



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()
SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE     				=						torch.device("cpu")







def snake_case__  (			)		->							Optional[int]:
  UpperCAmelCase_						=				"http://images.cocodataset.org/val2017/000000039769.jpg"
  UpperCAmelCase_						=				Image.open(requests.get(__SCREAMING_SNAKE_CASE			,     stream=__SCREAMING_SNAKE_CASE ).raw )
  return im







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Tuple:
  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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Dict:
  UpperCAmelCase_						=				dct.pop(__SCREAMING_SNAKE_CASE )
  UpperCAmelCase_						=				val







def snake_case__  (			__SCREAMING_SNAKE_CASE )		->							Tuple:
  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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							List[str]:
  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(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     repo_type="dataset" )			,     "r" ) )
  UpperCAmelCase_						=				{int(__SCREAMING_SNAKE_CASE ): 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(__SCREAMING_SNAKE_CASE			,     map_location="cpu"			,     check_hash=__SCREAMING_SNAKE_CASE )
    else:
      UpperCAmelCase_						=				torch.load(__SCREAMING_SNAKE_CASE			,     map_location="cpu" )
  UpperCAmelCase_						=				checkpoint

  UpperCAmelCase_						=				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
  UpperCAmelCase_						=				SwiftFormerForImageClassification(__SCREAMING_SNAKE_CASE ).eval()
  hf_model.load_state_dict(__SCREAMING_SNAKE_CASE )

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

  # compare outputs from both models
  UpperCAmelCase_						=				get_expected_output(__SCREAMING_SNAKE_CASE )
  UpperCAmelCase_						=				hf_model(inputs["pixel_values"] ).logits

  assert hf_logits.shape == torch.Size([1, 1000] )
  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__":
   SCREAMING_SNAKE_CASE     				=						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.")

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE     				=						{"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["FNetTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						[
			    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "FNetForMaskedLM",
			    "FNetForMultipleChoice",
			    "FNetForNextSentencePrediction",
			    "FNetForPreTraining",
			    "FNetForQuestionAnswering",
			    "FNetForSequenceClassification",
			    "FNetForTokenClassification",
			    "FNetLayer",
			    "FNetModel",
			    "FNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

