(__snake_case	)[0]

								UpperCAmelCase				: List[Any]											=	50265

								UpperCAmelCase				: Optional[Any]											=	torch.Size((1, 256, vocab_size)	)
								self.assertEqual(output.shape	,    __snake_case	)

								UpperCAmelCase				: Optional[int]											=	torch.tensor(
								    [[[9.25_95, -3.60_38, 11.88_19], [9.38_69, -3.26_93, 11.09_56], [11.85_24, -3.49_38, 13.12_10]]]	)

								self.assertTrue(torch.allclose(output[:, :3, :3]	,    __snake_case	,    atol=1E-4	)	)







	@slow
	def   A			(		self    :  Union[str, Any]	)    ->	Tuple:
								UpperCAmelCase				: Dict											=	MraForMaskedLM.from_pretrained('''uw-madison/mra-base-4096-8-d3'''	)
								UpperCAmelCase				: Any											=	torch.arange(4096	).unsqueeze(0	)

								with torch.no_grad():
															UpperCAmelCase				: str											=	model(__snake_case	)[0]

								UpperCAmelCase				: str											=	50265

								UpperCAmelCase				: str											=	torch.Size((1, 4096, vocab_size)	)
								self.assertEqual(output.shape	,    __snake_case	)

								UpperCAmelCase				: Optional[int]											=	torch.tensor(
								    [[[5.47_89, -2.35_64, 7.50_64], [7.90_67, -1.33_69, 9.96_68], [9.07_12, -1.81_06, 7.03_80]]]	)

								self.assertTrue(torch.allclose(output[:, :3, :3]	,    __snake_case	,    atol=1E-4	)	)










'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict


@pytest.mark.parametrize(
    '''split_dict'''     ,   [
        SplitDict(),
        SplitDict({'''train''': SplitInfo(name='''train'''     ,   num_bytes=1337     ,   num_examples=42     ,   dataset_name='''my_dataset'''						)}						),
        SplitDict({'''train''': SplitInfo(name='''train'''     ,   num_bytes=1337     ,   num_examples=42						)}						),
        SplitDict({'''train''': SplitInfo()}						),
    ]     ,   )
def 				snake_case_  (      _lowerCAmelCase						:		SplitDict						)   ->     List[Any]:
							UpperCAmelCase				: str											=	split_dict._to_yaml_list()
							assert len(_lowerCAmelCase						) == len(_lowerCAmelCase						)
							UpperCAmelCase				: Optional[int]											=	SplitDict._from_yaml_list(_lowerCAmelCase						)
							for split_name, split_info in split_dict.items():
														# dataset_name field is deprecated, and is therefore not part of the YAML dump
														UpperCAmelCase				: Optional[Any]											=	None
														# the split name of split_dict takes over the name of the split info object
														UpperCAmelCase				: Dict											=	split_name
							assert split_dict == reloaded




@pytest.mark.parametrize(
    '''split_info'''     ,   [SplitInfo(), SplitInfo(dataset_name=_lowerCAmelCase						), SplitInfo(dataset_name='''my_dataset'''						)]						)
def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Optional[Any]:
							# For backward compatibility, we need asdict(split_dict) to return split info dictrionaries with the "dataset_name"
							# field even if it's deprecated. This way old versionso of `datasets` can still reload dataset_infos.json files
							UpperCAmelCase				: Any											=	asdict(SplitDict({'''train''': split_info}						)						)
							assert "dataset_name" in split_dict_asdict["train"]
							assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name





