from __future__ import annotations
class SCREAMING_SNAKE_CASE_		:







   def __init__(    self	:	List[Any]      ,      lowerCamelCase_	:	int = 0							):





    """simple docstring"""


    UpperCamelCase      =			value
    UpperCamelCase      =			None
    UpperCamelCase      =			None





def  lowercase(  UpperCamelCase_       ,				UpperCamelCase_					)		->  Node | None:







 '''simple docstring'''
 if treea is None:
  return treea
 if treea is None:
  return treea

 UpperCamelCase      =			treea.value + treea.value
 UpperCamelCase      =			merge_two_binary_trees(treea.left       ,				treea.left					)
 UpperCamelCase      =			merge_two_binary_trees(treea.right       ,				treea.right					)
 return treea





def  lowercase(  UpperCamelCase_					)		->  None:







 '''simple docstring'''
 if root:
  print(root.value					)
  print_preorder(root.left					)
  print_preorder(root.right					)


if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE   		=			Node(1)
   _SCREAMING_SNAKE_CASE   		=			Node(2)
   _SCREAMING_SNAKE_CASE   		=			Node(3)
   _SCREAMING_SNAKE_CASE   		=			Node(4)

   _SCREAMING_SNAKE_CASE   		=			Node(2)
   _SCREAMING_SNAKE_CASE   		=			Node(4)
   _SCREAMING_SNAKE_CASE   		=			Node(6)
   _SCREAMING_SNAKE_CASE   		=			Node(9)
   _SCREAMING_SNAKE_CASE   		=			Node(5)

   print("""Tree1 is: """)
   print_preorder(treea)
   print("""Tree2 is: """)
   print_preorder(treea)
   _SCREAMING_SNAKE_CASE   		=			merge_two_binary_trees(treea, treea)
   print("""Merged Tree is: """)
   print_preorder(merged_tree)

import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         CpmAntConfig,
         CpmAntForCausalLM,
         CpmAntModel,
         CpmAntTokenizer,
     )
@require_torch
class SCREAMING_SNAKE_CASE_		:







   def __init__(    self	:	Optional[int]      ,      lowerCamelCase_	:	List[str]      ,      lowerCamelCase_	:	Tuple=2      ,      lowerCamelCase_	:	List[Any]=8      ,      lowerCamelCase_	:	Any=True      ,      lowerCamelCase_	:	int=False      ,      lowerCamelCase_	:	Optional[Any]=False      ,      lowerCamelCase_	:	Optional[int]=False      ,      lowerCamelCase_	:	Any=False      ,      lowerCamelCase_	:	List[Any]=99      ,      lowerCamelCase_	:	Optional[int]=32      ,      lowerCamelCase_	:	List[str]=3      ,      lowerCamelCase_	:	List[str]=4      ,      lowerCamelCase_	:	Tuple=37      ,      lowerCamelCase_	:	str=32      ,      lowerCamelCase_	:	Any=128      ,      lowerCamelCase_	:	List[str]=8      ,      lowerCamelCase_	:	Union[str, Any]=8      ,      lowerCamelCase_	:	Any=8      ,      lowerCamelCase_	:	Optional[int]=1.0      ,      lowerCamelCase_	:	int=True      ,      ):





    """simple docstring"""


    UpperCamelCase      =			parent
    UpperCamelCase      =			batch_size
    UpperCamelCase      =			seq_length
    UpperCamelCase      =			is_training
    UpperCamelCase      =			use_token_type_ids
    UpperCamelCase      =			use_input_mask
    UpperCamelCase      =			use_labels
    UpperCamelCase      =			use_mc_token_ids
    UpperCamelCase      =			vocab_size
    UpperCamelCase      =			hidden_size
    UpperCamelCase      =			num_hidden_layers
    UpperCamelCase      =			num_attention_heads
    UpperCamelCase      =			intermediate_size
    UpperCamelCase      =			num_buckets
    UpperCamelCase      =			max_distance
    UpperCamelCase      =			prompt_length
    UpperCamelCase      =			prompt_types
    UpperCamelCase      =			segment_types
    UpperCamelCase      =			init_std
    UpperCamelCase      =			return_dict







   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    UpperCamelCase      =			{}
    UpperCamelCase      =			ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size							).type(torch.intaa							)
    UpperCamelCase      =			False

    UpperCamelCase      =			self.get_config()

    return (config, input_ids)







   def 	lowerCamelCase_  (    self	:	Optional[Any]							):





    """simple docstring"""


    return CpmAntConfig(
        vocab_size=self.vocab_size      ,      hidden_size=self.hidden_size      ,      num_hidden_layers=self.num_hidden_layers      ,      num_attention_heads=self.num_attention_heads      ,      dim_ff=self.intermediate_size      ,      position_bias_num_buckets=self.num_buckets      ,      position_bias_max_distance=self.max_distance      ,      prompt_types=self.prompt_types      ,      prompt_length=self.prompt_length      ,      segment_types=self.segment_types      ,      use_cache=lowerCamelCase_      ,      init_std=self.init_std      ,      return_dict=self.return_dict      ,      )







   def 	lowerCamelCase_  (    self	:	Optional[int]      ,      lowerCamelCase_	:	List[str]      ,      lowerCamelCase_	:	List[str]      ,      *lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			CpmAntModel(config=lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    model.eval()

    UpperCamelCase      =			model(**lowerCamelCase_							).last_hidden_state

    self.parent.assertEqual(hidden_states.shape      ,      (self.batch_size, self.seq_length, config.hidden_size)							)







   def 	lowerCamelCase_  (    self	:	Any      ,      lowerCamelCase_	:	Dict      ,      lowerCamelCase_	:	Optional[Any]      ,      *lowerCamelCase_	:	str							):





    """simple docstring"""


    UpperCamelCase      =			CpmAntForCausalLM(lowerCamelCase_							)
    model.to(lowerCamelCase_							)
    UpperCamelCase      =			input_ids["""input_ids"""].to(lowerCamelCase_							)
    model.eval()

    UpperCamelCase      =			model(**lowerCamelCase_							)
    self.parent.assertEqual(
        model_output.logits.shape      ,      (self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)      ,      )







   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.prepare_config_and_inputs()
    return config, inputs_dict





@require_torch
class SCREAMING_SNAKE_CASE_		(			__lowerCAmelCase     ,    __lowerCAmelCase     ,    unittest.TestCase		):
   __lowerCAmelCase											=							(CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
   __lowerCAmelCase											=							(
       {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
   )

   __lowerCAmelCase											=							False
   __lowerCAmelCase											=							False
   __lowerCAmelCase											=							False
   __lowerCAmelCase											=							False
   __lowerCAmelCase											=							False







   def 	lowerCamelCase_  (    self	:	List[Any]							):





    """simple docstring"""


    UpperCamelCase      =			CpmAntModelTester(self							)
    UpperCamelCase      =			ConfigTester(self      ,      config_class=lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    self.config_tester.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()







   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    unittest.skip("""CPMAnt doesn't support input_embeds."""							)(self.test_inputs_embeds							)







   def 	lowerCamelCase_  (    self	:	Tuple							):





    """simple docstring"""


    unittest.skip(
        """CPMAnt doesn't support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model."""							)(self.test_retain_grad_hidden_states_attentions							)







   def 	lowerCamelCase_  (    self	:	Optional[int]							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_cpmant_model(lowerCamelCase_      ,      lowerCamelCase_							)







   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase     ,    UpperCamelCase      =			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_lm_head_model(lowerCamelCase_      ,      lowerCamelCase_							)





@require_torch
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase		):







   @tooslow
   def 	lowerCamelCase_  (    self	:	List[str]							):





    """simple docstring"""


    UpperCamelCase      =			"""今天天气真好！"""
    UpperCamelCase      =			"""openbmb/cpm-ant-10b"""
    UpperCamelCase      =			CpmAntModel.from_pretrained(lowerCamelCase_							)
    UpperCamelCase      =			CpmAntTokenizer.from_pretrained(lowerCamelCase_							)
    UpperCamelCase      =			tokenizer(lowerCamelCase_      ,      return_tensors="""pt"""							)
    UpperCamelCase      =			model(**lowerCamelCase_							).last_hidden_state

    UpperCamelCase      =			torch.tensor(
        [[[6.1_7_0_8, 5.9_2_4_4, 1.0_8_3_5], [6.5_2_0_7, 6.2_8_9_3, -1_1.3_3_2_4], [-1.0_1_0_7, -0.0_5_7_6, -5.9_5_7_7]]]      ,      )
    self.assertTrue(torch.allclose(hidden_states[:, :3, :3]      ,      lowerCamelCase_      ,      atol=1E-2							)							)





@require_torch
class SCREAMING_SNAKE_CASE_		(			unittest.TestCase		):







   @tooslow
   def 	lowerCamelCase_  (    self	:	str							):





    """simple docstring"""


    UpperCamelCase      =			"""今天天气真好！"""
    UpperCamelCase      =			"""openbmb/cpm-ant-10b"""
    UpperCamelCase      =			CpmAntForCausalLM.from_pretrained(lowerCamelCase_							)
    UpperCamelCase      =			CpmAntTokenizer.from_pretrained(lowerCamelCase_							)
    UpperCamelCase      =			tokenizer(lowerCamelCase_      ,      return_tensors="""pt"""							)
    UpperCamelCase      =			model(**lowerCamelCase_							).logits

    UpperCamelCase      =			torch.tensor(
        [[[-6.4_2_6_7, -6.4_0_8_3, -6.3_9_5_8], [-5.8_8_0_2, -5.9_4_4_7, -5.7_8_1_1], [-5.3_8_9_6, -5.4_8_2_0, -5.4_2_9_5]]]      ,      )
    self.assertTrue(torch.allclose(hidden_states[:, :3, :3]      ,      lowerCamelCase_      ,      atol=1E-2							)							)







   @tooslow
   def 	lowerCamelCase_  (    self	:	Dict							):





    """simple docstring"""


    UpperCamelCase      =			"""openbmb/cpm-ant-10b"""
    UpperCamelCase      =			CpmAntForCausalLM.from_pretrained(lowerCamelCase_							)
    UpperCamelCase      =			CpmAntTokenizer.from_pretrained(lowerCamelCase_							)
    UpperCamelCase      =			"""今天天气不错，"""
    UpperCamelCase      =			"""今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的"""
    UpperCamelCase      =			tokenizer(lowerCamelCase_      ,      return_tensors="""pt"""							)
    UpperCamelCase      =			model.generate(**lowerCamelCase_							)
    UpperCamelCase      =			tokenizer.batch_decode(lowerCamelCase_							)
    self.assertEqual(lowerCamelCase_      ,      lowerCamelCase_							)







   @tooslow
   def 	lowerCamelCase_  (    self	:	int							):





    """simple docstring"""


    UpperCamelCase      =			"""openbmb/cpm-ant-10b"""
    UpperCamelCase      =			CpmAntForCausalLM.from_pretrained(lowerCamelCase_							)
    UpperCamelCase      =			CpmAntTokenizer.from_pretrained(lowerCamelCase_							)
    UpperCamelCase      =			["""今天天气不错，""", """新年快乐，万事如意！"""]
    UpperCamelCase      =			[
        """今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的""",
        """新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的""",
    ]
    UpperCamelCase      =			tokenizer(lowerCamelCase_      ,      return_tensors="""pt"""      ,      padding=lowerCamelCase_							)
    UpperCamelCase      =			model.generate(**lowerCamelCase_							)
    UpperCamelCase      =			tokenizer.batch_decode(lowerCamelCase_							)
    self.assertEqual(lowerCamelCase_      ,      lowerCamelCase_							)
