


"""simple docstring"""
SCREAMING_SNAKE_CASE					:       dict[str, float]							   =							{
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0_009_842_073,
    "short-ton": 0.0_011_023_122,
    "pound": 2.2_046_244_202,
    "stone": 0.1_574_731_728,
    "ounce": 35.273_990_723,
    "carrat": 5000,
    "atomic-mass-unit": 6.022136652E26,
}

SCREAMING_SNAKE_CASE					:       dict[str, float]							   =							{
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1_016.04_608,
    "short-ton": 907.184,
    "pound": 0.453_592,
    "stone": 6.35_029,
    "ounce": 0.0_283_495,
    "carrat": 0.0_002,
    "atomic-mass-unit": 1.660540199E-27,
}






def 						lowercase				(	_snake_case		: str	,  _snake_case		: str	,  _snake_case		: float	)	->float:
	"""simple docstring"""





	if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
		__snake_case      :		Union[str, Any]         =							(
		    f"""Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
		    f"""Supported values are: {', '.join(_snake_case	)}"""
		)
		raise ValueError(_snake_case	)
	return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()








import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       from transformers.models.electra.modeling_flax_electra import (
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForMultipleChoice,
           FlaxElectraForPreTraining,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForSequenceClassification,
           FlaxElectraForTokenClassification,
           FlaxElectraModel,
       )







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   def __init__(							self,       lowercase_,       lowercase_=13,       lowercase_=7,       lowercase_=True,       lowercase_=True,       lowercase_=True,       lowercase_=True,       lowercase_=99,       lowercase_=24,       lowercase_=32,       lowercase_=5,       lowercase_=4,       lowercase_=37,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=512,       lowercase_=16,       lowercase_=2,       lowercase_=0.02,       lowercase_=4,       )				->       int:






     """simple docstring"""






     a__    =parent
     a__    =batch_size
     a__    =seq_length
     a__    =is_training
     a__    =use_attention_mask
     a__    =use_token_type_ids
     a__    =use_labels
     a__    =vocab_size
     a__    =hidden_size
     a__    =embedding_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_choices



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =ids_tensor([self.batch_size, self.seq_length],       self.vocab_size			)

     a__    =None
     if self.use_attention_mask:
       a__    =random_attention_mask([self.batch_size, self.seq_length]			)

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

     a__    =ElectraConfig(
         vocab_size=self.vocab_size,       hidden_size=self.hidden_size,       embedding_size=self.embedding_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,       )

     return config, input_ids, token_type_ids, attention_mask



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =self.prepare_config_and_inputs()
     a__, a__, a__, a__    =config_and_inputs
     a__    ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict







@require_flax
class 		__magic_name__     (				lowerCamelCase__						,			unittest.TestCase				):

   '''simple docstring'''




   lowerCamelCase__					:      List[str]        =  True

   lowerCamelCase__					:      Tuple        =  (
       (
           FlaxElectraModel,
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForPreTraining,
           FlaxElectraForTokenClassification,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForMultipleChoice,
           FlaxElectraForSequenceClassification,
       )
       if is_flax_available()
       else ()
   )



   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     a__    =FlaxElectraModelTester(self			)



   @slow
   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     for model_class_name in self.all_model_classes:
       if model_class_name == FlaxElectraForMaskedLM:
         a__    =model_class_name.from_pretrained('''google/electra-small-generator'''			)
       else:
         a__    =model_class_name.from_pretrained('''google/electra-small-discriminator'''			)
       a__    =model(np.ones((1, 1)			)			)
       self.assertIsNotNone(lowercase_			)
