
"""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					:
    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_=99    ,     snake_case_=32    ,     snake_case_=3    ,     snake_case_=4    ,     snake_case_=37    ,     snake_case_=32    ,     snake_case_=1_28    ,     snake_case_=8    ,     snake_case_=8    ,     snake_case_=8    ,     snake_case_=1.0    ,     snake_case_=True    ,     ):
      _snake_case  :    Optional[Any]            = parent
      _snake_case  :    str            = batch_size
      _snake_case  :    Union[str, Any]            = seq_length
      _snake_case  :    Optional[int]            = is_training
      _snake_case  :    Optional[Any]            = use_token_type_ids
      _snake_case  :    List[str]            = use_input_mask
      _snake_case  :    Any            = use_labels
      _snake_case  :    Tuple            = use_mc_token_ids
      _snake_case  :    Dict            = vocab_size
      _snake_case  :    List[str]            = hidden_size
      _snake_case  :    Any            = num_hidden_layers
      _snake_case  :    int            = num_attention_heads
      _snake_case  :    List[str]            = intermediate_size
      _snake_case  :    Union[str, Any]            = num_buckets
      _snake_case  :    List[str]            = max_distance
      _snake_case  :    Any            = prompt_length
      _snake_case  :    Dict            = prompt_types
      _snake_case  :    str            = segment_types
      _snake_case  :    List[str]            = init_std
      _snake_case  :    Tuple            = return_dict
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = {}
      _snake_case  :    List[Any]            = ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size  ).type(torch.intaa  )
      _snake_case  :    Union[str, Any]            = False

      _snake_case  :    Union[str, Any]            = self.get_config()

      return (config, input_ids)
    def        lowerCamelCase__						(     self  ):
      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_  ):
      _snake_case  :    Union[str, Any]            = CpmAntModel(config=snake_case_  )
      model.to(snake_case_  )
      model.eval()

      _snake_case  :    List[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_  ):
      _snake_case  :    List[Any]            = CpmAntForCausalLM(snake_case_  )
      model.to(snake_case_  )
      _snake_case  :    Union[str, Any]            = input_ids["input_ids"].to(snake_case_  )
      model.eval()

      _snake_case  :    List[str]            = 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  ):
      _snake_case      ,       _snake_case  :    Any            = self.prepare_config_and_inputs()
      return config, inputs_dict






@require_torch
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			List[str]	=       (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
    __lowercase				:			Optional[int]	=       (
        {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
    )

    __lowercase				:			Union[str, Any]	=       False
    __lowercase				:			int	=       False
    __lowercase				:			List[str]	=       False
    __lowercase				:			List[Any]	=       False
    __lowercase				:			Optional[Any]	=       False
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = CpmAntModelTester(self  )
      _snake_case  :    List[str]            = ConfigTester(self    ,     config_class=snake_case_  )
    def        lowerCamelCase__						(     self  ):
      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  ):
      unittest.skip("CPMAnt doesn't support input_embeds."  )(self.test_inputs_embeds  )
    def        lowerCamelCase__						(     self  ):
      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  ):
      _snake_case      ,       _snake_case  :    Optional[Any]            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_cpmant_model(snake_case_    ,     snake_case_  )


    def        lowerCamelCase__						(     self  ):
      _snake_case      ,       _snake_case  :    Optional[int]            = 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):
    @tooslow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = "今天天气真好！"
      _snake_case  :    str            = "openbmb/cpm-ant-10b"
      _snake_case  :    str            = CpmAntModel.from_pretrained(snake_case_  )
      _snake_case  :    List[Any]            = CpmAntTokenizer.from_pretrained(snake_case_  )
      _snake_case  :    Dict            = tokenizer(snake_case_    ,     return_tensors="pt"  )
      _snake_case  :    List[str]            = model(**snake_case_  ).last_hidden_state

      _snake_case  :    Tuple            = torch.tensor(
          [[[6.1708, 5.9244, 1.0835], [6.5207, 6.2893, -11.3324], [-1.0107, -0.0576, -5.9577]]]    ,     )
      self.assertTrue(torch.allclose(hidden_states[:, :3, :3]    ,     snake_case_    ,     atol=1E-2  )  )






@require_torch
class   _UpperCAmelCase					(       unittest.TestCase):
    @tooslow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = "今天天气真好！"
      _snake_case  :    Union[str, Any]            = "openbmb/cpm-ant-10b"
      _snake_case  :    int            = CpmAntForCausalLM.from_pretrained(snake_case_  )
      _snake_case  :    List[str]            = CpmAntTokenizer.from_pretrained(snake_case_  )
      _snake_case  :    Union[str, Any]            = tokenizer(snake_case_    ,     return_tensors="pt"  )
      _snake_case  :    List[str]            = model(**snake_case_  ).logits

      _snake_case  :    List[Any]            = torch.tensor(
          [[[-6.4267, -6.4083, -6.3958], [-5.8802, -5.9447, -5.7811], [-5.3896, -5.4820, -5.4295]]]    ,     )
      self.assertTrue(torch.allclose(hidden_states[:, :3, :3]    ,     snake_case_    ,     atol=1E-2  )  )
    @tooslow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = "openbmb/cpm-ant-10b"
      _snake_case  :    str            = CpmAntForCausalLM.from_pretrained(snake_case_  )
      _snake_case  :    List[str]            = CpmAntTokenizer.from_pretrained(snake_case_  )
      _snake_case  :    Optional[Any]            = "今天天气不错，"
      _snake_case  :    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"
      _snake_case  :    List[str]            = tokenizer(snake_case_    ,     return_tensors="pt"  )
      _snake_case  :    List[str]            = model.generate(**snake_case_  )
      _snake_case  :    str            = tokenizer.batch_decode(snake_case_  )
      self.assertEqual(snake_case_    ,     snake_case_  )


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



"""simple docstring"""
import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


_a   :      List[Any]       =				-1 if torch_device == """cpu""" else 0






@is_pipeline_test
class   _UpperCAmelCase					(       unittest.TestCase):
    __lowercase				:			List[str]	=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    __lowercase				:			str	=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    int            = SummarizationPipeline(model=snake_case_    ,     tokenizer=snake_case_  )
      return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    str            = summarizer.model

      _snake_case  :    Optional[int]            = summarizer("(CNN)The Palestinian Authority officially became"  )
      self.assertEqual(snake_case_    ,     [{"summary_text": ANY(snake_case_  )}]  )

      _snake_case  :    List[str]            = summarizer(
          "(CNN)The Palestinian Authority officially became "    ,     num_beams=2    ,     min_length=2    ,     max_length=5    ,     )
      self.assertEqual(snake_case_    ,     [{"summary_text": ANY(snake_case_  )}]  )

      # Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
      _snake_case  :    Tuple            = [
          "SwitchTransformersConfig",
          "T5Config",
          "LongT5Config",
          "LEDConfig",
          "PegasusXConfig",
          "FSMTConfig",
          "M2M100Config",
          "ProphetNetConfig",  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
      ]
      if model.config.__class__.__name__ not in model_can_handle_longer_seq:
        # Too long and exception is expected.
        # For TF models, if the weights are initialized in GPU context, we won't get expected index error from
        # the embedding layer.
        if not (
            isinstance(snake_case_    ,     snake_case_  )
            and get_gpu_count() > 0
            and len(summarizer.model.trainable_weights  ) > 0
        ):
          with self.assertRaises(snake_case_  ):
            _snake_case  :    Tuple            = summarizer("This " * 10_00  )
      _snake_case  :    Any            = summarizer("This " * 10_00    ,     truncation=TruncationStrategy.ONLY_FIRST  )
    @require_torch
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = pipeline(task="summarization"    ,     model="sshleifer/tiny-mbart"    ,     framework="pt"  )
      _snake_case  :    List[Any]            = summarizer("This is a small test"  )
      self.assertEqual(
          snake_case_    ,     [
              {
                  "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
              }
          ]    ,     )
    @require_tf
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = pipeline(task="summarization"    ,     model="sshleifer/tiny-mbart"    ,     framework="tf"  )
      _snake_case  :    Union[str, Any]            = summarizer("This is a small test"  )
      self.assertEqual(
          snake_case_    ,     [
              {
                  "summary_text": "เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป"
              }
          ]    ,     )


    @require_torch
    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = pipeline(task="summarization"    ,     device=snake_case_  )
      _snake_case  :    Any            = (
          " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on"
          " Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The"
          " formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based."
          " The Palestinians signed the ICC's founding Rome Statute in January, when they also accepted its"
          " jurisdiction over alleged crimes committed \"in the occupied Palestinian territory, including East"
          " Jerusalem, since June 13, 2014.\" Later that month, the ICC opened a preliminary examination into the"
          " situation in Palestinian territories, paving the way for possible war crimes investigations against"
          " Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and"
          " the United States, neither of which is an ICC member, opposed the Palestinians' efforts to join the"
          " body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday's ceremony, said it was a"
          " move toward greater justice. \"As Palestine formally becomes a State Party to the Rome Statute today, the"
          " world is also a step closer to ending a long era of impunity and injustice,\" he said, according to an"
          " ICC news release. \"Indeed, today brings us closer to our shared goals of justice and peace.\" Judge"
          " Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the"
          " Palestinians. \"As the Rome Statute today enters into force for the State of Palestine, Palestine"
          " acquires all the rights as well as responsibilities that come with being a State Party to the Statute."
          " These are substantive commitments, which cannot be taken lightly,\" she said. Rights group Human Rights"
          " Watch welcomed the development. \"Governments seeking to penalize Palestine for joining the ICC should"
          " immediately end their pressure, and countries that support universal acceptance of the court's treaty"
          " should speak out to welcome its membership,\" said Balkees Jarrah, international justice counsel for the"
          " group. \"What's objectionable is the attempts to undermine international justice, not Palestine's"
          " decision to join a treaty to which over 100 countries around the world are members.\" In January, when"
          " the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an"
          " outrage, saying the court was overstepping its boundaries. The United States also said it \"strongly\""
          " disagreed with the court's decision. \"As we have said repeatedly, we do not believe that Palestine is a"
          " state and therefore we do not believe that it is eligible to join the ICC,\" the State Department said in"
          " a statement. It urged the warring sides to resolve their differences through direct negotiations. \"We"
          " will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,\""
          " it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the"
          " territories as \"Palestine.\" While a preliminary examination is not a formal investigation, it allows the"
          " court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou"
          " Bensouda said her office would \"conduct its analysis in full independence and impartiality.\" The war"
          " between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry"
          " will include alleged war crimes committed since June. The International Criminal Court was set up in"
          " 2002 to prosecute genocide, crimes against humanity and war crimes. CNN's Vasco Cotovio, Kareem Khadder"
          " and Faith Karimi contributed to this report."
      )
      _snake_case  :    int            = (
          " The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives"
          " the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States"
          " opposed the Palestinians' efforts to join the court . Rights group Human Rights Watch welcomes the move,"
          " says governments seeking to penalize Palestine should end pressure ."
      )
      _snake_case  :    Union[str, Any]            = summarizer(snake_case_  )
      self.assertEqual(result[0]["summary_text"]    ,     snake_case_  )

