



"""simple docstring"""

from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros





def       __magic_name__    (   _lowerCamelCase				: List[Any]  ,       _lowerCamelCase				: List[str]					):
							__a		:		Dict									=       k_size // 2
							__a		:		str									=       mgrid[0 - center : k_size - center, 0 - center : k_size - center]
							__a		:		Optional[Any]									=       1 / (2 * pi * sigma) * exp(-(square(_lowerCamelCase					) + square(_lowerCamelCase					)) / (2 * square(_lowerCamelCase					))					)
							return g





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: int  ,       _lowerCamelCase				: Union[str, Any]					):
							__a		:		Optional[Any]									=       image.shape[0], image.shape[1]
							# dst image height and width
							__a		:		int									=       height - k_size + 1
							__a		:		Dict									=       width - k_size + 1

							# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
							__a		:		Any									=       zeros((dst_height * dst_width, k_size * k_size)					)
							__a		:		List[str]									=       0
							for i, j in product(range(_lowerCamelCase					)  ,       range(_lowerCamelCase					)					):
														__a		:		Optional[int]									=       ravel(image[i : i + k_size, j : j + k_size]					)
														__a		:		Union[str, Any]									=       window
														row += 1

							#  turn the kernel into shape(k*k, 1)
							__a		:		List[Any]									=       gen_gaussian_kernel(_lowerCamelCase  ,       _lowerCamelCase					)
							__a		:		List[str]									=       ravel(_lowerCamelCase					)

							# reshape and get the dst image
							__a		:		Optional[Any]									=       dot(_lowerCamelCase  ,       _lowerCamelCase					).reshape(_lowerCamelCase  ,       _lowerCamelCase					).astype(_lowerCamelCase					)

							return dst


if __name__ == "__main__":
				# read original image
				lowercase__			   =  imread(R"../image_data/lena.jpg")
				# turn image in gray scale value
				lowercase__			   =  cvtColor(img, COLOR_BGR2GRAY)

				# get values with two different mask size
				lowercase__			   =  gaussian_filter(gray, 3, sigma=1)
				lowercase__			   =  gaussian_filter(gray, 5, sigma=0.8)

				# show result images
				imshow("gaussian filter with 3x3 mask", gaussianaxa)
				imshow("gaussian filter with 5x5 mask", gaussianaxa)
				waitKey()






"""simple docstring"""

import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
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 (
	    TFEfficientFormerForImageClassification,
	    TFEfficientFormerForImageClassificationWithTeacher,
	    TFEfficientFormerModel,
	)
	from transformers.models.efficientformer.modeling_tf_efficientformer import (
	    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
	)


if is_vision_available():
	from PIL import Image

	from transformers import EfficientFormerImageProcessor




class SCREAMING_SNAKE_CASE__					:



						def __init__(self						,     _lowercase						,     _lowercase = 13						,     _lowercase = 64						,     _lowercase = 2						,     _lowercase = 3						,     _lowercase = 3						,     _lowercase = True						,     _lowercase = True						,     _lowercase = 128						,     _lowercase=[16, 32, 64, 128]						,     _lowercase = 7						,     _lowercase = 4						,     _lowercase = 37						,     _lowercase = "gelu"						,     _lowercase = 0.1						,     _lowercase = 0.1						,     _lowercase = 10						,     _lowercase = 0.02						,     _lowercase = 2						,     _lowercase = 1						,     _lowercase = 128						,     _lowercase = [2, 2, 2, 2]						,     _lowercase = 2						,     _lowercase = 2						,     ):





													'''simple docstring'''

													__a		:		str									=       parent
													__a		:		List[Any]									=       batch_size
													__a		:		int									=       image_size
													__a		:		Tuple									=       patch_size
													__a		:		str									=       num_channels
													__a		:		Union[str, Any]									=       is_training
													__a		:		List[Any]									=       use_labels
													__a		:		int									=       hidden_size
													__a		:		Optional[Any]									=       num_hidden_layers
													__a		:		List[Any]									=       num_attention_heads
													__a		:		Dict									=       intermediate_size
													__a		:		str									=       hidden_act
													__a		:		Dict									=       hidden_dropout_prob
													__a		:		str									=       attention_probs_dropout_prob
													__a		:		Optional[int]									=       type_sequence_label_size
													__a		:		Dict									=       initializer_range
													__a		:		Dict									=       encoder_stride
													__a		:		int									=       num_attention_outputs
													__a		:		List[Any]									=       embed_dim
													__a		:		Optional[Any]									=       embed_dim + 1
													__a		:		Optional[Any]									=       resolution
													__a		:		Optional[Any]									=       depths
													__a		:		Union[str, Any]									=       hidden_sizes
													__a		:		List[str]									=       dim
													__a		:		Any									=       mlp_expansion_ratio



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

													__a		:		str									=       None
													if self.use_labels:
																				__a		:		List[str]									=       ids_tensor([self.batch_size]						,     self.type_sequence_label_size							)

													__a		:		List[str]									=       self.get_config()

													return config, pixel_values, labels



						def     lowerCAmelCase__(self							):





													'''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=_lowercase						,     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     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       TFEfficientFormerModel(config=_lowercase							)
													__a		:		List[Any]									=       model(_lowercase						,     training=_lowercase							)
													self.parent.assertEqual(result.last_hidden_state.shape						,     (self.batch_size, self.seq_length, self.hidden_size)							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.type_sequence_label_size
													__a		:		Any									=       TFEfficientFormerForImageClassification(_lowercase							)
													__a		:		Union[str, Any]									=       model(_lowercase						,     labels=_lowercase						,     training=_lowercase							)
													self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.type_sequence_label_size)							)

													# test greyscale images
													__a		:		Optional[Any]									=       1
													__a		:		int									=       TFEfficientFormerForImageClassification(_lowercase							)

													__a		:		List[str]									=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
													__a		:		str									=       model(_lowercase						,     labels=_lowercase							)
													self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.type_sequence_label_size)							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       self.prepare_config_and_inputs()
													__a							,     __a							,     __a		:		Tuple									=       config_and_inputs
													__a		:		Tuple									=       {"""pixel_values""": pixel_values}
													return config, inputs_dict







@require_tf
class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					(
						    (
						        TFEfficientFormerModel,
						        TFEfficientFormerForImageClassificationWithTeacher,
						        TFEfficientFormerForImageClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCAmelCase							=					(
						    {
						        "feature-extraction": TFEfficientFormerModel,
						        "image-classification": (
						            TFEfficientFormerForImageClassification,
						            TFEfficientFormerForImageClassificationWithTeacher,
						        ),
						    }
						    if is_tf_available()
						    else {}
						)

						_lowerCAmelCase							=					False

						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False
						_lowerCAmelCase							=					False



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Tuple									=       TFEfficientFormerModelTester(self							)
													__a		:		Any									=       ConfigTester(
													    self						,     config_class=_lowercase						,     has_text_modality=_lowercase						,     hidden_size=37							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													self.config_tester.run_common_tests()



						@unittest.skip(reason="""EfficientFormer does not use inputs_embeds"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						@unittest.skip(reason="""EfficientFormer does not support input and output embeddings"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													pass



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		Optional[int]									=       self.model_tester.prepare_config_and_inputs_for_common()

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

																				__a		:		Union[str, Any]									=       ["""pixel_values"""]
																				self.assertListEqual(arg_names[:1]						,     _lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													def check_hidden_states_output(_lowercase						,     _lowercase						,     _lowercase							):
																				__a		:		Tuple									=       model_class(_lowercase							)

																				__a		:		int									=       model(**self._prepare_for_class(_lowercase						,     _lowercase							)						,     training=_lowercase							)
																				__a		:		Tuple									=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																				__a		:		str									=       getattr(
																				    self.model_tester						,     """expected_num_hidden_layers"""						,     self.model_tester.num_hidden_layers + 1							)
																				self.assertEqual(len(_lowercase							)						,     _lowercase							)

																				if hasattr(self.model_tester						,     """encoder_seq_length"""							):
																											__a		:		Any									=       self.model_tester.encoder_seq_length
																											if hasattr(self.model_tester						,     """chunk_length"""							) and self.model_tester.chunk_length > 1:
																																		__a		:		int									=       seq_length * self.model_tester.chunk_length
																				else:
																											__a		:		Any									=       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		:		Optional[int]									=       outputs.decoder_hidden_states

																											self.asseretIsInstance(_lowercase						,     (list, tuple)							)
																											self.assertEqual(len(_lowercase							)						,     _lowercase							)
																											__a		:		Any									=       getattr(self.model_tester						,     """seq_length"""						,     _lowercase							)
																											__a		:		List[Any]									=       getattr(self.model_tester						,     """decoder_seq_length"""						,     _lowercase							)

																											self.assertListEqual(
																											    list(hidden_states[-1].shape[-2:]							)						,     [decoder_seq_length, self.model_tester.hidden_size]						,     )

													__a							,     __a		:		Union[str, Any]									=       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__a		:		Dict									=       True
																				check_hidden_states_output(_lowercase						,     _lowercase						,     _lowercase							)

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				__a		:		int									=       True

																				check_hidden_states_output(_lowercase						,     _lowercase						,     _lowercase							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase						,     _lowercase=False							):





													'''simple docstring'''

													__a		:		Any									=       super()._prepare_for_class(_lowercase						,     _lowercase						,     return_labels=_lowercase							)

													if return_labels:
																				if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
																											del inputs_dict["labels"]

													return inputs_dict



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*_lowercase							)



						@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet"""							)
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Union[str, Any]									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_image_modeling(*_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*_lowercase							)



						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__a		:		Union[str, Any]									=       TFEfficientFormerModel.from_pretrained(_lowercase							)
																				self.assertIsNotNone(_lowercase							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		Optional[Any]									=       self.model_tester.prepare_config_and_inputs_for_common()
													__a		:		int									=       True

													__a		:		Optional[int]									=       getattr(self.model_tester						,     """seq_length"""						,     _lowercase							)
													__a		:		Dict									=       getattr(self.model_tester						,     """encoder_seq_length"""						,     _lowercase							)
													__a		:		Dict									=       getattr(self.model_tester						,     """key_length"""						,     _lowercase							)
													__a		:		int									=       getattr(self.model_tester						,     """chunk_length"""						,     _lowercase							)

													if chunk_length is not None and hasattr(self.model_tester						,     """num_hashes"""							):
																				__a		:		List[str]									=       encoder_seq_length * self.model_tester.num_hashes

													for model_class in self.all_model_classes:
																				__a		:		List[Any]									=       True
																				__a		:		Tuple									=       False
																				__a		:		List[Any]									=       True
																				__a		:		int									=       model_class(_lowercase							)

																				__a		:		List[Any]									=       model(**self._prepare_for_class(_lowercase						,     _lowercase							)						,     training=_lowercase							)
																				__a		:		Dict									=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																				self.assertEqual(len(_lowercase							)						,     self.model_tester.num_attention_outputs							)

																				# check that output_attentions also work using config
																				del inputs_dict["output_attentions"]
																				__a		:		Optional[Any]									=       True
																				__a		:		List[str]									=       model_class(_lowercase							)
																				__a		:		Dict									=       model(**self._prepare_for_class(_lowercase						,     _lowercase							)						,     training=_lowercase							)

																				__a		:		int									=       outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
																				self.assertEqual(len(_lowercase							)						,     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     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a							,     __a		:		List[str]									=       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				# Prepare our model
																				__a		:		Dict									=       model_class(_lowercase							)
																				# These are maximally general inputs for the model, with multiple None dimensions
																				# Hopefully this will catch any conditionals that fail for flexible shapes
																				__a		:		Optional[Any]									=       {
																				    key: tf.keras.Input(shape=val.shape[1:]						,     dtype=val.dtype						,     name=_lowercase							)
																				    for key, val in model.input_signature.items()
																				    if key in model.dummy_inputs
																				}
																				__a		:		Optional[Any]									=       model(_lowercase							)
																				self.assertTrue(outputs_dict is not None							)







def       __magic_name__    (   ):
							__a		:		int									=       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
							return image







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



						@cached_property
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													return (
													    EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300"""							)
													    if is_vision_available()
													    else None
													)



						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300"""							)
													__a		:		Optional[Any]									=       self.default_image_processor
													__a		:		List[str]									=       prepare_img()
													__a		:		int									=       image_processor(images=_lowercase						,     return_tensors="""tf"""							)
													# forward pass
													__a		:		Optional[Any]									=       model(**_lowercase						,     training=_lowercase							)
													# verify the logits
													__a		:		str									=       tf.TensorShape((1, 1000)							)
													self.assertEqual(outputs.logits.shape						,     _lowercase							)
													__a		:		Dict									=       tf.constant([-0.0555, 0.4825, -0.0852]							)
													self.assertTrue(np.allclose(outputs.logits[0, :3]						,     _lowercase						,     atol=1e-4							)							)





						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Any									=       TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
													    """snap-research/efficientformer-l1-300"""							)
													__a		:		Any									=       self.default_image_processor
													__a		:		str									=       prepare_img()
													__a		:		str									=       image_processor(images=_lowercase						,     return_tensors="""tf"""							)
													# forward pass
													__a		:		List[Any]									=       model(**_lowercase						,     training=_lowercase							)
													# verify the logits
													__a		:		int									=       tf.TensorShape((1, 1000)							)
													self.assertEqual(outputs.logits.shape						,     _lowercase							)
													__a		:		List[str]									=       tf.constant([-0.1312, 0.4353, -1.0499]							)
													self.assertTrue(np.allclose(outputs.logits[0, :3]						,     _lowercase						,     atol=1e-4							)							)

