
"""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 		a__						:
   def __init__(			self	,    _a	,    _a=13	,    _a=7	,    _a=True	,    _a=True	,    _a=99	,    _a=32	,    _a=5	,    _a=4	,    _a=37	,    _a="gelu"	,    _a=0.1	,    _a=0.1	,    _a=50	,    _a=0.0_2	,    _a=True	,    _a=None	,    ):
          lowercase    :				Union[str, Any]       		=							parent
          lowercase    :				List[Any]       		=							batch_size
          lowercase    :				Tuple       		=							seq_length
          lowercase    :				List[Any]       		=							is_training
          lowercase    :				str       		=							use_input_mask
          lowercase    :				str       		=							vocab_size
          lowercase    :				str       		=							hidden_size
          lowercase    :				Tuple       		=							num_hidden_layers
          lowercase    :				int       		=							num_attention_heads
          lowercase    :				Union[str, Any]       		=							intermediate_size
          lowercase    :				Tuple       		=							hidden_act
          lowercase    :				List[Any]       		=							hidden_dropout_prob
          lowercase    :				Any       		=							attention_probs_dropout_prob
          lowercase    :				Any       		=							max_position_embeddings
          lowercase    :				int       		=							initializer_range
          lowercase    :				Tuple       		=							use_labels
          lowercase    :				Any       		=							scope
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size				)

          lowercase    :				Any       		=							None
          if self.use_input_mask:
                 lowercase    :				Union[str, Any]       		=							random_attention_mask([self.batch_size, self.seq_length]				)

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

          lowercase    :				int       		=							self.get_config()

          return config, input_ids, input_mask, token_labels
   def 			__magic_name__			(			self				):
          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=__lowercase	,    initializer_range=self.initializer_range	,    )
   def 			__magic_name__			(			self				):
          (
              lowercase       
          )    :				Dict       		=							self.prepare_config_and_inputs()

          lowercase    :				Dict       		=							True
          lowercase    :				str       		=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
          lowercase    :				str       		=							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 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    **_a	,    ):
          lowercase    :				Tuple       		=							BertGenerationEncoder(config=__lowercase				)
          model.to(__lowercase				)
          model.eval()
          lowercase    :				Tuple       		=							model(__lowercase	,    attention_mask=__lowercase				)
          lowercase    :				Dict       		=							model(__lowercase				)
          self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    **_a	,    ):
          lowercase    :				str       		=							True
          lowercase    :				Any       		=							BertGenerationEncoder(config=__lowercase				)
          model.to(__lowercase				)
          model.eval()
          lowercase    :				Union[str, Any]       		=							model(
              __lowercase	,    attention_mask=__lowercase	,    encoder_hidden_states=__lowercase	,    encoder_attention_mask=__lowercase	,    )
          lowercase    :				Optional[Any]       		=							model(
              __lowercase	,    attention_mask=__lowercase	,    encoder_hidden_states=__lowercase	,    )
          self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    _a	,    _a	,    **_a	,    ):
          lowercase    :				Any       		=							True
          lowercase    :				Union[str, Any]       		=							True
          lowercase    :				Optional[int]       		=							BertGenerationDecoder(config=__lowercase				).to(__lowercase				).eval()

          # first forward pass
          lowercase    :				int       		=							model(
              __lowercase	,    attention_mask=__lowercase	,    encoder_hidden_states=__lowercase	,    encoder_attention_mask=__lowercase	,    use_cache=__lowercase	,    )
          lowercase    :				List[Any]       		=							outputs.past_key_values

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

          # append to next input_ids and
          lowercase    :				Optional[int]       		=							torch.cat([input_ids, next_tokens]	,    dim=-1				)
          lowercase    :				int       		=							torch.cat([input_mask, next_mask]	,    dim=-1				)

          lowercase    :				Optional[Any]       		=							model(
              __lowercase	,    attention_mask=__lowercase	,    encoder_hidden_states=__lowercase	,    encoder_attention_mask=__lowercase	,    output_hidden_states=__lowercase	,    )['''hidden_states'''][0]
          lowercase    :				str       		=							model(
              __lowercase	,    attention_mask=__lowercase	,    encoder_hidden_states=__lowercase	,    encoder_attention_mask=__lowercase	,    past_key_values=__lowercase	,    output_hidden_states=__lowercase	,    )['''hidden_states'''][0]

          # select random slice
          lowercase    :				Union[str, Any]       		=							ids_tensor((1,)	,    output_from_past.shape[-1]				).item()
          lowercase    :				Optional[int]       		=							output_from_no_past[:, -3:, random_slice_idx].detach()
          lowercase    :				Tuple       		=							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(__lowercase	,    __lowercase	,    atol=1E-3				)				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a	,    _a	,    *_a	,    ):
          lowercase    :				str       		=							BertGenerationDecoder(__lowercase				)
          model.to(__lowercase				)
          model.eval()
          lowercase    :				List[str]       		=							model(__lowercase	,    attention_mask=__lowercase	,    labels=__lowercase				)
          self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)				)

   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							self.prepare_config_and_inputs()
          lowercase    :				Union[str, Any]       		=							{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict


@require_torch
class 		a__						(      UpperCAmelCase_,			UpperCAmelCase_,			UpperCAmelCase_,			unittest.TestCase						):
   __lowerCAmelCase            =				(BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
   __lowerCAmelCase            =				(BertGenerationDecoder,) if is_torch_available() else ()
   __lowerCAmelCase            =				(
       {"""feature-extraction""": BertGenerationEncoder, """text-generation""": BertGenerationDecoder}
       if is_torch_available()
       else {}
   )
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							BertGenerationEncoderTester(self				)
          lowercase    :				Optional[Any]       		=							ConfigTester(self	,    config_class=__lowercase	,    hidden_size=37				)
   def 			__magic_name__			(			self				):
          self.config_tester.run_common_tests()
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__lowercase				)
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							self.model_tester.prepare_config_and_inputs()
          lowercase    :				Any       		=							'''bert'''
          self.model_tester.create_and_check_model(__lowercase	,    __lowercase	,    __lowercase	,    __lowercase				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(*__lowercase				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowercase				)
   def 			__magic_name__			(			self				):
          # This regression test was failing with PyTorch < 1.3
          (
              lowercase       
          )    :				int       		=							self.model_tester.prepare_config_and_inputs_for_decoder()

          lowercase    :				List[Any]       		=							None

          self.model_tester.create_and_check_model_as_decoder(
              __lowercase	,    __lowercase	,    __lowercase	,    __lowercase	,    __lowercase	,    __lowercase	,    )
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_for_causal_lm(*__lowercase				)

   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"				)
          self.assertIsNotNone(__lowercase				)


@require_torch
class 		a__						(      unittest.TestCase						):
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				Any       		=							BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"				)
          lowercase    :				Optional[Any]       		=							torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]				)
          with torch.no_grad():
                 lowercase    :				Any       		=							model(__lowercase				)[0]
          lowercase    :				Optional[Any]       		=							torch.Size([1, 8, 1_024]				)
          self.assertEqual(output.shape	,    __lowercase				)
          lowercase    :				str       		=							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]	,    __lowercase	,    atol=1E-4				)				)


@require_torch
class 		a__						(      unittest.TestCase						):
   @slow
   def 			__magic_name__			(			self				):
          lowercase    :				List[str]       		=							BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"				)
          lowercase    :				Tuple       		=							torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]				)
          with torch.no_grad():
                 lowercase    :				int       		=							model(__lowercase				)[0]
          lowercase    :				Optional[int]       		=							torch.Size([1, 8, 50_358]				)
          self.assertEqual(output.shape	,    __lowercase				)
          lowercase    :				Optional[Any]       		=							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]	,    __lowercase	,    atol=1E-4				)				)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE = 1  ,			SCREAMING_SNAKE_CASE = 1_000	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		1
    __UpperCamelCase     :Any			  =		0

    for divide_by_number in range(SCREAMING_SNAKE_CASE  ,			digit + 1	):
        __UpperCamelCase     :list[int]			  =		[]
        __UpperCamelCase     :Optional[int]			  =		numerator
        for _ in range(1  ,			digit + 1	):
            if now_divide in has_been_divided:
                if longest_list_length < len(SCREAMING_SNAKE_CASE	):
                    __UpperCamelCase     :Optional[Any]			  =		len(SCREAMING_SNAKE_CASE	)
                    __UpperCamelCase     :int			  =		divide_by_number
            else:
                has_been_divided.append(SCREAMING_SNAKE_CASE	)
                __UpperCamelCase     :Any			  =		now_divide * 10 % divide_by_number

    return the_digit


# Tests
if __name__ == "__main__":
    import doctest

    doctest.testmod()





