


'''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 	lowerCAmelCase__			:




   def __init__(		self  :     int	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     List[str]=13	,  lowerCamelCase__  :     List[Any]=3	,  lowerCamelCase__  :     List[Any]=True	,  lowerCamelCase__  :     Optional[Any]=True	,  lowerCamelCase__  :     Any=0.1	,  lowerCamelCase__  :     List[Any]=0.1	,  lowerCamelCase__  :     Any=2_24	,  lowerCamelCase__  :     List[str]=10_00	,  lowerCamelCase__  :     List[Any]=[3, 3, 6, 4]	,  lowerCamelCase__  :     Union[str, Any]=[48, 56, 1_12, 2_20]	,  )   ->Optional[int]:



     '''simple docstring'''







     _UpperCAmelCase		:       Any								=      parent
     _UpperCAmelCase		:       Union[str, Any]								=      batch_size
     _UpperCAmelCase		:       Union[str, Any]								=      num_channels
     _UpperCAmelCase		:       int								=      is_training
     _UpperCAmelCase		:       int								=      use_labels
     _UpperCAmelCase		:       List[Any]								=      hidden_dropout_prob
     _UpperCAmelCase		:       Dict								=      attention_probs_dropout_prob
     _UpperCAmelCase		:       List[Any]								=      num_labels
     _UpperCAmelCase		:       Any								=      image_size
     _UpperCAmelCase		:       Any								=      layer_depths
     _UpperCAmelCase		:       List[Any]								=      embed_dims




   def 							lowerCAmelCase__    (		self  :     Tuple )   ->Union[str, Any]:



     '''simple docstring'''







     _UpperCAmelCase		:       Optional[int]								=      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		:       Optional[Any]								=      self.get_config()

     return config, pixel_values, labels




   def 							lowerCAmelCase__    (		self  :     Tuple )   ->Dict:



     '''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=UpperCamelCase__	,  layer_scale_init_value=1E-5	,  )




   def 							lowerCAmelCase__    (		self  :     int	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Optional[int] )   ->Optional[int]:



     '''simple docstring'''







     _UpperCAmelCase		:       Dict								=      SwiftFormerModel(config=UpperCamelCase__ )
     model.to(UpperCamelCase__ )
     model.eval()
     _UpperCAmelCase		:       Union[str, Any]								=      model(UpperCamelCase__ )
     self.parent.assertEqual(result.last_hidden_state.shape	,  (self.batch_size, self.embed_dims[-1], 7, 7) )




   def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Optional[Any] )   ->Optional[Any]:



     '''simple docstring'''







     _UpperCAmelCase		:       Tuple								=      self.num_labels
     _UpperCAmelCase		:       List[str]								=      SwiftFormerForImageClassification(UpperCamelCase__ )
     model.to(UpperCamelCase__ )
     model.eval()
     _UpperCAmelCase		:       Dict								=      model(UpperCamelCase__	,  labels=UpperCamelCase__ )
     self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels) )

     _UpperCAmelCase		:       List[Any]								=      SwiftFormerForImageClassification(UpperCamelCase__ )
     model.to(UpperCamelCase__ )
     model.eval()

     _UpperCAmelCase		:       Optional[int]								=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
     _UpperCAmelCase		:       str								=      model(UpperCamelCase__ )
     self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels) )






   def 							lowerCAmelCase__    (		self  :     str )   ->List[str]:



     '''simple docstring'''







     ((_UpperCAmelCase)					,    (_UpperCAmelCase)					,    (_UpperCAmelCase))		:       Tuple								=      self.prepare_config_and_inputs()
     _UpperCAmelCase		:       int								=      {"pixel_values": pixel_values}
     return config, inputs_dict






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

   lowerCAmelCase :	List[str]          =     False
   lowerCAmelCase :	str          =     False
   lowerCAmelCase :	Optional[int]          =     False
   lowerCAmelCase :	Optional[int]          =     False
   lowerCAmelCase :	str          =     False




   def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Any:



     '''simple docstring'''







     _UpperCAmelCase		:       Any								=      SwiftFormerModelTester(self )
     _UpperCAmelCase		:       int								=      ConfigTester(
         self	,  config_class=UpperCamelCase__	,  has_text_modality=UpperCamelCase__	,  hidden_size=37	,  num_attention_heads=12	,  num_hidden_layers=12	,  )




   def 							lowerCAmelCase__    (		self  :     int )   ->Dict:



     '''simple docstring'''







     self.config_tester.run_common_tests()




   @unittest.skip(reason="SwiftFormer does not use inputs_embeds" )
   def 							lowerCAmelCase__    (		self  :     List[str] )   ->Dict:



     '''simple docstring'''







     pass




   def 							lowerCAmelCase__    (		self  :     Tuple )   ->Union[str, Any]:



     '''simple docstring'''







     _UpperCAmelCase					,    _UpperCAmelCase		:       str								=      self.model_tester.prepare_config_and_inputs_for_common()

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




   def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Union[str, Any]:



     '''simple docstring'''







     _UpperCAmelCase					,    _UpperCAmelCase		:       Union[str, Any]								=      self.model_tester.prepare_config_and_inputs_for_common()

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

       _UpperCAmelCase		:       Optional[int]								=      ["pixel_values"]
       self.assertListEqual(arg_names[:1]	,  UpperCamelCase__ )




   def 							lowerCAmelCase__    (		self  :     Any )   ->Union[str, Any]:



     '''simple docstring'''







     _UpperCAmelCase		:       Optional[int]								=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*UpperCamelCase__ )




   def 							lowerCAmelCase__    (		self  :     List[str] )   ->Any:



     '''simple docstring'''







     _UpperCAmelCase		:       int								=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )




   @slow
   def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->List[Any]:



     '''simple docstring'''







     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       _UpperCAmelCase		:       Optional[Any]								=      SwiftFormerModel.from_pretrained(UpperCamelCase__ )
       self.assertIsNotNone(UpperCamelCase__ )




   @unittest.skip(reason="SwiftFormer does not output attentions" )
   def 							lowerCAmelCase__    (		self  :     List[Any] )   ->int:



     '''simple docstring'''







     pass




   def 							lowerCAmelCase__    (		self  :     int )   ->List[Any]:



     '''simple docstring'''







     def check_hidden_states_output(lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     int ):
       _UpperCAmelCase		:       Tuple								=      model_class(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()

       with torch.no_grad():
         _UpperCAmelCase		:       Dict								=      model(**self._prepare_for_class(UpperCamelCase__	,  UpperCamelCase__ ) )

       _UpperCAmelCase		:       List[Any]								=      outputs.hidden_states

       _UpperCAmelCase		:       List[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		:       List[Any]								=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       _UpperCAmelCase		:       Any								=      True
       check_hidden_states_output(UpperCamelCase__	,  UpperCamelCase__	,  UpperCamelCase__ )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _UpperCAmelCase		:       List[Any]								=      True

       check_hidden_states_output(UpperCamelCase__	,  UpperCamelCase__	,  UpperCamelCase__ )




   def 							lowerCAmelCase__    (		self  :     Dict )   ->int:



     '''simple docstring'''







     def _config_zero_init(lowerCamelCase__  :     int ):
       _UpperCAmelCase		:       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		:       Optional[int]								=      _config_zero_init(getattr(UpperCamelCase__	,  UpperCamelCase__ ) )
           setattr(UpperCamelCase__	,  UpperCamelCase__	,  UpperCamelCase__ )
       return configs_no_init

     _UpperCAmelCase					,    _UpperCAmelCase		:       int								=      self.model_tester.prepare_config_and_inputs_for_common()

     _UpperCAmelCase		:       Union[str, 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 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->int:



     '''simple docstring'''







     pass






def  __lowerCAmelCase ():
  _UpperCAmelCase		:       str								=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
  return image






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




   @cached_property
   def 							lowerCAmelCase__    (		self  :     List[str] )   ->Any:



     '''simple docstring'''







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






   @slow
   def 							lowerCAmelCase__    (		self  :     List[Any] )   ->List[Any]:



     '''simple docstring'''







     _UpperCAmelCase		:       Union[str, Any]								=      SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs" ).to(UpperCamelCase__ )

     _UpperCAmelCase		:       int								=      self.default_image_processor
     _UpperCAmelCase		:       Optional[Any]								=      prepare_img()
     _UpperCAmelCase		:       Union[str, Any]								=      image_processor(images=UpperCamelCase__	,  return_tensors="pt" ).to(UpperCamelCase__ )

     # forward pass
     with torch.no_grad():
       _UpperCAmelCase		:       Tuple								=      model(**UpperCamelCase__ )

     # verify the logits
     _UpperCAmelCase		:       Tuple								=      torch.Size((1, 10_00) )
     self.assertEqual(outputs.logits.shape	,  UpperCamelCase__ )

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







'''simple docstring'''


from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	Any          =     ["image_processor", "tokenizer"]
			lowerCAmelCase :	List[Any]          =     "BlipImageProcessor"
			lowerCAmelCase :	Union[str, Any]          =     "AutoTokenizer"




			def __init__(		self  :     Any	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     int )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      False
					super().__init__(lowerCamelCase__	,  lowerCamelCase__ )
					_UpperCAmelCase		:       Tuple								=      self.image_processor




			def __call__(		self  :     Dict	,  lowerCamelCase__  :     ImageInput = None	,  lowerCamelCase__  :     Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,  lowerCamelCase__  :     bool = True	,  lowerCamelCase__  :     Union[bool, str, PaddingStrategy] = False	,  lowerCamelCase__  :     Union[bool, str, TruncationStrategy] = None	,  lowerCamelCase__  :     Optional[int] = None	,  lowerCamelCase__  :     int = 0	,  lowerCamelCase__  :     Optional[int] = None	,  lowerCamelCase__  :     Optional[bool] = None	,  lowerCamelCase__  :     bool = False	,  lowerCamelCase__  :     bool = False	,  lowerCamelCase__  :     bool = False	,  lowerCamelCase__  :     bool = False	,  lowerCamelCase__  :     bool = False	,  lowerCamelCase__  :     bool = True	,  lowerCamelCase__  :     Optional[Union[str, TensorType]] = None	,  **lowerCamelCase__  :     Tuple	,  )   ->BatchEncoding:



					'''simple docstring'''







					if images is None and text is None:
							raise ValueError("You have to specify either images or text." )

					# Get only text
					if images is None:
							_UpperCAmelCase		:       Optional[int]								=      self.tokenizer
							_UpperCAmelCase		:       List[Any]								=      self.tokenizer(
							    text=lowerCamelCase__	,  add_special_tokens=lowerCamelCase__	,  padding=lowerCamelCase__	,  truncation=lowerCamelCase__	,  max_length=lowerCamelCase__	,  stride=lowerCamelCase__	,  pad_to_multiple_of=lowerCamelCase__	,  return_attention_mask=lowerCamelCase__	,  return_overflowing_tokens=lowerCamelCase__	,  return_special_tokens_mask=lowerCamelCase__	,  return_offsets_mapping=lowerCamelCase__	,  return_token_type_ids=lowerCamelCase__	,  return_length=lowerCamelCase__	,  verbose=lowerCamelCase__	,  return_tensors=lowerCamelCase__	,  **lowerCamelCase__	,  )
							return text_encoding

					# add pixel_values
					_UpperCAmelCase		:       Optional[int]								=      self.image_processor(lowerCamelCase__	,  return_tensors=lowerCamelCase__ )

					if text is not None:
							_UpperCAmelCase		:       Dict								=      self.tokenizer(
							    text=lowerCamelCase__	,  add_special_tokens=lowerCamelCase__	,  padding=lowerCamelCase__	,  truncation=lowerCamelCase__	,  max_length=lowerCamelCase__	,  stride=lowerCamelCase__	,  pad_to_multiple_of=lowerCamelCase__	,  return_attention_mask=lowerCamelCase__	,  return_overflowing_tokens=lowerCamelCase__	,  return_special_tokens_mask=lowerCamelCase__	,  return_offsets_mapping=lowerCamelCase__	,  return_token_type_ids=lowerCamelCase__	,  return_length=lowerCamelCase__	,  verbose=lowerCamelCase__	,  return_tensors=lowerCamelCase__	,  **lowerCamelCase__	,  )
					else:
							_UpperCAmelCase		:       int								=      None

					if text_encoding is not None:
							encoding_image_processor.update(lowerCamelCase__ )

					return encoding_image_processor




			def 							lowerCAmelCase__    (		self  :     List[Any]	,  *lowerCamelCase__  :     Optional[int]	,  **lowerCamelCase__  :     Dict )   ->Optional[Any]:



					'''simple docstring'''







					return self.tokenizer.batch_decode(*lowerCamelCase__	,  **lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     int	,  *lowerCamelCase__  :     Dict	,  **lowerCamelCase__  :     str )   ->Optional[int]:



					'''simple docstring'''







					return self.tokenizer.decode(*lowerCamelCase__	,  **lowerCamelCase__ )






			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def 							lowerCAmelCase__    (		self  :     Any )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      self.tokenizer.model_input_names
					_UpperCAmelCase		:       Dict								=      self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )



