
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			: Any ,    snake_case_			: List[str] ,    snake_case_			: Optional[int]=1_3 ,    snake_case_			: Dict=3 ,    snake_case_			: Dict=True ,    snake_case_			: Optional[Any]=True ,    snake_case_			: Any=0.1 ,    snake_case_			: Any=0.1 ,    snake_case_			: Union[str, Any]=2_2_4 ,    snake_case_			: List[str]=1_0_0_0 ,    snake_case_			: int=[3, 3, 6, 4] ,    snake_case_			: Union[str, Any]=[4_8, 5_6, 1_1_2, 2_2_0] ,    ):
    __a			:  int		    =  parent
    __a			:  Tuple		    =  batch_size
    __a			:  Optional[Any]		    =  num_channels
    __a			:  Optional[int]		    =  is_training
    __a			:  Any		    =  use_labels
    __a			:  str		    =  hidden_dropout_prob
    __a			:  Tuple		    =  attention_probs_dropout_prob
    __a			:  List[str]		    =  num_labels
    __a			:  Tuple		    =  image_size
    __a			:  Dict		    =  layer_depths
    __a			:  str		    =  embed_dims
   def 				lowerCAmelCase      (self			: List[Any]       ):
    __a			:  Tuple		    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

    __a			:  List[Any]		    =  None
    if self.use_labels:
     __a			:  Optional[int]		    =  ids_tensor([self.batch_size] ,    self.num_labels       )

    __a			:  Optional[Any]		    =  self.get_config()

    return config, pixel_values, labels
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    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=snake_case_ ,    layer_scale_init_value=1E-5 ,    )
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: int ,    snake_case_			: Union[str, Any] ,    snake_case_			: Optional[int]       ):
    __a			:  List[str]		    =  SwiftFormerModel(config=snake_case_       )
    model.to(snake_case_       )
    model.eval()
    __a			:  List[str]		    =  model(snake_case_       )
    self.parent.assertEqual(result.last_hidden_state.shape ,    (self.batch_size, self.embed_dims[-1], 7, 7)       )
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    snake_case_			: Optional[int] ,    snake_case_			: str ,    snake_case_			: str       ):
    __a			:  Tuple		    =  self.num_labels
    __a			:  Optional[int]		    =  SwiftFormerForImageClassification(snake_case_       )
    model.to(snake_case_       )
    model.eval()
    __a			:  Optional[Any]		    =  model(snake_case_ ,    labels=snake_case_       )
    self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels)       )

    __a			:  int		    =  SwiftFormerForImageClassification(snake_case_       )
    model.to(snake_case_       )
    model.eval()

    __a			:  Optional[int]		    =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )
    __a			:  Any		    =  model(snake_case_       )
    self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels)       )







   def 				lowerCAmelCase      (self			: List[Any]       ):
    (__a)			:  int		    =  self.prepare_config_and_inputs()
    __a			:  str		    =  {'''pixel_values''': pixel_values}
    return config, inputs_dict





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

   _SCREAMING_SNAKE_CASE				:							Union[str, Any]     	=				False
   _SCREAMING_SNAKE_CASE				:							str     	=				False
   _SCREAMING_SNAKE_CASE				:							Tuple     	=				False
   _SCREAMING_SNAKE_CASE				:							Optional[Any]     	=				False
   _SCREAMING_SNAKE_CASE				:							Optional[int]     	=				False
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  int		    =  SwiftFormerModelTester(self       )
    __a			:  Tuple		    =  ConfigTester(
        self ,    config_class=snake_case_ ,    has_text_modality=snake_case_ ,    hidden_size=3_7 ,    num_attention_heads=1_2 ,    num_hidden_layers=1_2 ,    )
   def 				lowerCAmelCase      (self			: List[str]       ):
    self.config_tester.run_common_tests()
   @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''       )
   def 				lowerCAmelCase      (self			: Any       ):
    pass
   def 				lowerCAmelCase      (self			: int       ):
    __a			:  Optional[int]		    =  self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     __a			:  Tuple		    =  model_class(snake_case_       )
     __a			:  Dict		    =  model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(snake_case_ ,    nn.Linear       )       )
   def 				lowerCAmelCase      (self			: Optional[int]       ):
    __a			:  Dict		    =  self.model_tester.prepare_config_and_inputs_for_common()

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

     __a			:  Any		    =  ['''pixel_values''']
     self.assertListEqual(arg_names[:1] ,    snake_case_       )
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    __a			:  int		    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*snake_case_       )
   def 				lowerCAmelCase      (self			: int       ):
    __a			:  Optional[int]		    =  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_image_classification(*snake_case_       )
   @slow
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __a			:  str		    =  SwiftFormerModel.from_pretrained(snake_case_       )
     self.assertIsNotNone(snake_case_       )
   @unittest.skip(reason='''SwiftFormer does not output attentions'''       )
   def 				lowerCAmelCase      (self			: int       ):
    pass
   def 				lowerCAmelCase      (self			: List[str]       ):
    def check_hidden_states_output(snake_case_			: List[str] ,    snake_case_			: str ,    snake_case_			: Dict       ):
     __a			:  Optional[int]		    =  model_class(snake_case_       )
     model.to(snake_case_       )
     model.eval()

     with torch.no_grad():
      __a			:  Any		    =  model(**self._prepare_for_class(snake_case_ ,    snake_case_       )       )

     __a			:  Dict		    =  outputs.hidden_states

     __a			:  Optional[int]		    =  8
     self.assertEqual(len(snake_case_       ) ,    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(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),
              ]       ) ,    )

    __a			:  str		    =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

     check_hidden_states_output(snake_case_ ,    snake_case_ ,    snake_case_       )
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    def _config_zero_init(snake_case_			: List[str]       ):
     __a			:  List[Any]		    =  copy.deepcopy(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(snake_case_ ,    snake_case_ ,    1E-10       )
      if isinstance(getattr(snake_case_ ,    snake_case_ ,    snake_case_       ) ,    snake_case_       ):
       __a			:  Optional[Any]		    =  _config_zero_init(getattr(snake_case_ ,    snake_case_       )       )
       setattr(snake_case_ ,    snake_case_ ,    snake_case_       )
     return configs_no_init

    __a			:  int		    =  self.model_tester.prepare_config_and_inputs_for_common()

    __a			:  List[Any]		    =  _config_zero_init(snake_case_       )
    for model_class in self.all_model_classes:
     __a			:  Any		    =  model_class(config=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 				lowerCAmelCase      (self			: List[Any]       ):
    pass





def        __UpperCamelCase					(    ):
 __a			:  Optional[Any]		    =  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
 return image



@require_torch
@require_vision
class 		UpperCamelCase__  (	unittest.TestCase   ):
   @cached_property
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''       ) if is_vision_available() else None







   @slow
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    __a			:  Dict		    =  SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''       ).to(snake_case_       )

    __a			:  List[Any]		    =  self.default_image_processor
    __a			:  Dict		    =  prepare_img()
    __a			:  Union[str, Any]		    =  image_processor(images=snake_case_ ,    return_tensors='''pt'''       ).to(snake_case_       )

    # forward pass
    with torch.no_grad():
     __a			:  List[Any]		    =  model(**snake_case_       )

    # verify the logits
    __a			:  Tuple		    =  torch.Size((1, 1_0_0_0)       )
    self.assertEqual(outputs.logits.shape ,    snake_case_       )

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


def        __UpperCamelCase					(    lowerCAmelCase__							:    str  ,       lowerCAmelCase__							:    str  ):
 __a			:  Any		    =  len(lowerCAmelCase__  )
 __a			:  Union[str, Any]		    =  []
 for i in range(len(lowerCAmelCase__  ) - pat_len + 1  ):
  __a			:  List[Any]		    =  True
  for j in range(lowerCAmelCase__  ):
   if s[i + j] != pattern[j]:
    __a			:  Union[str, Any]		    =  False
    break
  if match_found:
   position.append(lowerCAmelCase__  )
 return position


if __name__ == "__main__":
      assert naive_pattern_search('ABCDEFG', 'DE') == [3]
      print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))
