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







  def    __A     (							self :       List[str]   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size   )

        SCREAMING_SNAKE_CASE_          =  None
        if self.use_input_mask:
              SCREAMING_SNAKE_CASE_          =  random_attention_mask([self.batch_size, self.seq_length]   )

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

        SCREAMING_SNAKE_CASE_          =  self.get_config()

        return config, input_ids, input_mask, token_labels







  def    __A     (							self :       int   )  ->	Tuple:
        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=__magic_name__  ,     initializer_range=self.initializer_range  ,     )







  def    __A     (							self :       Any   )  ->	List[str]:
        (
            (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , 
        )          =  self.prepare_config_and_inputs()

        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
        SCREAMING_SNAKE_CASE_          =  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    __A     (							self :       Tuple  ,     __magic_name__ :       str  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Optional[int]  ,     **__magic_name__ :       Optional[int]  ,     )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  BertGenerationEncoder(config=__magic_name__   )
        model.to(__magic_name__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     attention_mask=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )
        self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)   )







  def    __A     (							self :       Dict  ,     __magic_name__ :       int  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       str  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       str  ,     **__magic_name__ :       Optional[Any]  ,     )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  BertGenerationEncoder(config=__magic_name__   )
        model.to(__magic_name__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(
            __magic_name__  ,     attention_mask=__magic_name__  ,     encoder_hidden_states=__magic_name__  ,     encoder_attention_mask=__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  model(
            __magic_name__  ,     attention_mask=__magic_name__  ,     encoder_hidden_states=__magic_name__  ,     )
        self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)   )







  def    __A     (							self :       int  ,     __magic_name__ :       str  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       int  ,     **__magic_name__ :       List[str]  ,     )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  BertGenerationDecoder(config=__magic_name__   ).to(__magic_name__   ).eval()

        # first forward pass
        SCREAMING_SNAKE_CASE_          =  model(
            __magic_name__  ,     attention_mask=__magic_name__  ,     encoder_hidden_states=__magic_name__  ,     encoder_attention_mask=__magic_name__  ,     use_cache=__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        SCREAMING_SNAKE_CASE_          =  ids_tensor((self.batch_size, 3)  ,     config.vocab_size   )
        SCREAMING_SNAKE_CASE_          =  ids_tensor((self.batch_size, 3)  ,     vocab_size=2   )

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

        SCREAMING_SNAKE_CASE_          =  model(
            __magic_name__  ,     attention_mask=__magic_name__  ,     encoder_hidden_states=__magic_name__  ,     encoder_attention_mask=__magic_name__  ,     output_hidden_states=__magic_name__  ,     )["hidden_states"][0]
        SCREAMING_SNAKE_CASE_          =  model(
            __magic_name__  ,     attention_mask=__magic_name__  ,     encoder_hidden_states=__magic_name__  ,     encoder_attention_mask=__magic_name__  ,     past_key_values=__magic_name__  ,     output_hidden_states=__magic_name__  ,     )["hidden_states"][0]

        # select random slice
        SCREAMING_SNAKE_CASE_          =  ids_tensor((1,)  ,     output_from_past.shape[-1]   ).item()
        SCREAMING_SNAKE_CASE_          =  output_from_no_past[:, -3:, random_slice_idx].detach()
        SCREAMING_SNAKE_CASE_          =  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(__magic_name__  ,     __magic_name__  ,     atol=1e-3   )   )







  def    __A     (							self :       Any  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Optional[int]  ,     __magic_name__ :       Optional[int]  ,     *__magic_name__ :       Union[str, Any]  ,     )  ->	str:
        SCREAMING_SNAKE_CASE_          =  BertGenerationDecoder(__magic_name__   )
        model.to(__magic_name__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     attention_mask=__magic_name__  ,     labels=__magic_name__   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)   )







  def    __A     (							self :       Any   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict



@require_torch
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  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    __A     (							self :       str   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  BertGenerationEncoderTester(self   )
        SCREAMING_SNAKE_CASE_          =  ConfigTester(self  ,     config_class=__magic_name__  ,     hidden_size=37   )







  def    __A     (							self :       int   )  ->	Dict:
        self.config_tester.run_common_tests()







  def    __A     (							self :       Dict   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__magic_name__   )







  def    __A     (							self :       List[Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_          =  "bert"
        self.model_tester.create_and_check_model(__magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__   )







  def    __A     (							self :       Tuple   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*__magic_name__   )







  def    __A     (							self :       Dict   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*__magic_name__   )







  def    __A     (							self :       Any   )  ->	Union[str, Any]:
        # This regression test was failing with PyTorch < 1.3
        (
            (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , (
            SCREAMING_SNAKE_CASE_
        )     , 
        )          =  self.model_tester.prepare_config_and_inputs_for_decoder()

        SCREAMING_SNAKE_CASE_          =  None

        self.model_tester.create_and_check_model_as_decoder(
            __magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__  ,     __magic_name__  ,     )







  def    __A     (							self :       List[str]   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_for_causal_lm(*__magic_name__   )







  @slow
  def    __A     (							self :       int   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"   )
        self.assertIsNotNone(__magic_name__   )



@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       List[str]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]   )
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )[0]
        SCREAMING_SNAKE_CASE_          =  torch.Size([1, 8, 1_024]   )
        self.assertEqual(output.shape  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]   )
        self.assertTrue(torch.allclose(output[:, :3, :3]  ,     __magic_name__  ,     atol=1e-4   )   )



@require_torch
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  @slow
  def    __A     (							self :       str   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder"   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]   )
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )[0]
        SCREAMING_SNAKE_CASE_          =  torch.Size([1, 8, 50_358]   )
        self.assertEqual(output.shape  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  torch.tensor(
            [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]   )
        self.assertTrue(torch.allclose(output[:, :3, :3]  ,     __magic_name__  ,     atol=1e-4   )   )



from collections.abc import Callable


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  a
      SCREAMING_SNAKE_CASE_          =  b
      if function(__UpperCamelCase				) == 0:  # one of the a or b is a root for the function
            return a
      elif function(__UpperCamelCase				) == 0:
            return b
      elif (
          function(__UpperCamelCase				) * function(__UpperCamelCase				) > 0
      ):  # if none of these are root and they are both positive or negative,
            # then this algorithm can't find the root
            raise ValueError("could not find root in given interval."				)
      else:
            SCREAMING_SNAKE_CASE_          =  start + (end - start) / 2.0
            while abs(start - mid				) > 1_0**-7:  # until precisely equals to 10^-7
                  if function(__UpperCamelCase				) == 0:
                        return mid
                  elif function(__UpperCamelCase				) * function(__UpperCamelCase				) < 0:
                        SCREAMING_SNAKE_CASE_          =  mid
                  else:
                        SCREAMING_SNAKE_CASE_          =  mid
                  SCREAMING_SNAKE_CASE_          =  start + (end - start) / 2.0
            return mid


def      a__ (		__UpperCamelCase				):
      return x**3 - 2 * x - 5


if __name__ == "__main__":
      print(bisection(f, 1, 10_00))

      import doctest

      doctest.testmod()


