




'''simple docstring'''






from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers

class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self   ,		A ):






        '''simple docstring'''




        lowercase  :		int						=      vocab
        lowercase  :		Union[str, Any]						=      normalizers.BertNormalizer(
            clean_text=A   ,		handle_chinese_chars=A   ,		strip_accents=A   ,		lowercase=A   ,		)
        try:
               import rjieba
        except ImportError:
               raise ImportError(
                   '''You need to install rjieba to use RoFormerTokenizer. '''
                   '''See https://pypi.org/project/rjieba/ for installation.''' )
        lowercase  :		List[Any]						=      rjieba



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		str						=      []

        # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
        for token, start, end in self.jieba.tokenize(str(A )   ,		hmm=A ):
               if token in self.vocab:
                      splits.append(normalized_string[start:end] )
               else:
                      lowercase  :		Dict						=      self.normalizers.normalize_str(A ).split()
                      for token in token_list:
                             if token:
                                    lowercase  :		Tuple						=      start + len(A )
                                    splits.append(normalized_string[start:end] )
                                    lowercase  :		Any						=      end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

        return splits





 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        pretok.split(self.jieba_split )










'''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   SCREAMING_SNAKE_CASE_		:



 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=0.0_2   ,		A=3   ,		A=None   ,		A=[0, 1, 2, 3]   ,		):






        '''simple docstring'''




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

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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

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

        lowercase  :		int						=      self.get_config()

        return config, pixel_values, labels, pixel_labels



 def    SCREAMING_SNAKE_CASE(  self ):






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



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




        lowercase  :		List[str]						=      DataaVecVisionModel(config=A )
        model.to(A )
        model.eval()
        lowercase  :		Tuple						=      model(A )
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
        lowercase  :		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    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




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



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




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






        '''simple docstring'''




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






@require_torch
class   SCREAMING_SNAKE_CASE_		(       lowerCamelCase_		,     lowerCamelCase_		,     unittest.TestCase				):
 __snake_case	:     Tuple			   =      (
     (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 __snake_case	:     Optional[Any]			   =      (
     {
         "feature-extraction": DataaVecVisionModel,
         "image-classification": DataaVecVisionForImageClassification,
         "image-segmentation": DataaVecVisionForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __snake_case	:     str			   =      False
 __snake_case	:     str			   =      False
 __snake_case	:     int			   =      False



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Dict						=      DataaVecVisionModelTester(self )
        lowercase  :		List[str]						=      ConfigTester(
            self   ,		config_class=A   ,		has_text_modality=A   ,		hidden_size=37 )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.config_tester.run_common_tests()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        pass



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Dict						=      self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase  :		List[Any]						=      ['''pixel_values''']
               self.assertListEqual(arg_names[:1]   ,		A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        if not self.model_tester.is_training:
               return

        lowercase,     lowercase  :		Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()
        lowercase  :		Union[str, Any]						=      True

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

               lowercase  :		Optional[int]						=      model_class(A )
               model.to(A )
               model.train()
               lowercase  :		int						=      self._prepare_for_class(A   ,		A   ,		return_labels=A )
               lowercase  :		List[str]						=      model(**A ).loss
               loss.backward()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		Tuple						=      self.model_tester.prepare_config_and_inputs_for_common()
        if not self.model_tester.is_training:
               return

        lowercase  :		List[str]						=      False
        lowercase  :		str						=      True

        for model_class in self.all_model_classes:
               if model_class in [*get_values(A )] 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":
                      lowercase,     lowercase,     lowercase,     lowercase  :		Tuple						=      inputs_dict['''pixel_values'''].shape
                      lowercase  :		int						=      torch.zeros(
                          [self.model_tester.batch_size, height, width]   ,		device=A ).long()
               lowercase  :		Optional[Any]						=      model_class(A )
               model.gradient_checkpointing_enable()
               model.to(A )
               model.train()
               lowercase  :		Any						=      self._prepare_for_class(A   ,		A   ,		return_labels=A )
               lowercase  :		Optional[int]						=      model(**A ).loss
               loss.backward()



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase,     lowercase  :		List[Any]						=      self.model_tester.prepare_config_and_inputs_for_common()

        lowercase  :		Union[str, Any]						=      _config_zero_init(A )
        for model_class in self.all_model_classes:
               lowercase  :		Dict						=      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"""   ,		)



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A=2E-4   ,		A="outputs"   ,		A=None ):






        '''simple docstring'''




        super().check_pt_tf_outputs(A   ,		A   ,		A   ,		A   ,		A   ,		A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Any						=      self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*A )





 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase  :		List[Any]						=      DataaVecVisionModel.from_pretrained(A )
               self.assertIsNotNone(A )






def 		a_( )				->Any:






       '''simple docstring'''




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





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



 @cached_property
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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





 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		Union[str, Any]						=      DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k''' ).to(
            A )

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

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

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

        lowercase  :		str						=      torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1] ).to(A )

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

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




