
'''simple docstring'''
import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    DebertaForMaskedLM,
		    DebertaForQuestionAnswering,
		    DebertaForSequenceClassification,
		    DebertaForTokenClassification,
		    DebertaModel,
		)
		from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST







class   a_ (      UpperCamelCase__					):




			def __init__(       self					,   UpperCAmelCase					,   UpperCAmelCase=13					,   UpperCAmelCase=7					,   UpperCAmelCase=True					,   UpperCAmelCase=True					,   UpperCAmelCase=True					,   UpperCAmelCase=True					,   UpperCAmelCase=99					,   UpperCAmelCase=32					,   UpperCAmelCase=5					,   UpperCAmelCase=4					,   UpperCAmelCase=37					,   UpperCAmelCase="gelu"					,   UpperCAmelCase=0.1					,   UpperCAmelCase=0.1					,   UpperCAmelCase=5_12					,   UpperCAmelCase=16					,   UpperCAmelCase=2					,   UpperCAmelCase=0.02					,   UpperCAmelCase=False					,   UpperCAmelCase=True					,   UpperCAmelCase="None"					,   UpperCAmelCase=3					,   UpperCAmelCase=4					,   UpperCAmelCase=None					,   ):
				a_      				=						parent
				a_      				=						batch_size
				a_      				=						seq_length
				a_      				=						is_training
				a_      				=						use_input_mask
				a_      				=						use_token_type_ids
				a_      				=						use_labels
				a_      				=						vocab_size
				a_      				=						hidden_size
				a_      				=						num_hidden_layers
				a_      				=						num_attention_heads
				a_      				=						intermediate_size
				a_      				=						hidden_act
				a_      				=						hidden_dropout_prob
				a_      				=						attention_probs_dropout_prob
				a_      				=						max_position_embeddings
				a_      				=						type_vocab_size
				a_      				=						type_sequence_label_size
				a_      				=						initializer_range
				a_      				=						num_labels
				a_      				=						num_choices
				a_      				=						relative_attention
				a_      				=						position_biased_input
				a_      				=						pos_att_type
				a_      				=						scope




			def 					lowerCAmelCase__					(       self	):
				a_      				=						ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size	)

				a_      				=						None
				if self.use_input_mask:
					a_      				=						ids_tensor([self.batch_size, self.seq_length]					,   vocab_size=2	)

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

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

				a_      				=						self.get_config()

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




			def 					lowerCAmelCase__					(       self	):
				return DebertaConfig(
				    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					,   initializer_range=self.initializer_range					,   relative_attention=self.relative_attention					,   position_biased_input=self.position_biased_input					,   pos_att_type=self.pos_att_type					,   )




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.get_config()
				a_      				=						3_00
				return config




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase	):
				self.parent.assertListEqual(list(result.loss.size()	)					,   []	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						DebertaModel(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   token_type_ids=UpperCAmelCase	)[0]
				a_      				=						model(UpperCAmelCase					,   token_type_ids=UpperCAmelCase	)[0]
				a_      				=						model(UpperCAmelCase	)[0]

				self.parent.assertListEqual(list(sequence_output.size()	)					,   [self.batch_size, self.seq_length, self.hidden_size]	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						DebertaForMaskedLM(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						self.num_labels
				a_      				=						DebertaForSequenceClassification(UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase					,   token_type_ids=UpperCAmelCase					,   labels=UpperCAmelCase	)
				self.parent.assertListEqual(list(result.logits.size()	)					,   [self.batch_size, self.num_labels]	)
				self.check_loss_output(UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						self.num_labels
				a_      				=						DebertaForTokenClassification(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						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 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						DebertaForQuestionAnswering(config=UpperCAmelCase	)
				model.to(UpperCAmelCase	)
				model.eval()
				a_      				=						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 					lowerCAmelCase__					(       self	):
				a_      				=						self.prepare_config_and_inputs()
				(
				    (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  (
				    a_
				)				,  
				)      				=						config_and_inputs
				a_      				=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
				return config, inputs_dict







@require_torch
class   a_ (      UpperCamelCase__		,  UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			List[str]       =    (
			    (
			        DebertaModel,
			        DebertaForMaskedLM,
			        DebertaForSequenceClassification,
			        DebertaForTokenClassification,
			        DebertaForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__	:			Any       =    (
			    {
			        'feature-extraction': DebertaModel,
			        'fill-mask': DebertaForMaskedLM,
			        'question-answering': DebertaForQuestionAnswering,
			        'text-classification': DebertaForSequenceClassification,
			        'token-classification': DebertaForTokenClassification,
			        'zero-shot': DebertaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase__	:			List[Any]       =    True
			lowerCamelCase__	:			Tuple       =    False
			lowerCamelCase__	:			List[Any]       =    False
			lowerCamelCase__	:			Dict       =    False
			lowerCamelCase__	:			Any       =    False




			def 					lowerCAmelCase__					(       self	):
				a_      				=						DebertaModelTester(self	)
				a_      				=						ConfigTester(self					,   config_class=UpperCAmelCase					,   hidden_size=37	)




			def 					lowerCAmelCase__					(       self	):
				self.config_tester.run_common_tests()




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_model(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_question_answering(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_deberta_for_token_classification(*UpperCAmelCase	)






			@slow
			def 					lowerCAmelCase__					(       self	):
				for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					a_      				=						DebertaModel.from_pretrained(UpperCAmelCase	)
					self.assertIsNotNone(UpperCAmelCase	)







@require_torch
@require_sentencepiece
@require_tokenizers
class   a_ (      unittest.TestCase					):




			@unittest.skip(reason="""Model not available yet"""	)
			def 					lowerCAmelCase__					(       self	):
				pass






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						DebertaModel.from_pretrained("""microsoft/deberta-base"""	)

				a_      				=						torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	)
				a_      				=						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
				with torch.no_grad():
					a_      				=						model(UpperCAmelCase					,   attention_mask=UpperCAmelCase	)[0]
				# compare the actual values for a slice.
				a_      				=						torch.tensor(
				    [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]	)
				self.assertTrue(torch.allclose(output[:, 1:4, 1:4]					,   UpperCAmelCase					,   atol=1e-4	)					,   f'''{output[:, 1:4, 1:4]}'''	)


'''simple docstring'''
from math import log

from scipy.constants import Boltzmann, physical_constants

lowercase__											=3_00  # TEMPERATURE (unit = K)






def      UpperCamelCase_				(      A__  ,      A__  ,      A__  ,      ):

	if donor_conc <= 0:
		raise ValueError("""Donor concentration should be positive"""						)
	elif acceptor_conc <= 0:
		raise ValueError("""Acceptor concentration should be positive"""						)
	elif intrinsic_conc <= 0:
		raise ValueError("""Intrinsic concentration should be positive"""						)
	elif donor_conc <= intrinsic_conc:
		raise ValueError(
		    """Donor concentration should be greater than intrinsic concentration"""						)
	elif acceptor_conc <= intrinsic_conc:
		raise ValueError(
		    """Acceptor concentration should be greater than intrinsic concentration"""						)
	else:
		return (
		    Boltzmann
		    * T
		    * log((donor_conc * acceptor_conc) / intrinsic_conc**2						)
		    / physical_constants["electron volt"][0]
		)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
