






from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


_UpperCAmelCase					:						Optional[Any]         =     '\\n@inproceedings{xu-etal-2016-optimizing,\ntitle = {Optimizing Statistical Machine Translation for Text Simplification},\nauthors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\njournal = {Transactions of the Association for Computational Linguistics},\nvolume = {4},\nyear={2016},\nurl = {https://www.aclweb.org/anthology/Q16-1029},\npages = {401--415},\n}\n'

_UpperCAmelCase					:						str         =     '\\nSARI is a metric used for evaluating automatic text simplification systems.\nThe metric compares the predicted simplified sentences against the reference\nand the source sentences. It explicitly measures the goodness of words that are\nadded, deleted and kept by the system.\nSari = (F1_add + F1_keep + P_del) / 3\nwhere\nF1_add: n-gram F1 score for add operation\nF1_keep: n-gram F1 score for keep operation\nP_del: n-gram precision score for delete operation\nn = 4, as in the original paper.\n\nThis implementation is adapted from Tensorflow\'s tensor2tensor implementation [3].\nIt has two differences with the original GitHub [1] implementation:\n  (1) Defines 0/0=1 instead of 0 to give higher scores for predictions that match\n      a target exactly.\n  (2) Fixes an alleged bug [2] in the keep score computation.\n[1] https://github.com/cocoxu/simplification/blob/master/SARI.py\n    (commit 0210f15)\n[2] https://github.com/cocoxu/simplification/issues/6\n[3] https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py\n'


_UpperCAmelCase					:						int         =     '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known .","About 95 species are now accepted .","95 species are now accepted ."]]\n    >>> sari = datasets.load_metric("sari")\n    >>> results = sari.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 26.953601953601954}\n'






def  UpperCAmelCase__						(    lowerCamelCase	:			Any			,lowerCamelCase	:			Union[str, Any]			,lowerCamelCase	:			int			,lowerCamelCase	:			Optional[int]    ):
   lowerCamelCase									=[rgram for rgrams in rgramslist for rgram in rgrams]
   lowerCamelCase									=Counter(lowerCamelCase    )

   lowerCamelCase									=Counter(lowerCamelCase    )
   lowerCamelCase									=Counter()
   for sgram, scount in sgramcounter.items():
      lowerCamelCase									=scount * numref

   lowerCamelCase									=Counter(lowerCamelCase    )
   lowerCamelCase									=Counter()
   for cgram, ccount in cgramcounter.items():
      lowerCamelCase									=ccount * numref

   # KEEP
   lowerCamelCase									=sgramcounter_rep & cgramcounter_rep
   lowerCamelCase									=keepgramcounter_rep & rgramcounter
   lowerCamelCase									=sgramcounter_rep & rgramcounter

   lowerCamelCase									=0
   lowerCamelCase									=0
   for keepgram in keepgramcountergood_rep:
      keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
      # Fix an alleged bug [2] in the keep score computation.
      # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
      keeptmpscorea += keepgramcountergood_rep[keepgram]
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   #      a target exactly.
   lowerCamelCase									=1
   lowerCamelCase									=1
   if len(lowerCamelCase    ) > 0:
      lowerCamelCase									=keeptmpscorea / len(lowerCamelCase    )
   if len(lowerCamelCase    ) > 0:
      # Fix an alleged bug [2] in the keep score computation.
      # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
      lowerCamelCase									=keeptmpscorea / sum(keepgramcounterall_rep.values()    )
   lowerCamelCase									=0
   if keepscore_precision > 0 or keepscore_recall > 0:
      lowerCamelCase									=2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

   # DELETION
   lowerCamelCase									=sgramcounter_rep - cgramcounter_rep
   lowerCamelCase									=delgramcounter_rep - rgramcounter
   lowerCamelCase									=sgramcounter_rep - rgramcounter
   lowerCamelCase									=0
   lowerCamelCase									=0
   for delgram in delgramcountergood_rep:
      deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
      deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   # a target exactly.
   lowerCamelCase									=1
   if len(lowerCamelCase    ) > 0:
      lowerCamelCase									=deltmpscorea / len(lowerCamelCase    )

   # ADDITION
   lowerCamelCase									=set(lowerCamelCase    ) - set(lowerCamelCase    )
   lowerCamelCase									=set(lowerCamelCase    ) & set(lowerCamelCase    )
   lowerCamelCase									=set(lowerCamelCase    ) - set(lowerCamelCase    )

   lowerCamelCase									=0
   for addgram in addgramcountergood:
      addtmpscore += 1

   # Define 0/0=1 instead of 0 to give higher scores for predictions that match
   # a target exactly.
   lowerCamelCase									=1
   lowerCamelCase									=1
   if len(lowerCamelCase    ) > 0:
      lowerCamelCase									=addtmpscore / len(lowerCamelCase    )
   if len(lowerCamelCase    ) > 0:
      lowerCamelCase									=addtmpscore / len(lowerCamelCase    )
   lowerCamelCase									=0
   if addscore_precision > 0 or addscore_recall > 0:
      lowerCamelCase									=2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

   return (keepscore, delscore_precision, addscore)






def  UpperCAmelCase__						(    lowerCamelCase	:			List[str]			,lowerCamelCase	:			str			,lowerCamelCase	:			str    ):
   lowerCamelCase									=len(lowerCamelCase    )

   lowerCamelCase									=ssent.split(""" """    )
   lowerCamelCase									=csent.split(""" """    )
   lowerCamelCase									=[]
   lowerCamelCase									=[]
   lowerCamelCase									=[]
   lowerCamelCase									=[]
   lowerCamelCase									=[]
   lowerCamelCase									=[]

   lowerCamelCase									=[]
   lowerCamelCase									=[]
   lowerCamelCase									=[]
   lowerCamelCase									=[]
   for rsent in rsents:
      lowerCamelCase									=rsent.split(""" """    )
      lowerCamelCase									=[]
      lowerCamelCase									=[]
      lowerCamelCase									=[]
      ragramslist.append(lowerCamelCase    )
      for i in range(0			,len(lowerCamelCase    ) - 1    ):
         if i < len(lowerCamelCase    ) - 1:
            lowerCamelCase									=ragrams[i] + """ """ + ragrams[i + 1]
            ragrams.append(lowerCamelCase    )
         if i < len(lowerCamelCase    ) - 2:
            lowerCamelCase									=ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
            ragrams.append(lowerCamelCase    )
         if i < len(lowerCamelCase    ) - 3:
            lowerCamelCase									=ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
            ragrams.append(lowerCamelCase    )
      ragramslist.append(lowerCamelCase    )
      ragramslist.append(lowerCamelCase    )
      ragramslist.append(lowerCamelCase    )

   for i in range(0			,len(lowerCamelCase    ) - 1    ):
      if i < len(lowerCamelCase    ) - 1:
         lowerCamelCase									=sagrams[i] + """ """ + sagrams[i + 1]
         sagrams.append(lowerCamelCase    )
      if i < len(lowerCamelCase    ) - 2:
         lowerCamelCase									=sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
         sagrams.append(lowerCamelCase    )
      if i < len(lowerCamelCase    ) - 3:
         lowerCamelCase									=sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
         sagrams.append(lowerCamelCase    )

   for i in range(0			,len(lowerCamelCase    ) - 1    ):
      if i < len(lowerCamelCase    ) - 1:
         lowerCamelCase									=cagrams[i] + """ """ + cagrams[i + 1]
         cagrams.append(lowerCamelCase    )
      if i < len(lowerCamelCase    ) - 2:
         lowerCamelCase									=cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
         cagrams.append(lowerCamelCase    )
      if i < len(lowerCamelCase    ) - 3:
         lowerCamelCase									=cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
         cagrams.append(lowerCamelCase    )

   ((lowerCamelCase)				,       (lowerCamelCase)				,       (lowerCamelCase))									=SARIngram(lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )
   ((lowerCamelCase)				,       (lowerCamelCase)				,       (lowerCamelCase))									=SARIngram(lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )
   ((lowerCamelCase)				,       (lowerCamelCase)				,       (lowerCamelCase))									=SARIngram(lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )
   ((lowerCamelCase)				,       (lowerCamelCase)				,       (lowerCamelCase))									=SARIngram(lowerCamelCase			,lowerCamelCase			,lowerCamelCase			,lowerCamelCase    )
   lowerCamelCase									=sum([keepascore, keepascore, keepascore, keepascore]    ) / 4
   lowerCamelCase									=sum([delascore, delascore, delascore, delascore]    ) / 4
   lowerCamelCase									=sum([addascore, addascore, addascore, addascore]    ) / 4
   lowerCamelCase									=(avgkeepscore + avgdelscore + avgaddscore) / 3
   return finalscore






def  UpperCAmelCase__						(    lowerCamelCase	:			Tuple			,lowerCamelCase	:			bool = True			,lowerCamelCase	:			str = "13a"			,lowerCamelCase	:			bool = True    ):
   # Normalization is requried for the ASSET dataset (one of the primary
   # datasets in sentence simplification) to allow using space
   # to split the sentence. Even though Wiki-Auto and TURK datasets,
   # do not require normalization, we do it for consistency.
   # Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
   # [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7

   if lowercase:
      lowerCamelCase									=sentence.lower()

   if tokenizer in ["13a", "intl"]:
      if version.parse(sacrebleu.__version__    ).major >= 2:
         lowerCamelCase									=sacrebleu.metrics.bleu._get_tokenizer(lowerCamelCase    )()(lowerCamelCase    )
      else:
         lowerCamelCase									=sacrebleu.TOKENIZERS[tokenizer]()(lowerCamelCase    )
   elif tokenizer == "moses":
      lowerCamelCase									=sacremoses.MosesTokenizer().tokenize(lowerCamelCase			,return_str=lowerCamelCase			,escape=lowerCamelCase    )
   elif tokenizer == "penn":
      lowerCamelCase									=sacremoses.MosesTokenizer().penn_tokenize(lowerCamelCase			,return_str=lowerCamelCase    )
   else:
      lowerCamelCase									=sentence

   if not return_str:
      lowerCamelCase									=normalized_sent.split()

   return normalized_sent







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





     def 						lowerCamelCase							(	self  )		->				List[str]:
        return datasets.MetricInfo(
            description=_DESCRIPTION							, citation=_CITATION							, inputs_description=_KWARGS_DESCRIPTION							, features=datasets.Features(
                {
                    """sources""": datasets.Value("""string"""							, id="""sequence"""  ),
                    """predictions""": datasets.Value("""string"""							, id="""sequence"""  ),
                    """references""": datasets.Sequence(datasets.Value("""string"""							, id="""sequence"""  )							, id="""references"""  ),
                }  )							, codebase_urls=[
                """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
                """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
            ]							, reference_urls=["""https://www.aclweb.org/anthology/Q16-1029.pdf"""]							, )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				int:
        if not (len(_UpperCAmelCase  ) == len(_UpperCAmelCase  ) == len(_UpperCAmelCase  )):
           raise ValueError("""Sources length must match predictions and references lengths."""  )
        lowerCamelCase									=0
        for src, pred, refs in zip(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  ):
           sari_score += SARIsent(normalize(_UpperCAmelCase  )							, normalize(_UpperCAmelCase  )							, [normalize(_UpperCAmelCase  ) for sent in refs]  )
        lowerCamelCase									=sari_score / len(_UpperCAmelCase  )
        return {"sari": 100 * sari_score}







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 		lowerCAmelCase_				:





     def __init__(	self							, _UpperCAmelCase							, _UpperCAmelCase=100							, _UpperCAmelCase=13							, _UpperCAmelCase=30							, _UpperCAmelCase=2							, _UpperCAmelCase=3							, _UpperCAmelCase=True							, _UpperCAmelCase=True							, _UpperCAmelCase=32							, _UpperCAmelCase=4							, _UpperCAmelCase=4							, _UpperCAmelCase=37							, _UpperCAmelCase="gelu"							, _UpperCAmelCase=0.1							, _UpperCAmelCase=0.1							, _UpperCAmelCase=10							, _UpperCAmelCase=0.02							, _UpperCAmelCase=3							, _UpperCAmelCase=None							, _UpperCAmelCase=[0, 1, 2, 3]							, )		->				Dict:
        lowerCamelCase									=parent
        lowerCamelCase									=100
        lowerCamelCase									=batch_size
        lowerCamelCase									=image_size
        lowerCamelCase									=patch_size
        lowerCamelCase									=num_channels
        lowerCamelCase									=is_training
        lowerCamelCase									=use_labels
        lowerCamelCase									=hidden_size
        lowerCamelCase									=num_hidden_layers
        lowerCamelCase									=num_attention_heads
        lowerCamelCase									=intermediate_size
        lowerCamelCase									=hidden_act
        lowerCamelCase									=hidden_dropout_prob
        lowerCamelCase									=attention_probs_dropout_prob
        lowerCamelCase									=type_sequence_label_size
        lowerCamelCase									=initializer_range
        lowerCamelCase									=scope
        lowerCamelCase									=out_indices
        lowerCamelCase									=num_labels

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





     def 						lowerCamelCase							(	self  )		->				List[Any]:
        lowerCamelCase									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        lowerCamelCase									=None
        lowerCamelCase									=None
        if self.use_labels:
           lowerCamelCase									=ids_tensor([self.batch_size]							, self.type_sequence_label_size  )
           lowerCamelCase									=ids_tensor([self.batch_size, self.image_size, self.image_size]							, self.num_labels  )

        lowerCamelCase									=self.get_config()

        return config, pixel_values, labels, pixel_labels





     def 						lowerCamelCase							(	self  )		->				str:
        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							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Union[str, Any]:
        lowerCamelCase									=DataaVecVisionModel(config=_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase  )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowerCamelCase									=(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							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				Any:
        lowerCamelCase									=self.type_sequence_label_size
        lowerCamelCase									=DataaVecVisionForImageClassification(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase							, labels=_UpperCAmelCase  )
        self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)  )





     def 						lowerCamelCase							(	self							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )		->				str:
        lowerCamelCase									=self.num_labels
        lowerCamelCase									=DataaVecVisionForSemanticSegmentation(_UpperCAmelCase  )
        model.to(_UpperCAmelCase  )
        model.eval()
        lowerCamelCase									=model(_UpperCAmelCase  )
        self.parent.assertEqual(
            result.logits.shape							, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
        lowerCamelCase									=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  )		->				List[Any]:
        lowerCamelCase									=self.prepare_config_and_inputs()
        lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase				,       lowerCamelCase									=config_and_inputs
        lowerCamelCase									={"""pixel_values""": pixel_values}
        return config, inputs_dict




@require_torch
class 		lowerCAmelCase_				(  UpperCamelCase				,     UpperCamelCase				,     unittest.TestCase ):
     __lowerCamelCase       :    Optional[int] 	=     (
         (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
         if is_torch_available()
         else ()
     )
     __lowerCamelCase       :    str 	=     (
         {
             '''feature-extraction''': DataaVecVisionModel,
             '''image-classification''': DataaVecVisionForImageClassification,
             '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     __lowerCamelCase       :    Any 	=     False
     __lowerCamelCase       :    List[str] 	=     False
     __lowerCamelCase       :    int 	=     False





     def 						lowerCamelCase							(	self  )		->				Any:
        lowerCamelCase									=DataaVecVisionModelTester(self  )
        lowerCamelCase									=ConfigTester(
            self							, config_class=_UpperCAmelCase							, has_text_modality=_UpperCAmelCase							, hidden_size=37  )





     def 						lowerCamelCase							(	self  )		->				int:
        self.config_tester.run_common_tests()





     def 						lowerCamelCase							(	self  )		->				Dict:
        # Data2VecVision does not use inputs_embeds
        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  )		->				Dict:
        pass





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           self.assertIsInstance(model.get_input_embeddings()							, (nn.Module)  )
           lowerCamelCase									=model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(_UpperCAmelCase							, nn.Linear  )  )





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

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

           lowerCamelCase									=["""pixel_values"""]
           self.assertListEqual(arg_names[:1]							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				str:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        if not self.model_tester.is_training:
           return

        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        lowerCamelCase									=True

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

           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.to(_UpperCAmelCase  )
           model.train()
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
           lowerCamelCase									=model(**_UpperCAmelCase  ).loss
           loss.backward()





     def 						lowerCamelCase							(	self  )		->				int:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
           return

        lowerCamelCase									=False
        lowerCamelCase									=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									=inputs_dict["""pixel_values"""].shape
              lowerCamelCase									=torch.zeros(
                  [self.model_tester.batch_size, height, width]							, device=_UpperCAmelCase  ).long()
           lowerCamelCase									=model_class(_UpperCAmelCase  )
           model.gradient_checkpointing_enable()
           model.to(_UpperCAmelCase  )
           model.train()
           lowerCamelCase									=self._prepare_for_class(_UpperCAmelCase							, _UpperCAmelCase							, return_labels=_UpperCAmelCase  )
           lowerCamelCase									=model(**_UpperCAmelCase  ).loss
           loss.backward()





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase				,       lowerCamelCase									=self.model_tester.prepare_config_and_inputs_for_common()

        lowerCamelCase									=_config_zero_init(_UpperCAmelCase  )
        for model_class in self.all_model_classes:
           lowerCamelCase									=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							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase=2E-4							, _UpperCAmelCase="outputs"							, _UpperCAmelCase=None  )		->				int:
        # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
        super().check_pt_tf_outputs(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase  )





     @slow
     def 						lowerCamelCase							(	self  )		->				List[Any]:
        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCamelCase									=DataaVecVisionModel.from_pretrained(_UpperCAmelCase  )
           self.assertIsNotNone(_UpperCAmelCase  )






def  UpperCAmelCase__						(    ):
   lowerCamelCase									=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""    )
   return image







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





     @cached_property
     def 						lowerCamelCase							(	self  )		->				List[str]:
        return (
            BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ) if is_vision_available() else None
        )





     @slow
     def 						lowerCamelCase							(	self  )		->				Optional[int]:
        lowerCamelCase									=DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ).to(
            _UpperCAmelCase  )

        lowerCamelCase									=self.default_image_processor
        lowerCamelCase									=prepare_img()
        lowerCamelCase									=image_processor(images=_UpperCAmelCase							, return_tensors="""pt"""  ).to(_UpperCAmelCase  )

        # forward pass
        with torch.no_grad():
           lowerCamelCase									=model(**_UpperCAmelCase  )
        lowerCamelCase									=outputs.logits

        # verify the logits
        lowerCamelCase									=torch.Size((1, 1000)  )
        self.assertEqual(logits.shape							, _UpperCAmelCase  )

        lowerCamelCase									=torch.tensor([0.3277, -0.1395, 0.0911]  ).to(_UpperCAmelCase  )

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

        lowerCamelCase									=[model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
        self.assertEqual(logits[0].topk(2  ).indices.cpu().tolist()							, _UpperCAmelCase  )