






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        __UpperCamelCase    :







 def __init__(  self      :							Tuple       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							Optional[Any]=13       ,				_lowerCAmelCase      :							str=3       ,				_lowerCAmelCase      :							str=True       ,				_lowerCAmelCase      :							Union[str, Any]=True       ,				_lowerCAmelCase      :							str=0.1       ,				_lowerCAmelCase      :							int=0.1       ,				_lowerCAmelCase      :							str=224       ,				_lowerCAmelCase      :							Tuple=1000       ,				_lowerCAmelCase      :							str=[3, 3, 6, 4]       ,				_lowerCAmelCase      :							str=[48, 56, 112, 220]       ,				)		-> Tuple:
  """simple docstring"""







  __lowercase             =  parent
  __lowercase             =  batch_size
  __lowercase             =  num_channels
  __lowercase             =  is_training
  __lowercase             =  use_labels
  __lowercase             =  hidden_dropout_prob
  __lowercase             =  attention_probs_dropout_prob
  __lowercase             =  num_labels
  __lowercase             =  image_size
  __lowercase             =  layer_depths
  __lowercase             =  embed_dims







 def  _a   (  self      :							Any			)		-> Tuple:
  """simple docstring"""







  __lowercase             =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

  __lowercase             =  None
  if self.use_labels:
   __lowercase             =  ids_tensor([self.batch_size]       ,				self.num_labels			)

  __lowercase             =  self.get_config()

  return config, pixel_values, labels







 def  _a   (  self      :							Any			)		-> List[Any]:
  """simple docstring"""







  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=_lowerCAmelCase       ,				layer_scale_init_value=1e-5       ,				)







 def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							List[Any]       ,				_lowerCAmelCase      :							Optional[int]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Optional[Any]:
  """simple docstring"""







  __lowercase             =  SwiftFormerModel(config=_lowerCAmelCase			)
  model.to(_lowerCAmelCase			)
  model.eval()
  __lowercase             =  model(_lowerCAmelCase			)
  self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.embed_dims[-1], 7, 7)			)







 def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							int       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							Optional[Any]			)		-> Tuple:
  """simple docstring"""







  __lowercase             =  self.num_labels
  __lowercase             =  SwiftFormerForImageClassification(_lowerCAmelCase			)
  model.to(_lowerCAmelCase			)
  model.eval()
  __lowercase             =  model(_lowerCAmelCase       ,				labels=_lowerCAmelCase			)
  self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)			)

  __lowercase             =  SwiftFormerForImageClassification(_lowerCAmelCase			)
  model.to(_lowerCAmelCase			)
  model.eval()

  __lowercase             =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
  __lowercase             =  model(_lowerCAmelCase			)
  self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)			)







 def  _a   (  self      :							int			)		-> Any:
  """simple docstring"""







  ((__lowercase)       ,						(__lowercase)       ,						(__lowercase))             =  self.prepare_config_and_inputs()
  __lowercase             =  {"""pixel_values""": pixel_values}
  return config, inputs_dict


@require_torch
class        __UpperCamelCase    (     _lowerCAmelCase   ,			_lowerCAmelCase   ,			unittest.TestCase   ):
 __snake_case	:Union[str, Any]   =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
 __snake_case	:List[Any]   =       (
     {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
     if is_torch_available()
     else {}
 )

 __snake_case	:Tuple   =       False
 __snake_case	:List[Any]   =       False
 __snake_case	:List[str]   =       False
 __snake_case	:Tuple   =       False
 __snake_case	:int   =       False







 def  _a   (  self      :							Union[str, Any]			)		-> str:
  """simple docstring"""







  __lowercase             =  SwiftFormerModelTester(self			)
  __lowercase             =  ConfigTester(
      self       ,				config_class=_lowerCAmelCase       ,				has_text_modality=_lowerCAmelCase       ,				hidden_size=37       ,				num_attention_heads=12       ,				num_hidden_layers=12       ,				)







 def  _a   (  self      :							Optional[int]			)		-> str:
  """simple docstring"""







  self.config_tester.run_common_tests()







 @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""			)
 def  _a   (  self      :							Tuple			)		-> str:
  """simple docstring"""







  pass







 def  _a   (  self      :							int			)		-> Tuple:
  """simple docstring"""







  __lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __lowercase             =  model_class(_lowerCAmelCase			)
   __lowercase             =  model.get_output_embeddings()
   self.assertTrue(x is None or isinstance(_lowerCAmelCase       ,				nn.Linear			)			)







 def  _a   (  self      :							Optional[int]			)		-> Any:
  """simple docstring"""







  __lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __lowercase             =  model_class(_lowerCAmelCase			)
   __lowercase             =  inspect.signature(model.forward			)
   # signature.parameters is an OrderedDict => so arg_names order is deterministic
   __lowercase             =  [*signature.parameters.keys()]

   __lowercase             =  ["""pixel_values"""]
   self.assertListEqual(arg_names[:1]       ,				_lowerCAmelCase			)







 def  _a   (  self      :							Optional[int]			)		-> int:
  """simple docstring"""







  __lowercase             =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_lowerCAmelCase			)







 def  _a   (  self      :							Any			)		-> Tuple:
  """simple docstring"""







  __lowercase             =  self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase			)







 @slow
 def  _a   (  self      :							Dict			)		-> Union[str, Any]:
  """simple docstring"""







  for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __lowercase             =  SwiftFormerModel.from_pretrained(_lowerCAmelCase			)
   self.assertIsNotNone(_lowerCAmelCase			)







 @unittest.skip(reason="""SwiftFormer does not output attentions"""			)
 def  _a   (  self      :							Dict			)		-> Union[str, Any]:
  """simple docstring"""







  pass







 def  _a   (  self      :							int			)		-> Optional[int]:
  """simple docstring"""







  def check_hidden_states_output(_lowerCAmelCase      :							str       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							int			):
   __lowercase             =  model_class(_lowerCAmelCase			)
   model.to(_lowerCAmelCase			)
   model.eval()

   with torch.no_grad():
    __lowercase             =  model(**self._prepare_for_class(_lowerCAmelCase       ,				_lowerCAmelCase			)			)

   __lowercase             =  outputs.hidden_states

   __lowercase             =  8
   self.assertEqual(len(_lowerCAmelCase			)       ,				_lowerCAmelCase			)  # 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(_lowerCAmelCase			)			):
    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),
            ]			)       ,				)

  __lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __lowercase             =  True
   check_hidden_states_output(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)

   # check that output_hidden_states also work using config
   del inputs_dict["output_hidden_states"]
   __lowercase             =  True

   check_hidden_states_output(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)







 def  _a   (  self      :							Dict			)		-> Optional[Any]:
  """simple docstring"""







  def _config_zero_init(_lowerCAmelCase      :							Union[str, Any]			):
   __lowercase             =  copy.deepcopy(_lowerCAmelCase			)
   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(_lowerCAmelCase       ,				_lowerCAmelCase       ,				1e-10			)
    if isinstance(getattr(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)       ,				_lowerCAmelCase			):
     __lowercase             =  _config_zero_init(getattr(_lowerCAmelCase       ,				_lowerCAmelCase			)			)
     setattr(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase			)
   return configs_no_init

  __lowercase       ,						__lowercase             =  self.model_tester.prepare_config_and_inputs_for_common()

  __lowercase             =  _config_zero_init(_lowerCAmelCase			)
  for model_class in self.all_model_classes:
   __lowercase             =  model_class(config=_lowerCAmelCase			)
   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  _a   (  self      :							Optional[Any]			)		-> Union[str, Any]:
  """simple docstring"""







  pass





def 				snake_case  (     ):




 '''simple docstring'''







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




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







 @cached_property
 def  _a   (  self      :							int			)		-> Tuple:
  """simple docstring"""







  return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""			) if is_vision_available() else None







 @slow
 def  _a   (  self      :							Optional[int]			)		-> Optional[int]:
  """simple docstring"""







  __lowercase             =  SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""			).to(_lowerCAmelCase			)

  __lowercase             =  self.default_image_processor
  __lowercase             =  prepare_img()
  __lowercase             =  image_processor(images=_lowerCAmelCase       ,				return_tensors="""pt"""			).to(_lowerCAmelCase			)

  # forward pass
  with torch.no_grad():
   __lowercase             =  model(**_lowerCAmelCase			)

  # verify the logits
  __lowercase             =  torch.Size((1, 1000)			)
  self.assertEqual(outputs.logits.shape       ,				_lowerCAmelCase			)

  __lowercase             =  torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]			).to(_lowerCAmelCase			)
  self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				_lowerCAmelCase       ,				atol=1e-4			)			)









from __future__ import annotations

from scipy.special import comb  # type: ignore


class        __UpperCamelCase    :







	def __init__(  self      :							int       ,				_lowerCAmelCase      :							list[tuple[float, float]]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  list_of_points
		# Degree determines the flexibility of the curve.
		# Degree = 1 will produce a straight line.
		__lowercase             =  len(_lowerCAmelCase			) - 1







	def  _a   (  self      :							Tuple       ,				_lowerCAmelCase      :							float			)		-> list[float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."
		__lowercase             =  []
		for i in range(len(self.list_of_points			)			):
			# basis function for each i
			output_values.append(
			    comb(self.degree       ,				_lowerCAmelCase			) * ((1 - t) ** (self.degree - i)) * (t**i)			)
		# the basis must sum up to 1 for it to produce a valid Bezier curve.
		assert round(sum(_lowerCAmelCase			)       ,				5			) == 1
		return output_values







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							float			)		-> tuple[float, float]:
		"""simple docstring"""







		assert 0 <= t <= 1, "Time t must be between 0 and 1."

		__lowercase             =  self.basis_function(_lowerCAmelCase			)
		__lowercase             =  0.0
		__lowercase             =  0.0
		for i in range(len(self.list_of_points			)			):
			# For all points, sum up the product of i-th basis function and i-th point.
			x += basis_function[i] * self.list_of_points[i][0]
			y += basis_function[i] * self.list_of_points[i][1]
		return (x, y)







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							float = 0.01			)		-> Union[str, Any]:
		"""simple docstring"""







		from matplotlib import pyplot as plt  # type: ignore

		__lowercase             =  []  # x coordinates of points to plot
		__lowercase             =  []  # y coordinates of points to plot

		__lowercase             =  0.0
		while t <= 1:
			__lowercase             =  self.bezier_curve_function(_lowerCAmelCase			)
			to_plot_x.append(value[0]			)
			to_plot_y.append(value[1]			)
			t += step_size

		__lowercase             =  [i[0] for i in self.list_of_points]
		__lowercase             =  [i[1] for i in self.list_of_points]

		plt.plot(
		    _lowerCAmelCase       ,				_lowerCAmelCase       ,				color="""blue"""       ,				label="""Curve of Degree """ + str(self.degree			)       ,				)
		plt.scatter(_lowerCAmelCase       ,				_lowerCAmelCase       ,				color="""red"""       ,				label="""Control Points"""			)
		plt.legend()
		plt.show()


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
						BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
						BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3

