





"""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_    )


'''simple docstring'''

import sacrebleu as scb
from packaging import version

import datasets


lowercase__            =     "\\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

lowercase__            =     "\\nSacreBLEU provides hassle-free computation of shareable, comparable, and reproducible BLEU scores.\nInspired by Rico Sennrich's `multi-bleu-detok.perl`, it produces the official WMT scores but works with plain text.\nIt also knows all the standard test sets and handles downloading, processing, and tokenization for you.\n\nSee the [README.md] file at https://github.com/mjpost/sacreBLEU for more information.\n"

lowercase__            =     "\nProduces BLEU scores along with its sufficient statistics\nfrom a source against one or more references.\n\nArgs:\n    predictions (`list` of `str`): list of translations to score. Each translation should be tokenized into a list of tokens.\n    references (`list` of `list` of `str`): A list of lists of references. The contents of the first sub-list are the references for the first prediction, the contents of the second sub-list are for the second prediction, etc. Note that there must be the same number of references for each prediction (i.e. all sub-lists must be of the same length).\n    smooth_method (`str`): The smoothing method to use, defaults to `'exp'`. Possible values are:\n        - `'none'`: no smoothing\n        - `'floor'`: increment zero counts\n        - `'add-k'`: increment num/denom by k for n>1\n        - `'exp'`: exponential decay\n    smooth_value (`float`): The smoothing value. Only valid when `smooth_method='floor'` (in which case `smooth_value` defaults to `0.1`) or `smooth_method='add-k'` (in which case `smooth_value` defaults to `1`).\n    tokenize (`str`): Tokenization method to use for BLEU. If not provided, defaults to `'zh'` for Chinese, `'ja-mecab'` for Japanese and `'13a'` (mteval) otherwise. Possible values are:\n        - `'none'`: No tokenization.\n        - `'zh'`: Chinese tokenization.\n        - `'13a'`: mimics the `mteval-v13a` script from Moses.\n        - `'intl'`: International tokenization, mimics the `mteval-v14` script from Moses\n        - `'char'`: Language-agnostic character-level tokenization.\n        - `'ja-mecab'`: Japanese tokenization. Uses the [MeCab tokenizer](https://pypi.org/project/mecab-python3).\n    lowercase (`bool`): If `True`, lowercases the input, enabling case-insensitivity. Defaults to `False`.\n    force (`bool`): If `True`, insists that your tokenized input is actually detokenized. Defaults to `False`.\n    use_effective_order (`bool`): If `True`, stops including n-gram orders for which precision is 0. This should be `True`, if sentence-level BLEU will be computed. Defaults to `False`.\n\nReturns:\n    'score': BLEU score,\n    'counts': Counts,\n    'totals': Totals,\n    'precisions': Precisions,\n    'bp': Brevity penalty,\n    'sys_len': predictions length,\n    'ref_len': reference length,\n\nExamples:\n\n    Example 1:\n        >>> predictions = [\"hello there general kenobi\", \"foo bar foobar\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"], [\"foo bar foobar\", \"foo bar foobar\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions, references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        100.0\n\n    Example 2:\n        >>> predictions = [\"hello there general kenobi\",\n        ...                 \"on our way to ankh morpork\"]\n        >>> references = [[\"hello there general kenobi\", \"hello there !\"],\n        ...                 [\"goodbye ankh morpork\", \"ankh morpork\"]]\n        >>> sacrebleu = datasets.load_metric(\"sacrebleu\")\n        >>> results = sacrebleu.compute(predictions=predictions,\n        ...                             references=references)\n        >>> print(list(results.keys()))\n        ['score', 'counts', 'totals', 'precisions', 'bp', 'sys_len', 'ref_len']\n        >>> print(round(results[\"score\"], 1))\n        39.8\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,  _KWARGS_DESCRIPTION				)
class 			A_					(							datasets.Metric				):






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	Tuple			)		->      Union[str, Any]:
      if version.parse(scb.__version__			) < version.parse('1.4.12'			):
       raise ImportWarning(
           'To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'
           'You can install it with `pip install "sacrebleu>=1.4.12"`.'			)
      return datasets.MetricInfo(
          description=_DESCRIPTION	,     citation=_CITATION	,     homepage='https://github.com/mjpost/sacreBLEU'	,     inputs_description=_KWARGS_DESCRIPTION	,     features=datasets.Features(
              {
                  'predictions': datasets.Value('string'	,     id='sequence'			),
                  'references': datasets.Sequence(datasets.Value('string'	,     id='sequence'			)	,     id='references'			),
              }			)	,     codebase_urls=['https://github.com/mjpost/sacreBLEU']	,     reference_urls=[
              'https://github.com/mjpost/sacreBLEU',
              'https://en.wikipedia.org/wiki/BLEU',
              'https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213',
          ]	,     )




     def   UpperCAmelCase_			(						self					:	Union[str, Any]	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	int	,     lowercase_					:	List[Any]="exp"	,     lowercase_					:	Optional[int]=None	,     lowercase_					:	List[str]=False	,     lowercase_					:	Tuple=False	,     lowercase_					:	int=None	,     lowercase_					:	str=False	,     )		->      Dict:
      UpperCAmelCase			:      Union[str, Any]		   =					len(references[0]			)
      if any(len(lowercase_			) != references_per_prediction for refs in references			):
       raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
      UpperCAmelCase			:      Optional[Any]		   =					[[refs[i] for refs in references] for i in range(lowercase_			)]
      UpperCAmelCase			:      List[Any]		   =					scb.corpus_bleu(
          lowercase_	,     lowercase_	,     smooth_method=lowercase_	,     smooth_value=lowercase_	,     force=lowercase_	,     lowercase=lowercase_	,     use_effective_order=lowercase_	,     **({'tokenize': tokenize} if tokenize else {})	,     )
      UpperCAmelCase			:      Union[str, Any]		   =					{
          'score': output.score,
          'counts': output.counts,
          'totals': output.totals,
          'precisions': output.precisions,
          'bp': output.bp,
          'sys_len': output.sys_len,
          'ref_len': output.ref_len,
      }
      return output_dict




