

'''simple docstring'''



from itertools import compress, repeat
from math import ceil, sqrt

def   lowerCamelCase__       (      a__)		->    list[int]:



    """simple docstring"""







    if num <= 2:
        return []
    if num == 3:
        return [2]

    # Odd sieve for numbers in range [3, num - 1]
    _snake_case : Any												=		bytearray(B'\x01') * ((num >> 1) - 1)

    for i in range(3			,      int(sqrt(a__)) + 1			,      2):
        if sieve[(i >> 1) - 1]:
            _snake_case : Dict												=		i**2
            _snake_case : Any												=		repeat(
                0			,      ceil((num - i_squared) / (i << 1)))

    return [2] + list(compress(range(3			,      a__			,      2)			,      a__))


if __name__ == "__main__":
   import doctest

   doctest.testmod()






'''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 			SCREAMING_SNAKE_CASE					:






      '''simple docstring'''






      def __init__(					self :		List[str]     ,					snake_case :		Any     ,					snake_case :		List[Any]=13     ,					snake_case :		Dict=32     ,					snake_case :		Any=2     ,					snake_case :		List[Any]=3     ,					snake_case :		Tuple=16     ,					snake_case :		Union[str, Any]=[1, 2, 1]     ,					snake_case :		int=[2, 2, 4]     ,					snake_case :		Any=2     ,					snake_case :		Any=2.0     ,					snake_case :		List[str]=True     ,					snake_case :		Tuple=0.0     ,					snake_case :		Any=0.0     ,					snake_case :		Dict=0.1     ,					snake_case :		Union[str, Any]="gelu"     ,					snake_case :		Optional[Any]=False     ,					snake_case :		List[Any]=True     ,					snake_case :		str=0.02     ,					snake_case :		int=1e-5     ,					snake_case :		Optional[Any]=True     ,					snake_case :		Tuple=None     ,					snake_case :		List[str]=True     ,					snake_case :		int=10     ,					snake_case :		Optional[int]=8     ,					):



          """simple docstring"""





          _snake_case : List[str]												=		parent
          _snake_case : Tuple												=		batch_size
          _snake_case : str												=		image_size
          _snake_case : List[str]												=		patch_size
          _snake_case : Optional[int]												=		num_channels
          _snake_case : List[str]												=		embed_dim
          _snake_case : Union[str, Any]												=		depths
          _snake_case : Optional[int]												=		num_heads
          _snake_case : str												=		window_size
          _snake_case : Union[str, Any]												=		mlp_ratio
          _snake_case : str												=		qkv_bias
          _snake_case : Any												=		hidden_dropout_prob
          _snake_case : List[str]												=		attention_probs_dropout_prob
          _snake_case : Dict												=		drop_path_rate
          _snake_case : Dict												=		hidden_act
          _snake_case : Tuple												=		use_absolute_embeddings
          _snake_case : List[str]												=		patch_norm
          _snake_case : str												=		layer_norm_eps
          _snake_case : List[str]												=		initializer_range
          _snake_case : Optional[Any]												=		is_training
          _snake_case : Tuple												=		scope
          _snake_case : List[str]												=		use_labels
          _snake_case : List[str]												=		type_sequence_label_size
          _snake_case : int												=		encoder_stride




      def 	__UpperCAmelCase			(					self :		int   ):



          """simple docstring"""





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

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

          _snake_case : Optional[int]												=		self.get_config()

          return config, pixel_values, labels




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          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 	__UpperCAmelCase			(					self :		Tuple     ,					snake_case :		Any     ,					snake_case :		str     ,					snake_case :		List[str]   ):



          """simple docstring"""





          _snake_case : Any												=		TFSwinModel(config=snake_case   )
          _snake_case : List[str]												=		model(snake_case   )

          _snake_case : Tuple												=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
          _snake_case : Any												=		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 	__UpperCAmelCase			(					self :		Optional[Any]     ,					snake_case :		Union[str, Any]     ,					snake_case :		List[str]     ,					snake_case :		List[str]   ):



          """simple docstring"""





          _snake_case : Any												=		TFSwinForMaskedImageModeling(config=snake_case   )
          _snake_case : Tuple												=		model(snake_case   )
          self.parent.assertEqual(
              result.logits.shape     ,					(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

          # test greyscale images
          _snake_case : Any												=		1
          _snake_case : Any												=		TFSwinForMaskedImageModeling(snake_case   )

          _snake_case : int												=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
          _snake_case : List[Any]												=		model(snake_case   )
          self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, 1, self.image_size, self.image_size)   )




      def 	__UpperCAmelCase			(					self :		Any     ,					snake_case :		str     ,					snake_case :		int     ,					snake_case :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : List[str]												=		self.type_sequence_label_size
          _snake_case : int												=		TFSwinForImageClassification(snake_case   )
          _snake_case : int												=		model(snake_case     ,					labels=snake_case   )
          self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)   )

          # test greyscale images
          _snake_case : Union[str, Any]												=		1
          _snake_case : Tuple												=		TFSwinForImageClassification(snake_case   )
          _snake_case : Any												=		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
          _snake_case : Union[str, Any]												=		model(snake_case   )
          self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





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



@require_tf
class 			SCREAMING_SNAKE_CASE					(							lowercase_      ,lowercase_      ,unittest.TestCase					):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__ :				List[Any]    =				(
          (
              TFSwinModel,
              TFSwinForImageClassification,
              TFSwinForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      SCREAMING_SNAKE_CASE__ :				List[Any]    =				(
          {'''feature-extraction''': TFSwinModel, '''image-classification''': TFSwinForImageClassification}
          if is_tf_available()
          else {}
      )

      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				False
      SCREAMING_SNAKE_CASE__ :				List[Any]    =				False
      SCREAMING_SNAKE_CASE__ :				str    =				False
      SCREAMING_SNAKE_CASE__ :				Optional[Any]    =				False




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





          _snake_case : List[str]												=		TFSwinModelTester(self   )
          _snake_case : Tuple												=		ConfigTester(self     ,					config_class=snake_case     ,					embed_dim=37   )




      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """simple docstring"""





          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 	__UpperCAmelCase			(					self :		str   ):



          """simple docstring"""





          return




      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case : Tuple												=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case   )




      def 	__UpperCAmelCase			(					self :		Dict   ):



          """simple docstring"""





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




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : List[Any]												=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case   )




      @unittest.skip(reason='Swin does not use inputs_embeds'   )
      def 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """simple docstring"""





          pass




      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case					,							_snake_case : Tuple												=		self.model_tester.prepare_config_and_inputs_for_common()

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




      def 	__UpperCAmelCase			(					self :		Optional[Any]   ):



          """simple docstring"""





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

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

              _snake_case : List[Any]												=		['pixel_values']
              self.assertListEqual(arg_names[:1]     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		str   ):



          """simple docstring"""





          _snake_case					,							_snake_case : Dict												=		self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case : Any												=		True

          for model_class in self.all_model_classes:
              _snake_case : Optional[int]												=		True
              _snake_case : int												=		False
              _snake_case : Tuple												=		True
              _snake_case : Dict												=		model_class(snake_case   )
              _snake_case : int												=		model(**self._prepare_for_class(snake_case     ,					snake_case   )   )
              _snake_case : Any												=		outputs.attentions
              _snake_case : Optional[int]												=		len(self.model_tester.depths   )
              self.assertEqual(len(snake_case   )     ,					snake_case   )

              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              _snake_case : Any												=		True
              _snake_case : str												=		config.window_size**2
              _snake_case : Optional[int]												=		model_class(snake_case   )
              _snake_case : Optional[int]												=		model(**self._prepare_for_class(snake_case     ,					snake_case   )   )
              _snake_case : List[Any]												=		outputs.attentions
              self.assertEqual(len(snake_case   )     ,					snake_case   )

              self.assertListEqual(
                  list(attentions[0].shape[-3:]   )     ,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]     ,					)
              _snake_case : Optional[int]												=		len(snake_case   )

              # Check attention is always last and order is fine
              _snake_case : Any												=		True
              _snake_case : Tuple												=		True
              _snake_case : int												=		model_class(snake_case   )
              _snake_case : Dict												=		model(**self._prepare_for_class(snake_case     ,					snake_case   )   )

              if hasattr(self.model_tester     ,					'num_hidden_states_types'   ):
                  _snake_case : Any												=		self.model_tester.num_hidden_states_types
              else:
                  # also another +1 for reshaped_hidden_states
                  _snake_case : Union[str, Any]												=		2
              self.assertEqual(out_len + added_hidden_states     ,					len(snake_case   )   )

              _snake_case : Tuple												=		outputs.attentions

              self.assertEqual(len(snake_case   )     ,					snake_case   )

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




      def 	__UpperCAmelCase			(					self :		int     ,					snake_case :		List[str]     ,					snake_case :		Union[str, Any]     ,					snake_case :		Dict     ,					snake_case :		Tuple   ):



          """simple docstring"""





          _snake_case : int												=		model_class(snake_case   )
          _snake_case : Any												=		model(**self._prepare_for_class(snake_case     ,					snake_case   )   )
          _snake_case : Optional[int]												=		outputs.hidden_states

          _snake_case : List[str]												=		getattr(
              self.model_tester     ,					'expected_num_hidden_layers'     ,					len(self.model_tester.depths   ) + 1   )
          self.assertEqual(len(snake_case   )     ,					snake_case   )

          # Swin has a different seq_length
          _snake_case : Optional[int]												=		to_atuple(config.patch_size   )

          _snake_case : Optional[int]												=		(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]     ,					)

          _snake_case : Optional[int]												=		outputs.reshaped_hidden_states
          self.assertEqual(len(snake_case   )     ,					snake_case   )

          _snake_case					,							_snake_case					,							_snake_case					,							_snake_case : List[str]												=		reshaped_hidden_states[0].shape

          _snake_case : Any												=		tf.reshape(reshaped_hidden_states[0]     ,					(batch_size, num_channels, height * width)   )
          _snake_case : List[Any]												=		tf.transpose(snake_case     ,					(0, 2, 1)   )

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




      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case					,							_snake_case : List[Any]												=		self.model_tester.prepare_config_and_inputs_for_common()

          _snake_case : Optional[int]												=		to_atuple(self.model_tester.image_size   )

          for model_class in self.all_model_classes:
              _snake_case : Dict												=		True
              self.check_hidden_states_output(snake_case     ,					snake_case     ,					snake_case     ,					snake_case   )

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

              self.check_hidden_states_output(snake_case     ,					snake_case     ,					snake_case     ,					snake_case   )




      def 	__UpperCAmelCase			(					self :		Union[str, Any]   ):



          """simple docstring"""





          _snake_case					,							_snake_case : Tuple												=		self.model_tester.prepare_config_and_inputs_for_common()
          _snake_case : Tuple												=		3

          _snake_case : Any												=		to_atuple(self.model_tester.image_size   )
          _snake_case : str												=		to_atuple(config.patch_size   )

          _snake_case : List[str]												=		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          _snake_case : Tuple												=		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
              _snake_case : Union[str, Any]												=		True
              self.check_hidden_states_output(snake_case     ,					snake_case     ,					snake_case     ,					(padded_height, padded_width)   )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _snake_case : int												=		True
              self.check_hidden_states_output(snake_case     ,					snake_case     ,					snake_case     ,					(padded_height, padded_width)   )




      @slow
      def 	__UpperCAmelCase			(					self :		int   ):



          """simple docstring"""





          for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _snake_case : Optional[int]												=		TFSwinModel.from_pretrained(snake_case   )
              self.assertIsNotNone(snake_case   )



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






      '''simple docstring'''






      @cached_property
      def 	__UpperCAmelCase			(					self :		List[str]   ):



          """simple docstring"""





          return (
              AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'   )
              if is_vision_available()
              else None
          )




      @slow
      def 	__UpperCAmelCase			(					self :		Any   ):



          """simple docstring"""





          _snake_case : Dict												=		TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'   )
          _snake_case : str												=		self.default_image_processor

          _snake_case : List[str]												=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
          _snake_case : int												=		image_processor(images=snake_case     ,					return_tensors='tf'   )

          # forward pass
          _snake_case : Union[str, Any]												=		model(snake_case   )

          # verify the logits
          _snake_case : List[Any]												=		tf.TensorShape((1, 1000)   )
          self.assertEqual(outputs.logits.shape     ,					snake_case   )
          _snake_case : List[Any]												=		tf.constant([-0.0948, -0.6454, -0.0921]   )
          self.assertTrue(np.allclose(outputs.logits[0, :3]     ,					snake_case     ,					atol=1e-4   )   )



