


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   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



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



       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->Dict:
          lowerCamelCase_						:   Tuple		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          lowerCamelCase_						:   Union[str, Any]		= None
          if self.use_labels:
             lowerCamelCase_						:   Any		= ids_tensor([self.batch_size]		,				self.num_labels							)

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

          return config, pixel_values, labels



       def 				_lowerCAmelCase							(						self				:				Dict							)					->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=__a		,				layer_scale_init_value=1e-5		,				)



       def 				_lowerCAmelCase							(						self				:				Tuple		,				__a				:				int		,				__a				:				Dict		,				__a				:				Optional[int]							)					->Tuple:
          lowerCamelCase_						:   List[Any]		= SwiftFormerModel(config=__a							)
          model.to(__a							)
          model.eval()
          lowerCamelCase_						:   Optional[Any]		= model(__a							)
          self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.embed_dims[-1], 7, 7)							)



       def 				_lowerCAmelCase							(						self				:				List[str]		,				__a				:				int		,				__a				:				Tuple		,				__a				:				int							)					->List[Any]:
          lowerCamelCase_						:   List[Any]		= self.num_labels
          lowerCamelCase_						:   Any		= SwiftFormerForImageClassification(__a							)
          model.to(__a							)
          model.eval()
          lowerCamelCase_						:   Union[str, Any]		= model(__a		,				labels=__a							)
          self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)

          lowerCamelCase_						:   Any		= SwiftFormerForImageClassification(__a							)
          model.to(__a							)
          model.eval()

          lowerCamelCase_						:   Dict		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
          lowerCamelCase_						:   List[str]		= model(__a							)
          self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.num_labels)							)




       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Dict:
          ((lowerCamelCase_),	(lowerCamelCase_),	(lowerCamelCase_))						:   Union[str, Any]		= self.prepare_config_and_inputs()
          lowerCamelCase_						:   Union[str, Any]		= {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_torch
class   SCREAMING_SNAKE_CASE_			(a__						,       a__						,       unittest.TestCase						):




       '''simple docstring'''



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

       _a   							=							False
       _a   							=							False
       _a   							=							False
       _a   							=							False
       _a   							=							False



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->List[str]:
          lowerCamelCase_						:   Optional[int]		= SwiftFormerModelTester(self							)
          lowerCamelCase_						:   List[Any]		= ConfigTester(
              self		,				config_class=__a		,				has_text_modality=__a		,				hidden_size=37		,				num_attention_heads=12		,				num_hidden_layers=12		,				)



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->List[Any]:
          self.config_tester.run_common_tests()



       @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
       def 				_lowerCAmelCase							(						self				:				Optional[int]							)					->List[str]:
          pass



       def 				_lowerCAmelCase							(						self				:				Any							)					->Any:
          lowerCamelCase_,	lowerCamelCase_						:   List[Any]		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             lowerCamelCase_						:   str		= model_class(__a							)
             lowerCamelCase_						:   Tuple		= model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__a		,				nn.Linear							)							)



       def 				_lowerCAmelCase							(						self				:				Any							)					->Tuple:
          lowerCamelCase_,	lowerCamelCase_						:   Any		= self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCamelCase_						:   Optional[Any]		= ["""pixel_values"""]
             self.assertListEqual(arg_names[:1]		,				__a							)



       def 				_lowerCAmelCase							(						self				:				int							)					->int:
          lowerCamelCase_						:   List[Any]		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__a							)



       def 				_lowerCAmelCase							(						self				:				Dict							)					->Tuple:
          lowerCamelCase_						:   Union[str, Any]		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__a							)



       @slow
       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->int:
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_						:   Union[str, Any]		= SwiftFormerModel.from_pretrained(__a							)
             self.assertIsNotNone(__a							)



       @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->str:
          pass



       def 				_lowerCAmelCase							(						self				:				List[str]							)					->Optional[int]:
          def check_hidden_states_output(__a				:				Optional[int]		,				__a				:				Optional[int]		,				__a				:				int							):
             lowerCamelCase_						:   Optional[int]		= model_class(__a							)
             model.to(__a							)
             model.eval()

             with torch.no_grad():
                lowerCamelCase_						:   Dict		= model(**self._prepare_for_class(__a		,				__a							)							)

             lowerCamelCase_						:   List[str]		= outputs.hidden_states

             lowerCamelCase_						:   int		= 8
             self.assertEqual(len(__a							)		,				__a							)  # 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(__a							)							):
                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_,	lowerCamelCase_						:   Union[str, Any]		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             lowerCamelCase_						:   str		= True
             check_hidden_states_output(__a		,				__a		,				__a							)

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

             check_hidden_states_output(__a		,				__a		,				__a							)



       def 				_lowerCAmelCase							(						self				:				Any							)					->Dict:
          def _config_zero_init(__a				:				Tuple							):
             lowerCamelCase_						:   int		= copy.deepcopy(__a							)
             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(__a		,				__a		,				1e-10							)
                if isinstance(getattr(__a		,				__a		,				__a							)		,				__a							):
                   lowerCamelCase_						:   List[str]		= _config_zero_init(getattr(__a		,				__a							)							)
                   setattr(__a		,				__a		,				__a							)
             return configs_no_init

          lowerCamelCase_,	lowerCamelCase_						:   Optional[Any]		= self.model_tester.prepare_config_and_inputs_for_common()

          lowerCamelCase_						:   Tuple		= _config_zero_init(__a							)
          for model_class in self.all_model_classes:
             lowerCamelCase_						:   List[str]		= model_class(config=__a							)
             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				:				Dict							)					->int:
          pass







def    __lowerCamelCase   (      )     ->    Union[str, Any]:
   lowerCamelCase_						:   str		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
   return image





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




       '''simple docstring'''



       @cached_property
       def 				_lowerCAmelCase							(						self				:				Dict							)					->Any:
          return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None




       @slow
       def 				_lowerCAmelCase							(						self				:				Any							)					->Tuple:
          lowerCamelCase_						:   Tuple		= SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(__a							)

          lowerCamelCase_						:   List[Any]		= self.default_image_processor
          lowerCamelCase_						:   List[Any]		= prepare_img()
          lowerCamelCase_						:   Optional[int]		= image_processor(images=__a		,				return_tensors="""pt"""							).to(__a							)

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

          # verify the logits
          lowerCamelCase_						:   List[str]		= torch.Size((1, 1_000)							)
          self.assertEqual(outputs.logits.shape		,				__a							)

          lowerCamelCase_						:   Tuple		= torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]							).to(__a							)
          self.assertTrue(torch.allclose(outputs.logits[0, :3]		,				__a		,				atol=1e-4							)							)









import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					argparse.ArgumentParser()

      parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

      parser.add_argument(
          '--txt2img_unclip',
          default='kakaobrain/karlo-v1-alpha',
          type=str,
          required=False,
          help='The pretrained txt2img unclip.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

      snake_case__				:	Optional[int]					       =					CLIPImageProcessor()
      snake_case__				:	Optional[Any]					       =					CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

      snake_case__				:	Dict					       =					UnCLIPImageVariationPipeline(
          decoder=txtaimg.decoder,
          text_encoder=txtaimg.text_encoder,
          tokenizer=txtaimg.tokenizer,
          text_proj=txtaimg.text_proj,
          feature_extractor=feature_extractor,
          image_encoder=image_encoder,
          super_res_first=txtaimg.super_res_first,
          super_res_last=txtaimg.super_res_last,
          decoder_scheduler=txtaimg.decoder_scheduler,
          super_res_scheduler=txtaimg.super_res_scheduler,
      )

      imgaimg.save_pretrained(args.dump_path)





