

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
			import jax

			from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def __init__(      self				:			List[str]   ,   a				:			Any   ,   a				:			List[str]=13   ,   a				:			List[str]=30   ,   a				:			int=2   ,   a				:			str=3   ,   a				:			Optional[Any]=True   ,   a				:			Optional[Any]=True   ,   a				:			Any=32   ,   a				:			int=5   ,   a				:			Optional[int]=4   ,   a				:			int=37   ,   a				:			Tuple="gelu"   ,   a				:			Any=0.1   ,   a				:			Any=0.1   ,   a				:			List[str]=10   ,   a				:			List[str]=0.02   ,   )				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							parent
				SCREAMING_SNAKE_CASE :   List[Any]					  =							batch_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							image_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							patch_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_channels
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							is_training
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							use_labels
				SCREAMING_SNAKE_CASE :   Any					  =							hidden_size
				SCREAMING_SNAKE_CASE :   str					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Dict					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   List[str]					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   Any					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   List[Any]					  =							type_sequence_label_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							initializer_range

				# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				SCREAMING_SNAKE_CASE :   Tuple					  =							(image_size // patch_size) ** 2
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_patches + 1
			def 				__UpperCamelCase			(      self				:			str						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

				SCREAMING_SNAKE_CASE :   Any					  =							ViTConfig(
				    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=snake_case_   ,   initializer_range=self.initializer_range   ,   )

				return config, pixel_values
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			Dict   ,   a				:			Optional[int]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							FlaxViTModel(config=snake_case_						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							model(snake_case_						)
				# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
				SCREAMING_SNAKE_CASE :   int					  =							(self.image_size, self.image_size)
				SCREAMING_SNAKE_CASE :   int					  =							(self.patch_size, self.patch_size)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
				self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, num_patches + 1, self.hidden_size)						)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			str   ,   a				:			Dict						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.type_sequence_label_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							FlaxViTForImageClassification(config=snake_case_						)
				SCREAMING_SNAKE_CASE :   str					  =							model(snake_case_						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.type_sequence_label_size)						)

				# test greyscale images
				SCREAMING_SNAKE_CASE :   str					  =							1
				SCREAMING_SNAKE_CASE :   Tuple					  =							FlaxViTForImageClassification(snake_case_						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(snake_case_						)






			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.prepare_config_and_inputs()
				(
				    (
				    SCREAMING_SNAKE_CASE			
				)  ,(
				    SCREAMING_SNAKE_CASE			
				)  ,			
				) :   int					  =							config_and_inputs
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{"pixel_values": pixel_values}
				return config, inputs_dict






@require_flax
class _UpperCamelCase				(				__snake_case				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
			def 				__UpperCamelCase			(      self				:			int						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							FlaxViTModelTester(self						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							ConfigTester(self   ,   config_class=snake_case_   ,   has_text_modality=snake_case_   ,   hidden_size=37						)
			def 				__UpperCamelCase			(      self				:			str						)				->							Tuple:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*snake_case_						)
			def 				__UpperCamelCase			(      self				:			str						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*snake_case_						)
			def 				__UpperCamelCase			(      self				:			str						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE :   Dict					  =							model_class(snake_case_						)
					SCREAMING_SNAKE_CASE :   str					  =							inspect.signature(model.__call__						)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							[*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE :   Tuple					  =							["pixel_values"]
					self.assertListEqual(arg_names[:1]   ,   snake_case_						)
			def 				__UpperCamelCase			(      self				:			int						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   str					  =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__						):
						SCREAMING_SNAKE_CASE :   str					  =							self._prepare_for_class(snake_case_   ,   snake_case_						)
						SCREAMING_SNAKE_CASE :   str					  =							model_class(snake_case_						)

						@jax.jit
						def model_jitted(a				:			Tuple   ,   **a				:			Optional[Any]						):
							return model(pixel_values=snake_case_   ,   **snake_case_						)

						with self.subTest("JIT Enabled"						):
							SCREAMING_SNAKE_CASE :   List[str]					  =							model_jitted(**snake_case_						).to_tuple()

						with self.subTest("JIT Disabled"						):
							with jax.disable_jit():
								SCREAMING_SNAKE_CASE :   List[Any]					  =							model_jitted(**snake_case_						).to_tuple()

						self.assertEqual(len(snake_case_						)   ,   len(snake_case_						)						)
						for jitted_output, output in zip(snake_case_   ,   snake_case_						):
							self.assertEqual(jitted_output.shape   ,   output.shape						)






			@slow
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[Any]:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
					SCREAMING_SNAKE_CASE :   List[str]					  =							model_class_name.from_pretrained("google/vit-base-patch16-224"						)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(np.ones((1, 3, 224, 224)						)						)
					self.assertIsNotNone(snake_case_						)






from collections import defaultdict
from math import ceil, sqrt





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 1_000_000							,     _SCREAMING_SNAKE_CASE = 10				)							-> int:




    """simple docstring"""




    _A     =							defaultdict(_SCREAMING_SNAKE_CASE				)

    for outer_width in range(3							,     (t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            _A     =							max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)							,     1				)
        else:
            _A     =							1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_SCREAMING_SNAKE_CASE							,     outer_width - 1							,     2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


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





