




"""simple docstring"""

import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def 		_snake_case	(   _snake_case				:   List[Any]				)       ->   str:






      '''simple docstring'''


      _A     =  SwinConfig()
      _A     =  swin_name.split('_'				)

      _A     =  name_split[1]
      _A     =  int(name_split[4]				)
      _A     =  int(name_split[3][-1]				)

      if model_size == "tiny":
            _A     =  96
            _A     =  (2, 2, 6, 2)
            _A     =  (3, 6, 12, 24)
      elif model_size == "small":
            _A     =  96
            _A     =  (2, 2, 18, 2)
            _A     =  (3, 6, 12, 24)
      elif model_size == "base":
            _A     =  1_28
            _A     =  (2, 2, 18, 2)
            _A     =  (4, 8, 16, 32)
      else:
            _A     =  1_92
            _A     =  (2, 2, 18, 2)
            _A     =  (6, 12, 24, 48)

      if "in22k" in swin_name:
            _A     =  2_18_41
      else:
            _A     =  10_00
            _A     =  'huggingface/label-files'
            _A     =  'imagenet-1k-id2label.json'
            _A     =  json.load(open(hf_hub_download(_lowerCamelCase							,    _lowerCamelCase							,    repo_type='dataset'				)							,    'r'				)				)
            _A     =  {int(_lowerCamelCase				): v for k, v in idalabel.items()}
            _A     =  idalabel
            _A     =  {v: k for k, v in idalabel.items()}

      _A     =  img_size
      _A     =  num_classes
      _A     =  embed_dim
      _A     =  depths
      _A     =  num_heads
      _A     =  window_size

      return config






def 		_snake_case	(   _snake_case				:   str				)       ->   List[str]:






      '''simple docstring'''


      if "patch_embed.proj" in name:
            _A     =  name.replace('patch_embed.proj'							,    'embeddings.patch_embeddings.projection'				)
      if "patch_embed.norm" in name:
            _A     =  name.replace('patch_embed.norm'							,    'embeddings.norm'				)
      if "layers" in name:
            _A     =  'encoder.' + name
      if "attn.proj" in name:
            _A     =  name.replace('attn.proj'							,    'attention.output.dense'				)
      if "attn" in name:
            _A     =  name.replace('attn'							,    'attention.self'				)
      if "norm1" in name:
            _A     =  name.replace('norm1'							,    'layernorm_before'				)
      if "norm2" in name:
            _A     =  name.replace('norm2'							,    'layernorm_after'				)
      if "mlp.fc1" in name:
            _A     =  name.replace('mlp.fc1'							,    'intermediate.dense'				)
      if "mlp.fc2" in name:
            _A     =  name.replace('mlp.fc2'							,    'output.dense'				)

      if name == "norm.weight":
            _A     =  'layernorm.weight'
      if name == "norm.bias":
            _A     =  'layernorm.bias'

      if "head" in name:
            _A     =  name.replace('head'							,    'classifier'				)
      else:
            _A     =  'swin.' + name

      return name






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   Union[str, Any]				)       ->   Dict:






      '''simple docstring'''


      for key in orig_state_dict.copy().keys():
            _A     =  orig_state_dict.pop(_lowerCamelCase				)

            if "mask" in key:
                  continue
            elif "qkv" in key:
                  _A     =  key.split('.'				)
                  _A     =  int(key_split[1]				)
                  _A     =  int(key_split[3]				)
                  _A     =  model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        _A     =  val[:dim, :]
                        _A     =  val[
                            dim : dim * 2, :
                        ]
                        _A     =  val[-dim:, :]
                  else:
                        _A     =  val[
                            :dim
                        ]
                        _A     =  val[
                            dim : dim * 2
                        ]
                        _A     =  val[
                            -dim:
                        ]
            else:
                  _A     =  val

      return orig_state_dict






def 		_snake_case	(   _snake_case				:   str							,    _snake_case				:   Any				)       ->   List[Any]:






      '''simple docstring'''


      _A     =  timm.create_model(_lowerCamelCase							,    pretrained=_lowerCamelCase				)
      timm_model.eval()

      _A     =  get_swin_config(_lowerCamelCase				)
      _A     =  SwinForImageClassification(_lowerCamelCase				)
      model.eval()

      _A     =  convert_state_dict(timm_model.state_dict()							,    _lowerCamelCase				)
      model.load_state_dict(_lowerCamelCase				)

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

      _A     =  AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_'							,    '-'				)				)				)
      _A     =  Image.open(requests.get(_lowerCamelCase							,    stream=_lowerCamelCase				).raw				)
      _A     =  image_processor(images=_lowerCamelCase							,    return_tensors='pt'				)

      _A     =  timm_model(inputs['pixel_values']				)
      _A     =  model(**_lowerCamelCase				).logits

      assert torch.allclose(_lowerCamelCase							,    _lowerCamelCase							,    atol=1E-3				)

      print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''				)
      model.save_pretrained(_lowerCamelCase				)

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


if __name__ == "__main__":
       a							      =     argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--swin_name''',
           default='''swin_tiny_patch4_window7_224''',
           type=str,
           help='''Name of the Swin timm model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
       )

       a							      =     parser.parse_args()
       convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)







'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileViTImageProcessor





class      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=7     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=18     ,	__SCREAMING_SNAKE_CASE=30     ,	__SCREAMING_SNAKE_CASE=400     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=True     ,	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	size if size is not None else {'''shortest_edge''': 20}
            __snake_case            =	crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
            __snake_case            =	parent
            __snake_case            =	batch_size
            __snake_case            =	num_channels
            __snake_case            =	image_size
            __snake_case            =	min_resolution
            __snake_case            =	max_resolution
            __snake_case            =	do_resize
            __snake_case            =	size
            __snake_case            =	do_center_crop
            __snake_case            =	crop_size
            __snake_case            =	do_flip_channel_order




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



            '''simple docstring'''




            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_flip_channel_order": self.do_flip_channel_order,
            }





@require_torch
@require_vision
class      lowerCAmelCase (			__lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   Union[str, Any]										=      MobileViTImageProcessor if is_vision_available() else None


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	MobileViTImageProcessingTester(self	)


       @property
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            return self.image_processor_tester.prepare_image_processor_dict()


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



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_resize'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''size'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_center_crop'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''center_crop'''	)	)
            self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,	'''do_flip_channel_order'''	)	)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class.from_dict(self.image_processor_dict	)
            self.assertEqual(image_processor.size     ,	{'''shortest_edge''': 20}	)
            self.assertEqual(image_processor.crop_size     ,	{'''height''': 18, '''width''': 18}	)

            __snake_case            =	self.image_processing_class.from_dict(self.image_processor_dict     ,	size=42     ,	crop_size=84	)
            self.assertEqual(image_processor.size     ,	{'''shortest_edge''': 42}	)
            self.assertEqual(image_processor.crop_size     ,	{'''height''': 84, '''width''': 84}	)


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



            '''simple docstring'''




            pass


       def    lowerCAmelCase   ( self	)   ->	str:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PIL images
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	Image.Image	)

            # Test not batched input
            __snake_case            =	image_processing(image_inputs[0]     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random numpy tensors
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE     ,	numpify=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	np.ndarray	)

            # Test not batched input
            __snake_case            =	image_processing(image_inputs[0]     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)




       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	self.image_processing_class(**self.image_processor_dict	)
            # create random PyTorch tensors
            __snake_case            =	prepare_image_inputs(self.image_processor_tester     ,	equal_resolution=__SCREAMING_SNAKE_CASE     ,	torchify=__SCREAMING_SNAKE_CASE	)
            for image in image_inputs:
                 self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,	torch.Tensor	)

            # Test not batched input
            __snake_case            =	image_processing(image_inputs[0]     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)

            # Test batched
            __snake_case            =	image_processing(__SCREAMING_SNAKE_CASE     ,	return_tensors='''pt'''	).pixel_values
            self.assertEqual(
                encoded_images.shape     ,	(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size['''height'''],
                    self.image_processor_tester.crop_size['''width'''],
                )     ,	)






