



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						    =  logging.get_logger(__name__)

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






def 	_lowerCAmelCase						(				):



 """simple docstring"""





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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Any     ):



 """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						(				__lowerCamelCase   :     List[Any]		,	__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     Any     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Dict       =	dct.pop(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	val






def 	_lowerCAmelCase						(				__lowerCamelCase   :     List[str]     ):



 """simple docstring"""





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






@torch.no_grad()
def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[Any]		,	__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     int     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[str]       =	SwiftFormerConfig()

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

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

 elif swiftformer_name == "swiftformer_s":
  __SCREAMING_SNAKE_CASE       :    int       =	[3, 3, 9, 6]
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[48, 64, 168, 224]

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

 elif swiftformer_name == "swiftformer_l3":
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	[4, 4, 12, 6]
  __SCREAMING_SNAKE_CASE       :    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"     ):
   __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	torch.hub.load_state_dict_from_url(__lowerCamelCase		,	map_location="cpu"		,	check_hash=__lowerCamelCase     )
  else:
   __SCREAMING_SNAKE_CASE       :    Any       =	torch.load(__lowerCamelCase		,	map_location="cpu"     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	checkpoint

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

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

 # prepare test inputs
 __SCREAMING_SNAKE_CASE       :    Tuple       =	prepare_img()
 __SCREAMING_SNAKE_CASE       :    Any       =	ViTImageProcessor.from_pretrained("preprocessor_config"     )
 __SCREAMING_SNAKE_CASE       :    Any       =	processor(images=__lowerCamelCase		,	return_tensors="pt"     )

 # compare outputs from both models
 __SCREAMING_SNAKE_CASE       :    Any       =	get_expected_output(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	hf_model(inputs["pixel_values"]     ).logits

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

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


if __name__ == "__main__":
 _lowerCamelCase						    =  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						    =  parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)











from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Tuple       =	analyze_text(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	list(" " + ascii_lowercase     )
 # what is our total sum of probabilities.
 __SCREAMING_SNAKE_CASE       :    int       =	sum(single_char_strings.values()     )

 # one length string
 __SCREAMING_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:
   __SCREAMING_SNAKE_CASE       :    List[Any]       =	single_char_strings[ch]
   __SCREAMING_SNAKE_CASE       :    str       =	my_str / all_sum
   my_fir_sum += prob * math.loga(__lowerCamelCase     )  # entropy formula.

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

 # two len string
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	sum(two_char_strings.values()     )
 __SCREAMING_SNAKE_CASE       :    int       =	0
 # for each alpha (two in size) calculate entropy.
 for cha in my_alphas:
  for cha in my_alphas:
   __SCREAMING_SNAKE_CASE       :    int       =	cha + cha
   if sequence in two_char_strings:
    __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	two_char_strings[sequence]
    __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	int(__lowerCamelCase     ) / all_sum
    my_sec_sum += prob * math.loga(__lowerCamelCase     )

    # 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 	_lowerCAmelCase						(				__lowerCamelCase   :     str     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	Counter()  # type: ignore
 __SCREAMING_SNAKE_CASE       :    int       =	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(__lowerCamelCase     ) - 1     ):
  single_char_strings[text[i]] += 1
  two_char_strings[text[i : i + 2]] += 1
 return single_char_strings, two_char_strings






def 	_lowerCAmelCase						(				):



 """simple docstring"""





 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()






