






'''simple docstring'''



import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.albert.modeling_flax_albert import (
          FlaxAlbertForMaskedLM,
          FlaxAlbertForMultipleChoice,
          FlaxAlbertForPreTraining,
          FlaxAlbertForQuestionAnswering,
          FlaxAlbertForSequenceClassification,
          FlaxAlbertForTokenClassification,
          FlaxAlbertModel,
      )
class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




      def __init__(			self      :		Tuple  ,   UpperCAmelCase      :		Optional[int]  ,   UpperCAmelCase      :		Dict=13  ,   UpperCAmelCase      :		Optional[Any]=7  ,   UpperCAmelCase      :		Tuple=True  ,   UpperCAmelCase      :		Dict=True  ,   UpperCAmelCase      :		List[Any]=True  ,   UpperCAmelCase      :		Union[str, Any]=True  ,   UpperCAmelCase      :		List[str]=99  ,   UpperCAmelCase      :		Any=32  ,   UpperCAmelCase      :		int=5  ,   UpperCAmelCase      :		Tuple=4  ,   UpperCAmelCase      :		Dict=37  ,   UpperCAmelCase      :		List[str]="gelu"  ,   UpperCAmelCase      :		Any=0.1  ,   UpperCAmelCase      :		Union[str, Any]=0.1  ,   UpperCAmelCase      :		List[str]=512  ,   UpperCAmelCase      :		List[str]=16  ,   UpperCAmelCase      :		str=2  ,   UpperCAmelCase      :		int=0.0_2  ,   UpperCAmelCase      :		int=4  ,   )	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : int						=parent
       lowercase       : Tuple						=batch_size
       lowercase       : List[Any]						=seq_length
       lowercase       : List[Any]						=is_training
       lowercase       : str						=use_attention_mask
       lowercase       : str						=use_token_type_ids
       lowercase       : Optional[Any]						=use_labels
       lowercase       : Optional[Any]						=vocab_size
       lowercase       : Dict						=hidden_size
       lowercase       : Optional[int]						=num_hidden_layers
       lowercase       : Any						=num_attention_heads
       lowercase       : Dict						=intermediate_size
       lowercase       : int						=hidden_act
       lowercase       : List[str]						=hidden_dropout_prob
       lowercase       : Optional[Any]						=attention_probs_dropout_prob
       lowercase       : Any						=max_position_embeddings
       lowercase       : Any						=type_vocab_size
       lowercase       : Tuple						=type_sequence_label_size
       lowercase       : int						=initializer_range
       lowercase       : int						=num_choices






      def 					A__   (			self      :		Tuple		)	->	Optional[int]:


       '''simple docstring'''

       lowercase       : Union[str, Any]						=ids_tensor([self.batch_size, self.seq_length]  ,   self.vocab_size		)

       lowercase       : Optional[Any]						=None
       if self.use_attention_mask:
        lowercase       : Tuple						=random_attention_mask([self.batch_size, self.seq_length]		)

       lowercase       : Union[str, Any]						=None
       if self.use_token_type_ids:
        lowercase       : Union[str, Any]						=ids_tensor([self.batch_size, self.seq_length]  ,   self.type_vocab_size		)

       lowercase       : Union[str, Any]						=AlbertConfig(
           vocab_size=self.vocab_size  ,   hidden_size=self.hidden_size  ,   num_hidden_layers=self.num_hidden_layers  ,   num_attention_heads=self.num_attention_heads  ,   intermediate_size=self.intermediate_size  ,   hidden_act=self.hidden_act  ,   hidden_dropout_prob=self.hidden_dropout_prob  ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,   max_position_embeddings=self.max_position_embeddings  ,   type_vocab_size=self.type_vocab_size  ,   is_decoder=UpperCAmelCase  ,   initializer_range=self.initializer_range  ,   )

       return config, input_ids, token_type_ids, attention_mask






      def 					A__   (			self      :		Dict		)	->	List[Any]:


       '''simple docstring'''

       lowercase       : Optional[int]						=self.prepare_config_and_inputs()
       lowercase	,		lowercase	,		lowercase	,		lowercase       : int						=config_and_inputs
       lowercase       : int						={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict





@require_flax
class UpperCAmelCase_       (     __A					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      (
          (
              FlaxAlbertModel,
              FlaxAlbertForPreTraining,
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )






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


       '''simple docstring'''

       lowercase       : Optional[int]						=FlaxAlbertModelTester(self		)






      @slow
      def 					A__   (			self      :		Dict		)	->	Optional[int]:


       '''simple docstring'''

       for model_class_name in self.all_model_classes:
        lowercase       : int						=model_class_name.from_pretrained('''albert-base-v2'''		)
        lowercase       : Optional[Any]						=model(np.ones((1, 1)		)		)
        self.assertIsNotNone(UpperCAmelCase		)





@require_flax
class UpperCAmelCase_       (     unittest.TestCase					):




      """simple docstring"""




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


       '''simple docstring'''

       lowercase       : List[str]						=FlaxAlbertModel.from_pretrained('''albert-base-v2'''		)
       lowercase       : Tuple						=np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
       lowercase       : str						=np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
       lowercase       : Dict						=model(UpperCAmelCase  ,   attention_mask=UpperCAmelCase		)[0]
       lowercase       : Any						=(1, 11, 768)
       self.assertEqual(output.shape  ,   UpperCAmelCase		)
       lowercase       : int						=np.array(
           [[[-0.6_5_1_3, 1.5_0_3_5, -0.2_7_6_6], [-0.6_5_1_5, 1.5_0_4_6, -0.2_7_8_0], [-0.6_5_1_2, 1.5_0_4_9, -0.2_7_8_4]]]		)

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]  ,   UpperCAmelCase  ,   atol=1e-4		)		)








'''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        UpperCAmelCase_			:
    """simple docstring"""







    def __init__(       self   :	Any					,	snake_case_   :	List[Any]					,	snake_case_   :	Union[str, Any]=13					,	snake_case_   :	Optional[int]=3					,	snake_case_   :	List[Any]=True					,	snake_case_   :	Dict=True					,	snake_case_   :	Optional[Any]=0.1					,	snake_case_   :	Tuple=0.1					,	snake_case_   :	Dict=224					,	snake_case_   :	Dict=1_000					,	snake_case_   :	Tuple=[3, 3, 6, 4]					,	snake_case_   :	Tuple=[48, 56, 112, 220]					,	):
          snake_case__    :       str       		= parent
          snake_case__    :       Dict       		= batch_size
          snake_case__    :       Dict       		= num_channels
          snake_case__    :       Any       		= is_training
          snake_case__    :       Union[str, Any]       		= use_labels
          snake_case__    :       Tuple       		= hidden_dropout_prob
          snake_case__    :       Optional[int]       		= attention_probs_dropout_prob
          snake_case__    :       List[str]       		= num_labels
          snake_case__    :       Any       		= image_size
          snake_case__    :       str       		= layer_depths
          snake_case__    :       Union[str, Any]       		= embed_dims




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       List[Any]       		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

          snake_case__    :       List[str]       		= self.get_config()

          return config, pixel_values, labels




    def lowerCamelCase			(       self   :	str		):
          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   :	Optional[int]					,	snake_case_   :	Any					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	List[str]		):
          snake_case__    :       Dict       		= SwiftFormerModel(config=snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       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   :	List[Any]					,	snake_case_   :	Tuple					,	snake_case_   :	Optional[int]					,	snake_case_   :	Optional[int]		):
          snake_case__    :       Optional[int]       		= self.num_labels
          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()
          snake_case__    :       Optional[Any]       		= model(snake_case_					,	labels=snake_case_		)
          self.parent.assertEqual(result.logits.shape					,	(self.batch_size, self.num_labels)		)

          snake_case__    :       Union[str, Any]       		= SwiftFormerForImageClassification(snake_case_		)
          model.to(snake_case_		)
          model.eval()

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




    def lowerCamelCase			(       self   :	List[Any]		):
          ((snake_case__)		,							(snake_case__)		,							(snake_case__))    :       Union[str, Any]       		= self.prepare_config_and_inputs()
          snake_case__    :       int       		= {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class        UpperCAmelCase_			(			_a						,						_a						,						unittest.TestCase      ):
    """simple docstring"""







    lowercase   =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    lowercase   =						(
        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False
    lowercase   =						False




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Optional[Any]       		= SwiftFormerModelTester(self		)
          snake_case__    :       Union[str, Any]       		= ConfigTester(
              self					,	config_class=snake_case_					,	has_text_modality=snake_case_					,	hidden_size=37					,	num_attention_heads=12					,	num_hidden_layers=12					,	)




    def lowerCamelCase			(       self   :	Optional[int]		):
          self.config_tester.run_common_tests()




    @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""		)
    def lowerCamelCase			(       self   :	Optional[Any]		):
          pass




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__		,							snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= model_class(snake_case_		)
                snake_case__    :       Any       		= model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case_					,	nn.Linear		)		)




    def lowerCamelCase			(       self   :	List[str]		):
          snake_case__		,							snake_case__    :       Dict       		= self.model_tester.prepare_config_and_inputs_for_common()

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

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




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       List[str]       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_		)




    def lowerCamelCase			(       self   :	Tuple		):
          snake_case__    :       Any       		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_		)




    @slow
    def lowerCamelCase			(       self   :	List[str]		):
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__    :       Union[str, Any]       		= SwiftFormerModel.from_pretrained(snake_case_		)
                self.assertIsNotNone(snake_case_		)




    @unittest.skip(reason="""SwiftFormer does not output attentions"""		)
    def lowerCamelCase			(       self   :	Union[str, Any]		):
          pass




    def lowerCamelCase			(       self   :	List[Any]		):
          def check_hidden_states_output(snake_case_   :	str					,	snake_case_   :	Tuple					,	snake_case_   :	List[Any]		):
                snake_case__    :       Optional[Any]       		= model_class(snake_case_		)
                model.to(snake_case_		)
                model.eval()

                with torch.no_grad():
                      snake_case__    :       Tuple       		= model(**self._prepare_for_class(snake_case_					,	snake_case_		)		)

                snake_case__    :       Optional[Any]       		= outputs.hidden_states

                snake_case__    :       List[Any]       		= 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),
                              ]		)					,	)

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

          for model_class in self.all_model_classes:
                snake_case__    :       Any       		= 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"]
                snake_case__    :       str       		= True

                check_hidden_states_output(snake_case_					,	snake_case_					,	snake_case_		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          def _config_zero_init(snake_case_   :	Optional[Any]		):
                snake_case__    :       Union[str, 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-1_0		)
                      if isinstance(getattr(snake_case_					,	snake_case_					,	snake_case_		)					,	snake_case_		):
                            snake_case__    :       List[str]       		= _config_zero_init(getattr(snake_case_					,	snake_case_		)		)
                            setattr(snake_case_					,	snake_case_					,	snake_case_		)
                return configs_no_init

          snake_case__		,							snake_case__    :       Tuple       		= self.model_tester.prepare_config_and_inputs_for_common()

          snake_case__    :       List[str]       		= _config_zero_init(snake_case_		)
          for model_class in self.all_model_classes:
                snake_case__    :       Optional[int]       		= 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   :	Tuple		):
          pass





def 		__snake_case(							)						->					Optional[int]:
      snake_case__    :       Optional[int]       		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
      return image







@require_torch
@require_vision
class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    @cached_property
    def lowerCamelCase			(       self   :	List[str]		):
          return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""		) if is_vision_available() else None




    @slow
    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       str       		= SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""		).to(snake_case_		)

          snake_case__    :       Dict       		= self.default_image_processor
          snake_case__    :       Optional[Any]       		= prepare_img()
          snake_case__    :       Union[str, Any]       		= image_processor(images=snake_case_					,	return_tensors="""pt"""		).to(snake_case_		)

          # forward pass
          with torch.no_grad():
                snake_case__    :       Dict       		= model(**snake_case_		)

          # verify the logits
          snake_case__    :       int       		= torch.Size((1, 1_000)		)
          self.assertEqual(outputs.logits.shape					,	snake_case_		)

          snake_case__    :       int       		= torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]		).to(snake_case_		)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]					,	snake_case_					,	atol=1E-4		)		)
