
'''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()
lowercase__  :      Tuple        =					logging.get_logger(__name__)

lowercase__  :      Dict        =					torch.device("cpu")



def  __lowerCamelCase       (      ):


      '''simple docstring'''


      UpperCAmelCase_            =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCAmelCase_            =  Image.open(requests.get(_UpperCamelCase							,    stream=_UpperCamelCase					).raw					)
      return im



def  __lowerCamelCase       (      _UpperCamelCase  :  Any					):


      '''simple docstring'''


      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  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Tuple							,    _UpperCamelCase  :  Dict					):


      '''simple docstring'''


      UpperCAmelCase_            =  dct.pop(_UpperCamelCase					)
      UpperCAmelCase_            =  val



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


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



@torch.no_grad()
def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[Any]							,    _UpperCamelCase  :  Tuple					):


      '''simple docstring'''


      UpperCAmelCase_            =  SwiftFormerConfig()

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

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

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

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

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

      # load state_dict of original model, remove and rename some keys
      if original_ckpt:
            if original_ckpt.startswith('''https'''					):
                  UpperCAmelCase_            =  torch.hub.load_state_dict_from_url(_UpperCamelCase							,    map_location='''cpu'''							,    check_hash=_UpperCamelCase					)
            else:
                  UpperCAmelCase_            =  torch.load(_UpperCamelCase							,    map_location='''cpu'''					)
      UpperCAmelCase_            =  checkpoint

      UpperCAmelCase_            =  create_rename_keys(_UpperCamelCase					)
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)

      # load HuggingFace model
      UpperCAmelCase_            =  SwiftFormerForImageClassification(_UpperCamelCase					).eval()
      hf_model.load_state_dict(_UpperCamelCase					)

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

      # compare outputs from both models
      UpperCAmelCase_            =  get_expected_output(_UpperCamelCase					)
      UpperCAmelCase_            =  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__":
      lowercase__  :      Optional[Any]        =					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.")

      lowercase__  :      int        =					parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






'''simple docstring'''
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__  :      int        =					logging.get_logger(__name__)

lowercase__  :      List[str]        =					{
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	'''time_series_transformer'''
  lowerCAmelCase__										=	{
      '''hidden_size''': '''d_model''',
      '''num_attention_heads''': '''encoder_attention_heads''',
      '''num_hidden_layers''': '''encoder_layers''',
  }






  def __init__(      self			:       Optional[int]  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       Optional[int] = None  ,							UpperCAmelCase__			:       str = "student_t"  ,							UpperCAmelCase__			:       str = "nll"  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       List[int] = [1, 2, 3, 4, 5, 6, 7]  ,							UpperCAmelCase__			:       Optional[Union[str, bool]] = "mean"  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       int = 0  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       Optional[List[int]] = None  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       int = 2  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       str = "gelu"  ,							UpperCAmelCase__			:       int = 64  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       float = 0.1  ,							UpperCAmelCase__			:       int = 100  ,							UpperCAmelCase__			:       float = 0.02  ,							UpperCAmelCase__			:       Union[str, Any]=True  ,							**UpperCAmelCase__			:       Tuple  ,							)				->Optional[int]:
        # time series specific configuration
        UpperCAmelCase_            =  prediction_length
        UpperCAmelCase_            =  context_length or prediction_length
        UpperCAmelCase_            =  distribution_output
        UpperCAmelCase_            =  loss
        UpperCAmelCase_            =  input_size
        UpperCAmelCase_            =  num_time_features
        UpperCAmelCase_            =  lags_sequence
        UpperCAmelCase_            =  scaling
        UpperCAmelCase_            =  num_dynamic_real_features
        UpperCAmelCase_            =  num_static_real_features
        UpperCAmelCase_            =  num_static_categorical_features
        if cardinality and num_static_categorical_features > 0:
              if len(UpperCAmelCase__  ) != num_static_categorical_features:
                    raise ValueError(
                        '''The cardinality should be a list of the same length as `num_static_categorical_features`'''  )
              UpperCAmelCase_            =  cardinality
        else:
              UpperCAmelCase_            =  [0]
        if embedding_dimension and num_static_categorical_features > 0:
              if len(UpperCAmelCase__  ) != num_static_categorical_features:
                    raise ValueError(
                        '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''  )
              UpperCAmelCase_            =  embedding_dimension
        else:
              UpperCAmelCase_            =  [min(50  ,							(cat + 1) // 2  ) for cat in self.cardinality]
        UpperCAmelCase_            =  num_parallel_samples

        # Transformer architecture configuration
        UpperCAmelCase_            =  input_size * len(UpperCAmelCase__  ) + self._number_of_features
        UpperCAmelCase_            =  d_model
        UpperCAmelCase_            =  encoder_attention_heads
        UpperCAmelCase_            =  decoder_attention_heads
        UpperCAmelCase_            =  encoder_ffn_dim
        UpperCAmelCase_            =  decoder_ffn_dim
        UpperCAmelCase_            =  encoder_layers
        UpperCAmelCase_            =  decoder_layers

        UpperCAmelCase_            =  dropout
        UpperCAmelCase_            =  attention_dropout
        UpperCAmelCase_            =  activation_dropout
        UpperCAmelCase_            =  encoder_layerdrop
        UpperCAmelCase_            =  decoder_layerdrop

        UpperCAmelCase_            =  activation_function
        UpperCAmelCase_            =  init_std

        UpperCAmelCase_            =  use_cache

        super().__init__(is_encoder_decoder=UpperCAmelCase__  ,							**UpperCAmelCase__  )






  @property
  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        return (
            sum(self.embedding_dimension  )
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )





