import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel


class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       List[str]  ,     __magic_name__ :       int  ,     __magic_name__ :       Tuple=13  ,     __magic_name__ :       Dict=7  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       List[Any]=True  ,     __magic_name__ :       Optional[Any]=True  ,     __magic_name__ :       str=True  ,     __magic_name__ :       List[str]=99  ,     __magic_name__ :       List[str]=32  ,     __magic_name__ :       Any=5  ,     __magic_name__ :       Optional[Any]=4  ,     __magic_name__ :       Union[str, Any]=4  ,     __magic_name__ :       Optional[int]="gelu"  ,     __magic_name__ :       str=0.0  ,     __magic_name__ :       Optional[Any]=0.1  ,     __magic_name__ :       List[str]=True  ,     __magic_name__ :       Optional[int]=512  ,     __magic_name__ :       Dict=16  ,     __magic_name__ :       str=2  ,     __magic_name__ :       Optional[int]=0.02  ,     __magic_name__ :       Tuple=3  ,     __magic_name__ :       Tuple=4  ,     __magic_name__ :       List[str]=None  ,     )  ->	Optional[Any]:
        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_          =  use_token_type_ids
        SCREAMING_SNAKE_CASE_          =  use_labels
        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_multiple_size
        SCREAMING_SNAKE_CASE_          =  hidden_act
        SCREAMING_SNAKE_CASE_          =  hidden_dropout
        SCREAMING_SNAKE_CASE_          =  attention_dropout
        SCREAMING_SNAKE_CASE_          =  weight_tying
        SCREAMING_SNAKE_CASE_          =  max_position_embeddings
        SCREAMING_SNAKE_CASE_          =  type_vocab_size
        SCREAMING_SNAKE_CASE_          =  type_sequence_label_size
        SCREAMING_SNAKE_CASE_          =  initializer_range
        SCREAMING_SNAKE_CASE_          =  num_labels
        SCREAMING_SNAKE_CASE_          =  num_choices
        SCREAMING_SNAKE_CASE_          =  scope







  def    __A     (							self :       Any   )  ->	Optional[int]:
        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]   )

        SCREAMING_SNAKE_CASE_          =  None
        if self.use_labels:
              SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size, self.seq_length]  ,     self.num_labels   )

        SCREAMING_SNAKE_CASE_          =  self.get_config()

        return config, input_ids, input_mask, token_labels







  def    __A     (							self :       Any   )  ->	str:
        return GPTNeoXJapaneseConfig(
            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_multiple_size=self.intermediate_multiple_size  ,     hidden_act=self.hidden_act  ,     hidden_dropout=self.hidden_dropout  ,     attention_dropout=self.attention_dropout  ,     weight_tying=self.weight_tying  ,     max_position_embeddings=self.max_position_embeddings  ,     type_vocab_size=self.type_vocab_size  ,     is_decoder=__magic_name__  ,     initializer_range=self.initializer_range  ,     )







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

        SCREAMING_SNAKE_CASE_          =  True

        return config, input_ids, input_mask, token_labels







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







  def    __A     (							self :       Any  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       str  ,     __magic_name__ :       Any  ,     __magic_name__ :       int   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseForCausalLM(config=__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 :       Optional[Any]  ,     __magic_name__ :       Any  ,     __magic_name__ :       int  ,     __magic_name__ :       Any   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseForCausalLM(config=__magic_name__   )
        model.to(__magic_name__   )
        model.eval()

        # first forward pass
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     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__  ,     output_hidden_states=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output_from_no_past["hidden_states"][0]
        SCREAMING_SNAKE_CASE_          =  model(
            __magic_name__  ,     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 :       Optional[Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  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__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
  lowerCamelCase__     =						(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
  lowerCamelCase__     =						(
      {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
      if is_torch_available()
      else {}
  )
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False







  def    __A     (							self :       List[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseModelTester(self   )
        SCREAMING_SNAKE_CASE_          =  ConfigTester(self  ,     config_class=__magic_name__  ,     hidden_size=37   )







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







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







  def    __A     (							self :       Union[str, Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(__magic_name__  ,     __magic_name__  ,     __magic_name__   )







  def    __A     (							self :       Tuple   )  ->	Optional[int]:
        # This regression test was failing with PyTorch < 1.3
        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__   )







  def    __A     (							self :       Tuple   )  ->	int:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(__magic_name__  ,     __magic_name__  ,     __magic_name__   )







  def    __A     (							self :       str   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*__magic_name__   )







  @slow
  def    __A     (							self :       Dict   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  "abeja/gpt-neox-japanese-2.7b"

        SCREAMING_SNAKE_CASE_          =  ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]

        SCREAMING_SNAKE_CASE_          =  [
            "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
            "100年後に必要とされる会社は、「人」が中心の会社です。",
            "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
            "国境の長いトンネルを抜けると、そこは雪国だった。",
            "美味しい日本食といえば、やっぱりお寿司ですよね。",
        ]

        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseTokenizer.from_pretrained(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseForCausalLM.from_pretrained(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  []
        for prompt in prompts:
              SCREAMING_SNAKE_CASE_          =  tokenizer(__magic_name__  ,     return_tensors="pt"   ).input_ids
              SCREAMING_SNAKE_CASE_          =  model.generate(__magic_name__  ,     max_length=50   )
              SCREAMING_SNAKE_CASE_          =  tokenizer.batch_decode(__magic_name__  ,     skip_special_tokens=__magic_name__   )
              predicted_outputs += generated_string
        self.assertListEqual(__magic_name__  ,     __magic_name__   )



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()


