



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					)					)




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)