

'''simple docstring'''






import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


lowerCAmelCase__   		=      '''\
@inproceedings{popovic-2015-chrf,
    title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Tenth Workshop on Statistical Machine Translation",
    month = sep,
    year = "2015",
    address = "Lisbon, Portugal",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W15-3049",
    doi = "10.18653/v1/W15-3049",
    pages = "392--395",
}
@inproceedings{popovic-2017-chrf,
    title = "chr{F}++: words helping character n-grams",
    author = "Popovi{\'c}, Maja",
    booktitle = "Proceedings of the Second Conference on Machine Translation",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4770",
    doi = "10.18653/v1/W17-4770",
    pages = "612--618",
}
@inproceedings{post-2018-call,
    title = "A Call for Clarity in Reporting {BLEU} Scores",
    author = "Post, Matt",
    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",
    month = oct,
    year = "2018",
    address = "Belgium, Brussels",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/W18-6319",
    pages = "186--191",
}
'''

lowerCAmelCase__   		=      '''\
ChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,
and ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation
that is already present in sacrebleu.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.
'''

lowerCAmelCase__   		=      '''
Produces ChrF(++) scores for hypotheses given reference translations.

Args:
    predictions (list of str): The predicted sentences.
    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.
    char_order (int): Character n-gram order. Defaults to `6`.
    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.
    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.
    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.
    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.
    eps_smoothing (bool): If `True`, applies epsilon smoothing similar
    to reference chrF++.py, NLTK and Moses implementations. If `False`,
    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.

Returns:
    \'score\' (float): The chrF (chrF++) score,
    \'char_order\' (int): The character n-gram order,
    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,
    \'beta\' (int): Determine the importance of recall w.r.t precision

Examples:
    Example 1--a simple example of calculating chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction, references=reference)
        >>> print(results)
        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}

    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2)
        >>> print(results)
        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}

    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:
        >>> prediction = ["The relationship between cats and dogs is not exactly friendly.", "a good bookshop is just a genteel black hole that knows how to read."]
        >>> reference = [["The relationship between dogs and cats is not exactly friendly."], ["A good bookshop is just a genteel Black Hole that knows how to read."]]
        >>> chrf = datasets.load_metric("chrf")
        >>> results = chrf.compute(predictions=prediction,
        ...                         references=reference,
        ...                         word_order=2,
        ...                         lowercase=True)
        >>> print(results)
        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}
'''






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




  """simple docstring"""




  def    SCREAMING_SNAKE_CASE ( self  :	List[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#chrf--chrf'''   ,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#chrf--chrf''']   ,reference_urls=[
           '''https://github.com/m-popovic/chrF''',
       ]   ,)






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]   ,lowercase__  :	str   ,lowercase__  :	List[Any]   ,lowercase__  :	int = CHRF.CHAR_ORDER   ,lowercase__  :	int = CHRF.WORD_ORDER   ,lowercase__  :	int = CHRF.BETA   ,lowercase__  :	bool = False   ,lowercase__  :	bool = False   ,lowercase__  :	bool = False   ,):
   __lowercase				    =					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'''     )
   __lowercase				    =					[[refs[i] for refs in references] for i in range(lowercase__     )]

   __lowercase				    =					CHRF(lowercase__   ,lowercase__   ,lowercase__   ,lowercase__   ,lowercase__   ,lowercase__     )
   __lowercase				    =					sb_chrf.corpus_score(lowercase__   ,lowercase__     )

   return {
       "score": output.score,
       "char_order": output.char_order,
       "word_order": output.word_order,
       "beta": output.beta,
   }





import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
   from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image
class 		__magic_name__						:





     '''simple docstring'''



     def __init__(					self:Optional[Any]     ,		_a:int     ,		_a:str=3     ,		_a:Optional[int]=32     ,		_a:Optional[Any]=3     ,		_a:Tuple=10     ,		_a:List[Any]=[8, 16, 32, 64]     ,		_a:str=[1, 1, 2, 1]     ,		_a:Any=True     ,		_a:List[Any]=True     ,		_a:List[str]="relu"     ,		_a:int=3     ,		_a:Tuple=None     ,		_a:Tuple=["stage2", "stage3", "stage4"]     ,		_a:List[Any]=[2, 3, 4]     ,		_a:Union[str, Any]=1     ,		):
       snake_case__						= parent
       snake_case__						= batch_size
       snake_case__						= image_size
       snake_case__						= num_channels
       snake_case__						= embeddings_size
       snake_case__						= hidden_sizes
       snake_case__						= depths
       snake_case__						= is_training
       snake_case__						= use_labels
       snake_case__						= hidden_act
       snake_case__						= num_labels
       snake_case__						= scope
       snake_case__						= len(_a   )
       snake_case__						= out_features
       snake_case__						= out_indices
       snake_case__						= num_groups

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

       snake_case__						= None
       if self.use_labels:
         snake_case__						= ids_tensor([self.batch_size]     ,		self.num_labels   )

       snake_case__						= self.get_config()

       return config, pixel_values, labels

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return BitConfig(
           num_channels=self.num_channels     ,		embeddings_size=self.embeddings_size     ,		hidden_sizes=self.hidden_sizes     ,		depths=self.depths     ,		hidden_act=self.hidden_act     ,		num_labels=self.num_labels     ,		out_features=self.out_features     ,		out_indices=self.out_indices     ,		num_groups=self.num_groups     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:Any     ,		_a:Optional[int]     ,		_a:Tuple     ,		_a:int   ):
       snake_case__						= BitModel(config=_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a   )
       self.parent.assertEqual(
           result.last_hidden_state.shape     ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		_a:Tuple     ,		_a:Any     ,		_a:Union[str, Any]   ):
       snake_case__						= self.num_labels
       snake_case__						= BitForImageClassification(_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a     ,		labels=_a   )
       self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:str     ,		_a:List[str]     ,		_a:Any   ):
       snake_case__						= BitBackbone(config=_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a   )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps   )     ,		len(config.out_features   )   )
       self.parent.assertListEqual(list(result.feature_maps[0].shape   )     ,		[self.batch_size, self.hidden_sizes[1], 4, 4]   )

       # verify channels
       self.parent.assertEqual(len(model.channels   )     ,		len(config.out_features   )   )
       self.parent.assertListEqual(model.channels     ,		config.hidden_sizes[1:]   )

       # verify backbone works with out_features=None
       snake_case__						= None
       snake_case__						= BitBackbone(config=_a   )
       model.to(_a   )
       model.eval()
       snake_case__						= model(_a   )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps   )     ,		1   )
       self.parent.assertListEqual(list(result.feature_maps[0].shape   )     ,		[self.batch_size, self.hidden_sizes[-1], 1, 1]   )

       # verify channels
       self.parent.assertEqual(len(model.channels   )     ,		1   )
       self.parent.assertListEqual(model.channels     ,		[config.hidden_sizes[-1]]   )







     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= self.prepare_config_and_inputs()
       snake_case__				,				snake_case__				,				snake_case__						= config_and_inputs
       snake_case__						= {'''pixel_values''': pixel_values}
       return config, inputs_dict




@require_torch
class 		__magic_name__						(snake_case_   ,snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Any	   =       (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
     __lowercase				:							int	   =       (
         {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
         if is_torch_available()
         else {}
     )

     __lowercase				:							Tuple	   =       False
     __lowercase				:							Optional[Any]	   =       False
     __lowercase				:							str	   =       False
     __lowercase				:							Tuple	   =       False
     __lowercase				:							Tuple	   =       False

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       snake_case__						= BitModelTester(self   )
       snake_case__						= ConfigTester(self     ,		config_class=_a     ,		has_text_modality=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       self.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.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       return

     @unittest.skip(reason='''Bit does not output attentions'''   )
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       pass

     @unittest.skip(reason='''Bit does not use inputs_embeds'''   )
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       pass

     @unittest.skip(reason='''Bit does not support input and output embeddings'''   )
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       pass

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__				,				snake_case__						= self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         snake_case__						= model_class(_a   )
         snake_case__						= inspect.signature(model.forward   )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         snake_case__						= [*signature.parameters.keys()]

         snake_case__						= ['''pixel_values''']
         self.assertListEqual(arg_names[:1]     ,		_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__				,				snake_case__						= self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         snake_case__						= model_class(config=_a   )
         for name, module in model.named_modules():
           if isinstance(_a     ,		(nn.BatchNormad, nn.GroupNorm)   ):
             self.assertTrue(
                 torch.all(module.weight == 1   )     ,		msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,		)
             self.assertTrue(
                 torch.all(module.bias == 0   )     ,		msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""     ,		)

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       def check_hidden_states_output(_a:List[Any]     ,		_a:int     ,		_a:Union[str, Any]   ):
         snake_case__						= model_class(_a   )
         model.to(_a   )
         model.eval()

         with torch.no_grad():
           snake_case__						= model(**self._prepare_for_class(_a     ,		_a   )   )

         snake_case__						= outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         snake_case__						= self.model_tester.num_stages
         self.assertEqual(len(_a   )     ,		expected_num_stages + 1   )

         # Bit's feature maps are of shape (batch_size, num_channels, height, width)
         self.assertListEqual(
             list(hidden_states[0].shape[-2:]   )     ,		[self.model_tester.image_size // 4, self.model_tester.image_size // 4]     ,		)

       snake_case__				,				snake_case__						= self.model_tester.prepare_config_and_inputs_for_common()
       snake_case__						= ['''preactivation''', '''bottleneck''']
       for model_class in self.all_model_classes:
         for layer_type in layers_type:
           snake_case__						= layer_type
           snake_case__						= True
           check_hidden_states_output(_a     ,		_a     ,		_a   )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           snake_case__						= True

           check_hidden_states_output(_a     ,		_a     ,		_a   )

     @unittest.skip(reason='''Bit does not use feedforward chunking'''   )
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       pass

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       snake_case__						= self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_a   )







     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[Any]   ):
       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case__						= BitModel.from_pretrained(_a   )
         self.assertIsNotNone(_a   )







def SCREAMING_SNAKE_CASE	(			)    ->				Any:
  snake_case__						= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
  return image
@require_torch
@require_vision
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     @cached_property
     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ) if is_vision_available() else None
       )







     @slow
     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(_a   )

       snake_case__						= self.default_image_processor
       snake_case__						= prepare_img()
       snake_case__						= image_processor(images=_a     ,		return_tensors='''pt'''   ).to(_a   )

       # forward pass
       with torch.no_grad():
         snake_case__						= model(**_a   )

       # verify the logits
       snake_case__						= torch.Size((1, 10_00)   )
       self.assertEqual(outputs.logits.shape     ,		_a   )

       snake_case__						= torch.tensor([[-0.6526, -0.5263, -1.4398]]   ).to(_a   )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,		_a     ,		atol=1e-4   )   )




@require_torch
class 		__magic_name__						(snake_case_   ,unittest.TestCase				):





     '''simple docstring'''



     __lowercase				:							Optional[Any]	   =       (BitBackbone,) if is_torch_available() else ()
     __lowercase				:							int	   =       BitConfig

     __lowercase				:							Any	   =       False

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= BitModelTester(self   )

