code: 
  
  
  
  import inspect
  import unittest
  import warnings
  from typing import List
  
  from transformers import EfficientFormerConfig
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
      import torch
  
      from transformers import (
          MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          MODEL_MAPPING,
          EfficientFormerForImageClassification,
          EfficientFormerForImageClassificationWithTeacher,
          EfficientFormerModel,
      )
      from transformers.models.efficientformer.modeling_efficientformer import (
          EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      )
  
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import EfficientFormerImageProcessor
  
  
  
  class 		_snake_case      :
  
  
  
  
  
   """simple docstring"""
  
  
  
   def __init__(						self	,    a	,    a = 1_3	,    a = 6_4	,    a = 2	,    a = 3	,    a = 3	,    a = True	,    a = True	,    a = 1_2_8	,    a=[1_6, 3_2, 6_4, 1_2_8]	,    a = 7	,    a = 4	,    a = 3_7	,    a = "gelu"	,    a = 0.1	,    a = 0.1	,    a = 1_0	,    a = 0.02	,    a = 2	,    a = 1	,    a = 1_2_8	,    a = [2, 2, 2, 2]	,    a = 2	,    a = 2	,    )	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    parent
     _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           =    encoder_stride
     _A           =    num_attention_outputs
     _A           =    embed_dim
     _A           =    embed_dim + 1
     _A           =    resolution
     _A           =    depths
     _A           =    hidden_sizes
     _A           =    dim
     _A           =    mlp_expansion_ratio
   def lowercase_		(						self					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)
  
     _A           =    None
     if self.use_labels:
       _A           =    ids_tensor([self.batch_size]	,    self.type_sequence_label_size					)
  
     _A           =    self.get_config()
     return config, pixel_values, labels
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return EfficientFormerConfig(
         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	,    encoder_stride=self.encoder_stride	,    resolution=self.resolution	,    depths=self.depths	,    hidden_sizes=self.hidden_sizes	,    dim=self.dim	,    mlp_expansion_ratio=self.mlp_expansion_ratio	,    )
   def lowercase_		(						self	,    a	,    a	,    a					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    EfficientFormerModel(config=a					)
     model.to(a					)
     model.eval()
     _A           =    model(a					)
     self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)					)
   def lowercase_		(						self	,    a	,    a	,    a					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.type_sequence_label_size
     _A           =    EfficientFormerForImageClassification(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)					)
  
     # test greyscale images
     _A           =    1
     _A           =    EfficientFormerForImageClassification(a					)
     model.to(a					)
     model.eval()
  
     _A           =    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
     _A           =    model(a					)
     self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.type_sequence_label_size)					)
  
  
  
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.prepare_config_and_inputs()
     (
         _A  
     )           =    config_and_inputs
     _A           =    {"pixel_values": pixel_values}
     return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class 		_snake_case      (			A__					,A__					,unittest.TestCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			(
       (
           EfficientFormerModel,
           EfficientFormerForImageClassificationWithTeacher,
           EfficientFormerForImageClassification,
       )
       if is_torch_available()
       else ()
   )
   lowerCamelCase_          =			(
       {
           '''feature-extraction''': EfficientFormerModel,
           '''image-classification''': (
               EfficientFormerForImageClassification,
               EfficientFormerForImageClassificationWithTeacher,
           ),
       }
       if is_torch_available()
       else {}
   )
   lowerCamelCase_          =			False
  
   lowerCamelCase_          =			False
   lowerCamelCase_          =			False
   lowerCamelCase_          =			False
   def lowercase_		(						self					)	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    EfficientFormerModelTester(self					)
     _A           =    ConfigTester(
         self	,    config_class=a	,    has_text_modality=a	,    hidden_size=3_7					)
   def lowercase_		(						self					)	->							List[str]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     self.config_tester.run_common_tests()
   @unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''					)
   def lowercase_		(						self					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     pass
   @unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''					)
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     pass
   def lowercase_		(						self					)	->							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 lowercase_		(						self					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     def check_hidden_states_output(a	,    a	,    a					):
       _A           =    model_class(a					)
       model.to(a					)
       model.eval()
  
       with torch.no_grad():
         _A           =    model(**self._prepare_for_class(a	,    a					)					)
  
       _A           =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
  
       _A           =    getattr(
           self.model_tester	,    '''expected_num_hidden_layers'''	,    self.model_tester.num_hidden_layers + 1					)
       self.assertEqual(len(a					)	,    a					)
  
       if hasattr(self.model_tester	,    '''encoder_seq_length'''					):
         _A           =    self.model_tester.encoder_seq_length
         if hasattr(self.model_tester	,    '''chunk_length'''					) and self.model_tester.chunk_length > 1:
           _A           =    seq_length * self.model_tester.chunk_length
       else:
         _A           =    self.model_tester.seq_length
  
       self.assertListEqual(
           list(hidden_states[-1].shape[-2:]					)	,    [seq_length, self.model_tester.hidden_size]	,    )
  
       if config.is_encoder_decoder:
         _A           =    outputs.decoder_hidden_states
  
         self.assertIsInstance(a	,    (list, tuple)					)
         self.assertEqual(len(a					)	,    a					)
         _A           =    getattr(self.model_tester	,    '''seq_length'''	,    a					)
         _A           =    getattr(self.model_tester	,    '''decoder_seq_length'''	,    a					)
  
         self.assertListEqual(
             list(hidden_states[-1].shape[-2:]					)	,    [decoder_seq_length, self.model_tester.hidden_size]	,    )
  
     _A           =    self.model_tester.prepare_config_and_inputs_for_common()
  
     for model_class in self.all_model_classes:
       _A           =    True
       check_hidden_states_output(a	,    a	,    a					)
  
       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _A           =    True
  
       check_hidden_states_output(a	,    a	,    a					)
   def lowercase_		(						self	,    a	,    a	,    a=False					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    super()._prepare_for_class(a	,    a	,    return_labels=a					)
  
     if return_labels:
       if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
         del inputs_dict["labels"]
  
     return inputs_dict
   def lowercase_		(						self					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*a					)
   @unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''					)
   def lowercase_		(						self					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*a					)
   def lowercase_		(						self					)	->							List[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*a					)
   def lowercase_		(						self					)	->							List[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:
       # EfficientFormerForImageClassificationWithTeacher supports inference-only
       if (
           model_class in get_values(a					)
           or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
       ):
         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 lowercase_		(						self					)	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.model_tester.prepare_config_and_inputs_for_common()
  
     _A           =    [
         {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
         {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
         {"title": "regression", "num_labels": 1, "dtype": torch.float},
     ]
  
     for model_class in self.all_model_classes:
       if (
           model_class
           not in [
               *get_values(a					),
           ]
           or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
       ):
         continue
  
       for problem_type in problem_types:
         with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}'''					):
           _A           =    problem_type["title"]
           _A           =    problem_type["num_labels"]
  
           _A           =    model_class(a					)
           model.to(a					)
           model.train()
  
           _A           =    self._prepare_for_class(a	,    a	,    return_labels=a					)
  
           if problem_type["num_labels"] > 1:
             _A           =    inputs["labels"].unsqueeze(1					).repeat(1	,    problem_type['''num_labels''']					)
  
           _A           =    inputs["labels"].to(problem_type['''dtype''']					)
  
           # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
           # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
           # they have the same size." which is a symptom something in wrong for the regression problem.
           # See https://github.com/huggingface/transformers/issues/11780
           with warnings.catch_warnings(record=a					) as warning_list:
             _A           =    model(**a					).loss
           for w in warning_list:
             if "Using a target size that is different to the input size" in str(w.message					):
               raise ValueError(
                   f'''Something is going wrong in the regression problem: intercepted {w.message}'''					)
  
           loss.backward()
   @slow
   def lowercase_		(						self					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _A           =    EfficientFormerModel.from_pretrained(a					)
       self.assertIsNotNone(a					)
  
  
  
   def lowercase_		(						self					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.model_tester.prepare_config_and_inputs_for_common()
     _A           =    True
  
     _A           =    getattr(self.model_tester	,    '''seq_length'''	,    a					)
     _A           =    getattr(self.model_tester	,    '''encoder_seq_length'''	,    a					)
     _A           =    getattr(self.model_tester	,    '''key_length'''	,    a					)
     _A           =    getattr(self.model_tester	,    '''chunk_length'''	,    a					)
  
     if chunk_length is not None and hasattr(self.model_tester	,    '''num_hashes'''					):
       _A           =    encoder_seq_length * self.model_tester.num_hashes
  
     for model_class in self.all_model_classes:
       _A           =    True
       _A           =    False
       _A           =    True
       _A           =    model_class(a					)
       model.to(a					)
       model.eval()
       with torch.no_grad():
         _A           =    model(**self._prepare_for_class(a	,    a					)					)
       _A           =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(a					)	,    self.model_tester.num_attention_outputs					)
  
       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       _A           =    True
       _A           =    model_class(a					)
       model.to(a					)
       model.eval()
       with torch.no_grad():
         _A           =    model(**self._prepare_for_class(a	,    a					)					)
       _A           =    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(a					)	,    self.model_tester.num_attention_outputs					)
  
       if chunk_length is not None:
         self.assertListEqual(
             list(attentions[0].shape[-4:]					)	,    [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]	,    )
       else:
         self.assertListEqual(
             list(attentions[0].shape[-3:]					)	,    [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]	,    )
  
  
  
  
  
  
  
  def     UpperCAmelCase__ (							)   -> List[Any]:
    _A           =    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
    return image
  
  
  
  @require_torch
  @require_vision
  class 		_snake_case      (			unittest.TestCase				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   @cached_property
   def lowercase_		(						self					)	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return (
         EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''					)
         if is_vision_available()
         else None
     )
   @slow
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''					).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					)
  
     # verify the logits
     _A           =    (1, 1_0_0_0)
     self.assertEqual(outputs.logits.shape	,    a					)
  
     _A           =    torch.tensor([-0.0_555, 0.4_825, -0.0_852]					).to(a					)
     self.assertTrue(torch.allclose(outputs.logits[0][:3]	,    a	,    atol=1e-4					)					)
  
  
  
   @slow
   def lowercase_		(						self					)	->							str:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    EfficientFormerForImageClassificationWithTeacher.from_pretrained(
         '''snap-research/efficientformer-l1-300'''					).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					)
  
     # verify the logits
     _A           =    (1, 1_0_0_0)
     self.assertEqual(outputs.logits.shape	,    a					)
  
     _A           =    torch.tensor([-0.1_312, 0.4_353, -1.0_499]					).to(a					)
     self.assertTrue(torch.allclose(outputs.logits[0][:3]	,    a	,    atol=1e-4					)					)
style_context: 
  
  
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  __lowerCamelCase  				=					1_8_0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> Dict:
    _A           =    k_size // 2
    _A    ,   _A           =    np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    _A           =    (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(__snake_case							) + np.square(__snake_case							)) / (2 * np.square(__snake_case							))							)
    )
    return g
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> int:
    _A           =    np.zeros(__snake_case							)
  
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        _A           =    gradient_direction[row, col]
  
        if (
            0 <= direction < PI / 8
            or 15 * PI / 8 <= direction <= 2 * PI
            or 7 * PI / 8 <= direction <= 9 * PI / 8
        ):
          _A           =    sobel_grad[row, col - 1]
          _A           =    sobel_grad[row, col + 1]
          if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
            _A           =    sobel_grad[row, col]
  
        elif (
            PI / 8 <= direction < 3 * PI / 8
            or 9 * PI / 8 <= direction < 11 * PI / 8
        ):
          _A           =    sobel_grad[row + 1, col - 1]
          _A           =    sobel_grad[row - 1, col + 1]
          if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
            _A           =    sobel_grad[row, col]
  
        elif (
            3 * PI / 8 <= direction < 5 * PI / 8
            or 11 * PI / 8 <= direction < 13 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col]
          _A           =    sobel_grad[row + 1, col]
          if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
            _A           =    sobel_grad[row, col]
  
        elif (
            5 * PI / 8 <= direction < 7 * PI / 8
            or 13 * PI / 8 <= direction < 15 * PI / 8
        ):
          _A           =    sobel_grad[row - 1, col - 1]
          _A           =    sobel_grad[row + 1, col + 1]
          if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
            _A           =    sobel_grad[row, col]
  
    return destination
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Optional[int]:
    for row in range(1						,				image_shape[0] - 1							):
      for col in range(1						,				image_shape[1] - 1							):
        if destination[row, col] >= threshold_high:
          _A           =    strong
        elif destination[row, col] <= threshold_low:
          _A           =    0
        else:
          _A           =    weak
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> List[Any]:
    for row in range(1						,				image_shape[0]							):
      for col in range(1						,				image_shape[1]							):
        if destination[row, col] == weak:
          if 255 in (
              destination[row, col + 1],
              destination[row, col - 1],
              destination[row - 1, col],
              destination[row + 1, col],
              destination[row - 1, col - 1],
              destination[row + 1, col - 1],
              destination[row - 1, col + 1],
              destination[row + 1, col + 1],
          ):
            _A           =    strong
          else:
            _A           =    0
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case=15						,				__snake_case=30						,				__snake_case=128						,				__snake_case=255							)   -> List[Any]:
    # gaussian_filter
    _A           =    img_convolve(__snake_case						,				gen_gaussian_kernel(9						,				sigma=1.4							)							)
    # get the gradient and degree by sobel_filter
    _A    ,   _A           =    sobel_filter(__snake_case							)
    _A           =    PI + np.radadeg(__snake_case							)
  
    _A           =    suppress_non_maximum(image.shape						,				__snake_case						,				__snake_case							)
  
    detect_high_low_threshold(
        image.shape						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)
  
    track_edge(image.shape						,				__snake_case						,				__snake_case						,				__snake_case							)
  
    return destination
  
  
  if __name__ == "__main__":
      # read original image in gray mode
      __lowerCamelCase  				=					cva.imread(R"""../image_data/lena.jpg""", 0)
      # canny edge detection
      __lowerCamelCase  				=					canny(lena)
      cva.imshow("""canny""", canny_destination)
      cva.waitKey(0)