


"""simple docstring"""




import inspect
import unittest

from transformers import DataaVecVisionConfig
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,
     DataaVecVisionForImageClassification,
     DataaVecVisionForSemanticSegmentation,
     DataaVecVisionModel,
 )
 from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import BeitImageProcessor




class A  :






      def __init__( self						:					Dict      ,       lowercase_						:					int      ,       lowercase_						:					List[Any]=100      ,       lowercase_						:					Any=13      ,       lowercase_						:					str=30      ,       lowercase_						:					str=2      ,       lowercase_						:					Optional[Any]=3      ,       lowercase_						:					Optional[Any]=True      ,       lowercase_						:					Optional[int]=True      ,       lowercase_						:					Optional[Any]=32      ,       lowercase_						:					str=4      ,       lowercase_						:					Any=4      ,       lowercase_						:					Dict=37      ,       lowercase_						:					Union[str, Any]="gelu"      ,       lowercase_						:					Union[str, Any]=0.1      ,       lowercase_						:					List[Any]=0.1      ,       lowercase_						:					Optional[Any]=10      ,       lowercase_						:					Tuple=0.02      ,       lowercase_						:					int=3      ,       lowercase_						:					Union[str, Any]=None      ,       lowercase_						:					Tuple=[0, 1, 2, 3]      ,       ) ->						Any:
             """simple docstring"""




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

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






      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

             _lowerCamelCase	:		Dict          =self.get_config()

             return config, pixel_values, labels, pixel_labels






      def 	lowerCamelCase	( self						:					Any			) ->						Tuple:
             """simple docstring"""




             return DataaVecVisionConfig(
                 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      ,       out_indices=self.out_indices      ,       )






      def 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Any      ,       lowercase_						:					int      ,       lowercase_						:					Any      ,       lowercase_						:					Any			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =DataaVecVisionModel(config=__UpperCamelCase			)
             model.to(__UpperCamelCase			)
             model.eval()
             _lowerCamelCase	:		Optional[Any]          =model(__UpperCamelCase			)
             # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
             _lowerCamelCase	:		Union[str, Any]          =(self.image_size // self.patch_size) ** 2
             self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, num_patches + 1, self.hidden_size)			)






      def 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					Any      ,       lowercase_						:					int      ,       lowercase_						:					Dict      ,       lowercase_						:					Dict			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Any          =self.type_sequence_label_size
             _lowerCamelCase	:		Any          =DataaVecVisionForImageClassification(__UpperCamelCase			)
             model.to(__UpperCamelCase			)
             model.eval()
             _lowerCamelCase	:		Any          =model(__UpperCamelCase      ,       labels=__UpperCamelCase			)
             self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)			)






      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					Optional[Any]      ,       lowercase_						:					Optional[int]      ,       lowercase_						:					str			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Any          =self.num_labels
             _lowerCamelCase	:		Union[str, Any]          =DataaVecVisionForSemanticSegmentation(__UpperCamelCase			)
             model.to(__UpperCamelCase			)
             model.eval()
             _lowerCamelCase	:		Union[str, Any]          =model(__UpperCamelCase			)
             self.parent.assertEqual(
                 result.logits.shape      ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)
             _lowerCamelCase	:		Optional[Any]          =model(__UpperCamelCase      ,       labels=__UpperCamelCase			)
             self.parent.assertEqual(
                 result.logits.shape      ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)






      def 	lowerCamelCase	( self						:					Dict			) ->						int:
             """simple docstring"""




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




@require_torch
class A  (       lowercase_   ,							lowercase_   ,							unittest.TestCase					):
      UpperCamelCase__							:  List[Any]				=(
          (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
          if is_torch_available()
          else ()
      )
      UpperCamelCase__							:  Optional[Any]				=(
          {
              'feature-extraction': DataaVecVisionModel,
              'image-classification': DataaVecVisionForImageClassification,
              'image-segmentation': DataaVecVisionForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      UpperCamelCase__							:  Optional[Any]				=False
      UpperCamelCase__							:  int				=False
      UpperCamelCase__							:  str				=False






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =DataaVecVisionModelTester(self			)
             _lowerCamelCase	:		Union[str, Any]          =ConfigTester(
                 self      ,       config_class=__UpperCamelCase      ,       has_text_modality=__UpperCamelCase      ,       hidden_size=37			)






      def 	lowerCamelCase	( self						:					List[Any]			) ->						Optional[int]:
             """simple docstring"""




             self.config_tester.run_common_tests()






      def 	lowerCamelCase	( self						:					List[Any]			) ->						str:
             """simple docstring"""




             pass






      @require_torch_multi_gpu
      @unittest.skip(
          reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'			)
      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Any:
             """simple docstring"""




             pass






      def 	lowerCamelCase	( self						:					int			) ->						Any:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		Tuple          =self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    _lowerCamelCase	:		Optional[Any]          =model_class(__UpperCamelCase			)
                    self.assertIsInstance(model.get_input_embeddings()      ,       (nn.Module)			)
                    _lowerCamelCase	:		Dict          =model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(__UpperCamelCase      ,       nn.Linear			)			)






      def 	lowerCamelCase	( self						:					Any			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		Tuple          =self.model_tester.prepare_config_and_inputs_for_common()

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

                    _lowerCamelCase	:		Optional[int]          =['pixel_values']
                    self.assertListEqual(arg_names[:1]      ,       __UpperCamelCase			)






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		int          =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__UpperCamelCase			)






      def 	lowerCamelCase	( self						:					Any			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_segmentation(*__UpperCamelCase			)






      def 	lowerCamelCase	( self						:					str			) ->						str:
             """simple docstring"""




             if not self.model_tester.is_training:
                    return

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

             for model_class in self.all_model_classes:
                    if model_class in [*get_values(__UpperCamelCase			)]:
                           continue

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






      def 	lowerCamelCase	( self						:					str			) ->						List[Any]:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =self.model_tester.prepare_config_and_inputs_for_common()
             if not self.model_tester.is_training:
                    return

             _lowerCamelCase	:		Optional[int]          =False
             _lowerCamelCase	:		Optional[int]          =True

             for model_class in self.all_model_classes:
                    if model_class in [*get_values(__UpperCamelCase			)] or not model_class.supports_gradient_checkpointing:
                           continue
                    # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
                    # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                    elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
                           _lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase     ,					_lowerCamelCase	:		List[str]          =inputs_dict['pixel_values'].shape
                           _lowerCamelCase	:		Union[str, Any]          =torch.zeros(
                               [self.model_tester.batch_size, height, width]      ,       device=__UpperCamelCase			).long()
                    _lowerCamelCase	:		Optional[int]          =model_class(__UpperCamelCase			)
                    model.gradient_checkpointing_enable()
                    model.to(__UpperCamelCase			)
                    model.train()
                    _lowerCamelCase	:		Any          =self._prepare_for_class(__UpperCamelCase      ,       __UpperCamelCase      ,       return_labels=__UpperCamelCase			)
                    _lowerCamelCase	:		List[Any]          =model(**__UpperCamelCase			).loss
                    loss.backward()






      def 	lowerCamelCase	( self						:					Any			) ->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase     ,					_lowerCamelCase	:		Union[str, Any]          =self.model_tester.prepare_config_and_inputs_for_common()

             _lowerCamelCase	:		List[Any]          =_config_zero_init(__UpperCamelCase			)
             for model_class in self.all_model_classes:
                    _lowerCamelCase	:		List[Any]          =model_class(config=__UpperCamelCase			)
                    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'''      ,       )






      def 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					List[Any]      ,       lowercase_						:					List[Any]      ,       lowercase_						:					Union[str, Any]      ,       lowercase_						:					int=2E-4      ,       lowercase_						:					Dict="outputs"      ,       lowercase_						:					Dict=None			) ->						Tuple:
             """simple docstring"""




             super().check_pt_tf_outputs(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase			)






      def 	lowerCamelCase	( self						:					Optional[int]			) ->						List[str]:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase			)






      @slow
      def 	lowerCamelCase	( self						:					str			) ->						Optional[Any]:
             """simple docstring"""




             for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    _lowerCamelCase	:		Optional[Any]          =DataaVecVisionModel.from_pretrained(__UpperCamelCase			)
                    self.assertIsNotNone(__UpperCamelCase			)




def      a_				(   ):
       '''simple docstring'''





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




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






      @cached_property
      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						str:
             """simple docstring"""




             return (
                 BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k'			) if is_vision_available() else None
             )






      @slow
      def 	lowerCamelCase	( self						:					str			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		List[str]          =DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k'			).to(
                 __UpperCamelCase			)

             _lowerCamelCase	:		int          =self.default_image_processor
             _lowerCamelCase	:		Any          =prepare_img()
             _lowerCamelCase	:		Optional[Any]          =image_processor(images=__UpperCamelCase      ,       return_tensors='pt'			).to(__UpperCamelCase			)

             # forward pass
             with torch.no_grad():
                    _lowerCamelCase	:		List[Any]          =model(**__UpperCamelCase			)
             _lowerCamelCase	:		Optional[Any]          =outputs.logits

             # verify the logits
             _lowerCamelCase	:		List[Any]          =torch.Size((1, 1000)			)
             self.assertEqual(logits.shape      ,       __UpperCamelCase			)

             _lowerCamelCase	:		Union[str, Any]          =torch.tensor([0.3277, -0.1395, 0.0911]			).to(__UpperCamelCase			)

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

             _lowerCamelCase	:		Any          =[model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
             self.assertEqual(logits[0].topk(2			).indices.cpu().tolist()      ,       __UpperCamelCase			)






import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments

import datasets


lowerCamelCase						 =	'\\n@article{eddine2021frugalscore,\n  title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},\n  author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},\n  journal={arXiv preprint arXiv:2110.08559},\n  year={2021}\n}\n'

lowerCamelCase						 =	'\\nFrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.\n'


lowerCamelCase						 =	'\nCalculates how good are predictions given some references, using certain scores.\nArgs:\n    predictions (list of str): list of predictions to score. Each predictions\n        should be a string.\n    references (list of str): list of reference for each prediction. Each\n        reference should be a string.\n    batch_size (int): the batch size for predictions.\n    max_length (int): maximum sequence length.\n    device (str): either gpu or cpu\nReturns:\n    scores (list of int): list of scores.\nExamples:\n    >>> frugalscore = datasets.load_metric("frugalscore")\n    >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])\n    >>> print([round(s, 3) for s in results["scores"]])\n    [0.631, 0.645]\n'




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






      def 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """simple docstring"""




             return datasets.MetricInfo(
                 description=_DESCRIPTION      ,       citation=_CITATION      ,       inputs_description=_KWARGS_DESCRIPTION      ,       features=datasets.Features(
                     {
                         'predictions': datasets.Value('string'			),
                         'references': datasets.Value('string'			),
                     }			)      ,       homepage='https://github.com/moussaKam/FrugalScore'      ,       )






      def 	lowerCamelCase	( self						:					Optional[int]      ,       lowercase_						:					Union[str, Any]			) ->						int:
             """simple docstring"""




             if self.config_name == "default":
                    _lowerCamelCase	:		Optional[int]          ='moussaKam/frugalscore_tiny_bert-base_bert-score'
             else:
                    _lowerCamelCase	:		List[Any]          =self.config_name
             _lowerCamelCase	:		str          =AutoModelForSequenceClassification.from_pretrained(lowercase_			)
             _lowerCamelCase	:		str          =AutoTokenizer.from_pretrained(lowercase_			)






      def 	lowerCamelCase	( self						:					List[Any]      ,       lowercase_						:					List[str]      ,       lowercase_						:					str      ,       lowercase_						:					Optional[int]=32      ,       lowercase_						:					int=128      ,       lowercase_						:					Tuple=None      ,       ) ->						str:
             """simple docstring"""




             assert len(lowercase_			) == len(
                 lowercase_			), "predictions and references should have the same number of sentences."
             if device is not None:
                    assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
             else:
                    _lowerCamelCase	:		List[str]          ='gpu' if torch.cuda.is_available() else 'cpu'
             _lowerCamelCase	:		List[str]          =TrainingArguments(
                 'trainer'      ,       fpaa=(device == 'gpu')      ,       per_device_eval_batch_size=lowercase_      ,       report_to='all'      ,       no_cuda=(device == 'cpu')      ,       log_level='warning'      ,       )
             _lowerCamelCase	:		str          ={'sentence1': predictions, 'sentence2': references}
             _lowerCamelCase	:		Tuple          =datasets.Dataset.from_dict(lowercase_			)

             def tokenize_function(lowercase_						:					Any			):
                    return self.tokenizer(
                        data['sentence1']      ,       data['sentence2']      ,       max_length=lowercase_      ,       truncation=lowercase_      ,       padding=lowercase_			)

             _lowerCamelCase	:		Dict          =raw_datasets.map(lowercase_      ,       batched=lowercase_			)
             tokenized_datasets.remove_columns(['sentence1', 'sentence2']			)
             _lowerCamelCase	:		Union[str, Any]          =Trainer(self.model      ,       lowercase_      ,       tokenizer=self.tokenizer			)
             _lowerCamelCase	:		List[str]          =trainer.predict(lowercase_			)
             return {"scores": list(predictions.predictions.squeeze(-1			)			)}


