
'''simple docstring'''







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
					    TFDeiTForImageClassification,
					    TFDeiTForImageClassificationWithTeacher,
					    TFDeiTForMaskedImageModeling,
					    TFDeiTModel,
					)
					from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor


class 						__UpperCAmelCase      :


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_=13    ,   lowerCAmelCase_=30    ,   lowerCAmelCase_=2    ,   lowerCAmelCase_=3    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=32    ,   lowerCAmelCase_=2    ,   lowerCAmelCase_=4    ,   lowerCAmelCase_=37    ,   lowerCAmelCase_="gelu"    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=10    ,   lowerCAmelCase_=0.02    ,   lowerCAmelCase_=3    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=2    ,   ):





								"""simple docstring"""






								_snake_case   						=		parent
								_snake_case   						=		batch_size
								_snake_case   						=		image_size
								_snake_case   						=		patch_size
								_snake_case   						=		num_channels
								_snake_case   						=		is_training
								_snake_case   						=		use_labels
								_snake_case   						=		hidden_size
								_snake_case   						=		num_hidden_layers
								_snake_case   						=		num_attention_heads
								_snake_case   						=		intermediate_size
								_snake_case   						=		hidden_act
								_snake_case   						=		hidden_dropout_prob
								_snake_case   						=		attention_probs_dropout_prob
								_snake_case   						=		type_sequence_label_size
								_snake_case   						=		initializer_range
								_snake_case   						=		scope
								_snake_case   						=		encoder_stride

								# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
								_snake_case   						=		(image_size // patch_size) ** 2
								_snake_case   						=		num_patches + 2


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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

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

								_snake_case   						=		self.get_config()

								return config, pixel_values, labels


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return DeiTConfig(
								    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=lowerCAmelCase_    ,   initializer_range=self.initializer_range    ,   encoder_stride=self.encoder_stride    ,   )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		TFDeiTModel(config=lowerCAmelCase_  )
								_snake_case   						=		model(lowerCAmelCase_  )
								self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		TFDeiTForMaskedImageModeling(config=lowerCAmelCase_  )
								_snake_case   						=		model(lowerCAmelCase_  )
								self.parent.assertEqual(
								    result.reconstruction.shape    ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

								# test greyscale images
								_snake_case   						=		1
								_snake_case   						=		TFDeiTForMaskedImageModeling(lowerCAmelCase_  )

								_snake_case   						=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
								_snake_case   						=		model(lowerCAmelCase_  )
								self.parent.assertEqual(result.reconstruction.shape    ,   (self.batch_size, 1, self.image_size, self.image_size)  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		self.type_sequence_label_size
								_snake_case   						=		TFDeiTForImageClassification(lowerCAmelCase_  )
								_snake_case   						=		model(lowerCAmelCase_    ,   labels=lowerCAmelCase_  )
								self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)  )

								# test greyscale images
								_snake_case   						=		1
								_snake_case   						=		TFDeiTForImageClassification(lowerCAmelCase_  )

								_snake_case   						=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
								_snake_case   						=		model(lowerCAmelCase_    ,   labels=lowerCAmelCase_  )
								self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)  )




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


@require_tf
class 						__UpperCAmelCase      (					_lowerCamelCase						,   _lowerCamelCase						,   unittest.TestCase						):
							__lowercase      		=       (
							    (
							        TFDeiTModel,
							        TFDeiTForImageClassification,
							        TFDeiTForImageClassificationWithTeacher,
							        TFDeiTForMaskedImageModeling,
							    )
							    if is_tf_available()
							    else ()
							)
							__lowercase      		=       (
							    {
							        """feature-extraction""": TFDeiTModel,
							        """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
							    }
							    if is_tf_available()
							    else {}
							)

							__lowercase      		=       False
							__lowercase      		=       False
							__lowercase      		=       False
							__lowercase      		=       False


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		TFDeiTModelTester(self  )
								_snake_case   						=		ConfigTester(self    ,   config_class=lowerCAmelCase_    ,   has_text_modality=lowerCAmelCase_    ,   hidden_size=37  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								self.config_tester.run_common_tests()


							@unittest.skip(reason='DeiT does not use inputs_embeds'  )
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								pass


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case						, _snake_case   						=		self.model_tester.prepare_config_and_inputs_for_common()

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case						, _snake_case   						=		self.model_tester.prepare_config_and_inputs_for_common()

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

									_snake_case   						=		['pixel_values']
									self.assertListEqual(arg_names[:1]    ,   lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_=False  ):





								"""simple docstring"""






								_snake_case   						=		super()._prepare_for_class(lowerCAmelCase_    ,   lowerCAmelCase_    ,   return_labels=lowerCAmelCase_  )

								if return_labels:
									if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call  ).parameters:
										del inputs_dict["labels"]

								return inputs_dict




							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_snake_case   						=		TFDeiTModel.from_pretrained(lowerCAmelCase_  )
									self.assertIsNotNone(lowerCAmelCase_  )







def 				SCREAMING_SNAKE_CASE__	(   )       ->	Any:
	_snake_case   						=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image





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


							@cached_property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return (
								    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'  )
								    if is_vision_available()
								    else None
								)




							@slow
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'  )

								_snake_case   						=		self.default_image_processor
								_snake_case   						=		prepare_img()
								_snake_case   						=		image_processor(images=lowerCAmelCase_    ,   return_tensors='tf'  )

								# forward pass
								_snake_case   						=		model(**lowerCAmelCase_  )

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

								_snake_case   						=		tf.constant([-1.0266, 0.1912, -1.2861]  )

								self.assertTrue(np.allclose(outputs.logits[0, :3]    ,   lowerCAmelCase_    ,   atol=1E-4  )  )



'''simple docstring'''







def 				SCREAMING_SNAKE_CASE__	(   __A = 1_000_000	)       ->	int:
	_snake_case   						=		limit + 1
	_snake_case   						=		[0] * limit
	for first_term in range(1			,				__A	):
		for n in range(__A			,				__A			,				__A	):
			_snake_case   						=		first_term + n / first_term
			if common_difference % 4:  # d must be divisble by 4
				continue
			else:
				common_difference /= 4
				if (
				    first_term > common_difference
				    and first_term < 4 * common_difference
				):  # since x,y,z are positive integers
					frequency[n] += 1  # so z>0 and a>d ,also 4d<a

	_snake_case   						=		sum(1 for x in frequency[1:limit] if x == 10	)

	return count


if __name__ == "__main__":
					print(F'''{solution() = }''')

