code: 
  
  
  
  
  
  
  """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 					UpperCamelCase__   :
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				def __init__(		self   :  List[str]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  Dict=1_3							,						UpperCamelCase_   :  Union[str, Any]=3_2							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Any=3							,						UpperCamelCase_   :  Any=1_6							,						UpperCamelCase_   :  Union[str, Any]=[1, 2, 1]							,						UpperCamelCase_   :  str=[2, 2, 4]							,						UpperCamelCase_   :  Union[str, Any]=2							,						UpperCamelCase_   :  Optional[int]=2.0							,						UpperCamelCase_   :  Any=True							,						UpperCamelCase_   :  int=0.0							,						UpperCamelCase_   :  int=0.0							,						UpperCamelCase_   :  Tuple=0.1							,						UpperCamelCase_   :  Optional[Any]="gelu"							,						UpperCamelCase_   :  Union[str, Any]=False							,						UpperCamelCase_   :  List[Any]=True							,						UpperCamelCase_   :  Optional[int]=0.02							,						UpperCamelCase_   :  Optional[Any]=1e-5							,						UpperCamelCase_   :  Optional[int]=True							,						UpperCamelCase_   :  List[str]=None							,						UpperCamelCase_   :  Optional[int]=True							,						UpperCamelCase_   :  List[str]=1_0							,						UpperCamelCase_   :  Dict=8							,						):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						parent
  											__magic_name__										=						batch_size
  											__magic_name__										=						image_size
  											__magic_name__										=						patch_size
  											__magic_name__										=						num_channels
  											__magic_name__										=						embed_dim
  											__magic_name__										=						depths
  											__magic_name__										=						num_heads
  											__magic_name__										=						window_size
  											__magic_name__										=						mlp_ratio
  											__magic_name__										=						qkv_bias
  											__magic_name__										=						hidden_dropout_prob
  											__magic_name__										=						attention_probs_dropout_prob
  											__magic_name__										=						drop_path_rate
  											__magic_name__										=						hidden_act
  											__magic_name__										=						use_absolute_embeddings
  											__magic_name__										=						patch_norm
  											__magic_name__										=						layer_norm_eps
  											__magic_name__										=						initializer_range
  											__magic_name__										=						is_training
  											__magic_name__										=						scope
  											__magic_name__										=						use_labels
  											__magic_name__										=						type_sequence_label_size
  											__magic_name__										=						encoder_stride
  
  
  
  
  
  
  
  				def 							a__ (		self   :  str   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
  
  											__magic_name__										=						None
  											if self.use_labels:
  																		__magic_name__										=						ids_tensor([self.batch_size]							,						self.type_sequence_label_size   )
  
  											__magic_name__										=						self.get_config()
  
  											return config, pixel_values, labels
  
  
  
  
  
  
  
  				def 							a__ (		self   :  List[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 							a__ (		self   :  Union[str, Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  List[str]							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						TFSwinModel(config=__a   )
  											__magic_name__										=						model(__a   )
  
  											__magic_name__										=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
  											__magic_name__										=						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   :  Tuple							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  str   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						TFSwinForMaskedImageModeling(config=__a   )
  											__magic_name__										=						model(__a   )
  											self.parent.assertEqual(
  											    result.logits.shape							,						(self.batch_size, self.num_channels, self.image_size, self.image_size)   )
  
  											# test greyscale images
  											__magic_name__										=						1
  											__magic_name__										=						TFSwinForMaskedImageModeling(__a   )
  
  											__magic_name__										=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
  											__magic_name__										=						model(__a   )
  											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, 1, self.image_size, self.image_size)   )
  
  
  
  
  
  
  
  				def 							a__ (		self   :  Optional[Any]							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  List[str]   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						self.type_sequence_label_size
  											__magic_name__										=						TFSwinForImageClassification(__a   )
  											__magic_name__										=						model(__a							,						labels=__a   )
  											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size)   )
  
  											# test greyscale images
  											__magic_name__										=						1
  											__magic_name__										=						TFSwinForImageClassification(__a   )
  											__magic_name__										=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
  											__magic_name__										=						model(__a   )
  											self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.type_sequence_label_size)   )
  
  
  
  
  
  
  
  				def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						self.prepare_config_and_inputs()
  											__magic_name__										=						config_and_inputs
  											__magic_name__										=						{'pixel_values': pixel_values}
  											return config, inputs_dict
  
  
  
  @require_tf
  class 					UpperCamelCase__   (							lowerCAmelCase_       ,  lowerCAmelCase_       ,  unittest.TestCase):
  
  
  
  
  
  
  
  				"""simple docstring"""
  				__UpperCAmelCase											=   (
  				    (
  				        TFSwinModel,
  				        TFSwinForImageClassification,
  				        TFSwinForMaskedImageModeling,
  				    )
  				    if is_tf_available()
  				    else ()
  				)
  				__UpperCAmelCase											=   (
  				    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
  				    if is_tf_available()
  				    else {}
  				)
  
  				__UpperCAmelCase											=   False
  				__UpperCAmelCase											=   False
  				__UpperCAmelCase											=   False
  				__UpperCAmelCase											=   False
  
  
  
  
  
  
  
  				def 							a__ (		self   :  List[str]   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						TFSwinModelTester(self   )
  											__magic_name__										=						ConfigTester(self							,						config_class=__a							,						embed_dim=3_7   )
  
  
  
  
  
  
  
  				def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
  											'''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 							a__ (		self   :  Optional[int]   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											return
  
  
  
  
  
  
  
  				def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_model(*__a   )
  
  
  
  
  
  
  
  				def 							a__ (		self   :  Tuple   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						self.model_tester.prepare_config_and_inputs()
  											self.model_tester.create_and_check_for_masked_image_modeling(*__a   )
  
  
  
  
  
  
  
  				def 							a__ (		self   :  Any   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						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   :  Optional[int]   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											pass
  
  
  
  
  
  
  
  				def 							a__ (		self   :  Any   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						self.model_tester.prepare_config_and_inputs_for_common()
  
  											for model_class in self.all_model_classes:
  																		__magic_name__										=						model_class(__a   )
  																		self.assertIsInstance(model.get_input_embeddings()							,						tf.keras.layers.Layer   )
  																		__magic_name__										=						model.get_output_embeddings()
  																		self.assertTrue(x is None or isinstance(__a							,						tf.keras.layers.Dense   )   )
  
  
  
  
  
  
  
  				def 							a__ (		self   :  int   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						self.model_tester.prepare_config_and_inputs_for_common()
  
  											for model_class in self.all_model_classes:
  																		__magic_name__										=						model_class(__a   )
  																		__magic_name__										=						inspect.signature(model.call   )
  																		# signature.parameters is an OrderedDict => so arg_names order is deterministic
  																		__magic_name__										=						[*signature.parameters.keys()]
  
  																		__magic_name__										=						['pixel_values']
  																		self.assertListEqual(arg_names[:1]							,						__a   )
  
  
  
  
  
  
  
  				def 							a__ (		self   :  Optional[Any]   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						self.model_tester.prepare_config_and_inputs_for_common()
  											__magic_name__										=						True
  
  											for model_class in self.all_model_classes:
  																		__magic_name__										=						True
  																		__magic_name__										=						False
  																		__magic_name__										=						True
  																		__magic_name__										=						model_class(__a   )
  																		__magic_name__										=						model(**self._prepare_for_class(__a							,						__a   )   )
  																		__magic_name__										=						outputs.attentions
  																		__magic_name__										=						len(self.model_tester.depths   )
  																		self.assertEqual(len(__a   )							,						__a   )
  
  																		# check that output_attentions also work using config
  																		del inputs_dict["output_attentions"]
  																		__magic_name__										=						True
  																		__magic_name__										=						config.window_size**2
  																		__magic_name__										=						model_class(__a   )
  																		__magic_name__										=						model(**self._prepare_for_class(__a							,						__a   )   )
  																		__magic_name__										=						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]							,						)
  																		__magic_name__										=						len(__a   )
  
  																		# Check attention is always last and order is fine
  																		__magic_name__										=						True
  																		__magic_name__										=						True
  																		__magic_name__										=						model_class(__a   )
  																		__magic_name__										=						model(**self._prepare_for_class(__a							,						__a   )   )
  
  																		if hasattr(self.model_tester							,						'num_hidden_states_types'   ):
  																									__magic_name__										=						self.model_tester.num_hidden_states_types
  																		else:
  																									# also another +1 for reshaped_hidden_states
  																									__magic_name__										=						2
  																		self.assertEqual(out_len + added_hidden_states							,						len(__a   )   )
  
  																		__magic_name__										=						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   :  int							,						UpperCamelCase_   :  List[Any]							,						UpperCamelCase_   :  str							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Union[str, Any]   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						model_class(__a   )
  											__magic_name__										=						model(**self._prepare_for_class(__a							,						__a   )   )
  											__magic_name__										=						outputs.hidden_states
  
  											__magic_name__										=						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
  											__magic_name__										=						to_atuple(config.patch_size   )
  
  											__magic_name__										=						(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]							,						)
  
  											__magic_name__										=						outputs.reshaped_hidden_states
  											self.assertEqual(len(__a   )							,						__a   )
  
  											__magic_name__										=						reshaped_hidden_states[0].shape
  
  											__magic_name__										=						tf.reshape(reshaped_hidden_states[0]							,						(batch_size, num_channels, height * width)   )
  											__magic_name__										=						tf.transpose(__a							,						(0, 2, 1)   )
  
  											self.assertListEqual(
  											    list(reshaped_hidden_states.shape[-2:]   )							,						[num_patches, self.model_tester.embed_dim]							,						)
  
  
  
  
  
  
  
  				def 							a__ (		self   :  Any   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						self.model_tester.prepare_config_and_inputs_for_common()
  
  											__magic_name__										=						to_atuple(self.model_tester.image_size   )
  
  											for model_class in self.all_model_classes:
  																		__magic_name__										=						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"]
  																		__magic_name__										=						True
  
  																		self.check_hidden_states_output(__a							,						__a							,						__a							,						__a   )
  
  
  
  
  
  
  
  				def 							a__ (		self   :  List[Any]   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						self.model_tester.prepare_config_and_inputs_for_common()
  											__magic_name__										=						3
  
  											__magic_name__										=						to_atuple(self.model_tester.image_size   )
  											__magic_name__										=						to_atuple(config.patch_size   )
  
  											__magic_name__										=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
  											__magic_name__										=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
  
  											for model_class in self.all_model_classes:
  																		__magic_name__										=						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"]
  																		__magic_name__										=						True
  																		self.check_hidden_states_output(__a							,						__a							,						__a							,						(padded_height, padded_width)   )
  
  
  
  
  
  
  
  				@slow
  				def 							a__ (		self   :  int   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  																		__magic_name__										=						TFSwinModel.from_pretrained(__a   )
  																		self.assertIsNotNone(__a   )
  
  
  
  @require_vision
  @require_tf
  class 					UpperCamelCase__   (							unittest.TestCase):
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				@cached_property
  				def 							a__ (		self   :  Dict   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											return (
  											    AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'   )
  											    if is_vision_available()
  											    else None
  											)
  
  
  
  
  
  
  
  				@slow
  				def 							a__ (		self   :  List[str]   ):
  
  
  
  
  
  											'''simple docstring'''
  
  											__magic_name__										=						TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'   )
  											__magic_name__										=						self.default_image_processor
  
  											__magic_name__										=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
  											__magic_name__										=						image_processor(images=__a							,						return_tensors='tf'   )
  
  											# forward pass
  											__magic_name__										=						model(__a   )
  
  											# verify the logits
  											__magic_name__										=						tf.TensorShape((1, 1_0_0_0)   )
  											self.assertEqual(outputs.logits.shape							,						__a   )
  											__magic_name__										=						tf.constant([-0.0_948, -0.6_454, -0.0_921]   )
  											self.assertTrue(np.allclose(outputs.logits[0, :3]							,						__a							,						atol=1e-4   )   )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  def        A	(							__snake_case:   Dict   ,    __snake_case:   Tuple     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         sequence.append(__snake_case     )
  
         __magic_name__										=						0
         while sequence[index] != target:
                index += 1
  
         sequence.pop()
  
         if index == len(__snake_case     ):
                return None
  
         return index
  
  
  if __name__ == "__main__":
   snake_case   :   Dict    		=		input("""Enter numbers separated by comma:\n""").strip()
   snake_case   :   List[str]    		=		[int(item) for item in user_input.split(""",""")]
  
   snake_case   :   Union[str, Any]    		=		input("""Enter a single number to be found in the list:\n""")
   snake_case   :   str    		=		int(target_input)
   snake_case   :   Any    		=		sentinel_linear_search(sequence, target)
   if result is not None:
    print(f"""{target} found at positions: {result}""")
   else:
    print("""Not found""")