
"""simple docstring"""

from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.swin.modeling_tf_swin import (
       TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFSwinForImageClassification,
       TFSwinForMaskedImageModeling,
       TFSwinModel,
   )


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor
class        _UpperCAmelCase							:




      def __init__(							self				,    lowercase_				,    lowercase_=1_3				,    lowercase_=3_2				,    lowercase_=2				,    lowercase_=3				,    lowercase_=1_6				,    lowercase_=[1, 2, 1]				,    lowercase_=[2, 2, 4]				,    lowercase_=2				,    lowercase_=2.0				,    lowercase_=True				,    lowercase_=0.0				,    lowercase_=0.0				,    lowercase_=0.1				,    lowercase_="gelu"				,    lowercase_=False				,    lowercase_=True				,    lowercase_=0.0_2				,    lowercase_=1E-5				,    lowercase_=True				,    lowercase_=None				,    lowercase_=True				,    lowercase_=1_0				,    lowercase_=8				,    )  ->   None:
          UpperCAmelCase       =   parent
          UpperCAmelCase       =   batch_size
          UpperCAmelCase       =   image_size
          UpperCAmelCase       =   patch_size
          UpperCAmelCase       =   num_channels
          UpperCAmelCase       =   embed_dim
          UpperCAmelCase       =   depths
          UpperCAmelCase       =   num_heads
          UpperCAmelCase       =   window_size
          UpperCAmelCase       =   mlp_ratio
          UpperCAmelCase       =   qkv_bias
          UpperCAmelCase       =   hidden_dropout_prob
          UpperCAmelCase       =   attention_probs_dropout_prob
          UpperCAmelCase       =   drop_path_rate
          UpperCAmelCase       =   hidden_act
          UpperCAmelCase       =   use_absolute_embeddings
          UpperCAmelCase       =   patch_norm
          UpperCAmelCase       =   layer_norm_eps
          UpperCAmelCase       =   initializer_range
          UpperCAmelCase       =   is_training
          UpperCAmelCase       =   scope
          UpperCAmelCase       =   use_labels
          UpperCAmelCase       =   type_sequence_label_size
          UpperCAmelCase       =   encoder_stride




      def       a_ (							self							)  ->   Any:
          UpperCAmelCase       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          UpperCAmelCase       =   None
          if self.use_labels:
              UpperCAmelCase       =   ids_tensor([self.batch_size]				,    self.type_sequence_label_size							)

          UpperCAmelCase       =   self.get_config()

          return config, pixel_values, labels




      def       a_ (							self							)  ->   Union[str, Any]:
          return SwinConfig(
              image_size=self.image_size				,    patch_size=self.patch_size				,    num_channels=self.num_channels				,    embed_dim=self.embed_dim				,    depths=self.depths				,    num_heads=self.num_heads				,    window_size=self.window_size				,    mlp_ratio=self.mlp_ratio				,    qkv_bias=self.qkv_bias				,    hidden_dropout_prob=self.hidden_dropout_prob				,    attention_probs_dropout_prob=self.attention_probs_dropout_prob				,    drop_path_rate=self.drop_path_rate				,    hidden_act=self.hidden_act				,    use_absolute_embeddings=self.use_absolute_embeddings				,    path_norm=self.patch_norm				,    layer_norm_eps=self.layer_norm_eps				,    initializer_range=self.initializer_range				,    encoder_stride=self.encoder_stride				,    )




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Any:
          UpperCAmelCase       =   TFSwinModel(config=lowercase_							)
          UpperCAmelCase       =   model(lowercase_							)

          UpperCAmelCase       =   ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
          UpperCAmelCase       =   int(config.embed_dim * 2 ** (len(config.depths							) - 1)							)

          self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, expected_seq_len, expected_dim)							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Dict:
          UpperCAmelCase       =   TFSwinForMaskedImageModeling(config=lowercase_							)
          UpperCAmelCase       =   model(lowercase_							)
          self.parent.assertEqual(
              result.logits.shape				,    (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

          # test greyscale images
          UpperCAmelCase       =   1
          UpperCAmelCase       =   TFSwinForMaskedImageModeling(lowercase_							)

          UpperCAmelCase       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
          UpperCAmelCase       =   model(lowercase_							)
          self.parent.assertEqual(result.logits.shape				,    (self.batch_size, 1, self.image_size, self.image_size)							)




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   Tuple:
          UpperCAmelCase       =   self.type_sequence_label_size
          UpperCAmelCase       =   TFSwinForImageClassification(lowercase_							)
          UpperCAmelCase       =   model(lowercase_				,    labels=lowercase_							)
          self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.type_sequence_label_size)							)

          # test greyscale images
          UpperCAmelCase       =   1
          UpperCAmelCase       =   TFSwinForImageClassification(lowercase_							)
          UpperCAmelCase       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
          UpperCAmelCase       =   model(lowercase_							)
          self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.type_sequence_label_size)							)




      def       a_ (							self							)  ->   Dict:
          UpperCAmelCase       =   self.prepare_config_and_inputs()
          UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase       =   config_and_inputs
          UpperCAmelCase       =   {'pixel_values': pixel_values}
          return config, inputs_dict

@require_tf
class        _UpperCAmelCase							(						SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_       ,     unittest.TestCase							):
      __SCREAMING_SNAKE_CASE				:							List[Any]							 =		(
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE				:							Optional[Any]							 =		(
          {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE				:							List[str]							 =		False
      __SCREAMING_SNAKE_CASE				:							List[str]							 =		False
      __SCREAMING_SNAKE_CASE				:							Optional[Any]							 =		False
      __SCREAMING_SNAKE_CASE				:							Tuple							 =		False




      def       a_ (							self							)  ->   str:
          UpperCAmelCase       =   TFSwinModelTester(self							)
          UpperCAmelCase       =   ConfigTester(self				,    config_class=lowercase_				,    embed_dim=3_7							)




      def       a_ (							self							)  ->   Optional[Any]:
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()




      def       a_ (							self							)  ->   Union[str, Any]:
          return




      def       a_ (							self							)  ->   int:
          UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_							)




      def       a_ (							self							)  ->   Optional[Any]:
          UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_							)




      def       a_ (							self							)  ->   str:
          UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_							)




      @unittest.skip(reason='Swin does not use inputs_embeds'							)
      def       a_ (							self							)  ->   Any:
          pass




      def       a_ (							self							)  ->   Any:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              UpperCAmelCase       =   model_class(lowercase_							)
              self.assertIsInstance(model.get_input_embeddings()				,    tf.keras.layers.Layer							)
              UpperCAmelCase       =   model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowercase_				,    tf.keras.layers.Dense							)							)




      def       a_ (							self							)  ->   Union[str, Any]:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase       =   ['pixel_values']
              self.assertListEqual(arg_names[:1]				,    lowercase_							)




      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase       =   True

          for model_class in self.all_model_classes:
              UpperCAmelCase       =   True
              UpperCAmelCase       =   False
              UpperCAmelCase       =   True
              UpperCAmelCase       =   model_class(lowercase_							)
              UpperCAmelCase       =   model(**self._prepare_for_class(lowercase_				,    lowercase_							)							)
              UpperCAmelCase       =   outputs.attentions
              UpperCAmelCase       =   len(self.model_tester.depths							)
              self.assertEqual(len(lowercase_							)				,    lowercase_							)

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              UpperCAmelCase       =   True
              UpperCAmelCase       =   config.window_size**2
              UpperCAmelCase       =   model_class(lowercase_							)
              UpperCAmelCase       =   model(**self._prepare_for_class(lowercase_				,    lowercase_							)							)
              UpperCAmelCase       =   outputs.attentions
              self.assertEqual(len(lowercase_							)				,    lowercase_							)

              self.assertListEqual(
                  list(attentions[0].shape[-3:]							)				,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,    )
              UpperCAmelCase       =   len(lowercase_							)

              # Check attention is always last and order is fine
              UpperCAmelCase       =   True
              UpperCAmelCase       =   True
              UpperCAmelCase       =   model_class(lowercase_							)
              UpperCAmelCase       =   model(**self._prepare_for_class(lowercase_				,    lowercase_							)							)

              if hasattr(self.model_tester				,    'num_hidden_states_types'							):
                  UpperCAmelCase       =   self.model_tester.num_hidden_states_types
              else:
                  # also another +1 for reshaped_hidden_states
                  UpperCAmelCase       =   2
              self.assertEqual(out_len + added_hidden_states				,    len(lowercase_							)							)

              UpperCAmelCase       =   outputs.attentions

              self.assertEqual(len(lowercase_							)				,    lowercase_							)

              self.assertListEqual(
                  list(self_attentions[0].shape[-3:]							)				,    [self.model_tester.num_heads[0], window_size_squared, window_size_squared]				,    )




      def       a_ (							self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)  ->   int:
          UpperCAmelCase       =   model_class(lowercase_							)
          UpperCAmelCase       =   model(**self._prepare_for_class(lowercase_				,    lowercase_							)							)
          UpperCAmelCase       =   outputs.hidden_states

          UpperCAmelCase       =   getattr(
              self.model_tester				,    'expected_num_hidden_layers'				,    len(self.model_tester.depths							) + 1							)
          self.assertEqual(len(lowercase_							)				,    lowercase_							)

          # Swin has a different seq_length
          UpperCAmelCase       =   to_atuple(config.patch_size							)

          UpperCAmelCase       =   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

          self.assertListEqual(
              list(hidden_states[0].shape[-2:]							)				,    [num_patches, self.model_tester.embed_dim]				,    )

          UpperCAmelCase       =   outputs.reshaped_hidden_states
          self.assertEqual(len(lowercase_							)				,    lowercase_							)

          UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase     ,							UpperCAmelCase       =   reshaped_hidden_states[0].shape

          UpperCAmelCase       =   tf.reshape(reshaped_hidden_states[0]				,    (batch_size, num_channels, height * width)							)
          UpperCAmelCase       =   tf.transpose(lowercase_				,    (0, 2, 1)							)

          self.assertListEqual(
              list(reshaped_hidden_states.shape[-2:]							)				,    [num_patches, self.model_tester.embed_dim]				,    )




      def       a_ (							self							)  ->   Tuple:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()

          UpperCAmelCase       =   to_atuple(self.model_tester.image_size							)

          for model_class in self.all_model_classes:
              UpperCAmelCase       =   True
              self.check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase       =   True

              self.check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_				,    lowercase_							)




      def       a_ (							self							)  ->   Any:
          UpperCAmelCase     ,							UpperCAmelCase       =   self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase       =   3

          UpperCAmelCase       =   to_atuple(self.model_tester.image_size							)
          UpperCAmelCase       =   to_atuple(config.patch_size							)

          UpperCAmelCase       =   image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          UpperCAmelCase       =   image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
              UpperCAmelCase       =   True
              self.check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_				,    (padded_height, padded_width)							)

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase       =   True
              self.check_hidden_states_output(lowercase_				,    lowercase_				,    lowercase_				,    (padded_height, padded_width)							)




      @slow
      def       a_ (							self							)  ->   List[str]:
          for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase       =   TFSwinModel.from_pretrained(lowercase_							)
              self.assertIsNotNone(lowercase_							)

@require_vision
@require_tf
class        _UpperCAmelCase							(						unittest.TestCase							):




      @cached_property
      def       a_ (							self							)  ->   Optional[Any]:
          return (
              AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
              if is_vision_available()
              else None
          )




      @slow
      def       a_ (							self							)  ->   List[Any]:
          UpperCAmelCase       =   TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'							)
          UpperCAmelCase       =   self.default_image_processor

          UpperCAmelCase       =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
          UpperCAmelCase       =   image_processor(images=lowercase_				,    return_tensors='tf'							)

          # forward pass
          UpperCAmelCase       =   model(lowercase_							)

          # verify the logits
          UpperCAmelCase       =   tf.TensorShape((1, 1_0_0_0)							)
          self.assertEqual(outputs.logits.shape				,    lowercase_							)
          UpperCAmelCase       =   tf.constant([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]							)
          self.assertTrue(np.allclose(outputs.logits[0, :3]				,    lowercase_				,    atol=1E-4							)							)





"""simple docstring"""

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

SCREAMING_SNAKE_CASE_         =  180




def       lowercase__  (   lowerCAmelCase			:   str	,			lowerCAmelCase			:   List[Any]	)	-> Any:






    """simple docstring"""




    UpperCAmelCase       =   k_size // 2
    UpperCAmelCase     ,							UpperCAmelCase       =   np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    UpperCAmelCase       =   (
        1
        / (2 * np.pi * sigma)
        * np.exp(-(np.square(lowerCAmelCase	) + np.square(lowerCAmelCase	)) / (2 * np.square(lowerCAmelCase	))	)
    )
    return g




def       lowercase__  (   lowerCAmelCase			:   int	,			lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Union[str, Any]	)	-> List[Any]:






    """simple docstring"""




    UpperCAmelCase       =   np.zeros(lowerCAmelCase	)

    for row in range(1	,			image_shape[0] - 1	):
        for col in range(1	,			image_shape[1] - 1	):
            UpperCAmelCase       =   gradient_direction[row, col]

            if (
                0 <= direction < PI / 8
                or 15 * PI / 8 <= direction <= 2 * PI
                or 7 * PI / 8 <= direction <= 9 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row, col - 1]
                UpperCAmelCase       =   sobel_grad[row, col + 1]
                if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                PI / 8 <= direction < 3 * PI / 8
                or 9 * PI / 8 <= direction < 11 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row + 1, col - 1]
                UpperCAmelCase       =   sobel_grad[row - 1, col + 1]
                if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                3 * PI / 8 <= direction < 5 * PI / 8
                or 11 * PI / 8 <= direction < 13 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row - 1, col]
                UpperCAmelCase       =   sobel_grad[row + 1, col]
                if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                    UpperCAmelCase       =   sobel_grad[row, col]

            elif (
                5 * PI / 8 <= direction < 7 * PI / 8
                or 13 * PI / 8 <= direction < 15 * PI / 8
            ):
                UpperCAmelCase       =   sobel_grad[row - 1, col - 1]
                UpperCAmelCase       =   sobel_grad[row + 1, col + 1]
                if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                    UpperCAmelCase       =   sobel_grad[row, col]

    return destination




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Any	,			lowerCAmelCase			:   List[str]	)	-> Any:






    """simple docstring"""




    for row in range(1	,			image_shape[0] - 1	):
        for col in range(1	,			image_shape[1] - 1	):
            if destination[row, col] >= threshold_high:
                UpperCAmelCase       =   strong
            elif destination[row, col] <= threshold_low:
                UpperCAmelCase       =   0
            else:
                UpperCAmelCase       =   weak




def       lowercase__  (   lowerCAmelCase			:   List[str]	,			lowerCAmelCase			:   Union[str, Any]	,			lowerCAmelCase			:   Tuple	,			lowerCAmelCase			:   Dict	)	-> List[str]:






    """simple docstring"""




    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],
                ):
                    UpperCAmelCase       =   strong
                else:
                    UpperCAmelCase       =   0




def       lowercase__  (   lowerCAmelCase			:   Optional[Any]	,			lowerCAmelCase			:   int=15	,			lowerCAmelCase			:   Dict=30	,			lowerCAmelCase			:   str=128	,			lowerCAmelCase			:   Optional[Any]=255	)	-> Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase       =   img_convolve(lowerCAmelCase	,			gen_gaussian_kernel(9	,			sigma=1.4	)	)
    # get the gradient and degree by sobel_filter
    UpperCAmelCase     ,							UpperCAmelCase       =   sobel_filter(lowerCAmelCase	)
    UpperCAmelCase       =   PI + np.radadeg(lowerCAmelCase	)

    UpperCAmelCase       =   suppress_non_maximum(image.shape	,			lowerCAmelCase	,			lowerCAmelCase	)

    detect_high_low_threshold(
        image.shape	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    track_edge(image.shape	,			lowerCAmelCase	,			lowerCAmelCase	,			lowerCAmelCase	)

    return destination


if __name__ == "__main__":
   # read original image in gray mode
   SCREAMING_SNAKE_CASE_         =  cva.imread(r'''../image_data/lena.jpg''', 0)
   # canny edge detection
   SCREAMING_SNAKE_CASE_         =  canny(lena)
   cva.imshow('''canny''', canny_destination)
   cva.waitKey(0)



