




"""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 							lowerCamelCase						:







   def __init__(							self			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_=13			,					SCREAMING_SNAKE_CASE_=32			,					SCREAMING_SNAKE_CASE_=2			,					SCREAMING_SNAKE_CASE_=3			,					SCREAMING_SNAKE_CASE_=16			,					SCREAMING_SNAKE_CASE_=[1, 2, 1]			,					SCREAMING_SNAKE_CASE_=[2, 2, 4]			,					SCREAMING_SNAKE_CASE_=2			,					SCREAMING_SNAKE_CASE_=2.0			,					SCREAMING_SNAKE_CASE_=True			,					SCREAMING_SNAKE_CASE_=0.0			,					SCREAMING_SNAKE_CASE_=0.0			,					SCREAMING_SNAKE_CASE_=0.1			,					SCREAMING_SNAKE_CASE_="gelu"			,					SCREAMING_SNAKE_CASE_=False			,					SCREAMING_SNAKE_CASE_=True			,					SCREAMING_SNAKE_CASE_=0.02			,					SCREAMING_SNAKE_CASE_=1e-5			,					SCREAMING_SNAKE_CASE_=True			,					SCREAMING_SNAKE_CASE_=None			,					SCREAMING_SNAKE_CASE_=True			,					SCREAMING_SNAKE_CASE_=10			,					SCREAMING_SNAKE_CASE_=8			,					):
       UpperCamelCase :      Any      				=							parent
       UpperCamelCase :      List[Any]      				=							batch_size
       UpperCamelCase :      Optional[int]      				=							image_size
       UpperCamelCase :      Optional[Any]      				=							patch_size
       UpperCamelCase :      List[Any]      				=							num_channels
       UpperCamelCase :      Optional[Any]      				=							embed_dim
       UpperCamelCase :      str      				=							depths
       UpperCamelCase :      List[Any]      				=							num_heads
       UpperCamelCase :      List[Any]      				=							window_size
       UpperCamelCase :      Dict      				=							mlp_ratio
       UpperCamelCase :      int      				=							qkv_bias
       UpperCamelCase :      Optional[Any]      				=							hidden_dropout_prob
       UpperCamelCase :      Any      				=							attention_probs_dropout_prob
       UpperCamelCase :      Union[str, Any]      				=							drop_path_rate
       UpperCamelCase :      Optional[Any]      				=							hidden_act
       UpperCamelCase :      str      				=							use_absolute_embeddings
       UpperCamelCase :      List[str]      				=							patch_norm
       UpperCamelCase :      Optional[Any]      				=							layer_norm_eps
       UpperCamelCase :      Dict      				=							initializer_range
       UpperCamelCase :      Dict      				=							is_training
       UpperCamelCase :      List[str]      				=							scope
       UpperCamelCase :      Any      				=							use_labels
       UpperCamelCase :      List[str]      				=							type_sequence_label_size
       UpperCamelCase :      int      				=							encoder_stride







   def 					a_							(							self       ):
       UpperCamelCase :      List[Any]      				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

       UpperCamelCase :      Tuple      				=							None
       if self.use_labels:
           UpperCamelCase :      Dict      				=							ids_tensor([self.batch_size]			,					self.type_sequence_label_size       )

       UpperCamelCase :      str      				=							self.get_config()

       return config, pixel_values, labels







   def 					a_							(							self       ):
       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 					a_							(							self			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_       ):
       UpperCamelCase :      str      				=							TFSwinModel(config=__A       )
       UpperCamelCase :      List[Any]      				=							model(__A       )

       UpperCamelCase :      Dict      				=							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
       UpperCamelCase :      str      				=							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 					a_							(							self			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_       ):
       UpperCamelCase :      Union[str, Any]      				=							TFSwinForMaskedImageModeling(config=__A       )
       UpperCamelCase :      Tuple      				=							model(__A       )
       self.parent.assertEqual(
           result.logits.shape			,					(self.batch_size, self.num_channels, self.image_size, self.image_size)       )

       # test greyscale images
       UpperCamelCase :      Union[str, Any]      				=							1
       UpperCamelCase :      Optional[int]      				=							TFSwinForMaskedImageModeling(__A       )

       UpperCamelCase :      List[str]      				=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
       UpperCamelCase :      List[str]      				=							model(__A       )
       self.parent.assertEqual(result.logits.shape			,					(self.batch_size, 1, self.image_size, self.image_size)       )







   def 					a_							(							self			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_       ):
       UpperCamelCase :      Tuple      				=							self.type_sequence_label_size
       UpperCamelCase :      Any      				=							TFSwinForImageClassification(__A       )
       UpperCamelCase :      List[Any]      				=							model(__A			,					labels=__A       )
       self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.type_sequence_label_size)       )

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







   def 					a_							(							self       ):
       UpperCamelCase :      int      				=							self.prepare_config_and_inputs()
       UpperCamelCase				,							UpperCamelCase				,							UpperCamelCase :      Tuple      				=							config_and_inputs
       UpperCamelCase :      Dict      				=							{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_tf
class 							lowerCamelCase						(       _UpperCAmelCase		,     _UpperCAmelCase		,     unittest.TestCase ):
   lowercase				:     Optional[Any]            = (
       (
           TFSwinModel,
           TFSwinForImageClassification,
           TFSwinForMaskedImageModeling,
       )
       if is_tf_available()
       else ()
   )
   lowercase				:     int            = (
       {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
       if is_tf_available()
       else {}
   )

   lowercase				:     Optional[Any]            = False
   lowercase				:     Union[str, Any]            = False
   lowercase				:     Tuple            = False
   lowercase				:     List[Any]            = False







   def 					a_							(							self       ):
       UpperCamelCase :      Optional[int]      				=							TFSwinModelTester(self       )
       UpperCamelCase :      Tuple      				=							ConfigTester(self			,					config_class=__A			,					embed_dim=37       )







   def 					a_							(							self       ):
       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 					a_							(							self       ):
       return







   def 					a_							(							self       ):
       UpperCamelCase :      str      				=							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__A       )







   def 					a_							(							self       ):
       UpperCamelCase :      int      				=							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*__A       )







   def 					a_							(							self       ):
       UpperCamelCase :      Optional[Any]      				=							self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__A       )







   @unittest.skip(reason="""Swin does not use inputs_embeds"""       )
   def 					a_							(							self       ):
       pass







   def 					a_							(							self       ):
       UpperCamelCase				,							UpperCamelCase :      int      				=							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase :      Union[str, Any]      				=							model_class(__A       )
           self.assertIsInstance(model.get_input_embeddings()			,					tf.keras.layers.Layer       )
           UpperCamelCase :      Dict      				=							model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(__A			,					tf.keras.layers.Dense       )       )







   def 					a_							(							self       ):
       UpperCamelCase				,							UpperCamelCase :      Dict      				=							self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase :      int      				=							model_class(__A       )
           UpperCamelCase :      List[Any]      				=							inspect.signature(model.call       )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCamelCase :      Union[str, Any]      				=							[*signature.parameters.keys()]

           UpperCamelCase :      List[str]      				=							["""pixel_values"""]
           self.assertListEqual(arg_names[:1]			,					__A       )







   def 					a_							(							self       ):
       UpperCamelCase				,							UpperCamelCase :      int      				=							self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase :      int      				=							True

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

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

           self.assertListEqual(
               list(attentions[0].shape[-3:]       )			,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,					)
           UpperCamelCase :      str      				=							len(__A       )

           # Check attention is always last and order is fine
           UpperCamelCase :      Any      				=							True
           UpperCamelCase :      List[Any]      				=							True
           UpperCamelCase :      List[str]      				=							model_class(__A       )
           UpperCamelCase :      Dict      				=							model(**self._prepare_for_class(__A			,					__A       )       )

           if hasattr(self.model_tester			,					"""num_hidden_states_types"""       ):
               UpperCamelCase :      str      				=							self.model_tester.num_hidden_states_types
           else:
               # also another +1 for reshaped_hidden_states
               UpperCamelCase :      int      				=							2
           self.assertEqual(out_len + added_hidden_states			,					len(__A       )       )

           UpperCamelCase :      List[Any]      				=							outputs.attentions

           self.assertEqual(len(__A       )			,					__A       )

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







   def 					a_							(							self			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_       ):
       UpperCamelCase :      str      				=							model_class(__A       )
       UpperCamelCase :      Optional[Any]      				=							model(**self._prepare_for_class(__A			,					__A       )       )
       UpperCamelCase :      Tuple      				=							outputs.hidden_states

       UpperCamelCase :      str      				=							getattr(
           self.model_tester			,					"""expected_num_hidden_layers"""			,					len(self.model_tester.depths       ) + 1       )
       self.assertEqual(len(__A       )			,					__A       )

       # Swin has a different seq_length
       UpperCamelCase :      Union[str, Any]      				=							to_atuple(config.patch_size       )

       UpperCamelCase :      Tuple      				=							(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]			,					)

       UpperCamelCase :      Optional[int]      				=							outputs.reshaped_hidden_states
       self.assertEqual(len(__A       )			,					__A       )

       UpperCamelCase				,							UpperCamelCase				,							UpperCamelCase				,							UpperCamelCase :      str      				=							reshaped_hidden_states[0].shape

       UpperCamelCase :      Optional[int]      				=							tf.reshape(reshaped_hidden_states[0]			,					(batch_size, num_channels, height * width)       )
       UpperCamelCase :      Any      				=							tf.transpose(__A			,					(0, 2, 1)       )

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







   def 					a_							(							self       ):
       UpperCamelCase				,							UpperCamelCase :      Any      				=							self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase :      Tuple      				=							to_atuple(self.model_tester.image_size       )

       for model_class in self.all_model_classes:
           UpperCamelCase :      Optional[int]      				=							True
           self.check_hidden_states_output(__A			,					__A			,					__A			,					__A       )

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

           self.check_hidden_states_output(__A			,					__A			,					__A			,					__A       )







   def 					a_							(							self       ):
       UpperCamelCase				,							UpperCamelCase :      Optional[Any]      				=							self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase :      Optional[int]      				=							3

       UpperCamelCase :      List[str]      				=							to_atuple(self.model_tester.image_size       )
       UpperCamelCase :      Dict      				=							to_atuple(config.patch_size       )

       UpperCamelCase :      Tuple      				=							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       UpperCamelCase :      Union[str, Any]      				=							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
           UpperCamelCase :      Dict      				=							True
           self.check_hidden_states_output(__A			,					__A			,					__A			,					(padded_height, padded_width)       )

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







   @slow
   def 					a_							(							self       ):
       for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase :      Optional[Any]      				=							TFSwinModel.from_pretrained(__A       )
           self.assertIsNotNone(__A       )







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







   @cached_property
   def 					a_							(							self       ):
       return (
           AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""       )
           if is_vision_available()
           else None
       )







   @slow
   def 					a_							(							self       ):
       UpperCamelCase :      str      				=							TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""       )
       UpperCamelCase :      Any      				=							self.default_image_processor

       UpperCamelCase :      Dict      				=							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
       UpperCamelCase :      Optional[Any]      				=							image_processor(images=__A			,					return_tensors="""tf"""       )

       # forward pass
       UpperCamelCase :      Optional[int]      				=							model(__A       )

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












from math import factorial




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					return factorial(_lowercase       ) / (factorial(_lowercase       ) * factorial(n - r       ))




def    __A    (   ):

					'''simple docstring'''







					_A									=       0

					for i in range(1    ,	1_01       ):
										for j in range(1    ,	i + 1       ):
															if combinations(_lowercase    ,	_lowercase       ) > 1e6:
																				total += 1
					return total


if __name__ == "__main__":
				print(solution())



