from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
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 import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class      __lowercase					:

						def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      int=3							,							__lowerCamelCase				:      Optional[Any]=32							,							__lowerCamelCase				:      Any=3							,							__lowerCamelCase				:      int=10							,							__lowerCamelCase				:      List[str]=[10, 20, 30, 40]							,							__lowerCamelCase				:      Optional[Any]=[1, 1, 2, 1]							,							__lowerCamelCase				:      Optional[int]=True							,							__lowerCamelCase				:      Union[str, Any]=True							,							__lowerCamelCase				:      Dict="relu"							,							__lowerCamelCase				:      Any=3							,							__lowerCamelCase				:      Optional[int]=None							,							)							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		image_size
									lowercase							=		num_channels
									lowercase							=		embeddings_size
									lowercase							=		hidden_sizes
									lowercase							=		depths
									lowercase							=		is_training
									lowercase							=		use_labels
									lowercase							=		hidden_act
									lowercase							=		num_labels
									lowercase							=		scope
									lowercase							=		len(__lowerCamelCase   )

						def        __a		(	self				:      Any   )							->		Dict:


									'''simple docstring'''

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

									lowercase							=		None
									if self.use_labels:
												lowercase							=		ids_tensor([self.batch_size]							,							self.num_labels   )

									lowercase							=		self.get_config()
									return config, pixel_values, labels

						def        __a		(	self				:      str   )							->		List[str]:


									'''simple docstring'''

									return RegNetConfig(
									    num_channels=self.num_channels							,							embeddings_size=self.embeddings_size							,							hidden_sizes=self.hidden_sizes							,							depths=self.depths							,							hidden_act=self.hidden_act							,							num_labels=self.num_labels							,							)

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      Optional[int]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Tuple   )							->		Union[str, Any]:


									'''simple docstring'''

									lowercase							=		TFRegNetModel(config=__lowerCamelCase   )
									lowercase							=		model(__lowerCamelCase							,							training=__lowerCamelCase   )
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape							,							(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,							)

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      int   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.num_labels
									lowercase							=		TFRegNetForImageClassification(__lowerCamelCase   )
									lowercase							=		model(__lowerCamelCase							,							labels=__lowerCamelCase							,							training=__lowerCamelCase   )
									self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.num_labels)   )



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


									'''simple docstring'''

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






@require_tf
class      __lowercase					(    _A       ,     _A       ,     unittest.TestCase    ):
						lowercase					 =     (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
						lowercase					 =     (
						    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
						    if is_tf_available()
						    else {}
						)

						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False
						lowercase					 =     False

						def        __a		(	self				:      Dict   )							->		Optional[int]:


									'''simple docstring'''

									lowercase							=		TFRegNetModelTester(self   )
									lowercase							=		ConfigTester(self							,							config_class=__lowerCamelCase							,							has_text_modality=__lowerCamelCase   )

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


									'''simple docstring'''

									return

						@unittest.skip(reason='''RegNet does not use inputs_embeds'''   )
						def        __a		(	self				:      List[str]   )							->		List[str]:


									'''simple docstring'''

									pass

						@unittest.skipIf(
						    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''   )   ) == 0							,							reason='''TF does not support backprop for grouped convolutions on CPU.'''							,							)
						@slow
						def        __a		(	self				:      List[Any]   )							->		Optional[int]:


									'''simple docstring'''

									super().test_keras_fit()

						@unittest.skip(reason='''RegNet does not support input and output embeddings'''   )
						def        __a		(	self				:      List[Any]   )							->		Any:


									'''simple docstring'''

									pass

						def        __a		(	self				:      str   )							->		Any:


									'''simple docstring'''

									lowercase		,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.call   )
												# 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        __a		(	self				:      Optional[int]   )							->		str:


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase   )

						def        __a		(	self				:      Tuple   )							->		int:


									'''simple docstring'''

									def check_hidden_states_output(__lowerCamelCase				:      str							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Tuple   ):
												lowercase							=		model_class(__lowerCamelCase   )
												lowercase							=		model(**self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )							,							training=__lowerCamelCase   )

												lowercase							=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												lowercase							=		self.model_tester.num_stages
												self.assertEqual(len(__lowerCamelCase   )							,							expected_num_stages + 1   )

												# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]   )							,							[self.model_tester.image_size // 2, self.model_tester.image_size // 2]							,							)

									lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()
									lowercase							=		['''basic''', '''bottleneck''']
									for model_class in self.all_model_classes:
												for layer_type in layers_type:
															lowercase							=		layer_type
															lowercase							=		True
															check_hidden_states_output(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

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

															check_hidden_states_output(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase		,lowercase							=		self.model_tester.prepare_config_and_inputs_for_common()

									def check_equivalence(__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Any={}   ):
												lowercase							=		model(__lowerCamelCase							,							return_dict=__lowerCamelCase							,							**__lowerCamelCase   )
												lowercase							=		model(__lowerCamelCase							,							return_dict=__lowerCamelCase							,							**__lowerCamelCase   ).to_tuple()

												def recursive_check(__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int   ):
															if isinstance(__lowerCamelCase							,							(List, Tuple)   ):
																		for tuple_iterable_value, dict_iterable_value in zip(__lowerCamelCase							,							__lowerCamelCase   ):
																					recursive_check(__lowerCamelCase							,							__lowerCamelCase   )
															elif tuple_object is None:
																		return
															else:
																		self.assertTrue(
																		    all(tf.equal(__lowerCamelCase							,							__lowerCamelCase   )   )							,							msg=(
																		        '''Tuple and dict output are not equal. Difference:'''
																		        f' {tf.math.reduce_max(tf.abs(tuple_object - dict_object   )   )}'
																		    )							,							)

															recursive_check(__lowerCamelCase							,							__lowerCamelCase   )

									for model_class in self.all_model_classes:
												lowercase							=		model_class(__lowerCamelCase   )

												lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )
												lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )
												check_equivalence(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase							,							return_labels=__lowerCamelCase   )
												lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase							,							return_labels=__lowerCamelCase   )
												check_equivalence(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )
												lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase   )
												check_equivalence(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							{'''output_hidden_states''': True}   )

												lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase							,							return_labels=__lowerCamelCase   )
												lowercase							=		self._prepare_for_class(__lowerCamelCase							,							__lowerCamelCase							,							return_labels=__lowerCamelCase   )
												check_equivalence(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							{'''output_hidden_states''': True}   )

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


									'''simple docstring'''

									lowercase							=		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase   )



						@slow
						def        __a		(	self				:      List[str]   )							->		List[str]:


									'''simple docstring'''

									for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase							=		TFRegNetModel.from_pretrained(__lowerCamelCase   )
												self.assertIsNotNone(__lowerCamelCase   )






def        __UpperCAmelCase    (       )->       Tuple:



			"""simple docstring"""







			lowercase							=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
			return image






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

						@cached_property
						def        __a		(	self				:      Tuple   )							->		Tuple:


									'''simple docstring'''

									return (
									    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
									    if is_vision_available()
									    else None
									)



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


									'''simple docstring'''

									lowercase							=		TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )

									lowercase							=		self.default_image_processor
									lowercase							=		prepare_img()
									lowercase							=		image_processor(images=__lowerCamelCase							,							return_tensors='''tf'''   )

									# forward pass
									lowercase							=		model(**__lowerCamelCase							,							training=__lowerCamelCase   )

									# verify the logits
									lowercase							=		tf.TensorShape((1, 10_00)   )
									self.assertEqual(outputs.logits.shape							,							__lowerCamelCase   )

									lowercase							=		tf.constant([-0.4180, -1.5051, -3.4836]   )

									tf.debugging.assert_near(outputs.logits[0, :3]							,							__lowerCamelCase							,							atol=1E-4   )






import math
import sys

import cva
import numpy as np






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		math.sqrt(UpperCAmelCase       )
			lowercase							=		1 / (sigma * math.sqrt(2 * math.pi       ))
			return cons * np.exp(-((img / sigma) ** 2) * 0.5       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		kernel_size // 2
			return img[x - half : x + half + 1, y - half : y + half + 1]






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase       )->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros((kernel_size, kernel_size)       )
			for i in range(0,		UpperCAmelCase       ):
						for j in range(0,		UpperCAmelCase       ):
									lowercase							=		math.sqrt(
									    abs(i - kernel_size // 2       ) ** 2 + abs(j - kernel_size // 2       ) ** 2       )
			return vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		)->       np.ndarray:



			"""simple docstring"""







			lowercase							=		np.zeros(img.shape       )
			lowercase							=		get_gauss_kernel(UpperCAmelCase,		UpperCAmelCase       )
			lowercase		,lowercase							=		img.shape
			for i in range(kernel_size // 2,		size_x - kernel_size // 2       ):
						for j in range(kernel_size // 2,		size_y - kernel_size // 2       ):
									lowercase							=		get_slice(UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		img_s - img_s[kernel_size // 2, kernel_size // 2]
									lowercase							=		vec_gaussian(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.multiply(UpperCAmelCase,		UpperCAmelCase       )
									lowercase							=		np.sum(UpperCAmelCase       ) / np.sum(UpperCAmelCase       )
									lowercase							=		val
			return imga






def        __UpperCAmelCase    (       UpperCAmelCase       )->       tuple:



			"""simple docstring"""







			lowercase							=		args[1] if args[1:] else '''../image_data/lena.jpg'''
			lowercase							=		float(args[2]       ) if args[2:] else 1.0
			lowercase							=		float(args[3]       ) if args[3:] else 1.0
			if args[4:]:
						lowercase							=		int(args[4]       )
						lowercase							=		kernel_size + abs(kernel_size % 2 - 1       )
			else:
						lowercase							=		5
			return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
						A_    ,    A_    ,    A_    ,    A_      =      parse_args(sys.argv)
						A_      =      cva.imread(filename, 0)
						cva.imshow("input image", img)

						A_      =      img / 255
						A_      =      out.astype("float32")
						A_      =      bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
						A_      =      out * 255
						A_      =      np.uinta(out)
						cva.imshow("output image", out)
						cva.waitKey(0)
						cva.destroyAllWindows()





