






'''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 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """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 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[int]							     =  dct.pop(_UpperCamelCase				)
   lowercase_						:     Dict							     =  val




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




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




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     str							     =  SwiftFormerConfig()

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

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

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

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

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

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

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

   # load HuggingFace model
   lowercase_						:     int							     =  SwiftFormerForImageClassification(_UpperCamelCase				).eval()
   hf_model.load_state_dict(_UpperCamelCase				)

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

   # compare outputs from both models
   lowercase_						:     Any							     =  get_expected_output(_UpperCamelCase				)
   lowercase_						:     List[Any]							     =  hf_model(inputs["pixel_values"]				).logits

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

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


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 builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


UpperCamelCase__			=      False
try:
 UpperCamelCase__			=      _is_package_available('google.colab')
except ModuleNotFoundError:
 pass


@input.register
class 							_UpperCAmelCase    :




    def __init__(      self	:							List[Any]	,  a	:							str = None	,  a	:							list = []      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  0
       lowercase_						:     Dict							     =  choices
       lowercase_						:     List[str]							     =  prompt
       if sys.platform == "win32":
          lowercase_						:     List[Any]							     =  "*"
       else:
          lowercase_						:     Union[str, Any]							     =  "➔ "




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							Optional[int]	,  a	:							str = ""      ):






       '''simple docstring'''






       if sys.platform != "win32":
          writeColor(self.choices[index]	,  3_2	,  a      )
       else:
          forceWrite(self.choices[index]	,  a      )




    def 					lowerCAmelCase__					(      self	:							List[Any]	,  a	:							int      ):






       '''simple docstring'''






       if index == self.position:
          forceWrite(f""" {self.arrow_char} """      )
          self.write_choice(a      )
       else:
          forceWrite(f"""    {self.choices[index]}"""      )
       reset_cursor()




    def 					lowerCAmelCase__					(      self	:							str	,  a	:							Direction	,  a	:							int = 1      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.position
       if direction == Direction.DOWN:
          if self.position + 1 >= len(self.choices      ):
             return
          self.position += num_spaces
       else:
          if self.position - 1 < 0:
             return
          self.position -= num_spaces
       clear_line()
       self.print_choice(a      )
       move_cursor(a	,  direction.name      )
       self.print_choice(self.position      )




    @input.mark(KEYMAP["up"]      )
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       self.move_direction(Direction.UP      )




    @input.mark(KEYMAP["down"]      )
    def 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       self.move_direction(Direction.DOWN      )




    @input.mark(KEYMAP["newline"]      )
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       move_cursor(len(self.choices      ) - self.position	,  "DOWN"      )
       return self.position




    @input.mark(KEYMAP["interrupt"]      )
    def 					lowerCAmelCase__					(      self	:							str      ):






       '''simple docstring'''






       move_cursor(len(self.choices      ) - self.position	,  "DOWN"      )
       raise KeyboardInterrupt




    @input.mark_multiple(*[KEYMAP[str(a      )] for number in range(1_0      )]      )
    def 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     str							     =  int(chr(self.current_selection      )      )
       lowercase_						:     Tuple							     =  index - self.position
       if index == self.position:
          return
       if index < len(self.choices      ):
          if self.position > index:
             self.move_direction(Direction.UP	,  -movement      )
          elif self.position < index:
             self.move_direction(Direction.DOWN	,  a      )
          else:
             return
       else:
          return





    def 					lowerCAmelCase__					(      self	:							Tuple	,  a	:							int = 0      ):






       '''simple docstring'''






       if self.prompt:
          linebreak()
          forceWrite(self.prompt	,  "\n"      )
          if in_colab:
             forceWrite("Please input a choice index (starting from 0), and press enter"	,  "\n"      )
          else:
             forceWrite("Please select a choice using the arrow or number keys, and selecting with enter"	,  "\n"      )
       lowercase_						:     Dict							     =  default_choice
       for i in range(len(self.choices      )      ):
          self.print_choice(a      )
          forceWrite("\n"      )
       move_cursor(len(self.choices      ) - self.position	,  "UP"      )
       with cursor.hide():
          while True:
             if in_colab:
                try:
                   lowercase_						:     List[str]							     =  int(builtins.input()      )
                except ValueError:
                   lowercase_						:     List[Any]							     =  default_choice
             else:
                lowercase_						:     Dict							     =  self.handle_input()
             if choice is not None:
                reset_cursor()
                for _ in range(len(self.choices      ) + 1      ):
                   move_cursor(1	,  "UP"      )
                   clear_line()
                self.write_choice(a	,  "\n"      )
                return choice




