

'''simple docstring'''
import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch
					from torch import nn

					from transformers import SwiftFormerForImageClassification, SwiftFormerModel
					from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor
class 					_snake_case		:






			def __init__(  self			,_snake_case			,_snake_case=13			,_snake_case=3			,_snake_case=True			,_snake_case=True			,_snake_case=0.1			,_snake_case=0.1			,_snake_case=2_24			,_snake_case=10_00			,_snake_case=[3, 3, 6, 4]			,_snake_case=[48, 56, 1_12, 2_20]			,):
				UpperCAmelCase_     :      str						=  parent
				UpperCAmelCase_     :      Optional[int]						=  batch_size
				UpperCAmelCase_     :      Optional[Any]						=  num_channels
				UpperCAmelCase_     :      List[str]						=  is_training
				UpperCAmelCase_     :      List[Any]						=  use_labels
				UpperCAmelCase_     :      Any						=  hidden_dropout_prob
				UpperCAmelCase_     :      List[Any]						=  attention_probs_dropout_prob
				UpperCAmelCase_     :      str						=  num_labels
				UpperCAmelCase_     :      List[str]						=  image_size
				UpperCAmelCase_     :      List[Any]						=  layer_depths
				UpperCAmelCase_     :      Tuple						=  embed_dims






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

				UpperCAmelCase_     :      str						=  None
				if self.use_labels:
					UpperCAmelCase_     :      Dict						=  ids_tensor([self.batch_size]			,self.num_labels     )

				UpperCAmelCase_     :      Tuple						=  self.get_config()

				return config, pixel_values, labels






			def 		UpperCamelCase__							(  self     ):
				return SwiftFormerConfig(
				    depths=self.layer_depths			,embed_dims=self.embed_dims			,mlp_ratio=4			,downsamples=[True, True, True, True]			,hidden_act="gelu"			,num_labels=self.num_labels			,down_patch_size=3			,down_stride=2			,down_pad=1			,drop_rate=0.0			,drop_path_rate=0.0			,use_layer_scale=__UpperCAmelCase			,layer_scale_init_value=1E-5			,)






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      List[str]						=  SwiftFormerModel(config=__UpperCAmelCase     )
				model.to(__UpperCAmelCase     )
				model.eval()
				UpperCAmelCase_     :      Tuple						=  model(__UpperCAmelCase     )
				self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.embed_dims[-1], 7, 7)     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Optional[Any]						=  self.num_labels
				UpperCAmelCase_     :      int						=  SwiftFormerForImageClassification(__UpperCAmelCase     )
				model.to(__UpperCAmelCase     )
				model.eval()
				UpperCAmelCase_     :      Optional[int]						=  model(__UpperCAmelCase			,labels=__UpperCAmelCase     )
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)     )

				UpperCAmelCase_     :      int						=  SwiftFormerForImageClassification(__UpperCAmelCase     )
				model.to(__UpperCAmelCase     )
				model.eval()

				UpperCAmelCase_     :      List[str]						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )
				UpperCAmelCase_     :      int						=  model(__UpperCAmelCase     )
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)     )






			def 		UpperCamelCase__							(  self     ):
				((UpperCAmelCase_)					,					(UpperCAmelCase_)					,					(UpperCAmelCase_))     :      Tuple						=  self.prepare_config_and_inputs()
				UpperCAmelCase_     :      Tuple						=  {"pixel_values": pixel_values}
				return config, inputs_dict




@require_torch
class 					_snake_case		(lowerCAmelCase__			,			lowerCAmelCase__			,			unittest.TestCase):
			__A  :	str        =(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
			__A  :	Union[str, Any]        =(
			    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
			    if is_torch_available()
			    else {}
			)

			__A  :	Union[str, Any]        =False
			__A  :	List[Any]        =False
			__A  :	Optional[Any]        =False
			__A  :	Any        =False
			__A  :	Optional[int]        =False






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Tuple						=  SwiftFormerModelTester(self     )
				UpperCAmelCase_     :      str						=  ConfigTester(
				    self			,config_class=__UpperCAmelCase			,has_text_modality=__UpperCAmelCase			,hidden_size=37			,num_attention_heads=12			,num_hidden_layers=12			,)






			def 		UpperCamelCase__							(  self     ):
				self.config_tester.run_common_tests()






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






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      Dict						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					UpperCAmelCase_     :      Tuple						=  model_class(__UpperCAmelCase     )
					UpperCAmelCase_     :      Dict						=  model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(__UpperCAmelCase			,nn.Linear     )     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      Optional[int]						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					UpperCAmelCase_     :      List[Any]						=  model_class(__UpperCAmelCase     )
					UpperCAmelCase_     :      Tuple						=  inspect.signature(model.forward     )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					UpperCAmelCase_     :      Dict						=  [*signature.parameters.keys()]

					UpperCAmelCase_     :      List[Any]						=  ["pixel_values"]
					self.assertListEqual(arg_names[:1]			,__UpperCAmelCase     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Union[str, Any]						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCAmelCase     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Dict						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase     )






			@slow
			def 		UpperCamelCase__							(  self     ):
				for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCAmelCase_     :      Any						=  SwiftFormerModel.from_pretrained(__UpperCAmelCase     )
					self.assertIsNotNone(__UpperCAmelCase     )






			@unittest.skip(reason="SwiftFormer does not output attentions"     )
			def 		UpperCamelCase__							(  self     ):
				pass






			def 		UpperCamelCase__							(  self     ):
				def check_hidden_states_output(_snake_case			,_snake_case			,_snake_case     ):
					UpperCAmelCase_     :      Union[str, Any]						=  model_class(__UpperCAmelCase     )
					model.to(__UpperCAmelCase     )
					model.eval()

					with torch.no_grad():
						UpperCAmelCase_     :      str						=  model(**self._prepare_for_class(__UpperCAmelCase			,__UpperCAmelCase     )     )

					UpperCAmelCase_     :      Dict						=  outputs.hidden_states

					UpperCAmelCase_     :      str						=  8
					self.assertEqual(len(__UpperCAmelCase     )			,__UpperCAmelCase     )  # TODO

					# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
					# with the width and height being successively divided by 2, after every 2 blocks
					for i in range(len(__UpperCAmelCase     )     ):
						self.assertEqual(
						    hidden_states[i].shape			,torch.Size(
						        [
						            self.model_tester.batch_size,
						            self.model_tester.embed_dims[i // 2],
						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
						        ]     )			,)

				UpperCAmelCase_					,					UpperCAmelCase_     :      Any						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					UpperCAmelCase_     :      List[str]						=  True
					check_hidden_states_output(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase     )

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

					check_hidden_states_output(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase     )






			def 		UpperCamelCase__							(  self     ):
				def _config_zero_init(_snake_case     ):
					UpperCAmelCase_     :      Union[str, Any]						=  copy.deepcopy(__UpperCAmelCase     )
					for key in configs_no_init.__dict__.keys():
						if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
							setattr(__UpperCAmelCase			,__UpperCAmelCase			,1E-10     )
						if isinstance(getattr(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase     )			,__UpperCAmelCase     ):
							UpperCAmelCase_     :      Tuple						=  _config_zero_init(getattr(__UpperCAmelCase			,__UpperCAmelCase     )     )
							setattr(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase     )
					return configs_no_init

				UpperCAmelCase_					,					UpperCAmelCase_     :      Dict						=  self.model_tester.prepare_config_and_inputs_for_common()

				UpperCAmelCase_     :      Optional[Any]						=  _config_zero_init(__UpperCAmelCase     )
				for model_class in self.all_model_classes:
					UpperCAmelCase_     :      Dict						=  model_class(config=__UpperCAmelCase     )
					for name, param in model.named_parameters():
						if param.requires_grad:
							self.assertIn(
							    ((param.data.mean() * 1E9) / 1E9).round().item()			,[0.0, 1.0]			,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''			,)






			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
			def 		UpperCamelCase__							(  self     ):
				pass




def  a__       (    )     ->					Dict:


	"""simple docstring"""





	UpperCAmelCase_     :      Dict						=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
	return image





@require_torch
@require_vision
class 					_snake_case		(unittest.TestCase):






			@cached_property
			def 		UpperCamelCase__							(  self     ):
				return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"     ) if is_vision_available() else None






			@slow
			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"     ).to(__UpperCAmelCase     )

				UpperCAmelCase_     :      Any						=  self.default_image_processor
				UpperCAmelCase_     :      Optional[Any]						=  prepare_img()
				UpperCAmelCase_     :      Any						=  image_processor(images=__UpperCAmelCase			,return_tensors="pt"     ).to(__UpperCAmelCase     )

				# forward pass
				with torch.no_grad():
					UpperCAmelCase_     :      Tuple						=  model(**__UpperCAmelCase     )

				# verify the logits
				UpperCAmelCase_     :      Union[str, Any]						=  torch.Size((1, 10_00)     )
				self.assertEqual(outputs.logits.shape			,__UpperCAmelCase     )

				UpperCAmelCase_     :      List[str]						=  torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]     ).to(__UpperCAmelCase     )
				self.assertTrue(torch.allclose(outputs.logits[0, :3]			,__UpperCAmelCase			,atol=1E-4     )     )









import numpy

# List of input, output pairs
a_								=       (
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
a_								=       (((515, 22, 13), 555), ((61, 35, 49), 150))
a_								=       [2, 4, 1, 5]
a_								=       len(train_data)
a_								=       0.0_09





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			List[Any]="train"				):
   return calculate_hypothesis_value(_UpperCamelCase			,_UpperCamelCase				) - output(
       _UpperCamelCase			,_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			Optional[Any]				):
   __lowerCamelCase    				=     0
   for i in range(len(_UpperCamelCase				) - 1				):
      hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
   hyp_val += parameter_vector[0]
   return hyp_val





def 		a__      (		_UpperCamelCase      :			Tuple			,_UpperCamelCase      :			List[Any]				):
   if data_set == "train":
      return train_data[example_no][1]
   elif data_set == "test":
      return test_data[example_no][1]
   return None





def 		a__      (		_UpperCamelCase      :			List[Any]			,_UpperCamelCase      :			Union[str, Any]				):
   if data_set == "train":
      return _hypothesis_value(train_data[example_no][0]				)
   elif data_set == "test":
      return _hypothesis_value(test_data[example_no][0]				)
   return None





def 		a__      (		_UpperCamelCase      :			Dict			,_UpperCamelCase      :			Dict=m				):
   __lowerCamelCase    				=     0
   for i in range(_UpperCamelCase				):
      if index == -1:
         summation_value += _error(_UpperCamelCase				)
      else:
         summation_value += _error(_UpperCamelCase				) * train_data[i][0][index]
   return summation_value





def 		a__      (		_UpperCamelCase      :			Optional[int]				):
   __lowerCamelCase    				=     summation_of_cost_derivative(_UpperCamelCase			,_UpperCamelCase				) / m
   return cost_derivative_value





def 		a__      (		):
   global parameter_vector
   # Tune these values to set a tolerance value for predicted output
   __lowerCamelCase    				=     0.000_002
   __lowerCamelCase    				=     0
   __lowerCamelCase    				=     0
   while True:
      j += 1
      __lowerCamelCase    				=     [0, 0, 0, 0]
      for i in range(0			,len(_UpperCamelCase				)				):
         __lowerCamelCase    				=     get_cost_derivative(i - 1				)
         __lowerCamelCase    				=     (
             parameter_vector[i] - LEARNING_RATE * cost_derivative
         )
      if numpy.allclose(
          _UpperCamelCase			,_UpperCamelCase			,atol=_UpperCamelCase			,rtol=_UpperCamelCase			,):
         break
      __lowerCamelCase    				=     temp_parameter_vector
   print(('''Number of iterations:''', j)				)





def 		a__      (		):
   for i in range(len(_UpperCamelCase				)				):
      print(('''Actual output value:''', output(_UpperCamelCase			,'''test'''				))				)
      print(('''Hypothesis output:''', calculate_hypothesis_value(_UpperCamelCase			,'''test'''				))				)


if __name__ == "__main__":
  run_gradient_descent()
  print("""\nTesting gradient descent for a linear hypothesis function.\n""")
  test_gradient_descent()






