






import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase								=			logging.get_logger(__name__)

__UpperCAmelCase								=			torch.device("""cpu""")





def      snake_case_				()      ->     int:
   __lowerCAmelCase  :      int    =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   __lowerCAmelCase  :      Union[str, Any]    =						Image.open(requests.get(SCREAMING_SNAKE_CASE_ ,       stream=SCREAMING_SNAKE_CASE_					).raw					)
   return im





def      snake_case_				(__A					:						Optional[Any]					)      ->     List[Any]:






   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]					)

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]					)

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]					)

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]					)





def      snake_case_				(__A					:						List[str] ,       __A					:						Optional[Any] ,       __A					:						Union[str, Any]					)      ->     Tuple:
   __lowerCAmelCase  :      Dict    =						dct.pop(SCREAMING_SNAKE_CASE_					)
   __lowerCAmelCase  :      List[str]    =						val





def      snake_case_				(__A					:						Optional[int]					)      ->     Any:
   __lowerCAmelCase  :      Tuple    =						[]
   for k in state_dict.keys():
      __lowerCAmelCase  :      Dict    =						k
      if ".pwconv" in k:
         __lowerCAmelCase  :      Dict    =						k_new.replace(""".pwconv""" ,       """.point_wise_conv"""					)
      if ".dwconv" in k:
         __lowerCAmelCase  :      int    =						k_new.replace(""".dwconv""" ,       """.depth_wise_conv"""					)
      if ".Proj." in k:
         __lowerCAmelCase  :      List[Any]    =						k_new.replace(""".Proj.""" ,       """.proj."""					)
      if "patch_embed" in k_new:
         __lowerCAmelCase  :      int    =						k_new.replace("""patch_embed""" ,       """swiftformer.patch_embed.patch_embedding"""					)
      if "network" in k_new:
         __lowerCAmelCase  :      int    =						k_new.split("""."""					)
         if ls[2].isdigit():
            __lowerCAmelCase  :      List[str]    =						"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]					)
         else:
            __lowerCAmelCase  :      Optional[Any]    =						k_new.replace("""network""" ,       """swiftformer.encoder.network"""					)
      rename_keys.append((k, k_new)					)
   return rename_keys





@torch.no_grad()
def      snake_case_				(__A					:						List[Any] ,       __A					:						Optional[int] ,       __A					:						Union[str, Any]					)      ->     Optional[Any]:
   __lowerCAmelCase  :      Dict    =						SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   __lowerCAmelCase  :      List[str]    =						1_0_0_0
   __lowerCAmelCase  :      Tuple    =						"""huggingface/label-files"""
   __lowerCAmelCase  :      Optional[int]    =						"""imagenet-1k-id2label.json"""
   __lowerCAmelCase  :      Dict    =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_ ,       repo_type="""dataset"""					) ,       """r"""					)					)
   __lowerCAmelCase  :      Any    =						{int(SCREAMING_SNAKE_CASE_					): v for k, v in idalabel.items()}
   __lowerCAmelCase  :      Optional[int]    =						idalabel
   __lowerCAmelCase  :      List[Any]    =						{v: k for k, v in idalabel.items()}

   # size of the architecture
   if swiftformer_name == "swiftformer_xs":
      __lowerCAmelCase  :      Dict    =						[3, 3, 6, 4]
      __lowerCAmelCase  :      List[str]    =						[4_8, 5_6, 1_1_2, 2_2_0]

   elif swiftformer_name == "swiftformer_s":
      __lowerCAmelCase  :      Optional[int]    =						[3, 3, 9, 6]
      __lowerCAmelCase  :      Any    =						[4_8, 6_4, 1_6_8, 2_2_4]

   elif swiftformer_name == "swiftformer_l1":
      __lowerCAmelCase  :      Optional[Any]    =						[4, 3, 1_0, 5]
      __lowerCAmelCase  :      Tuple    =						[4_8, 9_6, 1_9_2, 3_8_4]

   elif swiftformer_name == "swiftformer_l3":
      __lowerCAmelCase  :      int    =						[4, 4, 1_2, 6]
      __lowerCAmelCase  :      int    =						[6_4, 1_2_8, 3_2_0, 5_1_2]

   # load state_dict of original model, remove and rename some keys
   if original_ckpt:
      if original_ckpt.startswith("""https"""					):
         __lowerCAmelCase  :      List[str]    =						torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ ,       map_location="""cpu""" ,       check_hash=SCREAMING_SNAKE_CASE_					)
      else:
         __lowerCAmelCase  :      Any    =						torch.load(SCREAMING_SNAKE_CASE_ ,       map_location="""cpu"""					)
   __lowerCAmelCase  :      int    =						checkpoint

   __lowerCAmelCase  :      Union[str, Any]    =						create_rename_keys(SCREAMING_SNAKE_CASE_					)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_ ,       SCREAMING_SNAKE_CASE_					)

   # load HuggingFace model
   __lowerCAmelCase  :      Union[str, Any]    =						SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_					).eval()
   hf_model.load_state_dict(SCREAMING_SNAKE_CASE_					)

   # prepare test inputs
   __lowerCAmelCase  :      Optional[int]    =						prepare_img()
   __lowerCAmelCase  :      int    =						ViTImageProcessor.from_pretrained("""preprocessor_config"""					)
   __lowerCAmelCase  :      List[str]    =						processor(images=SCREAMING_SNAKE_CASE_ ,       return_tensors="""pt"""					)

   # compare outputs from both models
   __lowerCAmelCase  :      Tuple    =						get_expected_output(SCREAMING_SNAKE_CASE_					)
   __lowerCAmelCase  :      Optional[int]    =						hf_model(inputs["""pixel_values"""]					).logits

   assert hf_logits.shape == torch.Size([1, 1_0_0_0]					)
   assert torch.allclose(hf_logits[0, 0:5] ,       SCREAMING_SNAKE_CASE_ ,       atol=1e-3					)

   Path(SCREAMING_SNAKE_CASE_					).mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
   print(f'''Saving model {swiftformer_name} to {pytorch_dump_folder_path}'''					)
   hf_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
   __UpperCAmelCase								=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--swiftformer_name""",
       default="""swiftformer_xs""",
       choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
       type=str,
       help="""Name of the SwiftFormer model you'd like to convert.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default="""./converted_outputs/""",
       type=str,
       help="""Path to the output PyTorch model directory.""",
   )
   parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

   __UpperCAmelCase								=			parser.parse_args()
   convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)





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 SCREAMING_SNAKE_CASE     (  unittest.TestCase			):
  """simple docstring"""






  def __init__(      self			:   List[Any]	,							lowerCAmelCase			:   List[str]	,							lowerCAmelCase			:   Optional[Any]=7	,							lowerCAmelCase			:   List[str]=3	,							lowerCAmelCase			:   int=18	,							lowerCAmelCase			:   int=30	,							lowerCAmelCase			:   Optional[int]=4_00	,							lowerCAmelCase			:   Optional[Any]=True	,							lowerCAmelCase			:   Dict=None	,							lowerCAmelCase			:   List[str]=True	,							lowerCAmelCase			:   Tuple=None	,							lowerCAmelCase			:   Any=True	,							)							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						size if size is not None else {"""shortest_edge""": 20}
     __lowerCAmelCase  :      Any    =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
     __lowerCAmelCase  :      str    =						parent
     __lowerCAmelCase  :      List[str]    =						batch_size
     __lowerCAmelCase  :      int    =						num_channels
     __lowerCAmelCase  :      List[str]    =						image_size
     __lowerCAmelCase  :      Optional[int]    =						min_resolution
     __lowerCAmelCase  :      List[str]    =						max_resolution
     __lowerCAmelCase  :      List[Any]    =						do_resize
     __lowerCAmelCase  :      Optional[int]    =						size
     __lowerCAmelCase  :      List[Any]    =						do_center_crop
     __lowerCAmelCase  :      Optional[Any]    =						crop_size
     __lowerCAmelCase  :      int    =						do_flip_channel_order







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     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 SCREAMING_SNAKE_CASE     (  a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      List[str]										=MobileViTImageProcessor if is_vision_available() else None







  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Tuple:






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						MobileViTImageProcessingTester(self   )







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






     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()







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






     """simple docstring"""







     __lowerCAmelCase  :      Optional[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_resize"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""size"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""center_crop"""   )   )
     self.assertTrue(hasattr(lowerCAmelCase	,							"""do_flip_channel_order"""   )   )







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






     """simple docstring"""







     __lowerCAmelCase  :      List[str]    =						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}   )

     __lowerCAmelCase  :      Optional[Any]    =						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 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     int:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						self.image_processing_class(**self.image_processor_dict   )
     # create random PIL images
     __lowerCAmelCase  :      Union[str, Any]    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							Image.Image   )

     # Test not batched input
     __lowerCAmelCase  :      List[Any]    =						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
     __lowerCAmelCase  :      str    =						image_processing(lowerCAmelCase	,							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 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.image_processing_class(**self.image_processor_dict   )
     # create random numpy tensors
     __lowerCAmelCase  :      Any    =						prepare_image_inputs(self.image_processor_tester	,							equal_resolution=lowerCAmelCase	,							numpify=lowerCAmelCase   )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase	,							np.ndarray   )

     # Test not batched input
     __lowerCAmelCase  :      Optional[Any]    =						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
     __lowerCAmelCase  :      Tuple    =						image_processing(lowerCAmelCase	,							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 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Any:






     """simple docstring"""







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

     # Test not batched input
     __lowerCAmelCase  :      Optional[int]    =						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
     __lowerCAmelCase  :      Tuple    =						image_processing(lowerCAmelCase	,							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"""],
         )	,							)


