



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 		_lowercase		:

   def __init__(   self      :							int				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Optional[int]=100				, __lowerCAmelCase      :							Optional[int]=13				, __lowerCAmelCase      :							Dict=30				, __lowerCAmelCase      :							Any=2				, __lowerCAmelCase      :							List[Any]=3				, __lowerCAmelCase      :							str=True				, __lowerCAmelCase      :							Tuple=True				, __lowerCAmelCase      :							Union[str, Any]=32				, __lowerCAmelCase      :							int=4				, __lowerCAmelCase      :							int=4				, __lowerCAmelCase      :							List[str]=37				, __lowerCAmelCase      :							Tuple="gelu"				, __lowerCAmelCase      :							Dict=0.1				, __lowerCAmelCase      :							Optional[int]=0.1				, __lowerCAmelCase      :							int=10				, __lowerCAmelCase      :							List[Any]=0.0_2				, __lowerCAmelCase      :							Tuple=3				, __lowerCAmelCase      :							str=None				, __lowerCAmelCase      :							str=[0, 1, 2, 3]				, )							->					Optional[int]:


    """simple docstring"""






    a							=		parent
    a							=		100
    a							=		batch_size
    a							=		image_size
    a							=		patch_size
    a							=		num_channels
    a							=		is_training
    a							=		use_labels
    a							=		hidden_size
    a							=		num_hidden_layers
    a							=		num_attention_heads
    a							=		intermediate_size
    a							=		hidden_act
    a							=		hidden_dropout_prob
    a							=		attention_probs_dropout_prob
    a							=		type_sequence_label_size
    a							=		initializer_range
    a							=		scope
    a							=		out_indices
    a							=		num_labels

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

   def 		A				(   self      :							Optional[int]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

    a							=		self.get_config()

    return config, pixel_values, labels, pixel_labels

   def 		A				(   self      :							str				)							->					Any:


    """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 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							Union[str, Any]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		DataaVecVisionModel(config=__a				)
    model.to(__a				)
    model.eval()
    a							=		model(__a				)
    # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
    a							=		(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 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							Optional[Any]				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							int				)							->					int:


    """simple docstring"""






    a							=		self.type_sequence_label_size
    a							=		DataaVecVisionForImageClassification(__a				)
    model.to(__a				)
    model.eval()
    a							=		model(__a				, labels=__a				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.type_sequence_label_size)				)

   def 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							List[Any]				)							->					List[Any]:


    """simple docstring"""






    a							=		self.num_labels
    a							=		DataaVecVisionForSemanticSegmentation(__a				)
    model.to(__a				)
    model.eval()
    a							=		model(__a				)
    self.parent.assertEqual(
        result.logits.shape				, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)
    a							=		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 		A				(   self      :							Tuple				)							->					Optional[int]:


    """simple docstring"""






    a							=		self.prepare_config_and_inputs()
    a							=		config_and_inputs
    a							=		{'pixel_values': pixel_values}
    return config, inputs_dict



@require_torch
class 		_lowercase		(							UpperCamelCase_, UpperCamelCase_, unittest.TestCase						):
   _UpperCAmelCase  						=      (
       (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
       if is_torch_available()
       else ()
   )
   _UpperCAmelCase  						=      (
       {
           '''feature-extraction''': DataaVecVisionModel,
           '''image-classification''': DataaVecVisionForImageClassification,
           '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False
   _UpperCAmelCase  						=      False

   def 		A				(   self      :							str				)							->					Tuple:


    """simple docstring"""






    a							=		DataaVecVisionModelTester(self				)
    a							=		ConfigTester(
        self				, config_class=__a				, has_text_modality=__a				, hidden_size=37				)

   def 		A				(   self      :							List[Any]				)							->					Tuple:


    """simple docstring"""






    self.config_tester.run_common_tests()

   def 		A				(   self      :							Tuple				)							->					List[Any]:


    """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 		A				(   self      :							List[Any]				)							->					str:


    """simple docstring"""






    pass

   def 		A				(   self      :							List[str]				)							->					Optional[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()

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

   def 		A				(   self      :							Dict				)							->					Tuple:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()

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

     a							=		['pixel_values']
     self.assertListEqual(arg_names[:1]				, __a				)

   def 		A				(   self      :							List[Any]				)							->					List[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__a				)

   def 		A				(   self      :							List[Any]				)							->					Tuple:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_segmentation(*__a				)

   def 		A				(   self      :							Optional[int]				)							->					Union[str, Any]:


    """simple docstring"""






    if not self.model_tester.is_training:
     return

    a							=		self.model_tester.prepare_config_and_inputs_for_common()
    a							=		True

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

     a							=		model_class(__a				)
     model.to(__a				)
     model.train()
     a							=		self._prepare_for_class(__a				, __a				, return_labels=__a				)
     a							=		model(**__a				).loss
     loss.backward()

   def 		A				(   self      :							int				)							->					Any:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()
    if not self.model_tester.is_training:
     return

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

   def 		A				(   self      :							int				)							->					List[Any]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs_for_common()

    a							=		_config_zero_init(__a				)
    for model_class in self.all_model_classes:
     a							=		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 		A				(   self      :							Optional[Any]				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							List[Any]=2E-4				, __lowerCAmelCase      :							Tuple="outputs"				, __lowerCAmelCase      :							int=None				)							->					List[str]:


    """simple docstring"""






    super().check_pt_tf_outputs(__a				, __a				, __a				, __a				, __a				, __a				)

   def 		A				(   self      :							int				)							->					str:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*__a				)




   @slow
   def 		A				(   self      :							List[Any]				)							->					Optional[int]:


    """simple docstring"""






    for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     a							=		DataaVecVisionModel.from_pretrained(__a				)
     self.assertIsNotNone(__a				)


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
 return image




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

   @cached_property
   def 		A				(   self      :							List[Any]				)							->					Optional[Any]:


    """simple docstring"""






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




   @slow
   def 		A				(   self      :							Union[str, Any]				)							->					int:


    """simple docstring"""






    a							=		DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"				).to(
        __a				)

    a							=		self.default_image_processor
    a							=		prepare_img()
    a							=		image_processor(images=__a				, return_tensors="pt"				).to(__a				)

    # forward pass
    with torch.no_grad():
     a							=		model(**__a				)
    a							=		outputs.logits

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

    a							=		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				)				)

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









'''simple docstring'''






from math import gcd




def 							_SCREAMING_SNAKE_CASE	(					A	:							int = 1_20_00	)     ->			int:







   """simple docstring"""
   __snake_case					:   Dict  				=			0
   for d in range(max_d + 1	):
      for n in range(d // 3 + 1	,		(d + 1) // 2	):
         if gcd(A	,		A	) == 1:
            fractions_number += 1
   return fractions_number


if __name__ == "__main__":
      print(f'''{solution() = }''')