



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 						UpperCamelCase      (  )      ->	Tuple:
   UpperCamelCase       :    List[str]		      =      'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase       :    Tuple		      =      Image.open(requests.get(snake_case__			,	stream=snake_case__      ).raw      )
   return im
def 						UpperCamelCase      (  snake_case__			:       str      )      ->	int:
   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 						UpperCamelCase      (  snake_case__			:       List[Any]			,	snake_case__			:       Optional[int]			,	snake_case__			:       List[Any]      )      ->	str:
   UpperCamelCase       :    Optional[int]		      =      dct.pop(snake_case__      )
   UpperCamelCase       :    int		      =      val
def 						UpperCamelCase      (  snake_case__			:       Optional[int]      )      ->	Optional[Any]:
   UpperCamelCase       :    List[str]		      =      []
   for k in state_dict.keys():
      UpperCamelCase       :    Optional[Any]		      =      k
      if ".pwconv" in k:
         UpperCamelCase       :    Dict		      =      k_new.replace('.pwconv'			,	'.point_wise_conv'      )
      if ".dwconv" in k:
         UpperCamelCase       :    Optional[Any]		      =      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       :    Optional[Any]		      =      k_new.replace('patch_embed'			,	'swiftformer.patch_embed.patch_embedding'      )
      if "network" in k_new:
         UpperCamelCase       :    Dict		      =      k_new.split('.'      )
         if ls[2].isdigit():
            UpperCamelCase       :    Union[str, Any]		      =      'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]      )
         else:
            UpperCamelCase       :    int		      =      k_new.replace('network'			,	'swiftformer.encoder.network'      )
      rename_keys.append((k, k_new)      )
   return rename_keys
@torch.no_grad()
def 						UpperCamelCase      (  snake_case__			:       List[Any]			,	snake_case__			:       List[Any]			,	snake_case__			:       str      )      ->	List[Any]:
   UpperCamelCase       :    Dict		      =      SwiftFormerConfig()

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

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

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

   elif swiftformer_name == "swiftformer_l1":
      UpperCamelCase       :    Optional[Any]		      =      [4, 3, 10, 5]
      UpperCamelCase       :    Union[str, Any]		      =      [48, 96, 192, 384]

   elif swiftformer_name == "swiftformer_l3":
      UpperCamelCase       :    List[Any]		      =      [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       :    Any		      =      torch.hub.load_state_dict_from_url(snake_case__			,	map_location='cpu'			,	check_hash=snake_case__      )
      else:
         UpperCamelCase       :    Optional[Any]		      =      torch.load(snake_case__			,	map_location='cpu'      )
   UpperCamelCase       :    int		      =      checkpoint

   UpperCamelCase       :    str		      =      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
   UpperCamelCase       :    str		      =      SwiftFormerForImageClassification(snake_case__      ).eval()
   hf_model.load_state_dict(snake_case__      )

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

   # compare outputs from both models
   UpperCamelCase       :    Tuple		      =      get_expected_output(snake_case__      )
   UpperCamelCase       :    Optional[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__":
    __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 re
import string

import numpy as np

import datasets


_SCREAMING_SNAKE_CASE									=				"\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"

_SCREAMING_SNAKE_CASE									=				"\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"the cat\", \"theater\", \"YELLING\", \"agent007\"]\n    >>> preds = [\"cat?\", \"theater\", \"yelling\", \"agent\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"the \", \"yell\", \"YELL\"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results[\"exact_match\"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric(\"exact_match\")\n    >>> refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n    >>> preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results[\"exact_match\"], 1))\n    33.3\n\n"

_SCREAMING_SNAKE_CASE									=				"\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,_KWARGS_DESCRIPTION		)
class  lowerCAmelCase_		(		datasets.Metric		):




 def 				_snake_case (						self		)     ->				List[str]:
     return datasets.MetricInfo(
         description=_DESCRIPTION      ,  citation=_CITATION      ,  inputs_description=_KWARGS_DESCRIPTION      ,  features=datasets.Features(
             {
                 "predictions": datasets.Value("string"      ,  id="sequence"		),
                 "references": datasets.Value("string"      ,  id="sequence"		),
             }		)      ,  reference_urls=[]      ,  )





 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  )     ->				str:
     if regexes_to_ignore is not None:
         for s in regexes_to_ignore:
             _lowerCAmelCase						      =   np.array([re.sub(_lowerCAmelCase      ,  ""      ,  _lowerCAmelCase		) for x in predictions]		)
             _lowerCAmelCase						      =   np.array([re.sub(_lowerCAmelCase      ,  ""      ,  _lowerCAmelCase		) for x in references]		)
     else:
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.asarray(_lowerCAmelCase		)

     if ignore_case:
         _lowerCAmelCase						      =   np.char.lower(_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.lower(_lowerCAmelCase		)

     if ignore_punctuation:
         _lowerCAmelCase						      =   string.punctuation.maketrans(""      ,  ""      ,  string.punctuation		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)

     if ignore_numbers:
         _lowerCAmelCase						      =   string.digits.maketrans(""      ,  ""      ,  string.digits		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)
         _lowerCAmelCase						      =   np.char.translate(_lowerCAmelCase      ,  table=_lowerCAmelCase		)

     _lowerCAmelCase						      =   predictions == references

     return {"exact_match": np.mean(_lowerCAmelCase		) * 100}


