




'''simple docstring'''



import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
      import torch
      from torch import nn

      from transformers import (
          MODEL_MAPPING,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
      )
      from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      import PIL
      from PIL import Image

      from transformers import BeitImageProcessor






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A,							A=100,							A=13,							A=30,							A=2,							A=3,							A=True,							A=True,							A=32,							A=4,							A=4,							A=37,							A="gelu",							A=0.1,							A=0.1,							A=10,							A=0.02,							A=3,							A=None,							A=[0, 1, 2, 3],							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						parent
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						100
             SCREAMING_SNAKE_CASE   :      int    					=						batch_size
             SCREAMING_SNAKE_CASE   :      str    					=						image_size
             SCREAMING_SNAKE_CASE   :      List[str]    					=						patch_size
             SCREAMING_SNAKE_CASE   :      Dict    					=						num_channels
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						is_training
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						use_labels
             SCREAMING_SNAKE_CASE   :      List[str]    					=						hidden_size
             SCREAMING_SNAKE_CASE   :      Tuple    					=						num_hidden_layers
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						num_attention_heads
             SCREAMING_SNAKE_CASE   :      Any    					=						intermediate_size
             SCREAMING_SNAKE_CASE   :      str    					=						hidden_act
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						hidden_dropout_prob
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						attention_probs_dropout_prob
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						type_sequence_label_size
             SCREAMING_SNAKE_CASE   :      int    					=						initializer_range
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						scope
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						out_indices
             SCREAMING_SNAKE_CASE   :      Tuple    					=						num_labels

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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						None
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						None
             if self.use_labels:
                   SCREAMING_SNAKE_CASE   :      str    					=						ids_tensor([self.batch_size],							self.type_sequence_label_size  )
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						ids_tensor([self.batch_size, self.image_size, self.image_size],							self.num_labels  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.get_config()

             return config, pixel_values, labels, pixel_labels







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return 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=A,							initializer_range=self.initializer_range,							out_indices=self.out_indices,							)







       def        UpperCamelCase_							(      self,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						BeitModel(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      List[str]    					=						model(A  )
             self.parent.assertEqual(result.last_hidden_state.shape,							(self.batch_size, self.seq_length, self.hidden_size)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						BeitForMaskedImageModeling(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      str    					=						model(A  )
             self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.seq_length - 1, self.vocab_size)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.type_sequence_label_size
             SCREAMING_SNAKE_CASE   :      str    					=						BeitForImageClassification(A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model(A,							labels=A  )
             self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.type_sequence_label_size)  )

             # test greyscale images
             SCREAMING_SNAKE_CASE   :      Dict    					=						1
             SCREAMING_SNAKE_CASE   :      Any    					=						BeitForImageClassification(A  )
             model.to(A  )
             model.eval()

             SCREAMING_SNAKE_CASE   :      str    					=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
             SCREAMING_SNAKE_CASE   :      str    					=						model(A,							labels=A  )
             self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.type_sequence_label_size)  )







       def        UpperCamelCase_							(      self,							A,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.num_labels
             SCREAMING_SNAKE_CASE   :      List[str]    					=						BeitForSemanticSegmentation(A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						model(A  )
             self.parent.assertEqual(
                 result.logits.shape,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						model(A,							labels=A  )
             self.parent.assertEqual(
                 result.logits.shape,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.prepare_config_and_inputs()
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[str]    					=						config_and_inputs
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{'pixel_values': pixel_values}
             return config, inputs_dict






@require_torch
class 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				Tuple        =						(
           (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       A						:				Tuple        =						(
           {
               '''feature-extraction''': BeitModel,
               '''image-classification''': BeitForImageClassification,
               '''image-segmentation''': BeitForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       A						:				Optional[Any]        =						False
       A						:				Any        =						False
       A						:				List[str]        =						False







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						BeitModelTester(self  )
             SCREAMING_SNAKE_CASE   :      str    					=						ConfigTester(self,							config_class=A,							has_text_modality=A,							hidden_size=37  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             self.config_tester.run_common_tests()







       @unittest.skip(reason='BEiT does not use inputs_embeds'  )
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       @require_torch_multi_gpu
       @unittest.skip(reason='BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'  )
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						model_class(A  )
                   self.assertIsInstance(model.get_input_embeddings(),							(nn.Module)  )
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(A,							nn.Linear  )  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      str    					=						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE   :      int    					=						model_class(A  )
                   SCREAMING_SNAKE_CASE   :      Any    					=						inspect.signature(model.forward  )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						[*signature.parameters.keys()]

                   SCREAMING_SNAKE_CASE   :      Dict    					=						['pixel_values']
                   self.assertListEqual(arg_names[:1],							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if not self.model_tester.is_training:
                   return

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.model_tester.prepare_config_and_inputs_for_common()
             SCREAMING_SNAKE_CASE   :      List[str]    					=						True

             for model_class in self.all_model_classes:
                   # we don't test BeitForMaskedImageModeling
                   if model_class in [*get_values(A  ), BeitForMaskedImageModeling]:
                         continue

                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model_class(A  )
                   model.to(A  )
                   model.train()
                   SCREAMING_SNAKE_CASE   :      Any    					=						self._prepare_for_class(A,							A,							return_labels=A  )
                   SCREAMING_SNAKE_CASE   :      int    					=						model(**A  ).loss
                   loss.backward()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      str    					=						self.model_tester.prepare_config_and_inputs_for_common()
             if not self.model_tester.is_training:
                   return

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						False
             SCREAMING_SNAKE_CASE   :      str    					=						True

             for model_class in self.all_model_classes:
                   # we don't test BeitForMaskedImageModeling
                   if (
                       model_class in [*get_values(A  ), BeitForMaskedImageModeling]
                       or not model_class.supports_gradient_checkpointing
                   ):
                         continue

                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model_class(A  )
                   model.gradient_checkpointing_enable()
                   model.to(A  )
                   model.train()
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						self._prepare_for_class(A,							A,							return_labels=A  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						model(**A  ).loss
                   loss.backward()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[str]    					=						self.model_tester.prepare_config_and_inputs_for_common()

             SCREAMING_SNAKE_CASE   :      List[str]    					=						_config_zero_init(A  )
             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						model_class(config=A  )
                   for name, param in model.named_parameters():
                         # we skip lambda parameters as these require special initial values
                         # determined by config.layer_scale_init_value
                         if "lambda" in name:
                               continue
                         if param.requires_grad:
                               self.assertIn(
                                   ((param.data.mean() * 1E9).round() / 1E9).item(),							[0.0, 1.0],							msg=F"Parameter {name} of model {model_class} seems not properly initialized",							)







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   SCREAMING_SNAKE_CASE   :      str    					=						BeitModel.from_pretrained(A  )
                   self.assertIsNotNone(A  )




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[Any]    					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
      return image






@require_torch
@require_vision
class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       @cached_property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'  ) if is_vision_available() else None







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'  ).to(A  )

             SCREAMING_SNAKE_CASE   :      Any    					=						self.default_image_processor
             SCREAMING_SNAKE_CASE   :      Tuple    					=						prepare_img()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						image_processor(images=A,							return_tensors='pt'  ).pixel_values.to(A  )

             # prepare bool_masked_pos
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.ones((1, 196),							dtype=torch.bool  ).to(A  )

             # forward pass
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						model(pixel_values=A,							bool_masked_pos=A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						outputs.logits

             # verify the logits
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						torch.Size((1, 196, 8_192)  )
             self.assertEqual(logits.shape,							A  )

             SCREAMING_SNAKE_CASE   :      Any    					=						torch.tensor(
                 [[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]]  ).to(A  )

             self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3],							A,							atol=1E-2  )  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'  ).to(A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.default_image_processor
             SCREAMING_SNAKE_CASE   :      Any    					=						prepare_img()
             SCREAMING_SNAKE_CASE   :      List[str]    					=						image_processor(images=A,							return_tensors='pt'  ).to(A  )

             # forward pass
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model(**A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						outputs.logits

             # verify the logits
             SCREAMING_SNAKE_CASE   :      str    					=						torch.Size((1, 1_000)  )
             self.assertEqual(logits.shape,							A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						torch.tensor([-1.23_85, -1.09_87, -1.01_08]  ).to(A  )

             self.assertTrue(torch.allclose(logits[0, :3],							A,							atol=1E-4  )  )

             SCREAMING_SNAKE_CASE   :      int    					=						281
             self.assertEqual(logits.argmax(-1  ).item(),							A  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'  ).to(
                 A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.default_image_processor
             SCREAMING_SNAKE_CASE   :      str    					=						prepare_img()
             SCREAMING_SNAKE_CASE   :      str    					=						image_processor(images=A,							return_tensors='pt'  ).to(A  )

             # forward pass
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      Any    					=						model(**A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						outputs.logits

             # verify the logits
             SCREAMING_SNAKE_CASE   :      Tuple    					=						torch.Size((1, 21_841)  )
             self.assertEqual(logits.shape,							A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						torch.tensor([1.68_81, -0.27_87, 0.59_01]  ).to(A  )

             self.assertTrue(torch.allclose(logits[0, :3],							A,							atol=1E-4  )  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						2_396
             self.assertEqual(logits.argmax(-1  ).item(),							A  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						model.to(A  )

             SCREAMING_SNAKE_CASE   :      int    					=						BeitImageProcessor(do_resize=A,							size=640,							do_center_crop=A  )

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						load_dataset('hf-internal-testing/fixtures_ade20k',							split='test'  )
             SCREAMING_SNAKE_CASE   :      str    					=						Image.open(ds[0]['file']  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						image_processor(images=A,							return_tensors='pt'  ).to(A  )

             # forward pass
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						model(**A  )
             SCREAMING_SNAKE_CASE   :      str    					=						outputs.logits

             # verify the logits
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.Size((1, 150, 160, 160)  )
             self.assertEqual(logits.shape,							A  )

             SCREAMING_SNAKE_CASE   :      str    					=						version.parse(PIL.__version__  ) < version.parse('9.0.0'  )

             if is_pillow_less_than_a:
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						torch.tensor(
                       [
                           [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
                           [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
                           [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
                       ],							device=A,							)
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						torch.tensor(
                       [
                           [[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
                           [[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
                           [[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
                       ],							device=A,							)

             self.assertTrue(torch.allclose(logits[0, :3, :3, :3],							A,							atol=1E-4  )  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						model.to(A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						BeitImageProcessor(do_resize=A,							size=640,							do_center_crop=A  )

             SCREAMING_SNAKE_CASE   :      Any    					=						load_dataset('hf-internal-testing/fixtures_ade20k',							split='test'  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Image.open(ds[0]['file']  )
             SCREAMING_SNAKE_CASE   :      Any    					=						image_processor(images=A,							return_tensors='pt'  ).to(A  )

             # forward pass
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      Any    					=						model(**A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						outputs.logits.detach().cpu()

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						image_processor.post_process_semantic_segmentation(outputs=A,							target_sizes=[(500, 300)]  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						torch.Size((500, 300)  )
             self.assertEqual(segmentation[0].shape,							A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						image_processor.post_process_semantic_segmentation(outputs=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						torch.Size((160, 160)  )
             self.assertEqual(segmentation[0].shape,							A  )










'''simple docstring'''



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

import datasets


__UpperCamelCase   :							List[str]         =   """\
@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\",
}
"""

__UpperCamelCase   :							Union[str, Any]         =   """\
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.
"""

__UpperCamelCase   :							str         =   """
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 				__SCREAMING_SNAKE_CASE							(				datasets.Metric  ):




      def 			__UpperCamelCase				(	self   )				->Union[str, Any]:



         '''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 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase = CHRF.CHAR_ORDER			, lowerCamelCase = CHRF.WORD_ORDER			, lowerCamelCase = CHRF.BETA			, lowerCamelCase = False			, lowerCamelCase = False			, lowerCamelCase = False			, )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     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'   )
         __a					  =     [[refs[i] for refs in references] for i in range(lowerCamelCase   )]

         __a					  =     CHRF(lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )
         __a					  =     sb_chrf.corpus_score(lowerCamelCase			, lowerCamelCase   )

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