
'''simple docstring'''





from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

a__		: Optional[Any]  							=							TypeVar("T", bound=bool)


class 			UpperCamelCase__	(     Generic[T]):






 def __init__(      self	:List[Any]						,					_A	:T      )					->      None:







       '''simple docstring'''



       __A													=      value
       __A													=      None
       __A													=      None






 @property
 def 						lowercase_    (      self	:Tuple      )					->      T:







       '''simple docstring'''



       return self._value






 @staticmethod
 def 						lowercase_    (      _A	:SkewNode[T] | None						,					_A	:SkewNode[T] | None      )					->      SkewNode[T] | None:







       '''simple docstring'''



       if not roota:
             return roota

       if not roota:
             return roota

       if roota.value > roota.value:
             __A						,   __A													=      roota, roota

       __A													=      roota
       __A													=      roota.right
       __A													=      roota.left
       __A													=      SkewNode.merge(_A						,					_A      )

       return result


class 			UpperCamelCase__	(     Generic[T]):






 def __init__(      self	:Optional[int]						,					_A	:Iterable[T] | None = ()      )					->      None:







       '''simple docstring'''



       __A													=      None
       if data:
             for item in data:
                   self.insert(_A      )






 def __bool__(      self	:List[str]      )					->      bool:







       '''simple docstring'''



       return self._root is not None






 def __iter__(      self	:Dict      )					->      Iterator[T]:







       '''simple docstring'''



       __A													=      []
       while self:
             result.append(self.pop()      )

       # Pushing items back to the heap not to clear it.
       for item in result:
             self.insert(_A      )

       return iter(_A      )






 def 						lowercase_    (      self	:Tuple						,					_A	:T      )					->      None:







       '''simple docstring'''



       __A													=      SkewNode.merge(self._root						,					SkewNode(_A      )      )






 def 						lowercase_    (      self	:Dict      )					->      T | None:







       '''simple docstring'''



       __A													=      self.top()
       __A													=      (
           SkewNode.merge(self._root.left						,					self._root.right      ) if self._root else None
       )

       return result






 def 						lowercase_    (      self	:int      )					->      T:







       '''simple docstring'''



       if not self._root:
             raise IndexError('Can\'t get top element for the empty heap.'      )
       return self._root.value






 def 						lowercase_    (      self	:int      )					->      None:







       '''simple docstring'''



       __A													=      None


if __name__ == "__main__":
 import doctest

 doctest.testmod()













"""simple docstring"""




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 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=2   , snake_case_=8   , snake_case_=True   , snake_case_=False   , snake_case_=False   , snake_case_=False   , snake_case_=False   , snake_case_=9_9   , snake_case_=3_2   , snake_case_=3   , snake_case_=4   , snake_case_=3_7   , snake_case_=3_2   , snake_case_=1_2_8   , snake_case_=8   , snake_case_=8   , snake_case_=8   , snake_case_=1.0   , snake_case_=True   , ):

        """simple docstring"""

        A_    :			List[Any]									=    parent
        A_    :			Union[str, Any]									=    batch_size
        A_    :			Dict									=    seq_length
        A_    :			Tuple									=    is_training
        A_    :			Union[str, Any]									=    use_token_type_ids
        A_    :			Optional[Any]									=    use_input_mask
        A_    :			str									=    use_labels
        A_    :			Optional[int]									=    use_mc_token_ids
        A_    :			str									=    vocab_size
        A_    :			Any									=    hidden_size
        A_    :			List[Any]									=    num_hidden_layers
        A_    :			str									=    num_attention_heads
        A_    :			Optional[Any]									=    intermediate_size
        A_    :			List[str]									=    num_buckets
        A_    :			Tuple									=    max_distance
        A_    :			Optional[int]									=    prompt_length
        A_    :			List[str]									=    prompt_types
        A_    :			Dict									=    segment_types
        A_    :			List[str]									=    init_std
        A_    :			Union[str, Any]									=    return_dict

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    {}
        A_    :			Any									=    ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size    ).type(torch.intaa    )
        A_    :			Union[str, Any]									=    False

        A_    :			Optional[int]									=    self.get_config()

        return (config, input_ids)

      def    lowerCamelCase_     (	self    ):

        """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=snake_case_   , init_std=self.init_std   , return_dict=self.return_dict   , )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , *snake_case_    ):

        """simple docstring"""

        A_    :			Tuple									=    CpmAntModel(config=snake_case_    )
        model.to(snake_case_    )
        model.eval()

        A_    :			Union[str, Any]									=    model(**snake_case_    ).last_hidden_state

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

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , *snake_case_    ):

        """simple docstring"""

        A_    :			Dict									=    CpmAntForCausalLM(snake_case_    )
        model.to(snake_case_    )
        A_    :			str									=    input_ids['input_ids'].to(snake_case_    )
        model.eval()

        A_    :			int									=    model(**snake_case_    )
        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    ):

        """simple docstring"""

        A_		,			A_    :			Optional[Any]									=    self.prepare_config_and_inputs()
        return config, inputs_dict


@require_torch
class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					Any             =  (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
      lowercase_ :					int             =  (
          {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
      )

      lowercase_ :					Any             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Dict             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Union[str, Any]             =  False

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    CpmAntModelTester(self    )
        A_    :			Optional[int]									=    ConfigTester(self   , config_class=snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """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    ):

        """simple docstring"""

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

      def    lowerCamelCase_     (	self    ):

        """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    ):

        """simple docstring"""

        A_		,			A_    :			Optional[Any]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_cpmant_model(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			List[Any]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head_model(snake_case_   , snake_case_    )


@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    '今天天气真好！'
        A_    :			Tuple									=    'openbmb/cpm-ant-10b'
        A_    :			Tuple									=    CpmAntModel.from_pretrained(snake_case_    )
        A_    :			Dict									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			str									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Union[str, Any]									=    model(**snake_case_    ).last_hidden_state

        A_    :			Optional[int]									=    torch.tensor(
            [[[6.17_08, 5.92_44, 1.08_35], [6.52_07, 6.28_93, -11.33_24], [-1.01_07, -0.05_76, -5.95_77]]]   , )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]   , snake_case_   , atol=1E-2    )    )


@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    '今天天气真好！'
        A_    :			Union[str, Any]									=    'openbmb/cpm-ant-10b'
        A_    :			List[str]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Union[str, Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			int									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Tuple									=    model(**snake_case_    ).logits

        A_    :			Union[str, Any]									=    torch.tensor(
            [[[-6.42_67, -6.40_83, -6.39_58], [-5.88_02, -5.94_47, -5.78_11], [-5.38_96, -5.48_20, -5.42_95]]]   , )
        self.assertTrue(torch.allclose(hidden_states[:, :3, :3]   , snake_case_   , atol=1E-2    )    )

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    'openbmb/cpm-ant-10b'
        A_    :			Optional[int]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Optional[Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			List[str]									=    '今天天气不错，'
        A_    :			Dict									=    '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'
        A_    :			str									=    tokenizer(snake_case_   , return_tensors='pt'    )
        A_    :			Dict									=    model.generate(**snake_case_    )
        A_    :			Union[str, Any]									=    tokenizer.batch_decode(snake_case_    )
        self.assertEqual(snake_case_   , snake_case_    )

      @tooslow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Any									=    'openbmb/cpm-ant-10b'
        A_    :			List[Any]									=    CpmAntForCausalLM.from_pretrained(snake_case_    )
        A_    :			Optional[Any]									=    CpmAntTokenizer.from_pretrained(snake_case_    )
        A_    :			int									=    ['今天天气不错，', '新年快乐，万事如意！']
        A_    :			List[Any]									=    [
            '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',
        ]
        A_    :			Tuple									=    tokenizer(snake_case_   , return_tensors='pt'   , padding=snake_case_    )
        A_    :			int									=    model.generate(**snake_case_    )
        A_    :			Union[str, Any]									=    tokenizer.batch_decode(snake_case_    )
        self.assertEqual(snake_case_   , snake_case_    )