




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  A		:



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__=13,	UpperCamelCase__=3,	UpperCamelCase__=True,	UpperCamelCase__=True,	UpperCamelCase__=0.1,	UpperCamelCase__=0.1,	UpperCamelCase__=224,	UpperCamelCase__=1000,	UpperCamelCase__=[3, 3, 6, 4],	UpperCamelCase__=[48, 56, 112, 220],	):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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

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

								lowerCAmelCase_      			=  self.get_config()

								return config, pixel_values, labels



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""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=SCREAMING_SNAKE_CASE_,	layer_scale_init_value=1E-5,	)



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



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



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):







								"""simple docstring"""



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

								lowerCAmelCase_      			=  SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_   )
								model.to(SCREAMING_SNAKE_CASE_   )
								model.eval()

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







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								(lowerCAmelCase_)      			=  self.prepare_config_and_inputs()
								lowerCAmelCase_      			=  {'pixel_values': pixel_values}
								return config, inputs_dict







@require_torch
class  A		(  lowercase_      ,				lowercase_      ,				unittest.TestCase     ):
		__snake_case									=   (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
		__snake_case									=   (
		    {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__snake_case									=   False
		__snake_case									=   False
		__snake_case									=   False
		__snake_case									=   False
		__snake_case									=   False



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  SwiftFormerModelTester(self   )
								lowerCAmelCase_      			=  ConfigTester(
								    self,	config_class=SCREAMING_SNAKE_CASE_,	has_text_modality=SCREAMING_SNAKE_CASE_,	hidden_size=37,	num_attention_heads=12,	num_hidden_layers=12,	)



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self.config_tester.run_common_tests()



		@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''   )
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs_for_common()

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



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCAmelCase_      			=  ['pixel_values']
														self.assertListEqual(arg_names[:1],	SCREAMING_SNAKE_CASE_   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_   )



		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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



		@unittest.skip(reason='''SwiftFormer does not output attentions'''   )
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								def check_hidden_states_output(UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__   ):
														lowerCAmelCase_      			=  model_class(SCREAMING_SNAKE_CASE_   )
														model.to(SCREAMING_SNAKE_CASE_   )
														model.eval()

														with torch.no_grad():
																				lowerCAmelCase_      			=  model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_   )   )

														lowerCAmelCase_      			=  outputs.hidden_states

														lowerCAmelCase_      			=  8
														self.assertEqual(len(SCREAMING_SNAKE_CASE_   ),	SCREAMING_SNAKE_CASE_   )  # 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(SCREAMING_SNAKE_CASE_   )   ):
																				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),
																				        ]   ),	)

								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
														lowerCAmelCase_      			=  True
														check_hidden_states_output(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_   )

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

														check_hidden_states_output(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								def _config_zero_init(UpperCamelCase__   ):
														lowerCAmelCase_      			=  copy.deepcopy(SCREAMING_SNAKE_CASE_   )
														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(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	1E-10   )
																				if isinstance(getattr(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_   ),	SCREAMING_SNAKE_CASE_   ):
																										lowerCAmelCase_      			=  _config_zero_init(getattr(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_   )   )
																										setattr(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_   )
														return configs_no_init

								lowerCAmelCase_      			=  self.model_tester.prepare_config_and_inputs_for_common()

								lowerCAmelCase_      			=  _config_zero_init(SCREAMING_SNAKE_CASE_   )
								for model_class in self.all_model_classes:
														lowerCAmelCase_      			=  model_class(config=SCREAMING_SNAKE_CASE_   )
														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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass







def __UpperCamelCase			(       ):
						lowerCAmelCase_      			=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
						return image






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



		@cached_property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



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







		@slow
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''   ).to(SCREAMING_SNAKE_CASE_   )

								lowerCAmelCase_      			=  self.default_image_processor
								lowerCAmelCase_      			=  prepare_img()
								lowerCAmelCase_      			=  image_processor(images=SCREAMING_SNAKE_CASE_,	return_tensors='''pt'''   ).to(SCREAMING_SNAKE_CASE_   )

								# forward pass
								with torch.no_grad():
														lowerCAmelCase_      			=  model(**SCREAMING_SNAKE_CASE_   )

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

								lowerCAmelCase_      			=  torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]   ).to(SCREAMING_SNAKE_CASE_   )
								self.assertTrue(torch.allclose(outputs.logits[0, :3],	SCREAMING_SNAKE_CASE_,	atol=1E-4   )   )









"""simple docstring"""







import random

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				float    ,   lowerCAmelCase__      :				bool = False						)						->   dict:

  """simple docstring"""




  lowerCAmelCase_   :				dict								= {i: [] for i in range(lowerCAmelCase__						)}

  # if probability is greater or equal than 1, then generate a complete graph
  if probability >= 1:
    return complete_graph(lowerCAmelCase__						)
  # if probability is lower or equal than 0, then return a graph without edges
  if probability <= 0:
    return graph

  # for each couple of nodes, add an edge from u to v
  # if the number randomly generated is greater than probability probability
  for i in range(lowerCAmelCase__						):
    for j in range(i + 1    ,   lowerCAmelCase__						):
      if random.random() < probability:
        graph[i].append(lowerCAmelCase__						)
        if not directed:
          # if the graph is undirected, add an edge in from j to i, either
          graph[j].append(lowerCAmelCase__						)
  return graph

def 				UpperCamelCase_    (    lowerCAmelCase__      :				int						)						->   dict:

  """simple docstring"""




  return {
      i: [j for j in range(lowerCAmelCase__						) if i != j] for i in range(lowerCAmelCase__						)
  }


if __name__ == "__main__":
     import doctest

     doctest.testmod()





