
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    __A:

					def __init__(						self       :  Optional[int] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  str=2 ,       __UpperCamelCase       :  str=8 ,       __UpperCamelCase       :  Dict=True ,       __UpperCamelCase       :  Optional[int]=True ,       __UpperCamelCase       :  Optional[int]=True ,       __UpperCamelCase       :  Optional[int]=True ,       __UpperCamelCase       :  Any=9_9 ,       __UpperCamelCase       :  Optional[int]=1_6 ,       __UpperCamelCase       :  Optional[int]=5 ,       __UpperCamelCase       :  List[str]=2 ,       __UpperCamelCase       :  int=3_6 ,       __UpperCamelCase       :  str="gelu" ,       __UpperCamelCase       :  Optional[Any]=0.0 ,       __UpperCamelCase       :  Optional[Any]=0.0 ,       __UpperCamelCase       :  List[str]=5_1_2 ,       __UpperCamelCase       :  str=1_6 ,       __UpperCamelCase       :  int=2 ,       __UpperCamelCase       :  Tuple=0.02 ,       __UpperCamelCase       :  Tuple=3 ,       __UpperCamelCase       :  Dict=4 ,       __UpperCamelCase       :  Optional[Any]=None ,       ):
												lowerCamelCase_					=       parent
												lowerCamelCase_					=       batch_size
												lowerCamelCase_					=       seq_length
												lowerCamelCase_					=       is_training
												lowerCamelCase_					=       use_input_mask
												lowerCamelCase_					=       use_token_type_ids
												lowerCamelCase_					=       use_labels
												lowerCamelCase_					=       vocab_size
												lowerCamelCase_					=       hidden_size
												lowerCamelCase_					=       num_hidden_layers
												lowerCamelCase_					=       num_attention_heads
												lowerCamelCase_					=       intermediate_size
												lowerCamelCase_					=       hidden_act
												lowerCamelCase_					=       hidden_dropout_prob
												lowerCamelCase_					=       attention_probs_dropout_prob
												lowerCamelCase_					=       max_position_embeddings
												lowerCamelCase_					=       type_vocab_size
												lowerCamelCase_					=       type_sequence_label_size
												lowerCamelCase_					=       initializer_range
												lowerCamelCase_					=       num_labels
												lowerCamelCase_					=       num_choices
												lowerCamelCase_					=       scope

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size )

												lowerCamelCase_					=       None
												if self.use_input_mask:
																			lowerCamelCase_					=       random_attention_mask([self.batch_size, self.seq_length] )

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

												lowerCamelCase_					=       None
												lowerCamelCase_					=       None
												lowerCamelCase_					=       None
												if self.use_labels:
																			lowerCamelCase_					=       ids_tensor([self.batch_size] ,       self.type_sequence_label_size )
																			lowerCamelCase_					=       ids_tensor([self.batch_size, self.seq_length] ,       self.num_labels )
																			lowerCamelCase_					=       ids_tensor([self.batch_size] ,       self.num_choices )

												lowerCamelCase_					=       self.get_config()

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

					def  lowercase__   (						self       :  Tuple ):
												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=__UpperCamelCase ,       initializer_range=self.initializer_range ,       )

					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       self.get_config()
												lowerCamelCase_					=       3_0_0
												return config

					def  lowercase__   (						self       :  Any ):
												(
												    (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, 
												)					=       self.prepare_config_and_inputs()

												lowerCamelCase_					=       True
												lowerCamelCase_					=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
												lowerCamelCase_					=       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  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  Union[str, Any] ):
												lowerCamelCase_					=       MraModel(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase )
												lowerCamelCase_					=       model(__UpperCamelCase ,       token_type_ids=__UpperCamelCase )
												lowerCamelCase_					=       model(__UpperCamelCase )
												self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size) )

					def  lowercase__   (						self       :  int ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Any ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  List[str] ,       ):
												lowerCamelCase_					=       True
												lowerCamelCase_					=       MraModel(__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       encoder_hidden_states=__UpperCamelCase ,       encoder_attention_mask=__UpperCamelCase ,       )
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       encoder_hidden_states=__UpperCamelCase ,       )
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase )
												self.parent.assertEqual(result.last_hidden_state.shape ,       (self.batch_size, self.seq_length, self.hidden_size) )

					def  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  str ):
												lowerCamelCase_					=       MraForMaskedLM(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size) )

					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  List[Any] ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  List[str] ):
												lowerCamelCase_					=       MraForQuestionAnswering(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(
												    __UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       start_positions=__UpperCamelCase ,       end_positions=__UpperCamelCase ,       )
												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  lowercase__   (						self       :  Dict ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  List[Any] ):
												lowerCamelCase_					=       self.num_labels
												lowerCamelCase_					=       MraForSequenceClassification(__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels) )

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  Dict ,       __UpperCamelCase       :  List[str] ,       __UpperCamelCase       :  int ):
												lowerCamelCase_					=       self.num_labels
												lowerCamelCase_					=       MraForTokenClassification(config=__UpperCamelCase )
												model.to(__UpperCamelCase )
												model.eval()
												lowerCamelCase_					=       model(__UpperCamelCase ,       attention_mask=__UpperCamelCase ,       token_type_ids=__UpperCamelCase ,       labels=__UpperCamelCase )
												self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.num_labels) )

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



					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       self.prepare_config_and_inputs()
												(
												    (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, (
												    lowerCamelCase_
												)	, 
												)					=       config_and_inputs
												lowerCamelCase_					=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
												return config, inputs_dict





@require_torch
class    __A(					UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     (
					    (
					        MraModel,
					        MraForMaskedLM,
					        MraForMultipleChoice,
					        MraForQuestionAnswering,
					        MraForSequenceClassification,
					        MraForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False
					SCREAMING_SNAKE_CASE           =     False

					SCREAMING_SNAKE_CASE           =     ()

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       MraModelTester(self )
												lowerCamelCase_					=       ConfigTester(self ,       config_class=__UpperCamelCase ,       hidden_size=3_7 )

					def  lowercase__   (						self       :  Optional[Any] ):
												self.config_tester.run_common_tests()

					def  lowercase__   (						self       :  int ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase )

					def  lowercase__   (						self       :  Any ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																			lowerCamelCase_					=       type
																			self.model_tester.create_and_check_model(*__UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase )

					def  lowercase__   (						self       :  str ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase )

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase )

					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase )

					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase )

					@slow
					def  lowercase__   (						self       :  List[str] ):
												for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			lowerCamelCase_					=       MraModel.from_pretrained(__UpperCamelCase )
																			self.assertIsNotNone(__UpperCamelCase )



					@unittest.skip(reason="""MRA does not output attentions""" )
					def  lowercase__   (						self       :  int ):
												return





@require_torch
class    __A(					unittest.TestCase							):

					@slow
					def  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       MraModel.from_pretrained("""uw-madison/mra-base-512-4""" )
												lowerCamelCase_					=       torch.arange(2_5_6 ).unsqueeze(0 )

												with torch.no_grad():
																			lowerCamelCase_					=       model(__UpperCamelCase )[0]

												lowerCamelCase_					=       torch.Size((1, 2_5_6, 7_6_8) )
												self.assertEqual(output.shape ,       __UpperCamelCase )

												lowerCamelCase_					=       torch.tensor(
												    [[[-0.0140, 0.0830, -0.0381], [0.1546, 0.1402, 0.0220], [0.1162, 0.0851, 0.0165]]] )

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

					@slow
					def  lowercase__   (						self       :  Optional[Any] ):
												lowerCamelCase_					=       MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""" )
												lowerCamelCase_					=       torch.arange(2_5_6 ).unsqueeze(0 )

												with torch.no_grad():
																			lowerCamelCase_					=       model(__UpperCamelCase )[0]

												lowerCamelCase_					=       5_0_2_6_5

												lowerCamelCase_					=       torch.Size((1, 2_5_6, vocab_size) )
												self.assertEqual(output.shape ,       __UpperCamelCase )

												lowerCamelCase_					=       torch.tensor(
												    [[[9.2595, -3.6038, 11.8819], [9.3869, -3.2693, 11.0956], [11.8524, -3.4938, 13.1210]]] )

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



					@slow
					def  lowercase__   (						self       :  Union[str, Any] ):
												lowerCamelCase_					=       MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""" )
												lowerCamelCase_					=       torch.arange(4_0_9_6 ).unsqueeze(0 )

												with torch.no_grad():
																			lowerCamelCase_					=       model(__UpperCamelCase )[0]

												lowerCamelCase_					=       5_0_2_6_5

												lowerCamelCase_					=       torch.Size((1, 4_0_9_6, vocab_size) )
												self.assertEqual(output.shape ,       __UpperCamelCase )

												lowerCamelCase_					=       torch.tensor(
												    [[[5.4789, -2.3564, 7.5064], [7.9067, -1.3369, 9.9668], [9.0712, -1.8106, 7.0380]]] )

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







lowercase   =	8.314_4598



def   __lowerCAmelCase    (				UpperCAmelCase__					:   float      ,					UpperCAmelCase__					:   float							)	->     float:
							if temperature < 0:
														raise Exception("""Temperature cannot be less than 0 K"""							)
							if molar_mass <= 0:
														raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol"""							)
							else:
														return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_mass) ** 0.5


if __name__ == "__main__":
		import doctest

		# run doctest
		doctest.testmod()

		# example
		lowercase   =	3_0_0
		lowercase   =	2_8
		lowercase   =	rms_speed_of_molecule(temperature, molar_mass)
		print(F"""Vrms of Nitrogen gas at 300 K is {vrms} m/s""")





