import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    import jax.numpy as jnp

    from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
        FlaxRobertaPreLayerNormForCausalLM,
        FlaxRobertaPreLayerNormForMaskedLM,
        FlaxRobertaPreLayerNormForMultipleChoice,
        FlaxRobertaPreLayerNormForQuestionAnswering,
        FlaxRobertaPreLayerNormForSequenceClassification,
        FlaxRobertaPreLayerNormForTokenClassification,
        FlaxRobertaPreLayerNormModel,
    )




class 	__snake_case	( unittest.TestCase		):





   '''simple docstring'''






   def __init__(					self     :				str    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				List[Any]=13    ,  _UpperCamelCase     :				List[Any]=7    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				Optional[Any]=True    ,  _UpperCamelCase     :				Optional[int]=True    ,  _UpperCamelCase     :				str=99    ,  _UpperCamelCase     :				Optional[int]=32    ,  _UpperCamelCase     :				List[Any]=5    ,  _UpperCamelCase     :				Dict=4    ,  _UpperCamelCase     :				List[str]=37    ,  _UpperCamelCase     :				int="gelu"    ,  _UpperCamelCase     :				str=0.1    ,  _UpperCamelCase     :				List[str]=0.1    ,  _UpperCamelCase     :				int=512    ,  _UpperCamelCase     :				int=16    ,  _UpperCamelCase     :				List[Any]=2    ,  _UpperCamelCase     :				List[str]=0.0_2    ,  _UpperCamelCase     :				Any=4    ,  )				->Tuple:


       """simple docstring"""


       _lowerCamelCase     :		Union[str, Any] 						=						parent
       _lowerCamelCase     :		Union[str, Any] 						=						batch_size
       _lowerCamelCase     :		Optional[Any] 						=						seq_length
       _lowerCamelCase     :		str 						=						is_training
       _lowerCamelCase     :		Dict 						=						use_attention_mask
       _lowerCamelCase     :		Optional[Any] 						=						use_token_type_ids
       _lowerCamelCase     :		str 						=						use_labels
       _lowerCamelCase     :		List[Any] 						=						vocab_size
       _lowerCamelCase     :		Optional[Any] 						=						hidden_size
       _lowerCamelCase     :		List[str] 						=						num_hidden_layers
       _lowerCamelCase     :		Any 						=						num_attention_heads
       _lowerCamelCase     :		List[str] 						=						intermediate_size
       _lowerCamelCase     :		int 						=						hidden_act
       _lowerCamelCase     :		Any 						=						hidden_dropout_prob
       _lowerCamelCase     :		Any 						=						attention_probs_dropout_prob
       _lowerCamelCase     :		List[Any] 						=						max_position_embeddings
       _lowerCamelCase     :		str 						=						type_vocab_size
       _lowerCamelCase     :		Optional[int] 						=						type_sequence_label_size
       _lowerCamelCase     :		List[str] 						=						initializer_range
       _lowerCamelCase     :		Optional[Any] 						=						num_choices



   def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->int:


       """simple docstring"""


       _lowerCamelCase     :		str 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size)

       _lowerCamelCase     :		List[str] 						=						None
       if self.use_attention_mask:
           _lowerCamelCase     :		str 						=						random_attention_mask([self.batch_size, self.seq_length])

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

       _lowerCamelCase     :		int 						=						RobertaPreLayerNormConfig(
           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    ,  )

       return config, input_ids, token_type_ids, attention_mask



   def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Any:


       """simple docstring"""


       _lowerCamelCase     :		Union[str, Any] 						=						self.prepare_config_and_inputs()
       _lowerCamelCase     :		str 						=						config_and_inputs
       _lowerCamelCase     :		List[str] 						=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict




   def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Any:


       """simple docstring"""


       _lowerCamelCase     :		Any 						=						self.prepare_config_and_inputs()
       _lowerCamelCase     :		Optional[int] 						=						config_and_inputs

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

       return (
           config,
           input_ids,
           token_type_ids,
           encoder_hidden_states,
           encoder_attention_mask,
       )





@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class 	__snake_case	( _UpperCamelCase ,  unittest.TestCase		):





   '''simple docstring'''






   _snake_case    		=       True

   _snake_case    		=       (
       (
           FlaxRobertaPreLayerNormModel,
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )



   def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Optional[Any]:


       """simple docstring"""


       _lowerCamelCase     :		Dict 						=						FlaxRobertaPreLayerNormModelTester(self)




   @slow
   def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->str:


       """simple docstring"""


       for model_class_name in self.all_model_classes:
           _lowerCamelCase     :		Tuple 						=						model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""    ,  from_pt=_UpperCAmelCase)
           _lowerCamelCase     :		List[Any] 						=						model(np.ones((1, 1)))
           self.assertIsNotNone(_UpperCAmelCase)





@require_flax
class 	__snake_case	( unittest.TestCase		):





   '''simple docstring'''






   @slow
   def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->List[Any]:


       """simple docstring"""


       _lowerCamelCase     :		List[str] 						=						FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""    ,  from_pt=_UpperCAmelCase)

       _lowerCamelCase     :		Optional[int] 						=						np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]    ,  dtype=jnp.intaa)
       _lowerCamelCase     :		Dict 						=						model(_UpperCAmelCase)[0]
       _lowerCamelCase     :		Any 						=						[1, 11, 5_0265]
       self.assertEqual(list(output.shape)    ,  _UpperCAmelCase)
       # compare the actual values for a slice.
       _lowerCamelCase     :		int 						=						np.array(
           [[[40.4880, 18.0199, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 10.7085], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]    ,  dtype=np.floataa)
       self.assertTrue(np.allclose(output[:, :3, :3]    ,  _UpperCAmelCase    ,  atol=1E-4))




   @slow
   def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->int:


       """simple docstring"""


       _lowerCamelCase     :		Optional[int] 						=						FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""    ,  from_pt=_UpperCAmelCase)

       _lowerCamelCase     :		str 						=						np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]    ,  dtype=jnp.intaa)
       _lowerCamelCase     :		Optional[Any] 						=						model(_UpperCAmelCase)[0]
       # compare the actual values for a slice.
       _lowerCamelCase     :		Union[str, Any] 						=						np.array(
           [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]    ,  dtype=np.floataa)
       self.assertTrue(np.allclose(output[:, :3, :3]    ,  _UpperCAmelCase    ,  atol=1E-4))





from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase			:							List[Any]     =300  # TEMPERATURE (unit = K)







def 						A__  (					__A   , __A   , __A   , ):







    '''simple docstring'''

    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()




