


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 A    :






      def __init__(   self		:	str     ,  lowercase_		:	Tuple     ,  lowercase_		:	List[str]=13     ,  lowercase_		:	Optional[Any]=7     ,  lowercase_		:	Optional[Any]=True     ,  lowercase_		:	List[str]=True     ,  lowercase_		:	Optional[int]=True     ,  lowercase_		:	Optional[int]=True     ,  lowercase_		:	int=99     ,  lowercase_		:	List[str]=32     ,  lowercase_		:	Optional[Any]=5     ,  lowercase_		:	Dict=4     ,  lowercase_		:	Dict=4     ,  lowercase_		:	Optional[Any]="gelu"     ,  lowercase_		:	Optional[Any]=0.0     ,  lowercase_		:	Tuple=0.1     ,  lowercase_		:	List[Any]=True     ,  lowercase_		:	Union[str, Any]=512     ,  lowercase_		:	Optional[Any]=16     ,  lowercase_		:	Optional[int]=2     ,  lowercase_		:	List[str]=0.02     ,  lowercase_		:	Tuple=3     ,  lowercase_		:	str=4     ,  lowercase_		:	List[Any]=None     ,  )   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =parent
             _lowerCamelCase			:						Any            =batch_size
             _lowerCamelCase			:						str            =seq_length
             _lowerCamelCase			:						Optional[int]            =is_training
             _lowerCamelCase			:						Any            =use_input_mask
             _lowerCamelCase			:						List[Any]            =use_token_type_ids
             _lowerCamelCase			:						List[Any]            =use_labels
             _lowerCamelCase			:						Union[str, Any]            =vocab_size
             _lowerCamelCase			:						Optional[Any]            =hidden_size
             _lowerCamelCase			:						Tuple            =num_hidden_layers
             _lowerCamelCase			:						Dict            =num_attention_heads
             _lowerCamelCase			:						str            =intermediate_multiple_size
             _lowerCamelCase			:						List[str]            =hidden_act
             _lowerCamelCase			:						int            =hidden_dropout
             _lowerCamelCase			:						Optional[Any]            =attention_dropout
             _lowerCamelCase			:						Tuple            =weight_tying
             _lowerCamelCase			:						Dict            =max_position_embeddings
             _lowerCamelCase			:						List[Any]            =type_vocab_size
             _lowerCamelCase			:						Tuple            =type_sequence_label_size
             _lowerCamelCase			:						int            =initializer_range
             _lowerCamelCase			:						Any            =num_labels
             _lowerCamelCase			:						Any            =num_choices
             _lowerCamelCase			:						int            =scope






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)

             _lowerCamelCase			:						int            =None
             if self.use_input_mask:
                    _lowerCamelCase			:						Tuple            =random_attention_mask([self.batch_size, self.seq_length]					)

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

             _lowerCamelCase			:						List[str]            =self.get_config()

             return config, input_ids, input_mask, token_labels






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Tuple:
             """simple docstring"""




             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=lowercase_     ,  initializer_range=self.initializer_range     ,  )






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[str]            =self.prepare_config_and_inputs()

             _lowerCamelCase			:						str            =True

             return config, input_ids, input_mask, token_labels






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	Union[str, Any]     ,  lowercase_		:	Optional[Any]     ,  lowercase_		:	int					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =GPTNeoXJapaneseModel(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _lowerCamelCase			:						Optional[int]            =model(lowercase_     ,  attention_mask=lowercase_					)
             _lowerCamelCase			:						List[Any]            =model(lowercase_					)
             self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)					)






      def 			lowerCamelCase			(   self		:	Dict     ,  lowercase_		:	Optional[int]     ,  lowercase_		:	str     ,  lowercase_		:	Tuple					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =True
             _lowerCamelCase			:						Tuple            =GPTNeoXJapaneseModel(lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _lowerCamelCase			:						List[str]            =model(lowercase_     ,  attention_mask=lowercase_					)
             self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)					)






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	Optional[int]     ,  lowercase_		:	Tuple     ,  lowercase_		:	List[Any]     ,  lowercase_		:	Optional[Any]					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =GPTNeoXJapaneseForCausalLM(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()
             _lowerCamelCase			:						Tuple            =model(lowercase_     ,  attention_mask=lowercase_     ,  labels=lowercase_					)
             self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)					)






      def 			lowerCamelCase			(   self		:	List[Any]     ,  lowercase_		:	str     ,  lowercase_		:	Optional[Any]     ,  lowercase_		:	Dict					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =True
             _lowerCamelCase			:						List[Any]            =GPTNeoXJapaneseForCausalLM(config=lowercase_					)
             model.to(lowercase_					)
             model.eval()

             # first forward pass
             _lowerCamelCase			:						Union[str, Any]            =model(lowercase_     ,  attention_mask=lowercase_     ,  use_cache=lowercase_					)
             _lowerCamelCase			:						Optional[Any]            =outputs.past_key_values

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

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

             _lowerCamelCase			:						Optional[int]            =model(lowercase_     ,  attention_mask=lowercase_     ,  output_hidden_states=lowercase_					)
             _lowerCamelCase			:						Dict            =output_from_no_past['hidden_states'][0]
             _lowerCamelCase			:						Union[str, Any]            =model(
                 lowercase_     ,  attention_mask=lowercase_     ,  past_key_values=lowercase_     ,  output_hidden_states=lowercase_     ,  )['hidden_states'][0]

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






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.prepare_config_and_inputs()
             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Dict            =config_and_inputs
             _lowerCamelCase			:						Optional[int]            ={'input_ids': input_ids, 'attention_mask': input_mask}
             return config, inputs_dict




@require_torch
class A    (  UpperCamelCase_       ,				UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    int =(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
      UpperCamelCase__				:    Tuple =(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
      UpperCamelCase__				:    List[str] =(
          {'feature-extraction': GPTNeoXJapaneseModel, 'text-generation': GPTNeoXJapaneseForCausalLM}
          if is_torch_available()
          else {}
      )
      UpperCamelCase__				:    List[str] =False
      UpperCamelCase__				:    Dict =False
      UpperCamelCase__				:    Optional[int] =False
      UpperCamelCase__				:    Optional[int] =False






      def 			lowerCamelCase			(   self		:	int					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =GPTNeoXJapaneseModelTester(self					)
             _lowerCamelCase			:						Optional[int]            =ConfigTester(self     ,  config_class=lowercase_     ,  hidden_size=37					)






      def 			lowerCamelCase			(   self		:	List[Any]					)   ->		int:
             """simple docstring"""




             self.config_tester.run_common_tests()






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(lowercase_     ,  lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						Union[str, Any]            =self.model_tester.prepare_config_and_inputs_for_decoder()
             self.model_tester.create_and_check_model_as_decoder(lowercase_     ,  lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =self.model_tester.prepare_config_and_inputs_for_decoder()

             _lowerCamelCase			:						Optional[int]            =None

             self.model_tester.create_and_check_model_as_decoder(lowercase_     ,  lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	Dict					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past_large_inputs(lowercase_     ,  lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_causal_lm(*lowercase_					)






      @slow
      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            ='abeja/gpt-neox-japanese-2.7b'

             _lowerCamelCase			:						Tuple            =['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、']

             _lowerCamelCase			:						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。',
             ]

             _lowerCamelCase			:						Optional[Any]            =GPTNeoXJapaneseTokenizer.from_pretrained(lowercase_					)
             _lowerCamelCase			:						Dict            =GPTNeoXJapaneseForCausalLM.from_pretrained(lowercase_					)

             _lowerCamelCase			:						Optional[Any]            =[]
             for prompt in prompts:
                    _lowerCamelCase			:						Optional[int]            =tokenizer(lowercase_     ,  return_tensors='pt'					).input_ids
                    _lowerCamelCase			:						Tuple            =model.generate(lowercase_     ,  max_length=50					)
                    _lowerCamelCase			:						List[Any]            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    predicted_outputs += generated_string
             self.assertListEqual(lowercase_     ,  lowercase_					)






from collections.abc import Iterable
from typing import Any




class A    :






      def __init__(   self		:	Dict     ,  lowercase_		:	int | None = None					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =value
             _lowerCamelCase			:						Node | None            =None  # Added in order to delete a node easier
             _lowerCamelCase			:						Node | None            =None
             _lowerCamelCase			:						Node | None            =None






      def __repr__(   self		:	Dict					)   ->		str:
             """simple docstring"""




             from pprint import pformat

             if self.left is None and self.right is None:
                    return str(self.value					)
             return pformat({F'''{self.value}''': (self.left, self.right)}     ,  indent=1					)




class A    :






      def __init__(   self		:	Union[str, Any]     ,  lowercase_		:	Node | None = None					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =root






      def __str__(   self		:	Tuple					)   ->		str:
             """simple docstring"""




             return str(self.root					)






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	Node     ,  lowercase_		:	Node | None					)   ->		None:
             """simple docstring"""




             if new_children is not None:  # reset its kids
                    _lowerCamelCase			:						Optional[int]            =node.parent
             if node.parent is not None:  # reset its parent
                    if self.is_right(lowercase_					):  # If it is the right children
                           _lowerCamelCase			:						int            =new_children
                    else:
                           _lowerCamelCase			:						Dict            =new_children
             else:
                    _lowerCamelCase			:						Tuple            =new_children






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	Node					)   ->		bool:
             """simple docstring"""




             if node.parent and node.parent.right:
                    return node == node.parent.right
             return False






      def 			lowerCamelCase			(   self		:	int					)   ->		bool:
             """simple docstring"""




             return self.root is None






      def 			lowerCamelCase			(   self		:	List[Any]     ,  lowercase_		:	Union[str, Any]					)   ->		None:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =Node(lowercase_					)  # create a new Node
             if self.empty():  # if Tree is empty
                    _lowerCamelCase			:						Union[str, Any]            =new_node  # set its root
             else:  # Tree is not empty
                    _lowerCamelCase			:						Optional[int]            =self.root  # from root
                    if parent_node is None:
                           return
                    while True:  # While we don't get to a leaf
                           if value < parent_node.value:  # We go left
                                  if parent_node.left is None:
                                         _lowerCamelCase			:						Optional[int]            =new_node  # We insert the new node in a leaf
                                         break
                                  else:
                                         _lowerCamelCase			:						Optional[Any]            =parent_node.left
                           else:
                                  if parent_node.right is None:
                                         _lowerCamelCase			:						Optional[Any]            =new_node
                                         break
                                  else:
                                         _lowerCamelCase			:						Optional[int]            =parent_node.right
                    _lowerCamelCase			:						Optional[Any]            =parent_node






      def 			lowerCamelCase			(   self		:	Any     ,  *lowercase_		:	Union[str, Any]					)   ->		None:
             """simple docstring"""




             for value in values:
                    self.__insert(lowercase_					)






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  lowercase_		:	List[Any]					)   ->		Node | None:
             """simple docstring"""




             if self.empty():
                    raise IndexError('Warning: Tree is empty! please use another.'					)
             else:
                    _lowerCamelCase			:						int            =self.root
                    # use lazy evaluation here to avoid NoneType Attribute error
                    while node is not None and node.value is not value:
                           _lowerCamelCase			:						Dict            =node.left if value < node.value else node.right
                    return node






      def 			lowerCamelCase			(   self		:	Tuple     ,  lowercase_		:	Node | None = None					)   ->		Node | None:
             """simple docstring"""




             if node is None:
                    if self.root is None:
                           return None
                    _lowerCamelCase			:						Union[str, Any]            =self.root

             if not self.empty():
                    while node.right is not None:
                           _lowerCamelCase			:						Optional[int]            =node.right
             return node






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  lowercase_		:	Node | None = None					)   ->		Node | None:
             """simple docstring"""




             if node is None:
                    _lowerCamelCase			:						Union[str, Any]            =self.root
             if self.root is None:
                    return None
             if not self.empty():
                    _lowerCamelCase			:						Optional[int]            =self.root
                    while node.left is not None:
                           _lowerCamelCase			:						List[Any]            =node.left
             return node






      def 			lowerCamelCase			(   self		:	Tuple     ,  lowercase_		:	int					)   ->		None:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =self.search(lowercase_					)  # Look for the node with that label
             if node is not None:
                    if node.left is None and node.right is None:  # If it has no children
                           self.__reassign_nodes(lowercase_     ,  lowercase_					)
                    elif node.left is None:  # Has only right children
                           self.__reassign_nodes(lowercase_     ,  node.right					)
                    elif node.right is None:  # Has only left children
                           self.__reassign_nodes(lowercase_     ,  node.left					)
                    else:
                           _lowerCamelCase			:						List[str]            =self.get_max(
                               node.left					)  # Gets the max value of the left branch
                           self.remove(tmp_node.value					)  # type: ignore
                           _lowerCamelCase			:						Union[str, Any]            =(
                               tmp_node.value  # type: ignore
                           )  # Assigns the value to the node to delete and keep tree structure






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	Node | None					)   ->		Iterable:
             """simple docstring"""




             if node is not None:
                    yield node  # Preorder Traversal
                    yield from self.preorder_traverse(node.left					)
                    yield from self.preorder_traverse(node.right					)






      def 			lowerCamelCase			(   self		:	str     ,  lowercase_		:	Dict=None					)   ->		Any:
             """simple docstring"""




             if traversal_function is None:
                    return self.preorder_traverse(self.root					)
             else:
                    return traversal_function(self.root					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	list     ,  lowercase_		:	Node | None					)   ->		None:
             """simple docstring"""




             if node:
                    self.inorder(lowercase_     ,  node.left					)
                    arr.append(node.value					)
                    self.inorder(lowercase_     ,  node.right					)






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	int     ,  lowercase_		:	Node					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						list[int]            =[]
             self.inorder(lowercase_     ,  lowercase_					)  # append all values to list using inorder traversal
             return arr[k - 1]




def       a_ (       SCREAMING_SNAKE_CASE__   : Node | None	):
       '''simple docstring'''





       _lowerCamelCase			:						int            =[]
       if curr_node is not None:
              _lowerCamelCase			:						List[Any]            =postorder(curr_node.left	) + postorder(curr_node.right	) + [curr_node]
       return node_list




def       a_ (       ):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[int]            =(8, 3, 6, 1, 10, 14, 13, 4, 7)
       _lowerCamelCase			:						int            =BinarySearchTree()
       for i in testlist:
              t.insert(SCREAMING_SNAKE_CASE__	)

       # Prints all the elements of the list in order traversal
       print(SCREAMING_SNAKE_CASE__	)

       if t.search(6	) is not None:
              print('The value 6 exists'	)
       else:
              print('The value 6 doesn\'t exist'	)

       if t.search(-1	) is not None:
              print('The value -1 exists'	)
       else:
              print('The value -1 doesn\'t exist'	)

       if not t.empty():
              print('Max Value: '						,     t.get_max().value	)  # type: ignore
              print('Min Value: '						,     t.get_min().value	)  # type: ignore

       for i in testlist:
              t.remove(SCREAMING_SNAKE_CASE__	)
              print(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)


