





from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
    from transformers import (
        AutoConfig,
        BertConfig,
        GPTaConfig,
        TaConfig,
        TFAutoModel,
        TFAutoModelForCausalLM,
        TFAutoModelForMaskedLM,
        TFAutoModelForPreTraining,
        TFAutoModelForQuestionAnswering,
        TFAutoModelForSeqaSeqLM,
        TFAutoModelForSequenceClassification,
        TFAutoModelWithLMHead,
        TFBertForMaskedLM,
        TFBertForPreTraining,
        TFBertForQuestionAnswering,
        TFBertForSequenceClassification,
        TFBertModel,
        TFGPTaLMHeadModel,
        TFRobertaForMaskedLM,
        TFTaForConditionalGeneration,
    )
    from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
    from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
    from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
    from transformers import (
        AutoModel,
        AutoModelForCausalLM,
        AutoModelForMaskedLM,
        AutoModelForPreTraining,
        AutoModelForQuestionAnswering,
        AutoModelForSeqaSeqLM,
        AutoModelForSequenceClassification,
        AutoModelWithLMHead,
        BertForMaskedLM,
        BertForPreTraining,
        BertForQuestionAnswering,
        BertForSequenceClassification,
        BertModel,
        GPTaLMHeadModel,
        RobertaForMaskedLM,
        TaForConditionalGeneration,
    )





@is_pt_tf_cross_test
class  A__    (							unittest.TestCase	):




   """simple docstring"""



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







          '''simple docstring'''

          for model_name in ["bert-base-uncased"]:
                 a__						:	str =				AutoConfig.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	str =				TFAutoModel.from_pretrained(lowercase					,  from_pt=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	Optional[Any] =				AutoModel.from_pretrained(lowercase					,  from_tf=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

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







          '''simple docstring'''

          for model_name in ["bert-base-uncased"]:
                 a__						:	List[str] =				AutoConfig.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	Any =				TFAutoModelForPreTraining.from_pretrained(lowercase					,  from_pt=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	Optional[int] =				AutoModelForPreTraining.from_pretrained(lowercase					,  from_tf=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

   @slow
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[Any] =				AutoConfig.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	List[Any] =				TFAutoModelForCausalLM.from_pretrained(lowercase					,  from_pt=lowercase)
                 a__ ,							a__						:	Tuple =				TFAutoModelForCausalLM.from_pretrained(
                     lowercase					,  output_loading_info=lowercase					,  from_pt=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	Tuple =				AutoModelForCausalLM.from_pretrained(lowercase					,  from_tf=lowercase)
                 a__ ,							a__						:	Any =				AutoModelForCausalLM.from_pretrained(
                     lowercase					,  output_loading_info=lowercase					,  from_tf=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

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







          '''simple docstring'''

          for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[Any] =				AutoConfig.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	str =				TFAutoModelWithLMHead.from_pretrained(lowercase					,  from_pt=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	int =				AutoModelWithLMHead.from_pretrained(lowercase					,  from_tf=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

   @slow
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	str =				AutoConfig.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	List[str] =				TFAutoModelForMaskedLM.from_pretrained(lowercase					,  from_pt=lowercase)
                 a__ ,							a__						:	Optional[Any] =				TFAutoModelForMaskedLM.from_pretrained(
                     lowercase					,  output_loading_info=lowercase					,  from_pt=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	Tuple =				AutoModelForMaskedLM.from_pretrained(lowercase					,  from_tf=lowercase)
                 a__ ,							a__						:	Optional[int] =				AutoModelForMaskedLM.from_pretrained(
                     lowercase					,  output_loading_info=lowercase					,  from_tf=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

   @slow
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Optional[Any] =				AutoConfig.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	Optional[int] =				TFAutoModelForSeqaSeqLM.from_pretrained(lowercase					,  from_pt=lowercase)
                 a__ ,							a__						:	List[str] =				TFAutoModelForSeqaSeqLM.from_pretrained(
                     lowercase					,  output_loading_info=lowercase					,  from_pt=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	Union[str, Any] =				AutoModelForSeqaSeqLM.from_pretrained(lowercase					,  from_tf=lowercase)
                 a__ ,							a__						:	Optional[int] =				AutoModelForSeqaSeqLM.from_pretrained(
                     lowercase					,  output_loading_info=lowercase					,  from_tf=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

   @slow
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          for model_name in ["bert-base-uncased"]:
                 a__						:	Optional[Any] =				AutoConfig.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	int =				TFAutoModelForSequenceClassification.from_pretrained(lowercase					,  from_pt=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	Any =				AutoModelForSequenceClassification.from_pretrained(lowercase					,  from_tf=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

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







          '''simple docstring'''

          for model_name in ["bert-base-uncased"]:
                 a__						:	List[Any] =				AutoConfig.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	Optional[int] =				TFAutoModelForQuestionAnswering.from_pretrained(lowercase					,  from_pt=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

                 a__						:	str =				AutoModelForQuestionAnswering.from_pretrained(lowercase					,  from_tf=lowercase)
                 self.assertIsNotNone(lowercase)
                 self.assertIsInstance(lowercase					,  lowercase)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Tuple =				TFAutoModelWithLMHead.from_pretrained(lowercase					,  from_pt=lowercase)
          self.assertIsInstance(lowercase					,  lowercase)
          self.assertEqual(model.num_parameters()					,  1_4410)
          self.assertEqual(model.num_parameters(only_trainable=lowercase)					,  1_4410)

          a__						:	Any =				AutoModelWithLMHead.from_pretrained(lowercase					,  from_tf=lowercase)
          self.assertIsInstance(lowercase					,  lowercase)
          self.assertEqual(model.num_parameters()					,  1_4410)
          self.assertEqual(model.num_parameters(only_trainable=lowercase)					,  1_4410)

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	List[str] =				TFAutoModelWithLMHead.from_pretrained(lowercase					,  from_pt=lowercase)
          self.assertIsInstance(lowercase					,  lowercase)
          self.assertEqual(model.num_parameters()					,  1_4410)
          self.assertEqual(model.num_parameters(only_trainable=lowercase)					,  1_4410)

          a__						:	int =				AutoModelWithLMHead.from_pretrained(lowercase					,  from_tf=lowercase)
          self.assertIsInstance(lowercase					,  lowercase)
          self.assertEqual(model.num_parameters()					,  1_4410)
          self.assertEqual(model.num_parameters(only_trainable=lowercase)					,  1_4410)









"""simple docstring"""




from __future__ import annotations

from math import pi






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    float    ,       _lowercase						:    float    ,       _lowercase						:    float					) ->dict[str, float]:






	'''simple docstring'''






	if (inductance, frequency, reactance).count(0					) != 1:
		raise ValueError("One and only one argument must be 0"					)
	if inductance < 0:
		raise ValueError("Inductance cannot be negative"					)
	if frequency < 0:
		raise ValueError("Frequency cannot be negative"					)
	if reactance < 0:
		raise ValueError("Inductive reactance cannot be negative"					)
	if inductance == 0:
		return {"inductance": reactance / (2 * pi * frequency)}
	elif frequency == 0:
		return {"frequency": reactance / (2 * pi * inductance)}
	elif reactance == 0:
		return {"reactance": 2 * pi * frequency * inductance}
	else:
		raise ValueError("Exactly one argument must be 0"					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()


