

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()
__A      		=     logging.get_logger(__name__)

__A      		=     torch.device('''cpu''')



def 				snake_case_()							->     Dict:



  """simple docstring"""






  _snake_case						=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  _snake_case						=							Image.open(requests.get(snake_case_			,	stream=snake_case_					).raw					)
  return im



def 				snake_case_(_UpperCamelCase					)							->     int:



  """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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     str:



  """simple docstring"""






  _snake_case						=							dct.pop(snake_case_					)
  _snake_case						=							val



def 				snake_case_(_UpperCamelCase					)							->     List[Any]:



  """simple docstring"""






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



@torch.no_grad()
def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     List[str]:



  """simple docstring"""






  _snake_case						=							SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  _snake_case						=							1_000
  _snake_case						=							"""huggingface/label-files"""
  _snake_case						=							"""imagenet-1k-id2label.json"""
  _snake_case						=							json.load(open(hf_hub_download(snake_case_			,	snake_case_			,	repo_type='''dataset'''					)			,	'''r'''					)					)
  _snake_case						=							{int(snake_case_					): v for k, v in idalabel.items()}
  _snake_case						=							idalabel
  _snake_case						=							{v: k for k, v in idalabel.items()}

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

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

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

  elif swiftformer_name == "swiftformer_l3":
    _snake_case						=							[4, 4, 12, 6]
    _snake_case						=							[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						=							torch.hub.load_state_dict_from_url(snake_case_			,	map_location='''cpu'''			,	check_hash=snake_case_					)
    else:
      _snake_case						=							torch.load(snake_case_			,	map_location='''cpu'''					)
  _snake_case						=							checkpoint

  _snake_case						=							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
  _snake_case						=							SwiftFormerForImageClassification(snake_case_					).eval()
  hf_model.load_state_dict(snake_case_					)

  # prepare test inputs
  _snake_case						=							prepare_img()
  _snake_case						=							ViTImageProcessor.from_pretrained('''preprocessor_config'''					)
  _snake_case						=							processor(images=snake_case_			,	return_tensors='''pt'''					)

  # compare outputs from both models
  _snake_case						=							get_expected_output(snake_case_					)
  _snake_case						=							hf_model(inputs['''pixel_values''']					).logits

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

 __A      		=     parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)




from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer


__A      		=     logging.get_logger(__name__)

__A      		=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A      		=     {
    '''vocab_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''allegro/herbert-base-cased''': '''https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt'''
    },
}

__A      		=     {'''allegro/herbert-base-cased''': 5_14}
__A      		=     {}



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Any										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		Optional[int]										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		List[str]										=    PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase_      :		Dict										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Optional[Any]										=    HerbertTokenizer



      def __init__(      self				:    Tuple      , A__				:    str=None      , A__				:    Optional[Any]=None      , A__				:    Union[str, Any]=None      , A__				:    Optional[int]="<s>"      , A__				:    Optional[int]="<unk>"      , A__				:    str="<pad>"      , A__				:    List[Any]="<mask>"      , A__				:    Dict="</s>"      , **A__				:    Optional[int]      , )   ->					Optional[int]:
        super().__init__(
            A__      , A__      , tokenizer_file=A__      , cls_token=A__      , unk_token=A__      , pad_token=A__      , mask_token=A__      , sep_token=A__      , **A__      , )



      def 						UpperCamelCase_		(      self				:    str      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.cls_token_id]
        _snake_case						=							[self.sep_token_id]
        if token_ids_a is None:
          return cls + token_ids_a + sep

        return cls + token_ids_a + sep + token_ids_a + sep



      def 						UpperCamelCase_		(      self				:    Tuple      , A__				:    List[int]      , A__				:    Optional[List[int]] = None      , A__				:    bool = False		)   ->					List[int]:
        if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=A__      , token_ids_a=A__      , already_has_special_tokens=A__		)

        if token_ids_a is None:
          return [1] + ([0] * len(A__		)) + [1]
        return [1] + ([0] * len(A__		)) + [1] + ([0] * len(A__		)) + [1]



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.sep_token_id]
        _snake_case						=							[self.cls_token_id]

        if token_ids_a is None:
          return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]







      def 						UpperCamelCase_		(      self				:    Union[str, Any]      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        _snake_case						=							self._tokenizer.model.save(A__      , name=A__		)
        return tuple(A__		)

