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

import datasets


_A   =     "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\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"

_A   =     "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

_A   =     "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> 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.\"]\n        >>> 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.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> 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.\"]\n        >>> 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.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> 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.\"]\n        >>> 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.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n"





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




				def      __a						(					self     )				->      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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase = CHRF.CHAR_ORDER				,				_UpperCamelCase = CHRF.WORD_ORDER				,				_UpperCamelCase = CHRF.BETA				,				_UpperCamelCase = False				,				_UpperCamelCase = False				,				_UpperCamelCase = False				,				)				->      int:
							lowerCAmelCase_						=	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"     )
							lowerCAmelCase_						=	[[refs[i] for refs in references] for i in range(_lowerCamelCase     )]

							lowerCAmelCase_						=	CHRF(_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase				,				_lowerCamelCase     )
							lowerCAmelCase_						=	sb_chrf.corpus_score(_lowerCamelCase				,				_lowerCamelCase     )

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





"""simple docstring"""



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
 from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class       lowerCAmelCase__				:







     def __init__(							self			:			Dict    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Optional[int]=3    ,      _lowerCamelCase			:			List[str]=32    ,      _lowerCamelCase			:			Optional[int]=3    ,      _lowerCamelCase			:			Dict=10    ,      _lowerCamelCase			:			Tuple=[10, 20, 30, 40]    ,      _lowerCamelCase			:			int=[1, 1, 2, 1]    ,      _lowerCamelCase			:			int=True    ,      _lowerCamelCase			:			Optional[int]=True    ,      _lowerCamelCase			:			Optional[int]="relu"    ,      _lowerCamelCase			:			List[Any]=3    ,      _lowerCamelCase			:			Dict=None    ,      ):
       _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(_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[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      lowercase	(							self			:			Tuple			):
       return ResNetConfig(
           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    ,      image_size=self.image_size    ,      )







     def      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			List[Any]			):
       _snake_case					     =       TFResNetModel(config=_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase			)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape    ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,      )







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Tuple			):
       _snake_case					     =       self.num_labels
       _snake_case					     =       TFResNetForImageClassification(_lowerCamelCase			)
       _snake_case					     =       model(_lowerCamelCase    ,      labels=_lowerCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)			)







     def      lowercase	(							self			:			Tuple			):
       _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_tf
class       lowerCAmelCase__				(						A_ ,	A_ ,	unittest.TestCase	):
     __a						=					(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __a						=					(
         {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False
     __a						=					False







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       TFResNetModelTester(self			)
       _snake_case					     =       ConfigTester(self    ,      config_class=_lowerCamelCase    ,      has_text_modality=_lowerCamelCase			)







     def      lowercase	(							self			:			Tuple			):
       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      lowercase	(							self			:			List[Any]			):
       return







     @unittest.skip(reason='''ResNet does not use inputs_embeds'''			)
     def      lowercase	(							self			:			Any			):
       pass







     @unittest.skip(reason='''ResNet does not support input and output embeddings'''			)
     def      lowercase	(							self			:			List[str]			):
       pass







     def      lowercase	(							self			:			int			):
       _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(_lowerCamelCase			)
         _snake_case					     =       inspect.signature(model.call			)
         # 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]    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       def check_hidden_states_output(_lowerCamelCase			:			int    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			str			):
         _snake_case					     =       model_class(_lowerCamelCase			)
         _snake_case					     =       model(**self._prepare_for_class(_lowerCamelCase    ,      _lowerCamelCase			)			)

         _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(_lowerCamelCase			)    ,      expected_num_stages + 1			)

         # ResNet'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					     =       ['''basic''', '''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(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)

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

           check_hidden_states_output(_lowerCamelCase    ,      _lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase			)







     @slow
     def      lowercase	(							self			:			List[str]			):
       for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case					     =       TFResNetModel.from_pretrained(_lowerCamelCase			)
         self.assertIsNotNone(_lowerCamelCase			)






def 					_UpperCAmelCase      ( )     ->					Union[str, Any]:
  _snake_case					     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
  return image

@require_tf
@require_vision
class       lowerCAmelCase__				(						unittest.TestCase	):







     @cached_property
     def      lowercase	(							self			:			Dict			):
       return (
           AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)
           if is_vision_available()
           else None
       )







     @slow
     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]			)

       _snake_case					     =       self.default_image_processor
       _snake_case					     =       prepare_img()
       _snake_case					     =       image_processor(images=_lowerCamelCase    ,      return_tensors='''tf'''			)

       # forward pass
       _snake_case					     =       model(**_lowerCamelCase			)

       # verify the logits
       _snake_case					     =       tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape    ,      _lowerCamelCase			)

       _snake_case					     =       tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]			)

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



