



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       snake_case__:

					"""simple docstring"""






					def __init__(							self					:	Dict      ,   SCREAMING_SNAKE_CASE					:	Tuple      ,   SCREAMING_SNAKE_CASE					:	Any=13      ,   SCREAMING_SNAKE_CASE					:	Tuple=7      ,   SCREAMING_SNAKE_CASE					:	Optional[int]=True      ,   SCREAMING_SNAKE_CASE					:	int=True      ,   SCREAMING_SNAKE_CASE					:	Any=True      ,   SCREAMING_SNAKE_CASE					:	Optional[Any]=True      ,   SCREAMING_SNAKE_CASE					:	int=99      ,   SCREAMING_SNAKE_CASE					:	Dict=32      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]=5      ,   SCREAMING_SNAKE_CASE					:	str=4      ,   SCREAMING_SNAKE_CASE					:	Optional[Any]=4      ,   SCREAMING_SNAKE_CASE					:	Dict="gelu"      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]=0.0      ,   SCREAMING_SNAKE_CASE					:	Optional[Any]=0.1      ,   SCREAMING_SNAKE_CASE					:	int=True      ,   SCREAMING_SNAKE_CASE					:	str=512      ,   SCREAMING_SNAKE_CASE					:	Any=16      ,   SCREAMING_SNAKE_CASE					:	Optional[Any]=2      ,   SCREAMING_SNAKE_CASE					:	int=0.02      ,   SCREAMING_SNAKE_CASE					:	List[Any]=3      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]=4      ,   SCREAMING_SNAKE_CASE					:	List[str]=None      ,   ):
									lowercase__     :    str													=     parent
									lowercase__     :    Union[str, Any]													=     batch_size
									lowercase__     :    Optional[Any]													=     seq_length
									lowercase__     :    int													=     is_training
									lowercase__     :    int													=     use_input_mask
									lowercase__     :    Optional[Any]													=     use_token_type_ids
									lowercase__     :    Any													=     use_labels
									lowercase__     :    Optional[int]													=     vocab_size
									lowercase__     :    Optional[int]													=     hidden_size
									lowercase__     :    Any													=     num_hidden_layers
									lowercase__     :    str													=     num_attention_heads
									lowercase__     :    int													=     intermediate_multiple_size
									lowercase__     :    Any													=     hidden_act
									lowercase__     :    Union[str, Any]													=     hidden_dropout
									lowercase__     :    int													=     attention_dropout
									lowercase__     :    str													=     weight_tying
									lowercase__     :    Optional[int]													=     max_position_embeddings
									lowercase__     :    Optional[int]													=     type_vocab_size
									lowercase__     :    int													=     type_sequence_label_size
									lowercase__     :    Optional[int]													=     initializer_range
									lowercase__     :    str													=     num_labels
									lowercase__     :    List[Any]													=     num_choices
									lowercase__     :    Any													=     scope







					def  snake_case     (							self					:	List[str]     ):
									lowercase__     :    str													=     ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size     )

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

									lowercase__     :    Union[str, Any]													=     None
									if self.use_labels:
													lowercase__     :    Tuple													=     ids_tensor([self.batch_size, self.seq_length]      ,   self.num_labels     )

									lowercase__     :    Any													=     self.get_config()

									return config, input_ids, input_mask, token_labels







					def  snake_case     (							self					:	int     ):
									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=__UpperCAmelCase      ,   initializer_range=self.initializer_range      ,   )







					def  snake_case     (							self					:	Union[str, Any]     ):
									lowercase__     :    Tuple													=     self.prepare_config_and_inputs()

									lowercase__     :    str													=     True

									return config, input_ids, input_mask, token_labels







					def  snake_case     (							self					:	Any      ,   SCREAMING_SNAKE_CASE					:	Any      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	List[str]     ):
									lowercase__     :    str													=     GPTNeoXJapaneseModel(config=__UpperCAmelCase     )
									model.to(__UpperCAmelCase     )
									model.eval()
									lowercase__     :    str													=     model(__UpperCAmelCase      ,   attention_mask=__UpperCAmelCase     )
									lowercase__     :    Any													=     model(__UpperCAmelCase     )
									self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)     )







					def  snake_case     (							self					:	Optional[Any]      ,   SCREAMING_SNAKE_CASE					:	Dict      ,   SCREAMING_SNAKE_CASE					:	Tuple      ,   SCREAMING_SNAKE_CASE					:	str     ):
									lowercase__     :    Optional[Any]													=     True
									lowercase__     :    Dict													=     GPTNeoXJapaneseModel(__UpperCAmelCase     )
									model.to(__UpperCAmelCase     )
									model.eval()
									lowercase__     :    Tuple													=     model(__UpperCAmelCase      ,   attention_mask=__UpperCAmelCase     )
									self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)     )







					def  snake_case     (							self					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	str      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	Any      ,   SCREAMING_SNAKE_CASE					:	Optional[int]     ):
									lowercase__     :    List[Any]													=     GPTNeoXJapaneseForCausalLM(config=__UpperCAmelCase     )
									model.to(__UpperCAmelCase     )
									model.eval()
									lowercase__     :    List[Any]													=     model(__UpperCAmelCase      ,   attention_mask=__UpperCAmelCase      ,   labels=__UpperCAmelCase     )
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length, self.vocab_size)     )







					def  snake_case     (							self					:	List[Any]      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	Tuple      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]     ):
									lowercase__     :    List[Any]													=     True
									lowercase__     :    Any													=     GPTNeoXJapaneseForCausalLM(config=__UpperCAmelCase     )
									model.to(__UpperCAmelCase     )
									model.eval()

									# first forward pass
									lowercase__     :    Optional[Any]													=     model(__UpperCAmelCase      ,   attention_mask=__UpperCAmelCase      ,   use_cache=__UpperCAmelCase     )
									lowercase__     :    Optional[Any]													=     outputs.past_key_values

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

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

									lowercase__     :    Optional[Any]													=     model(__UpperCAmelCase      ,   attention_mask=__UpperCAmelCase      ,   output_hidden_states=__UpperCAmelCase     )
									lowercase__     :    List[Any]													=     output_from_no_past["hidden_states"][0]
									lowercase__     :    Tuple													=     model(
									    __UpperCAmelCase      ,   attention_mask=__UpperCAmelCase      ,   past_key_values=__UpperCAmelCase      ,   output_hidden_states=__UpperCAmelCase      ,   )["hidden_states"][0]

									# select random slice
									lowercase__     :    List[Any]													=     ids_tensor((1,)      ,   output_from_past.shape[-1]     ).item()
									lowercase__     :    List[str]													=     output_from_no_past[:, -3:, random_slice_idx].detach()
									lowercase__     :    List[Any]													=     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(__UpperCAmelCase      ,   __UpperCAmelCase      ,   atol=1E-3     )     )







					def  snake_case     (							self					:	Optional[int]     ):
									lowercase__     :    Tuple													=     self.prepare_config_and_inputs()
									lowercase__     :    Any													=     config_and_inputs
									lowercase__     :    int													=     {"input_ids": input_ids, "attention_mask": input_mask}
									return config, inputs_dict






@require_torch
class       snake_case__(_a				,			_a				,			unittest.TestCase			):

					"""simple docstring"""






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







					def  snake_case     (							self					:	Any     ):
									lowercase__     :    Optional[int]													=     GPTNeoXJapaneseModelTester(self     )
									lowercase__     :    Optional[int]													=     ConfigTester(self      ,   config_class=__UpperCAmelCase      ,   hidden_size=37     )







					def  snake_case     (							self					:	List[str]     ):
									self.config_tester.run_common_tests()







					def  snake_case     (							self					:	Tuple     ):
									lowercase__     :    str													=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase     )







					def  snake_case     (							self					:	Dict     ):
									lowercase__     :    Tuple													=     self.model_tester.prepare_config_and_inputs_for_decoder()
									self.model_tester.create_and_check_model_as_decoder(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase     )







					def  snake_case     (							self					:	Optional[int]     ):
									# This regression test was failing with PyTorch < 1.3
									lowercase__     :    int													=     self.model_tester.prepare_config_and_inputs_for_decoder()

									lowercase__     :    Union[str, Any]													=     None

									self.model_tester.create_and_check_model_as_decoder(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase     )







					def  snake_case     (							self					:	str     ):
									lowercase__     :    int													=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase     )







					def  snake_case     (							self					:	Optional[int]     ):
									lowercase__     :    Optional[Any]													=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_causal_lm(*__UpperCAmelCase     )







					@slow
					def  snake_case     (							self					:	List[str]     ):
									lowercase__     :    Optional[int]													=     "abeja/gpt-neox-japanese-2.7b"

									lowercase__     :    int													=     ["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、"]

									lowercase__     :    Union[str, Any]													=     [
									    "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。",
									]

									lowercase__     :    Tuple													=     GPTNeoXJapaneseTokenizer.from_pretrained(__UpperCAmelCase     )
									lowercase__     :    List[Any]													=     GPTNeoXJapaneseForCausalLM.from_pretrained(__UpperCAmelCase     )

									lowercase__     :    int													=     []
									for prompt in prompts:
													lowercase__     :    List[str]													=     tokenizer(__UpperCAmelCase      ,   return_tensors="pt"     ).input_ids
													lowercase__     :    List[str]													=     model.generate(__UpperCAmelCase      ,   max_length=50     )
													lowercase__     :    Union[str, Any]													=     tokenizer.batch_decode(__UpperCAmelCase      ,   skip_special_tokens=__UpperCAmelCase     )
													predicted_outputs += generated_string
									self.assertListEqual(__UpperCAmelCase      ,   __UpperCAmelCase     )








"""simple docstring"""







from bisect import bisect
from itertools import accumulate


def      lowercase			(				A_  ,   A_  ,   A_  ,   A_       )->      Union[str, Any]:
  '''simple docstring'''







  a						:		Any      							=			sorted(zip(A_  ,   A_       )  ,   key=lambda A_       : x[0] / x[1]  ,   reverse=A_       )
  a							,		a						:		int      							=			[i[0] for i in r], [i[1] for i in r]
  a						:		Union[str, Any]      							=			list(accumulate(A_       )       )
  a						:		Optional[Any]      							=			bisect(A_  ,   A_       )
  return (
      0
      if k == 0
      else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
      if k != n
      else sum(vl[:k]       )
  )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






