

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 			lowercase    :




    def __init__( self				,       snake_case				,       snake_case=13				,       snake_case=3				,       snake_case=True				,       snake_case=True				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=224				,       snake_case=1000				,       snake_case=[3, 3, 6, 4]				,       snake_case=[48, 56, 112, 220]				,       ):
           snake_case_          =   parent
           snake_case_          =   batch_size
           snake_case_          =   num_channels
           snake_case_          =   is_training
           snake_case_          =   use_labels
           snake_case_          =   hidden_dropout_prob
           snake_case_          =   attention_probs_dropout_prob
           snake_case_          =   num_labels
           snake_case_          =   image_size
           snake_case_          =   layer_depths
           snake_case_          =   embed_dims




    def    a							( self	):
           snake_case_          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

           snake_case_          =   self.get_config()

           return config, pixel_values, labels




    def    a							( self	):
           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    a							( self				,       snake_case				,       snake_case				,       snake_case	):
           snake_case_          =   SwiftFormerModel(config=UpperCAmelCase_	)
           model.to(UpperCAmelCase_	)
           model.eval()
           snake_case_          =   model(UpperCAmelCase_	)
           self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.embed_dims[-1], 7, 7)	)




    def    a							( self				,       snake_case				,       snake_case				,       snake_case	):
           snake_case_          =   self.num_labels
           snake_case_          =   SwiftFormerForImageClassification(UpperCAmelCase_	)
           model.to(UpperCAmelCase_	)
           model.eval()
           snake_case_          =   model(UpperCAmelCase_				,       labels=UpperCAmelCase_	)
           self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_labels)	)

           snake_case_          =   SwiftFormerForImageClassification(UpperCAmelCase_	)
           model.to(UpperCAmelCase_	)
           model.eval()

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




    def    a							( self	):
           (snake_case_)          =   self.prepare_config_and_inputs()
           snake_case_          =   {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class 			lowercase    (	snake_case__     ,  snake_case__     ,  unittest.TestCase      ):
    __SCREAMING_SNAKE_CASE :			Tuple	      =      (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    __SCREAMING_SNAKE_CASE :			Optional[int]	      =      (
        {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    __SCREAMING_SNAKE_CASE :			Any	      =      False
    __SCREAMING_SNAKE_CASE :			Optional[int]	      =      False
    __SCREAMING_SNAKE_CASE :			int	      =      False
    __SCREAMING_SNAKE_CASE :			Optional[int]	      =      False
    __SCREAMING_SNAKE_CASE :			Union[str, Any]	      =      False




    def    a							( self	):
           snake_case_          =   SwiftFormerModelTester(self	)
           snake_case_          =   ConfigTester(
               self				,       config_class=UpperCAmelCase_				,       has_text_modality=UpperCAmelCase_				,       hidden_size=37				,       num_attention_heads=12				,       num_hidden_layers=12				,       )




    def    a							( self	):
           self.config_tester.run_common_tests()




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




    def    a							( self	):
           snake_case_          =   self.model_tester.prepare_config_and_inputs_for_common()

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




    def    a							( self	):
           snake_case_          =   self.model_tester.prepare_config_and_inputs_for_common()

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

                  snake_case_          =   ["pixel_values"]
                  self.assertListEqual(arg_names[:1]				,       UpperCAmelCase_	)




    def    a							( self	):
           snake_case_          =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_	)




    def    a							( self	):
           snake_case_          =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_	)




    @slow
    def    a							( self	):
           for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  snake_case_          =   SwiftFormerModel.from_pretrained(UpperCAmelCase_	)
                  self.assertIsNotNone(UpperCAmelCase_	)




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




    def    a							( self	):
           def check_hidden_states_output(snake_case				,       snake_case				,       snake_case	):
                  snake_case_          =   model_class(UpperCAmelCase_	)
                  model.to(UpperCAmelCase_	)
                  model.eval()

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

                  snake_case_          =   outputs.hidden_states

                  snake_case_          =   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),
                                 ]	)				,       )

           snake_case_          =   self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  snake_case_          =   True
                  check_hidden_states_output(UpperCAmelCase_				,       UpperCAmelCase_				,       UpperCAmelCase_	)

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

                  check_hidden_states_output(UpperCAmelCase_				,       UpperCAmelCase_				,       UpperCAmelCase_	)




    def    a							( self	):
           def _config_zero_init(snake_case	):
                  snake_case_          =   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-1_0	)
                         if isinstance(getattr(UpperCAmelCase_				,       UpperCAmelCase_				,       UpperCAmelCase_	)				,       UpperCAmelCase_	):
                                snake_case_          =   _config_zero_init(getattr(UpperCAmelCase_				,       UpperCAmelCase_	)	)
                                setattr(UpperCAmelCase_				,       UpperCAmelCase_				,       UpperCAmelCase_	)
                  return configs_no_init

           snake_case_          =   self.model_tester.prepare_config_and_inputs_for_common()

           snake_case_          =   _config_zero_init(UpperCAmelCase_	)
           for model_class in self.all_model_classes:
                  snake_case_          =   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    a							( self	):
           pass





def  __lowerCamelCase     (			):







       '''simple docstring'''






       snake_case_          =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image







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




    @cached_property
    def    a							( self	):
           return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'	) if is_vision_available() else None




    @slow
    def    a							( self	):
           snake_case_          =   SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'	).to(UpperCAmelCase_	)

           snake_case_          =   self.default_image_processor
           snake_case_          =   prepare_img()
           snake_case_          =   image_processor(images=UpperCAmelCase_				,       return_tensors='pt'	).to(UpperCAmelCase_	)

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

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

           snake_case_          =   torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]	).to(UpperCAmelCase_	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]				,       UpperCAmelCase_				,       atol=1e-4	)	)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_UpperCAmelCase		:       Dict						    =	{
    """configuration_blip""": [
        """BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BlipConfig""",
        """BlipTextConfig""",
        """BlipVisionConfig""",
    ],
    """processing_blip""": ["""BlipProcessor"""],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       Optional[int]						    =	["""BlipImageProcessor"""]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       Any						    =	[
					    """BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BlipModel""",
					    """BlipPreTrainedModel""",
					    """BlipForConditionalGeneration""",
					    """BlipForQuestionAnswering""",
					    """BlipVisionModel""",
					    """BlipTextModel""",
					    """BlipForImageTextRetrieval""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       Any						    =	[
					    """TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFBlipModel""",
					    """TFBlipPreTrainedModel""",
					    """TFBlipForConditionalGeneration""",
					    """TFBlipForQuestionAnswering""",
					    """TFBlipVisionModel""",
					    """TFBlipTextModel""",
					    """TFBlipForImageTextRetrieval""",
					]

if TYPE_CHECKING:
					from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
					from .processing_blip import BlipProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_blip import BlipImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip import (
										    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipForConditionalGeneration,
										    BlipForImageTextRetrieval,
										    BlipForQuestionAnswering,
										    BlipModel,
										    BlipPreTrainedModel,
										    BlipTextModel,
										    BlipVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blip import (
										    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBlipForConditionalGeneration,
										    TFBlipForImageTextRetrieval,
										    TFBlipForQuestionAnswering,
										    TFBlipModel,
										    TFBlipPreTrainedModel,
										    TFBlipTextModel,
										    TFBlipVisionModel,
										)

else:
					import sys

					_UpperCAmelCase		:       Optional[int]						    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




