






"""simple docstring"""





from __future__ import annotations

from math import pow, sqrt



def     __lowercase					(    a :				float       ,		a :				float       ,		a :				float )					->					dict[str, float]:
      if (resistance, reactance, impedance).count(0 ) != 1:
            raise ValueError('''One and only one argument must be 0''' )
      if resistance == 0:
            return {"resistance": sqrt(pow(a       ,		2 ) - pow(a       ,		2 ) )}
      elif reactance == 0:
            return {"reactance": sqrt(pow(a       ,		2 ) - pow(a       ,		2 ) )}
      elif impedance == 0:
            return {"impedance": sqrt(pow(a       ,		2 ) + pow(a       ,		2 ) )}
      else:
            raise ValueError('''Exactly one argument must be 0''' )


if __name__ == "__main__":
    import doctest

    doctest.testmod()










"""simple docstring"""





import unittest

from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import BertGenerationDecoder, BertGenerationEncoder


class      _lowercase :


      def __init__(    self				:	Optional[Any]							,   a				:	List[str]							,   a				:	List[Any]=1_3							,   a				:	Any=7							,   a				:	List[str]=True							,   a				:	int=True							,   a				:	Optional[int]=9_9							,   a				:	List[Any]=3_2							,   a				:	Optional[int]=5							,   a				:	List[Any]=4							,   a				:	Dict=3_7							,   a				:	List[str]="gelu"							,   a				:	Union[str, Any]=0.1							,   a				:	str=0.1							,   a				:	List[str]=5_0							,   a				:	Tuple=0.0_2							,   a				:	Union[str, Any]=True							,   a				:	int=None							,   ):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=parent
            __snake_case      :    Dict  			=batch_size
            __snake_case      :    Union[str, Any]  			=seq_length
            __snake_case      :    Optional[Any]  			=is_training
            __snake_case      :    Any  			=use_input_mask
            __snake_case      :    Union[str, Any]  			=vocab_size
            __snake_case      :    Union[str, Any]  			=hidden_size
            __snake_case      :    int  			=num_hidden_layers
            __snake_case      :    Optional[int]  			=num_attention_heads
            __snake_case      :    int  			=intermediate_size
            __snake_case      :    str  			=hidden_act
            __snake_case      :    str  			=hidden_dropout_prob
            __snake_case      :    Tuple  			=attention_probs_dropout_prob
            __snake_case      :    Tuple  			=max_position_embeddings
            __snake_case      :    Optional[Any]  			=initializer_range
            __snake_case      :    List[str]  			=use_labels
            __snake_case      :    List[Any]  			=scope


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







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

            __snake_case      :    Optional[int]  			=None
            if self.use_input_mask:
                  __snake_case      :    Optional[Any]  			=random_attention_mask([self.batch_size, self.seq_length]						)

            if self.use_labels:
                  __snake_case      :    int  			=ids_tensor([self.batch_size, self.seq_length]							,   self.vocab_size						)

            __snake_case      :    Any  			=self.get_config()

            return config, input_ids, input_mask, token_labels


      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            return BertGenerationConfig(
                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							,   is_decoder=a							,   initializer_range=self.initializer_range							,   )


      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            (
                (
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				
            )      :    Dict  			=self.prepare_config_and_inputs()

            __snake_case      :    Dict  			=True
            __snake_case      :    int  			=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
            __snake_case      :    Union[str, Any]  			=ids_tensor([self.batch_size, self.seq_length]							,   vocab_size=2						)

            return (
                config,
                input_ids,
                input_mask,
                token_labels,
                encoder_hidden_states,
                encoder_attention_mask,
            )


      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	Union[str, Any]							,   a				:	Optional[int]							,   a				:	int							,   a				:	Tuple							,   **a				:	Any							,   ):





            """simple docstring"""







            __snake_case      :    List[Any]  			=BertGenerationEncoder(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    Dict  			=model(a							,   attention_mask=a						)
            __snake_case      :    Tuple  			=model(a						)
            self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)						)


      def _UpperCamelCase						(    self				:	Tuple							,   a				:	Union[str, Any]							,   a				:	Optional[Any]							,   a				:	str							,   a				:	List[Any]							,   a				:	Optional[int]							,   a				:	Optional[Any]							,   **a				:	int							,   ):





            """simple docstring"""







            __snake_case      :    Any  			=True
            __snake_case      :    List[str]  			=BertGenerationEncoder(config=a						)
            model.to(a						)
            model.eval()
            __snake_case      :    Union[str, Any]  			=model(
                a							,   attention_mask=a							,   encoder_hidden_states=a							,   encoder_attention_mask=a							,   )
            __snake_case      :    str  			=model(
                a							,   attention_mask=a							,   encoder_hidden_states=a							,   )
            self.parent.assertEqual(result.last_hidden_state.shape							,   (self.batch_size, self.seq_length, self.hidden_size)						)


      def _UpperCamelCase						(    self				:	Any							,   a				:	List[str]							,   a				:	Dict							,   a				:	Optional[Any]							,   a				:	Tuple							,   a				:	Dict							,   a				:	Tuple							,   **a				:	Tuple							,   ):





            """simple docstring"""







            __snake_case      :    List[Any]  			=True
            __snake_case      :    Any  			=True
            __snake_case      :    int  			=BertGenerationDecoder(config=a						).to(a						).eval()

            # first forward pass
            __snake_case      :    List[str]  			=model(
                a							,   attention_mask=a							,   encoder_hidden_states=a							,   encoder_attention_mask=a							,   use_cache=a							,   )
            __snake_case      :    Optional[int]  			=outputs.past_key_values

            # create hypothetical multiple next token and extent to next_input_ids
            __snake_case      :    List[str]  			=ids_tensor((self.batch_size, 3)							,   config.vocab_size						)
            __snake_case      :    List[Any]  			=ids_tensor((self.batch_size, 3)							,   vocab_size=2						)

            # append to next input_ids and
            __snake_case      :    Tuple  			=torch.cat([input_ids, next_tokens]							,   dim=-1						)
            __snake_case      :    List[Any]  			=torch.cat([input_mask, next_mask]							,   dim=-1						)

            __snake_case      :    Tuple  			=model(
                a							,   attention_mask=a							,   encoder_hidden_states=a							,   encoder_attention_mask=a							,   output_hidden_states=a							,   )['''hidden_states'''][0]
            __snake_case      :    Any  			=model(
                a							,   attention_mask=a							,   encoder_hidden_states=a							,   encoder_attention_mask=a							,   past_key_values=a							,   output_hidden_states=a							,   )['''hidden_states'''][0]

            # select random slice
            __snake_case      :    int  			=ids_tensor((1,)							,   output_from_past.shape[-1]						).item()
            __snake_case      :    int  			=output_from_no_past[:, -3:, random_slice_idx].detach()
            __snake_case      :    int  			=output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]						)

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(a							,   a							,   atol=1e-3						)						)


      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	List[Any]							,   a				:	Dict							,   a				:	Optional[Any]							,   a				:	List[str]							,   *a				:	Dict							,   ):





            """simple docstring"""







            __snake_case      :    List[str]  			=BertGenerationDecoder(a						)
            model.to(a						)
            model.eval()
            __snake_case      :    Any  			=model(a							,   attention_mask=a							,   labels=a						)
            self.parent.assertEqual(result.logits.shape							,   (self.batch_size, self.seq_length, self.vocab_size)						)







      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case			,				__snake_case			,				__snake_case			,				__snake_case      :    Any  			=self.prepare_config_and_inputs()
            __snake_case      :    List[Any]  			={'''input_ids''': input_ids, '''attention_mask''': input_mask}
            return config, inputs_dict



@require_torch
class      _lowercase (     lowerCAmelCase		,  lowerCAmelCase		,  lowerCAmelCase		,  unittest.TestCase						):
      _a   :     Union[str, Any]								=   (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
      _a   :     List[Any]								=   (BertGenerationDecoder,) if is_torch_available() else ()
      _a   :     Union[str, Any]								=   (
          {'''feature-extraction''': BertGenerationEncoder, '''text-generation''': BertGenerationDecoder}
          if is_torch_available()
          else {}
      )


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    int  			=BertGenerationEncoderTester(self						)
            __snake_case      :    Union[str, Any]  			=ConfigTester(self							,   config_class=a							,   hidden_size=3_7						)


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            self.config_tester.run_common_tests()


      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    str  			=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*a						)


      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case			,				__snake_case			,				__snake_case			,				__snake_case      :    Tuple  			=self.model_tester.prepare_config_and_inputs()
            __snake_case      :    int  			='''bert'''
            self.model_tester.create_and_check_model(a							,   a							,   a							,   a						)


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    List[str]  			=self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_model_as_decoder(*a						)


      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Dict  			=self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_decoder_model_past_large_inputs(*a						)


      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            (
                (
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				(
                __snake_case
            )			,				
            )      :    Union[str, Any]  			=self.model_tester.prepare_config_and_inputs_for_decoder()

            __snake_case      :    Tuple  			=None

            self.model_tester.create_and_check_model_as_decoder(
                a							,   a							,   a							,   a							,   a							,   a							,   )


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    Tuple  			=self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_for_causal_lm(*a						)







      @slow
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''						)
            self.assertIsNotNone(a						)



@require_torch
class      _lowercase (     unittest.TestCase						):


      @slow
      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''						)
            __snake_case      :    Any  			=torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]						)
            with torch.no_grad():
                  __snake_case      :    str  			=model(a						)[0]
            __snake_case      :    List[str]  			=torch.Size([1, 8, 1_0_2_4]						)
            self.assertEqual(output.shape							,   a						)
            __snake_case      :    int  			=torch.tensor(
                [[[0.1_7_7_5, 0.0_0_8_3, -0.0_3_2_1], [1.6_0_0_2, 0.1_2_8_7, 0.3_9_1_2], [2.1_4_7_3, 0.5_7_9_1, 0.6_0_6_6]]]						)
            self.assertTrue(torch.allclose(output[:, :3, :3]							,   a							,   atol=1e-4						)						)



@require_torch
class      _lowercase (     unittest.TestCase						):


      @slow
      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            __snake_case      :    int  			=BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''						)
            __snake_case      :    Dict  			=torch.tensor([[1_0_1, 7_5_9_2, 1_0_1_0, 2_0_2_6, 3_8_9_9, 2_0_0_3, 1_0_1_4_0, 1_0_2]]						)
            with torch.no_grad():
                  __snake_case      :    List[str]  			=model(a						)[0]
            __snake_case      :    str  			=torch.Size([1, 8, 5_0_3_5_8]						)
            self.assertEqual(output.shape							,   a						)
            __snake_case      :    Optional[int]  			=torch.tensor(
                [[[-0.5_7_8_8, -2.5_9_9_4, -3.7_0_5_4], [0.0_4_3_8, 4.7_9_9_7, 1.8_7_9_5], [1.5_8_6_2, 6.6_4_0_9, 4.4_6_3_8]]]						)
            self.assertTrue(torch.allclose(output[:, :3, :3]							,   a							,   atol=1e-4						)						)


