

"""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       _lowerCAmelCase						:







 """simple docstring"""







 def __init__(    self							:    Optional[int],      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    Dict=1_0_0,      UpperCAmelCase__							:    Optional[int]=1_3,      UpperCAmelCase__							:    Union[str, Any]=3_0,      UpperCAmelCase__							:    Optional[int]=2,      UpperCAmelCase__							:    Optional[Any]=3,      UpperCAmelCase__							:    int=True,      UpperCAmelCase__							:    Optional[int]=True,      UpperCAmelCase__							:    int=3_2,      UpperCAmelCase__							:    str=4,      UpperCAmelCase__							:    List[str]=4,      UpperCAmelCase__							:    List[Any]=3_7,      UpperCAmelCase__							:    Any="gelu",      UpperCAmelCase__							:    Optional[int]=0.1,      UpperCAmelCase__							:    Union[str, Any]=0.1,      UpperCAmelCase__							:    str=1_0,      UpperCAmelCase__							:    str=0.02,      UpperCAmelCase__							:    List[str]=3,      UpperCAmelCase__							:    Tuple=None,      UpperCAmelCase__							:    Dict=[0, 1, 2, 3],      ):
    __lowercase			=		parent
    __lowercase			=		1_0_0
    __lowercase			=		batch_size
    __lowercase			=		image_size
    __lowercase			=		patch_size
    __lowercase			=		num_channels
    __lowercase			=		is_training
    __lowercase			=		use_labels
    __lowercase			=		hidden_size
    __lowercase			=		num_hidden_layers
    __lowercase			=		num_attention_heads
    __lowercase			=		intermediate_size
    __lowercase			=		hidden_act
    __lowercase			=		hidden_dropout_prob
    __lowercase			=		attention_probs_dropout_prob
    __lowercase			=		type_sequence_label_size
    __lowercase			=		initializer_range
    __lowercase			=		scope
    __lowercase			=		out_indices
    __lowercase			=		num_labels

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


 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

    __lowercase			=		self.get_config()

    return config, pixel_values, labels, pixel_labels


 def       _lowercase				(    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=lowerCamelCase__,      initializer_range=self.initializer_range,      out_indices=self.out_indices,      )


 def       _lowercase				(    self							:    Tuple,      UpperCAmelCase__							:    Optional[Any],      UpperCAmelCase__							:    Dict,      UpperCAmelCase__							:    Dict,      UpperCAmelCase__							:    str			):
    __lowercase			=		DataaVecVisionModel(config=lowerCamelCase__			)
    model.to(lowerCamelCase__			)
    model.eval()
    __lowercase			=		model(lowerCamelCase__			)
    # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
    __lowercase			=		(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       _lowercase				(    self							:    str,      UpperCAmelCase__							:    Any,      UpperCAmelCase__							:    Optional[int],      UpperCAmelCase__							:    Union[str, Any],      UpperCAmelCase__							:    Optional[int]			):
    __lowercase			=		self.type_sequence_label_size
    __lowercase			=		DataaVecVisionForImageClassification(lowerCamelCase__			)
    model.to(lowerCamelCase__			)
    model.eval()
    __lowercase			=		model(lowerCamelCase__,      labels=lowerCamelCase__			)
    self.parent.assertEqual(result.logits.shape,      (self.batch_size, self.type_sequence_label_size)			)


 def       _lowercase				(    self							:    List[Any],      UpperCAmelCase__							:    List[Any],      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    Dict,      UpperCAmelCase__							:    str			):
    __lowercase			=		self.num_labels
    __lowercase			=		DataaVecVisionForSemanticSegmentation(lowerCamelCase__			)
    model.to(lowerCamelCase__			)
    model.eval()
    __lowercase			=		model(lowerCamelCase__			)
    self.parent.assertEqual(
        result.logits.shape,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)
    __lowercase			=		model(lowerCamelCase__,      labels=lowerCamelCase__			)
    self.parent.assertEqual(
        result.logits.shape,      (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)



 def       _lowercase				(    self							:    Dict			):
    __lowercase			=		self.prepare_config_and_inputs()
    __lowercase			=		config_and_inputs
    __lowercase			=		{"pixel_values": pixel_values}
    return config, inputs_dict







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







 """simple docstring"""







 __UpperCAmelCase			:      List[str]     	=			(
     (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 __UpperCAmelCase			:      int     	=			(
     {
         "feature-extraction": DataaVecVisionModel,
         "image-classification": DataaVecVisionForImageClassification,
         "image-segmentation": DataaVecVisionForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __UpperCAmelCase			:      Union[str, Any]     	=			False
 __UpperCAmelCase			:      Optional[int]     	=			False
 __UpperCAmelCase			:      Union[str, Any]     	=			False


 def       _lowercase				(    self							:    Optional[int]			):
    __lowercase			=		DataaVecVisionModelTester(self			)
    __lowercase			=		ConfigTester(
        self,      config_class=lowerCamelCase__,      has_text_modality=lowerCamelCase__,      hidden_size=3_7			)


 def       _lowercase				(    self							:    Optional[Any]			):
    self.config_tester.run_common_tests()


 def       _lowercase				(    self							:    List[str]			):
    # 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       _lowercase				(    self							:    Optional[int]			):
    pass


 def       _lowercase				(    self							:    Any			):
    __lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()

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


 def       _lowercase				(    self							:    str			):
    __lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()

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

       __lowercase			=		["pixel_values"]
       self.assertListEqual(arg_names[:1],      lowerCamelCase__			)


 def       _lowercase				(    self							:    Optional[Any]			):
    __lowercase			=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCamelCase__			)


 def       _lowercase				(    self							:    List[str]			):
    __lowercase			=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_segmentation(*lowerCamelCase__			)


 def       _lowercase				(    self							:    Optional[int]			):
    if not self.model_tester.is_training:
       return

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

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

       __lowercase			=		model_class(lowerCamelCase__			)
       model.to(lowerCamelCase__			)
       model.train()
       __lowercase			=		self._prepare_for_class(lowerCamelCase__,      lowerCamelCase__,      return_labels=lowerCamelCase__			)
       __lowercase			=		model(**lowerCamelCase__			).loss
       loss.backward()


 def       _lowercase				(    self							:    Optional[Any]			):
    __lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()
    if not self.model_tester.is_training:
       return

    __lowercase			=		False
    __lowercase			=		True

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


 def       _lowercase				(    self							:    Tuple			):
    __lowercase			=		self.model_tester.prepare_config_and_inputs_for_common()

    __lowercase			=		_config_zero_init(lowerCamelCase__			)
    for model_class in self.all_model_classes:
       __lowercase			=		model_class(config=lowerCamelCase__			)
       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       _lowercase				(    self							:    List[str],      UpperCAmelCase__							:    List[Any],      UpperCAmelCase__							:    Union[str, Any],      UpperCAmelCase__							:    int,      UpperCAmelCase__							:    Optional[int]=2E-4,      UpperCAmelCase__							:    Union[str, Any]="outputs",      UpperCAmelCase__							:    int=None			):
    # We override with a slightly higher tol value, as semseg models tend to diverge a bit more
    super().check_pt_tf_outputs(lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__,      lowerCamelCase__			)


 def       _lowercase				(    self							:    List[Any]			):
    __lowercase			=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__			)



 @slow
 def       _lowercase				(    self							:    List[Any]			):
    for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __lowercase			=		DataaVecVisionModel.from_pretrained(lowerCamelCase__			)
       self.assertIsNotNone(lowerCamelCase__			)







def 						_A						(						)			->		Optional[int]:
   '''simple docstring'''


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




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







 """simple docstring"""







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



 @slow
 def       _lowercase				(    self							:    Optional[Any]			):
    __lowercase			=		DataaVecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k"			).to(
        lowerCamelCase__			)

    __lowercase			=		self.default_image_processor
    __lowercase			=		prepare_img()
    __lowercase			=		image_processor(images=lowerCamelCase__,      return_tensors="pt"			).to(lowerCamelCase__			)

    # forward pass
    with torch.no_grad():
       __lowercase			=		model(**lowerCamelCase__			)
    __lowercase			=		outputs.logits

    # verify the logits
    __lowercase			=		torch.Size((1, 1_0_0_0)			)
    self.assertEqual(logits.shape,      lowerCamelCase__			)

    __lowercase			=		torch.tensor([0.3_277, -0.1_395, 0.0_911]			).to(lowerCamelCase__			)

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

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







"""simple docstring"""





def 						_A						(						UpperCamelCase_  : float,    UpperCamelCase_  : float)			->		float:
   '''simple docstring'''


   __lowercase			=		float()
   try:
      __lowercase			=		mass * acceleration
   except Exception:
      return -0.0
   return force


if __name__ == "__main__":
   import doctest

   # run doctest
   doctest.testmod()

   # demo
   _a				   =							12.5
   _a				   =							10
   _a				   =							newtons_second_law_of_motion(mass, acceleration)
   print('The force is ', force, 'N')




