"""simple docstring"""







import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[Any]      ):
     lowercase_	:				Optional[Any]													=	SwinConfig(image_size=1_92      )

     if "base" in model_name:
          lowercase_	:				Any													=	6
          lowercase_	:				Any													=	1_28
          lowercase_	:				int													=	(2, 2, 18, 2)
          lowercase_	:				Dict													=	(4, 8, 16, 32)
     elif "large" in model_name:
          lowercase_	:				Dict													=	12
          lowercase_	:				Any													=	1_92
          lowercase_	:				Union[str, Any]													=	(2, 2, 18, 2)
          lowercase_	:				str													=	(6, 12, 24, 48)
     else:
          raise ValueError('Model not supported, only supports base and large variants'      )

     lowercase_	:				str													=	window_size
     lowercase_	:				Dict													=	embed_dim
     lowercase_	:				Tuple													=	depths
     lowercase_	:				Dict													=	num_heads

     return config



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[Any]      ):
     if "encoder.mask_token" in name:
          lowercase_	:				Optional[int]													=	name.replace('encoder.mask_token'     ,			'embeddings.mask_token'      )
     if "encoder.patch_embed.proj" in name:
          lowercase_	:				int													=	name.replace('encoder.patch_embed.proj'     ,			'embeddings.patch_embeddings.projection'      )
     if "encoder.patch_embed.norm" in name:
          lowercase_	:				Optional[Any]													=	name.replace('encoder.patch_embed.norm'     ,			'embeddings.norm'      )
     if "attn.proj" in name:
          lowercase_	:				Optional[int]													=	name.replace('attn.proj'     ,			'attention.output.dense'      )
     if "attn" in name:
          lowercase_	:				Dict													=	name.replace('attn'     ,			'attention.self'      )
     if "norm1" in name:
          lowercase_	:				Union[str, Any]													=	name.replace('norm1'     ,			'layernorm_before'      )
     if "norm2" in name:
          lowercase_	:				Tuple													=	name.replace('norm2'     ,			'layernorm_after'      )
     if "mlp.fc1" in name:
          lowercase_	:				Optional[int]													=	name.replace('mlp.fc1'     ,			'intermediate.dense'      )
     if "mlp.fc2" in name:
          lowercase_	:				int													=	name.replace('mlp.fc2'     ,			'output.dense'      )

     if name == "encoder.norm.weight":
          lowercase_	:				Union[str, Any]													=	'layernorm.weight'
     if name == "encoder.norm.bias":
          lowercase_	:				Tuple													=	'layernorm.bias'

     if "decoder" in name:
          pass
     else:
          lowercase_	:				List[str]													=	'swin.' + name

     return name



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     List[str]     ,			__SCREAMING_SNAKE_CASE							:     str      ):
     for key in orig_state_dict.copy().keys():
          lowercase_	:				Tuple													=	orig_state_dict.pop(__SCREAMING_SNAKE_CASE      )

          if "attn_mask" in key:
               pass
          elif "qkv" in key:
               lowercase_	:				Tuple													=	key.split('.'      )
               lowercase_	:				List[Any]													=	int(key_split[2]      )
               lowercase_	:				Optional[int]													=	int(key_split[4]      )
               lowercase_	:				List[str]													=	model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    lowercase_	:				Any													=	val[:dim, :]
                    lowercase_	:				Dict													=	val[
                        dim : dim * 2, :
                    ]
                    lowercase_	:				List[str]													=	val[-dim:, :]
               else:
                    lowercase_	:				int													=	val[
                        :dim
                    ]
                    lowercase_	:				str													=	val[
                        dim : dim * 2
                    ]
                    lowercase_	:				List[str]													=	val[
                        -dim:
                    ]
          else:
               lowercase_	:				int													=	val

     return orig_state_dict



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Dict     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,			__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,			__SCREAMING_SNAKE_CASE							:     List[str]      ):
     lowercase_	:				List[str]													=	torch.load(__SCREAMING_SNAKE_CASE     ,			map_location='cpu'      )['model']

     lowercase_	:				Optional[Any]													=	get_swin_config(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				str													=	SwinForMaskedImageModeling(__SCREAMING_SNAKE_CASE      )
     model.eval()

     lowercase_	:				int													=	convert_state_dict(__SCREAMING_SNAKE_CASE     ,			__SCREAMING_SNAKE_CASE      )
     model.load_state_dict(__SCREAMING_SNAKE_CASE      )

     lowercase_	:				Any													=	'http://images.cocodataset.org/val2017/000000039769.jpg'

     lowercase_	:				Optional[Any]													=	ViTImageProcessor(size={'height': 1_92, 'width': 1_92}      )
     lowercase_	:				Optional[int]													=	Image.open(requests.get(__SCREAMING_SNAKE_CASE     ,			stream=__SCREAMING_SNAKE_CASE      ).raw      )
     lowercase_	:				int													=	image_processor(images=__SCREAMING_SNAKE_CASE     ,			return_tensors='pt'      )

     with torch.no_grad():
          lowercase_	:				int													=	model(**__SCREAMING_SNAKE_CASE      ).logits

     print(outputs.keys()      )
     print('Looks ok!'      )

     if pytorch_dump_folder_path is not None:
          print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''      )
          model.save_pretrained(__SCREAMING_SNAKE_CASE      )

          print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
          image_processor.save_pretrained(__SCREAMING_SNAKE_CASE      )

     if push_to_hub:
          print(F'''Pushing model and image processor for {model_name} to hub'''      )
          model.push_to_hub(F'''microsoft/{model_name}'''      )
          image_processor.push_to_hub(F'''microsoft/{model_name}'''      )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE    	=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="swin-base-simmim-window6-192",
           type=str,
           choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
           help="Name of the Swin SimMIM model you'd like to convert.",
       )
       parser.add_argument(
           "--checkpoint_path",
           default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
           type=str,
           help="Path to the original PyTorch checkpoint (.pth file).",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )

       __SCREAMING_SNAKE_CASE    	=parser.parse_args()
       convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""







import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
       from PIL import Image
else:







       class        UpperCamelCase		:
          @staticmethod
          def 			_UpperCAmelCase   (			*__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


               '''simple docstring'''


               pass







@is_pipeline_test
@require_vision
@require_torch
class        UpperCamelCase		(			unittest.TestCase  ):
   lowercase							=  MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				int													=	pipeline(
            'zero-shot-object-detection'     ,model='hf-internal-testing/tiny-random-owlvit-object-detection'			)

        lowercase_	:				Optional[int]													=	[
            {
                'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                'candidate_labels': ['cat', 'remote', 'couch'],
            }
        ]
        return object_detector, examples
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Tuple													=	object_detector(examples[0]     ,threshold=0.0			)

        lowercase_	:				Any													=	len(__UpperCamelCase			)
        self.assertGreater(__UpperCamelCase     ,0			)
        self.assertEqual(
            __UpperCamelCase     ,[
                {
                    'score': ANY(__UpperCamelCase			),
                    'label': ANY(__UpperCamelCase			),
                    'box': {'xmin': ANY(__UpperCamelCase			), 'ymin': ANY(__UpperCamelCase			), 'xmax': ANY(__UpperCamelCase			), 'ymax': ANY(__UpperCamelCase			)},
                }
                for i in range(__UpperCamelCase			)
            ]     ,)
   @require_tf
   @unittest.skip('Zero Shot Object Detection not implemented in TF'			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        pass
   @require_torch
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	pipeline(
            'zero-shot-object-detection'     ,model='hf-internal-testing/tiny-random-owlvit-object-detection'			)

        lowercase_	:				Optional[int]													=	object_detector(
            './tests/fixtures/tests_samples/COCO/000000039769.png'     ,candidate_labels=['cat', 'remote', 'couch']     ,threshold=0.64     ,)

        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.7235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
                {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
                {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
            ]     ,)

        lowercase_	:				Union[str, Any]													=	object_detector(
            [
                {
                    'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                    'candidate_labels': ['cat', 'remote', 'couch'],
                }
            ]     ,threshold=0.64     ,)

        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                [
                    {'score': 0.7235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                    {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                    {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
                    {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                    {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                    {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
                    {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
                    {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
                    {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
                ]
            ]     ,)
   @require_torch
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Any													=	pipeline('zero-shot-object-detection'			)

        lowercase_	:				List[str]													=	object_detector(
            'http://images.cocodataset.org/val2017/000000039769.jpg'     ,candidate_labels=['cat', 'remote', 'couch']     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
                {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
                {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
            ]     ,)

        lowercase_	:				Union[str, Any]													=	object_detector(
            [
                {
                    'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                    'candidate_labels': ['cat', 'remote', 'couch'],
                },
                {
                    'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                    'candidate_labels': ['cat', 'remote', 'couch'],
                },
            ]     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                [
                    {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                    {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                    {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
                    {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
                    {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
                ],
                [
                    {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                    {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                    {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
                    {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
                    {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
                ],
            ]     ,)
   @require_tf
   @unittest.skip('Zero Shot Object Detection not implemented in TF'			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        pass
   @require_torch
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	0.2
        lowercase_	:				Any													=	pipeline('zero-shot-object-detection'			)

        lowercase_	:				str													=	object_detector(
            'http://images.cocodataset.org/val2017/000000039769.jpg'     ,candidate_labels=['cat', 'remote', 'couch']     ,threshold=__UpperCamelCase     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
                {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
            ]     ,)







   @require_torch
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Tuple													=	2
        lowercase_	:				Optional[Any]													=	pipeline('zero-shot-object-detection'			)

        lowercase_	:				List[Any]													=	object_detector(
            'http://images.cocodataset.org/val2017/000000039769.jpg'     ,candidate_labels=['cat', 'remote', 'couch']     ,top_k=__UpperCamelCase     ,)
        self.assertEqual(
            nested_simplify(__UpperCamelCase     ,decimals=4			)     ,[
                {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
                {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
            ]     ,)

