'''simple docstring'''





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()
_lowerCAmelCase  :List[str]  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Union[str, Any]  				=    torch.device("""cpu""")


def     __lowerCAmelCase						(       )      ->	Any:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       Image.open(requests.get(snake_case_       ,      stream=snake_case_		).raw		)
     return im


def     __lowerCAmelCase						(       a_		)      ->	List[str]:





     '''simple docstring'''





     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     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Tuple    	=       dct.pop(snake_case_		)
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       val


def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





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


@torch.no_grad()
def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	Dict:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[Any]    	=       SwiftFormerConfig()

     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     SCREAMING_SNAKE_CASE					:			Dict    	=       1000
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       """huggingface/label-files"""
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       """imagenet-1k-id2label.json"""
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       json.load(open(hf_hub_download(snake_case_       ,      snake_case_       ,      repo_type='dataset'		)       ,      'r'		)		)
     SCREAMING_SNAKE_CASE					:			Any    	=       {int(snake_case_		): v for k, v in idalabel.items()}
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       idalabel
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {v: k for k, v in idalabel.items()}

     # size of the architecture
     if swiftformer_name == "swiftformer_xs":
          SCREAMING_SNAKE_CASE					:			Dict    	=       [3, 3, 6, 4]
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [48, 56, 112, 220]

     elif swiftformer_name == "swiftformer_s":
          SCREAMING_SNAKE_CASE					:			List[str]    	=       [3, 3, 9, 6]
          SCREAMING_SNAKE_CASE					:			List[str]    	=       [48, 64, 168, 224]

     elif swiftformer_name == "swiftformer_l1":
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       [4, 3, 10, 5]
          SCREAMING_SNAKE_CASE					:			Dict    	=       [48, 96, 192, 384]

     elif swiftformer_name == "swiftformer_l3":
          SCREAMING_SNAKE_CASE					:			List[str]    	=       [4, 4, 12, 6]
          SCREAMING_SNAKE_CASE					:			Any    	=       [64, 128, 320, 512]

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

     SCREAMING_SNAKE_CASE					:			List[Any]    	=       create_rename_keys(snake_case_		)
     for rename_key_src, rename_key_dest in rename_keys:
          rename_key(snake_case_       ,      snake_case_       ,      snake_case_		)

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

     # prepare test inputs
     SCREAMING_SNAKE_CASE					:			Tuple    	=       prepare_img()
     SCREAMING_SNAKE_CASE					:			Dict    	=       ViTImageProcessor.from_pretrained('preprocessor_config'		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       processor(images=snake_case_       ,      return_tensors='pt'		)

     # compare outputs from both models
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_expected_output(snake_case_		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       hf_model(inputs['pixel_values']		).logits

     assert hf_logits.shape == torch.Size([1, 1000]		)
     assert torch.allclose(hf_logits[0, 0:5]       ,      snake_case_       ,      atol=1e-3		)

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


if __name__ == "__main__":
 _lowerCAmelCase  :Optional[int]  				=    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.""")

 _lowerCAmelCase  :str  				=    parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




'''simple docstring'''





import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=None    ,							lowercase__=True    ,							lowercase__=None    ,							**lowercase__ )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       parent
         SCREAMING_SNAKE_CASE					:			str    	=       config_class
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       has_text_modality
         SCREAMING_SNAKE_CASE					:			Any    	=       kwargs
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       common_properties






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.config_class(**self.inputs_dict )
         SCREAMING_SNAKE_CASE					:			int    	=       (
             ['hidden_size', 'num_attention_heads', 'num_hidden_layers']
             if self.common_properties is None
             else self.common_properties
         )

         # Add common fields for text models
         if self.has_text_modality:
              common_properties.extend(['vocab_size'] )

         # Test that config has the common properties as getters
         for prop in common_properties:
              self.parent.assertTrue(hasattr(lowercase__    ,							lowercase__ )    ,							msg=F"""`{prop}` does not exist""" )

         # Test that config has the common properties as setter
         for idx, name in enumerate(lowercase__ ):
              try:
                   setattr(lowercase__    ,							lowercase__    ,							lowercase__ )
                   self.parent.assertEqual(
                       getattr(lowercase__    ,							lowercase__ )    ,							lowercase__    ,							msg=F"""`{name} value {idx} expected, but was {getattr(lowercase__    ,							lowercase__ )}""" )
              except NotImplementedError:
                   # Some models might not be able to implement setters for common_properties
                   # In that case, a NotImplementedError is raised
                   pass

        # Test if config class can be called with Config(prop_name=..)
         for idx, name in enumerate(lowercase__ ):
              try:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.config_class(**{name: idx} )
                   self.parent.assertEqual(
                       getattr(lowercase__    ,							lowercase__ )    ,							lowercase__    ,							msg=F"""`{name} value {idx} expected, but was {getattr(lowercase__    ,							lowercase__ )}""" )
              except NotImplementedError:
                   # Some models might not be able to implement setters for common_properties
                   # In that case, a NotImplementedError is raised
                   pass






    def       _UpperCamelCase    (       self )							-> List[str]:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.config_class(**self.inputs_dict )
         SCREAMING_SNAKE_CASE					:			Any    	=       json.loads(config.to_json_string() )
         for key, value in self.inputs_dict.items():
              self.parent.assertEqual(obj[key]    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.config_class(**self.inputs_dict )

         with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       os.path.join(lowercase__    ,							'config.json' )
              config_first.to_json_file(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.config_class.from_json_file(lowercase__ )

         self.parent.assertEqual(config_second.to_dict()    ,							config_first.to_dict() )






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.config_class(**self.inputs_dict )

         with tempfile.TemporaryDirectory() as tmpdirname:
              config_first.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.config_class.from_pretrained(lowercase__ )

         self.parent.assertEqual(config_second.to_dict()    ,							config_first.to_dict() )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.config_class(**self.inputs_dict )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       'test'
         with tempfile.TemporaryDirectory() as tmpdirname:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       os.path.join(lowercase__    ,							lowercase__ )
              config_first.save_pretrained(lowercase__ )
              SCREAMING_SNAKE_CASE					:			Dict    	=       self.config_class.from_pretrained(lowercase__    ,							subfolder=lowercase__ )

         self.parent.assertEqual(config_second.to_dict()    ,							config_first.to_dict() )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       self.config_class(**self.inputs_dict    ,							num_labels=5 )
         self.parent.assertEqual(len(config.idalabel )    ,							5 )
         self.parent.assertEqual(len(config.labelaid )    ,							5 )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       3
         self.parent.assertEqual(len(config.idalabel )    ,							3 )
         self.parent.assertEqual(len(config.labelaid )    ,							3 )






    def       _UpperCamelCase    (       self )							-> Dict:
         if self.config_class.is_composition:
              return
         SCREAMING_SNAKE_CASE					:			int    	=       self.config_class()
         self.parent.assertIsNotNone(lowercase__ )






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       copy.deepcopy(lowercase__ )
         SCREAMING_SNAKE_CASE					:			Any    	=       self.config_class(**lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       []
         for key, value in config_common_kwargs.items():
              if key == "torch_dtype":
                   if not is_torch_available():
                        continue
                   else:
                        import torch

                        if config.torch_dtype != torch.floataa:
                             wrong_values.append(('torch_dtype', config.torch_dtype, torch.floataa) )
              elif getattr(lowercase__    ,							lowercase__ ) != value:
                   wrong_values.append((key, getattr(lowercase__    ,							lowercase__ ), value) )

         if len(lowercase__ ) > 0:
              SCREAMING_SNAKE_CASE					:			List[Any]    	=       '\n'.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
              raise ValueError(F"""The following keys were not properly set in the config:\n{errors}""" )






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         self.create_and_test_config_common_properties()
         self.create_and_test_config_to_json_string()
         self.create_and_test_config_to_json_file()
         self.create_and_test_config_from_and_save_pretrained()
         self.create_and_test_config_from_and_save_pretrained_subfolder()
         self.create_and_test_config_with_num_labels()
         self.check_config_can_be_init_without_params()
         self.check_config_arguments_init()



