"""simple docstring"""




import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
				import jax

				from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
				from PIL import Image

				from transformers import BeitImageProcessor
class 			UpperCamelCase__(	unittest.TestCase       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=1_00						,__UpperCAmelCase=13						,__UpperCAmelCase=30						,__UpperCAmelCase=2						,__UpperCAmelCase=3						,__UpperCAmelCase=True						,__UpperCAmelCase=True						,__UpperCAmelCase=32						,__UpperCAmelCase=5						,__UpperCAmelCase=4						,__UpperCAmelCase=37						,__UpperCAmelCase="gelu"						,__UpperCAmelCase=0.1						,__UpperCAmelCase=0.1						,__UpperCAmelCase=10						,__UpperCAmelCase=0.0_2						,__UpperCAmelCase=3						,)    -> List[Any]:
													A__		   =							parent
													A__		   =							vocab_size
													A__		   =							batch_size
													A__		   =							image_size
													A__		   =							patch_size
													A__		   =							num_channels
													A__		   =							is_training
													A__		   =							use_labels
													A__		   =							hidden_size
													A__		   =							num_hidden_layers
													A__		   =							num_attention_heads
													A__		   =							intermediate_size
													A__		   =							hidden_act
													A__		   =							hidden_dropout_prob
													A__		   =							attention_probs_dropout_prob
													A__		   =							type_sequence_label_size
													A__		   =							initializer_range

													# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
													A__		   =							(image_size // patch_size) ** 2
													A__		   =							num_patches + 1





						def  snake_case__		(				self   )    -> Dict:
													A__		   =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

													A__		   =							None
													if self.use_labels:
																				A__		   =							ids_tensor([self.batch_size]						,self.type_sequence_label_size   )

													A__		   =							BeitConfig(
													    vocab_size=self.vocab_size						,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=__UpperCAmelCase						,initializer_range=self.initializer_range						,)

													return config, pixel_values, labels





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Any:
													A__		   =							FlaxBeitModel(config=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.last_hidden_state.shape						,(self.batch_size, self.seq_length, self.hidden_size)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							FlaxBeitForMaskedImageModeling(config=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.seq_length - 1, self.vocab_size)   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )    -> Tuple:
													A__		   =							self.type_sequence_label_size
													A__		   =							FlaxBeitForImageClassification(config=__UpperCAmelCase   )
													A__		   =							model(__UpperCAmelCase   )
													self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.type_sequence_label_size)   )

													# test greyscale images
													A__		   =							1
													A__		   =							FlaxBeitForImageClassification(__UpperCAmelCase   )

													A__		   =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
													A__		   =							model(__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							self.prepare_config_and_inputs()
													(
													    (
													    A__
													)   ,     (
													    A__
													)   ,     (
													    A__
													)   ,     
													)		   =							config_and_inputs
													A__		   =							{'pixel_values': pixel_values}
													return config, inputs_dict






@require_flax
class 			UpperCamelCase__(	__A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  List[Any]  							=						(
						    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
						)





						def  snake_case__		(				self   )    -> None:
													A__		   =							FlaxBeitModelTester(self   )
													A__		   =							ConfigTester(self						,config_class=__UpperCAmelCase						,has_text_modality=__UpperCAmelCase						,hidden_size=37   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													self.config_tester.run_common_tests()





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

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

																				A__		   =							['pixel_values']
																				self.assertListEqual(arg_names[:1]						,__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__   ,     A__		   =							self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				with self.subTest(model_class.__name__   ):
																											A__		   =							self._prepare_for_class(__UpperCAmelCase						,__UpperCAmelCase   )
																											A__		   =							model_class(__UpperCAmelCase   )

																											@jax.jit
																											def model_jitted(__UpperCAmelCase						,**__UpperCAmelCase   ):
																																		return model(pixel_values=__UpperCAmelCase						,**__UpperCAmelCase   )

																											with self.subTest('JIT Enabled'   ):
																																		A__		   =							model_jitted(**__UpperCAmelCase   ).to_tuple()

																											with self.subTest('JIT Disabled'   ):
																																		with jax.disable_jit():
																																									A__		   =							model_jitted(**__UpperCAmelCase   ).to_tuple()

																											self.assertEqual(len(__UpperCAmelCase   )						,len(__UpperCAmelCase   )   )
																											for jitted_output, output in zip(__UpperCAmelCase						,__UpperCAmelCase   ):
																																		self.assertEqual(jitted_output.shape						,output.shape   )





						def  snake_case__		(				self   )    -> str:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Union[str, Any]:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_lm(*__UpperCAmelCase   )





						def  snake_case__		(				self   )    -> Any:
													A__		   =							self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase   )





						@slow
						def  snake_case__		(				self   )    -> str:
													for model_class_name in self.all_model_classes:
																				A__		   =							model_class_name.from_pretrained('microsoft/beit-base-patch16-224'   )
																				A__		   =							model(np.ones((1, 3, 2_24, 2_24)   )   )
																				self.assertIsNotNone(__UpperCAmelCase   )






def      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
							return image


@require_vision
@require_flax
class 			UpperCamelCase__(	unittest.TestCase       ):





						@cached_property
						def  snake_case__		(				self   )    -> str:
													return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'   ) if is_vision_available() else None





						@slow
						def  snake_case__		(				self   )    -> str:
													A__		   =							FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'   )

													A__		   =							self.default_image_processor
													A__		   =							prepare_img()
													A__		   =							image_processor(images=__UpperCAmelCase						,return_tensors='np'   ).pixel_values

													# prepare bool_masked_pos
													A__		   =							np.ones((1, 1_96)						,dtype=__UpperCAmelCase   )

													# forward pass
													A__		   =							model(pixel_values=__UpperCAmelCase						,bool_masked_pos=__UpperCAmelCase   )
													A__		   =							outputs.logits

													# verify the logits
													A__		   =							(1, 1_96, 81_92)
													self.assertEqual(logits.shape						,__UpperCAmelCase   )

													A__		   =							np.array(
													    [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]   )

													self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]						,__UpperCAmelCase						,atol=1e-2   )   )





						@slow
						def  snake_case__		(				self   )    -> List[str]:
													A__		   =							FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'   )

													A__		   =							self.default_image_processor
													A__		   =							prepare_img()
													A__		   =							image_processor(images=__UpperCAmelCase						,return_tensors='np'   )

													# forward pass
													A__		   =							model(**__UpperCAmelCase   )
													A__		   =							outputs.logits

													# verify the logits
													A__		   =							(1, 10_00)
													self.assertEqual(logits.shape						,__UpperCAmelCase   )

													A__		   =							np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]   )

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

													A__		   =							2_81
													self.assertEqual(logits.argmax(-1   ).item()						,__UpperCAmelCase   )





						@slow
						def  snake_case__		(				self   )    -> Any:
													A__		   =							FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'   )

													A__		   =							self.default_image_processor
													A__		   =							prepare_img()
													A__		   =							image_processor(images=__UpperCAmelCase						,return_tensors='np'   )

													# forward pass
													A__		   =							model(**__UpperCAmelCase   )
													A__		   =							outputs.logits

													# verify the logits
													A__		   =							(1, 2_18_41)
													self.assertEqual(logits.shape						,__UpperCAmelCase   )

													A__		   =							np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]   )

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

													A__		   =							23_96
													self.assertEqual(logits.argmax(-1   ).item()						,__UpperCAmelCase   )






"""simple docstring"""




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

import datasets


__lowerCamelCase							 =     "\\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"

__lowerCamelCase							 =     "\\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"

__lowerCamelCase							 =     "\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 			UpperCamelCase__(	datasets.Metric       ):





						def  snake_case__		(				self   )    -> Tuple:
													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  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = CHRF.CHAR_ORDER						,__UpperCAmelCase = CHRF.WORD_ORDER						,__UpperCAmelCase = CHRF.BETA						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,)    -> Union[str, Any]:
													A__		   =							len(references[0]   )
													if any(len(__UpperCAmelCase   ) != references_per_prediction for refs in references   ):
																				raise ValueError('Sacrebleu requires the same number of references for each prediction'   )
													A__		   =							[[refs[i] for refs in references] for i in range(__UpperCAmelCase   )]

													A__		   =							CHRF(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase   )
													A__		   =							sb_chrf.corpus_score(__UpperCAmelCase						,__UpperCAmelCase   )

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





