






'''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()
UpperCamelCase      					=logging.get_logger(__name__)

UpperCamelCase      					=torch.device("cpu")



def 				snake_case       (  )							->					List[str]:





      """simple docstring"""

      UpperCamelCase_					: Dict            =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCamelCase_					: Tuple            =				Image.open(requests.get(a_     , stream=a_						).raw						)
      return im



def 				snake_case       (  a_				:			List[str]						)							->					Union[str, Any]:





      """simple docstring"""

      if swiftformer_name == "swiftformer_xs":
            return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]						)

      elif swiftformer_name == "swiftformer_s":
            return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]						)

      elif swiftformer_name == "swiftformer_l1":
            return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]						)

      elif swiftformer_name == "swiftformer_l3":
            return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]						)



def 				snake_case       (  a_				:			List[str]     , a_				:			Optional[Any]     , a_				:			Dict						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: Union[str, Any]            =				dct.pop(a_						)
      UpperCamelCase_					: List[Any]            =				val



def 				snake_case       (  a_				:			Union[str, Any]						)							->					Optional[Any]:





      """simple docstring"""

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



@torch.no_grad()
def 				snake_case       (  a_				:			Any     , a_				:			List[str]     , a_				:			Optional[Any]						)							->					int:





      """simple docstring"""

      UpperCamelCase_					: Union[str, Any]            =				SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      UpperCamelCase_					: Any            =				1_000
      UpperCamelCase_					: str            =				"""huggingface/label-files"""
      UpperCamelCase_					: Dict            =				"""imagenet-1k-id2label.json"""
      UpperCamelCase_					: int            =				json.load(open(hf_hub_download(a_     , a_     , repo_type="""dataset"""						)     , """r"""						)						)
      UpperCamelCase_					: Optional[int]            =				{int(a_						): v for k, v in idalabel.items()}
      UpperCamelCase_					: Union[str, Any]            =				idalabel
      UpperCamelCase_					: List[Any]            =				{v: k for k, v in idalabel.items()}

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

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

      elif swiftformer_name == "swiftformer_l1":
            UpperCamelCase_					: Dict            =				[4, 3, 10, 5]
            UpperCamelCase_					: int            =				[48, 96, 192, 384]

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

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

      UpperCamelCase_					: Optional[int]            =				create_rename_keys(a_						)
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(a_     , a_     , a_						)

      # load HuggingFace model
      UpperCamelCase_					: Any            =				SwiftFormerForImageClassification(a_						).eval()
      hf_model.load_state_dict(a_						)

      # prepare test inputs
      UpperCamelCase_					: Optional[Any]            =				prepare_img()
      UpperCamelCase_					: Any            =				ViTImageProcessor.from_pretrained("""preprocessor_config"""						)
      UpperCamelCase_					: Union[str, Any]            =				processor(images=a_     , return_tensors="""pt"""						)

      # compare outputs from both models
      UpperCamelCase_					: List[str]            =				get_expected_output(a_						)
      UpperCamelCase_					: Optional[Any]            =				hf_model(inputs["""pixel_values"""]						).logits

      assert hf_logits.shape == torch.Size([1, 1_000]						)
      assert torch.allclose(hf_logits[0, 0:5]     , a_     , atol=1e-3						)

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


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)














'''simple docstring'''

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCamelCase      					="\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"




class      A     (   unittest.TestCase, SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				load_tool("""text-question-answering"""			)
       self.tool.setup()
       UpperCamelCase_					: List[str]            =				load_tool("""text-question-answering"""      ,  remote=__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.tool(__lowerCAmelCase      ,  """What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				self.remote_tool(__lowerCAmelCase      ,  """What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[Any]            =				self.tool(text=__lowerCAmelCase      ,  question="""What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self.remote_tool(text=__lowerCAmelCase      ,  question="""What did Hugging Face do in April 2021?"""			)
       self.assertEqual(__lowerCAmelCase      ,  """launched the BigScience Research Workshop"""			)






