



"""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_	:				Tuple										= logging.get_logger(__name__)

UpperCamelCase_	:				List[Any]										= torch.device("""cpu""")



def     __lowercase					(    )					->					Optional[int]:
      __snake_case      :    Tuple  			='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      __snake_case      :    Tuple  			=Image.open(requests.get(_SCREAMING_SNAKE_CASE       ,		stream=_SCREAMING_SNAKE_CASE ).raw )
      return im



def     __lowercase					(    a :				List[str] )					->					List[Any]:



      if swiftformer_name == "swiftformer_xs":
            return torch.tensor([-2.17_03e00, 2.11_07e00, -2.08_11e00, 8.86_85e-01, 2.43_60e-01] )

      elif swiftformer_name == "swiftformer_s":
            return torch.tensor([3.96_36e-01, 2.34_78e-01, -1.69_63e00, -1.73_81e00, -8.63_37e-01] )

      elif swiftformer_name == "swiftformer_l1":
            return torch.tensor([-4.27_68e-01, -4.74_29e-01, -1.08_97e00, -1.02_48e00, 3.55_23e-02] )

      elif swiftformer_name == "swiftformer_l3":
            return torch.tensor([-2.53_30e-01, 2.42_11e-01, -6.01_85e-01, -8.27_89e-01, -6.04_46e-02] )



def     __lowercase					(    a :				int       ,		a :				List[Any]       ,		a :				Optional[int] )					->					Tuple:
      __snake_case      :    Dict  			=dct.pop(_SCREAMING_SNAKE_CASE )
      __snake_case      :    Tuple  			=val



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



@torch.no_grad()
def     __lowercase					(    a :				str       ,		a :				int       ,		a :				Optional[int] )					->					List[str]:
      __snake_case      :    Optional[Any]  			=SwiftFormerConfig()

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

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

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

      elif swiftformer_name == "swiftformer_l1":
            __snake_case      :    List[Any]  			=[4, 3, 10, 5]
            __snake_case      :    List[str]  			=[48, 96, 192, 384]

      elif swiftformer_name == "swiftformer_l3":
            __snake_case      :    Any  			=[4, 4, 12, 6]
            __snake_case      :    Dict  			=[64, 128, 320, 512]

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

      __snake_case      :    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
      __snake_case      :    Optional[Any]  			=SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
      hf_model.load_state_dict(_SCREAMING_SNAKE_CASE )

      # prepare test inputs
      __snake_case      :    Union[str, Any]  			=prepare_img()
      __snake_case      :    List[Any]  			=ViTImageProcessor.from_pretrained('''preprocessor_config''' )
      __snake_case      :    Optional[Any]  			=processor(images=_SCREAMING_SNAKE_CASE       ,		return_tensors='''pt''' )

      # compare outputs from both models
      __snake_case      :    Optional[int]  			=get_expected_output(_SCREAMING_SNAKE_CASE )
      __snake_case      :    int  			=hf_model(inputs['''pixel_values'''] ).logits

      assert hf_logits.shape == torch.Size([1, 1_000] )
      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_	:				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.""")

    UpperCamelCase_	:				Optional[Any]										= parser.parse_args()
    convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)










"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def     __lowercase					(    a :				str )					->					None:
      __snake_case			,				__snake_case      :    List[Any]  			=analyze_text(a )
      __snake_case      :    Union[str, Any]  			=list(''' ''' + ascii_lowercase )
      # what is our total sum of probabilities.
      __snake_case      :    List[Any]  			=sum(single_char_strings.values() )

      # one length string
      __snake_case      :    str  			=0
      # for each alpha we go in our dict and if it is in it we calculate entropy
      for ch in my_alphas:
            if ch in single_char_strings:
                  __snake_case      :    Union[str, Any]  			=single_char_strings[ch]
                  __snake_case      :    Any  			=my_str / all_sum
                  my_fir_sum += prob * math.loga(a )  # entropy formula.

    # print entropy
      print(f'''{round(-1 * my_fir_sum ):.1f}''' )

      # two len string
      __snake_case      :    Optional[Any]  			=sum(two_char_strings.values() )
      __snake_case      :    Dict  			=0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  __snake_case      :    Tuple  			=cha + cha
                  if sequence in two_char_strings:
                        __snake_case      :    List[Any]  			=two_char_strings[sequence]
                        __snake_case      :    Tuple  			=int(a ) / all_sum
                        my_sec_sum += prob * math.loga(a )

    # print second entropy
      print(f'''{round(-1 * my_sec_sum ):.1f}''' )

      # print the difference between them
      print(f'''{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}''' )



def     __lowercase					(    a :				str )					->					tuple[dict, dict]:
      __snake_case      :    List[str]  			=Counter()  # type: ignore
      __snake_case      :    Union[str, Any]  			=Counter()  # type: ignore
      single_char_strings[text[-1]] += 1

      # first case when we have space at start.
      two_char_strings[" " + text[0]] += 1
      for i in range(0       ,		len(a ) - 1 ):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings



def     __lowercase					(    )					->					int:
      import doctest

      doctest.testmod()
      # text = (
      #     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
      #     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
      #     "jointure saw horrible. He private he on be imagine suppose. Fertile "
      #     "beloved evident through no service elderly is. Blind there if every no so "
      #     "at. Own neglected you preferred way sincerity delivered his attempted. To "
      #     "of message cottage windows do besides against uncivil.  Delightful "
      #     "unreserved impossible few estimating men favourable see entreaties. She "
      #     "propriety immediate was improving. He or entrance humoured likewise "
      #     "moderate. Much nor game son say feel. Fat make met can must form into "
      #     "gate. Me we offending prevailed discovery. "
      # )

      # calculate_prob(text)


if __name__ == "__main__":
    main()


