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      lowerCamelCase_					:






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase=13			,			__lowercase=7			,			__lowercase=True			,			__lowercase=True			,			__lowercase=99			,			__lowercase=32			,			__lowercase=5			,			__lowercase=4			,			__lowercase=37			,			__lowercase="gelu"			,			__lowercase=0.1			,			__lowercase=0.1			,			__lowercase=50			,			__lowercase=0.02			,			__lowercase=True			,			__lowercase=None			,			)	->     Optional[int]:
        __UpperCamelCase					:List[Any]        =		parent
        __UpperCamelCase					:Optional[Any]        =		batch_size
        __UpperCamelCase					:str        =		seq_length
        __UpperCamelCase					:Optional[int]        =		is_training
        __UpperCamelCase					:int        =		use_input_mask
        __UpperCamelCase					:Any        =		vocab_size
        __UpperCamelCase					:Dict        =		hidden_size
        __UpperCamelCase					:int        =		num_hidden_layers
        __UpperCamelCase					:Tuple        =		num_attention_heads
        __UpperCamelCase					:List[str]        =		intermediate_size
        __UpperCamelCase					:str        =		hidden_act
        __UpperCamelCase					:Tuple        =		hidden_dropout_prob
        __UpperCamelCase					:List[str]        =		attention_probs_dropout_prob
        __UpperCamelCase					:Any        =		max_position_embeddings
        __UpperCamelCase					:Optional[int]        =		initializer_range
        __UpperCamelCase					:str        =		use_labels
        __UpperCamelCase					:List[str]        =		scope







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:Tuple        =		ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size)

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

        if self.use_labels:
            __UpperCamelCase					:Optional[Any]        =		ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size)

        __UpperCamelCase					:Tuple        =		self.get_config()

        return config, input_ids, input_mask, token_labels







    def       UpperCamelCase__       (     self)	->     List[Any]:
        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=__lowerCamelCase			,			initializer_range=self.initializer_range			,			)







    def       UpperCamelCase__       (     self)	->     Any:
        (
            (
            __UpperCamelCase     
        )  ,	(
            __UpperCamelCase     
        )  ,	(
            __UpperCamelCase     
        )  ,	(
            __UpperCamelCase     
        )  ,	     
        )					:Dict        =		self.prepare_config_and_inputs()

        __UpperCamelCase					:str        =		True
        __UpperCamelCase					:List[str]        =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
        __UpperCamelCase					:int        =		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			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase			,			)	->     Dict:
        __UpperCamelCase					:List[str]        =		BertGenerationEncoder(config=__lowerCamelCase)
        model.to(__lowerCamelCase)
        model.eval()
        __UpperCamelCase					:Optional[int]        =		model(__lowerCamelCase			,			attention_mask=__lowerCamelCase)
        __UpperCamelCase					:int        =		model(__lowerCamelCase)
        self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase			,			)	->     Optional[Any]:
        __UpperCamelCase					:str        =		True
        __UpperCamelCase					:List[Any]        =		BertGenerationEncoder(config=__lowerCamelCase)
        model.to(__lowerCamelCase)
        model.eval()
        __UpperCamelCase					:List[Any]        =		model(
            __lowerCamelCase			,			attention_mask=__lowerCamelCase			,			encoder_hidden_states=__lowerCamelCase			,			encoder_attention_mask=__lowerCamelCase			,			)
        __UpperCamelCase					:List[Any]        =		model(
            __lowerCamelCase			,			attention_mask=__lowerCamelCase			,			encoder_hidden_states=__lowerCamelCase			,			)
        self.parent.assertEqual(result.last_hidden_state.shape			,			(self.batch_size, self.seq_length, self.hidden_size))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase			,			)	->     Union[str, Any]:
        __UpperCamelCase					:List[Any]        =		True
        __UpperCamelCase					:str        =		True
        __UpperCamelCase					:List[str]        =		BertGenerationDecoder(config=__lowerCamelCase).to(__lowerCamelCase).eval()

        # first forward pass
        __UpperCamelCase					:List[Any]        =		model(
            __lowerCamelCase			,			attention_mask=__lowerCamelCase			,			encoder_hidden_states=__lowerCamelCase			,			encoder_attention_mask=__lowerCamelCase			,			use_cache=__lowerCamelCase			,			)
        __UpperCamelCase					:Dict        =		outputs.past_key_values

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

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

        __UpperCamelCase					:Optional[int]        =		model(
            __lowerCamelCase			,			attention_mask=__lowerCamelCase			,			encoder_hidden_states=__lowerCamelCase			,			encoder_attention_mask=__lowerCamelCase			,			output_hidden_states=__lowerCamelCase			,			)['''hidden_states'''][0]
        __UpperCamelCase					:Any        =		model(
            __lowerCamelCase			,			attention_mask=__lowerCamelCase			,			encoder_hidden_states=__lowerCamelCase			,			encoder_attention_mask=__lowerCamelCase			,			past_key_values=__lowerCamelCase			,			output_hidden_states=__lowerCamelCase			,			)['''hidden_states'''][0]

        # select random slice
        __UpperCamelCase					:List[Any]        =		ids_tensor((1,)			,			output_from_past.shape[-1]).item()
        __UpperCamelCase					:int        =		output_from_no_past[:, -3:, random_slice_idx].detach()
        __UpperCamelCase					:Optional[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(__lowerCamelCase			,			__lowerCamelCase			,			atol=1E-3))







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			*__lowercase			,			)	->     Optional[Any]:
        __UpperCamelCase					:Any        =		BertGenerationDecoder(__lowerCamelCase)
        model.to(__lowerCamelCase)
        model.eval()
        __UpperCamelCase					:Optional[Any]        =		model(__lowerCamelCase			,			attention_mask=__lowerCamelCase			,			labels=__lowerCamelCase)
        self.parent.assertEqual(result.logits.shape			,			(self.batch_size, self.seq_length, self.vocab_size))







    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		self.prepare_config_and_inputs()
        __UpperCamelCase					:Union[str, Any]        =		{'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict


@require_torch
class      lowerCamelCase_					(       UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						int      =				(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
    a__     :						Dict      =				(BertGenerationDecoder,) if is_torch_available() else ()
    a__     :						List[Any]      =				(
        {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
        if is_torch_available()
        else {}
    )







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase					:str        =		BertGenerationEncoderTester(self)
        __UpperCamelCase					:Optional[Any]        =		ConfigTester(self			,			config_class=__lowerCamelCase			,			hidden_size=37)







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        self.config_tester.run_common_tests()







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:Any        =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCamelCase)







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		self.model_tester.prepare_config_and_inputs()
        __UpperCamelCase					:str        =		'''bert'''
        self.model_tester.create_and_check_model(__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase)







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:List[str]        =		self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*__lowerCamelCase)







    def       UpperCamelCase__       (     self)	->     str:
        __UpperCamelCase					:List[Any]        =		self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCamelCase)







    def       UpperCamelCase__       (     self)	->     int:
        # This regression test was failing with PyTorch < 1.3
        (
            (
            __UpperCamelCase     
        )  ,	(
            __UpperCamelCase     
        )  ,	(
            __UpperCamelCase     
        )  ,	(
            __UpperCamelCase     
        )  ,	(
            __UpperCamelCase     
        )  ,	(
            __UpperCamelCase     
        )  ,	     
        )					:Optional[int]        =		self.model_tester.prepare_config_and_inputs_for_decoder()

        __UpperCamelCase					:List[Any]        =		None

        self.model_tester.create_and_check_model_as_decoder(
            __lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			__lowerCamelCase			,			)







    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:Dict        =		self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_for_causal_lm(*__lowerCamelCase)







    @slow
    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:List[Any]        =		BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')
        self.assertIsNotNone(__lowerCamelCase)


@require_torch
class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    @slow
    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:Tuple        =		BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')
        __UpperCamelCase					:int        =		torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]])
        with torch.no_grad():
            __UpperCamelCase					:Tuple        =		model(__lowerCamelCase)[0]
        __UpperCamelCase					:Any        =		torch.Size([1, 8, 1_024])
        self.assertEqual(output.shape			,			__lowerCamelCase)
        __UpperCamelCase					:Dict        =		torch.tensor(
            [[[0.17_75, 0.00_83, -0.03_21], [1.60_02, 0.12_87, 0.39_12], [2.14_73, 0.57_91, 0.60_66]]])
        self.assertTrue(torch.allclose(output[:, :3, :3]			,			__lowerCamelCase			,			atol=1E-4))


@require_torch
class      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    @slow
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        __UpperCamelCase					:List[str]        =		BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')
        __UpperCamelCase					:Tuple        =		torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]])
        with torch.no_grad():
            __UpperCamelCase					:Tuple        =		model(__lowerCamelCase)[0]
        __UpperCamelCase					:Optional[Any]        =		torch.Size([1, 8, 50_358])
        self.assertEqual(output.shape			,			__lowerCamelCase)
        __UpperCamelCase					:List[str]        =		torch.tensor(
            [[[-0.57_88, -2.59_94, -3.70_54], [0.04_38, 4.79_97, 1.87_95], [1.58_62, 6.64_09, 4.46_38]]])
        self.assertTrue(torch.allclose(output[:, :3, :3]			,			__lowerCamelCase			,			atol=1E-4))






from __future__ import annotations

from math import pi



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''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()





