


from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor





class   __a      :
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=13    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=32    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=4    ,			lowerCAmelCase__=37    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=10    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=None    ,			) ->    int:




        '''simple docstring'''
        lowercase__:       str        =       parent
        lowercase__:       Optional[Any]        =       batch_size
        lowercase__:       List[Any]        =       image_size
        lowercase__:       Tuple        =       patch_size
        lowercase__:       str        =       num_channels
        lowercase__:       List[Any]        =       is_training
        lowercase__:       List[str]        =       use_labels
        lowercase__:       Union[str, Any]        =       hidden_size
        lowercase__:       List[str]        =       num_hidden_layers
        lowercase__:       Optional[Any]        =       num_attention_heads
        lowercase__:       int        =       intermediate_size
        lowercase__:       List[str]        =       hidden_act
        lowercase__:       Optional[int]        =       hidden_dropout_prob
        lowercase__:       Union[str, Any]        =       attention_probs_dropout_prob
        lowercase__:       List[str]        =       type_sequence_label_size
        lowercase__:       Union[str, Any]        =       initializer_range
        lowercase__:       int        =       scope

        # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        lowercase__:       List[str]        =       (image_size // patch_size) ** 2
        lowercase__:       List[str]        =       num_patches + 1
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase__:       List[Any]        =       None
        if self.use_labels:
               lowercase__:       Optional[int]        =       ids_tensor([self.batch_size]    ,			self.type_sequence_label_size			)

        lowercase__:       List[Any]        =       self.get_config()

        return config, pixel_values, labels
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        return ViTConfig(
            image_size=self.image_size    ,			patch_size=self.patch_size    ,			num_channels=self.num_channels    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			is_decoder=lowerCAmelCase__    ,			initializer_range=self.initializer_range    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       TFViTModel(config=lowerCAmelCase__			)
        lowercase__:       Union[str, Any]        =       model(lowerCAmelCase__    ,			training=lowerCAmelCase__			)
        self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size)			)

        # Test with an image with different size than the one specified in config.
        lowercase__:       Optional[Any]        =       self.image_size // 2
        lowercase__:       Dict        =       pixel_values[:, :, :image_size, :image_size]
        lowercase__:       Any        =       model(lowerCAmelCase__    ,			interpolate_pos_encoding=lowerCAmelCase__    ,			training=lowerCAmelCase__			)
        lowercase__:       Optional[int]        =       (image_size // self.patch_size) ** 2 + 1
        self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, seq_length, self.hidden_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       self.type_sequence_label_size
        lowercase__:       Dict        =       TFViTForImageClassification(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       model(lowerCAmelCase__    ,			labels=lowerCAmelCase__    ,			training=lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)

        # Test with an image with different size than the one specified in config.
        lowercase__:       Optional[Any]        =       self.image_size // 2
        lowercase__:       Optional[int]        =       pixel_values[:, :, :image_size, :image_size]
        lowercase__:       str        =       model(lowerCAmelCase__    ,			interpolate_pos_encoding=lowerCAmelCase__    ,			training=lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase__:       List[str]        =       1
        lowercase__:       Any        =       TFViTForImageClassification(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase__:       Optional[int]        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Dict        =       self.prepare_config_and_inputs()
        lowercase__      ,						lowercase__      ,						lowercase__:       Union[str, Any]        =       config_and_inputs
        lowercase__:       Dict        =       {'pixel_values': pixel_values}
        return config, inputs_dict





@require_tf
class   __a      (   __UpperCamelCase	,	__UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Optional[Any]       					=			(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
 __lowercase			:							List[str]       					=			(
     {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
     if is_tf_available()
     else {}
 )

 __lowercase			:							Tuple       					=			False
 __lowercase			:							List[Any]       					=			False
 __lowercase			:							int       					=			False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Optional[Any]        =       TFViTModelTester(self			)
        lowercase__:       Dict        =       ConfigTester(self    ,			config_class=lowerCAmelCase__    ,			has_text_modality=lowerCAmelCase__    ,			hidden_size=37			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        self.config_tester.run_common_tests()
 @unittest.skip(reason='ViT does not use inputs_embeds'			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        pass
 @unittest.skip(reason='ViT does not use inputs_embeds'			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       Any        =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase__:       List[Any]        =       model_class(lowerCAmelCase__			)
               self.assertIsInstance(model.get_input_embeddings()    ,			(tf.keras.layers.Layer)			)
               lowercase__:       Any        =       model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase__    ,			tf.keras.layers.Layer			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       Dict        =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase__:       str        =       model_class(lowerCAmelCase__			)
               lowercase__:       Optional[Any]        =       inspect.signature(model.call			)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase__:       Any        =       [*signature.parameters.keys()]

               lowercase__:       int        =       ['pixel_values']
               self.assertListEqual(arg_names[:1]    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       Optional[int]        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       int        =       TFViTModel.from_pretrained('google/vit-base-patch16-224'			)
        self.assertIsNotNone(lowerCAmelCase__			)





def        snake_case_					(  )  ->    List[str]:
       lowercase__:       List[Any]        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image





@require_tf
@require_vision
class   __a      (   unittest.TestCase				):
 @cached_property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'			) if is_vision_available() else None
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        lowercase__:       str        =       TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'			)

        lowercase__:       Tuple        =       self.default_image_processor
        lowercase__:       List[Any]        =       prepare_img()
        lowercase__:       List[str]        =       image_processor(images=lowerCAmelCase__    ,			return_tensors='tf'			)

        # forward pass
        lowercase__:       str        =       model(**lowerCAmelCase__			)

        # verify the logits
        lowercase__:       Optional[int]        =       tf.TensorShape((1, 1_000)			)
        self.assertEqual(outputs.logits.shape    ,			lowerCAmelCase__			)

        lowercase__:       Optional[Any]        =       tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]			)

        tf.debugging.assert_near(outputs.logits[0, :3]    ,			lowerCAmelCase__    ,			atol=1E-4			)






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   __a      (   datasets.Metric				):
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    str:




        '''simple docstring'''
        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    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__ = CHRF.CHAR_ORDER    ,			lowerCAmelCase__ = CHRF.WORD_ORDER    ,			lowerCAmelCase__ = CHRF.BETA    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			lowerCAmelCase__ = False    ,			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       str        =       len(references[0]			)
        if any(len(lowerCAmelCase__			) != references_per_prediction for refs in references			):
               raise ValueError('Sacrebleu requires the same number of references for each prediction'			)
        lowercase__:       List[str]        =       [[refs[i] for refs in references] for i in range(lowerCAmelCase__			)]

        lowercase__:       Union[str, Any]        =       CHRF(lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			)
        lowercase__:       str        =       sb_chrf.corpus_score(lowerCAmelCase__    ,			lowerCAmelCase__			)

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


