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  :      Any            =    logging.get_logger(__name__)

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

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

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

   elif swiftformer_name == "swiftformer_s":
      lowerCAmelCase_:							List[Any]       =	[3, 3, 9, 6]
      lowerCAmelCase_:							int       =	[48, 64, 168, 224]

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

   elif swiftformer_name == "swiftformer_l3":
      lowerCAmelCase_:							Any       =	[4, 4, 12, 6]
      lowerCAmelCase_:							Optional[int]       =	[64, 128, 320, 512]

   # load state_dict of original model, remove and rename some keys
   if original_ckpt:
      if original_ckpt.startswith("https"					):
         lowerCAmelCase_:							Optional[int]       =	torch.hub.load_state_dict_from_url(lowercase							,				map_location="cpu"							,				check_hash=lowercase					)
      else:
         lowerCAmelCase_:							int       =	torch.load(lowercase							,				map_location="cpu"					)
   lowerCAmelCase_:							List[str]       =	checkpoint

   lowerCAmelCase_:							Optional[Any]       =	create_rename_keys(lowercase					)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(lowercase							,				lowercase							,				lowercase					)

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

   # prepare test inputs
   lowerCAmelCase_:							Union[str, Any]       =	prepare_img()
   lowerCAmelCase_:							int       =	ViTImageProcessor.from_pretrained("preprocessor_config"					)
   lowerCAmelCase_:							List[str]       =	processor(images=lowercase							,				return_tensors="pt"					)

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

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

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


if __name__ == "__main__":
 a  :      str            =    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  :      Optional[int]            =    parser.parse_args()
 convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase					):
   assert isinstance(lowercase							,				lowercase					)
   assert dataset.num_rows == 4
   assert dataset.num_columns == 3
   assert dataset.column_names == ["col_1", "col_2", "col_3"]
   for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize("keep_in_memory"							,				[False, True]					)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Any       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
   with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
      lowerCAmelCase_:							Optional[int]       =	SqlDatasetReader(
          "dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase							,				keep_in_memory=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
@require_sqlalchemy
@pytest.mark.parametrize(
    "features"							,				[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]							,				)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							List[str]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	{"col_1": "string", "col_2": "int64", "col_3": "float64"}
   lowerCAmelCase_:							List[str]       =	features.copy() if features else default_expected_features
   lowerCAmelCase_:							Any       =	(
       Features({feature: Value(lowercase					) for feature, dtype in features.items()}					) if features is not None else None
   )
   lowerCAmelCase_:							int       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				features=lowercase							,				cache_dir=lowercase					).read()
   _check_sql_dataset(lowercase							,				lowercase					)
def 		snake_case__	(		lowercase					):
   with contextlib.closing(sqlitea.connect(lowercase					)					) as con:
      lowerCAmelCase_:							Any       =	con.cursor()
      cur.execute("SELECT * FROM dataset"					)
      for row in cur:
         yield row
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Optional[int]       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[Any]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							str       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=1					).write()

   lowerCAmelCase_:							Union[str, Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							str       =	iter_sql_file(lowercase					)

   for rowa, rowa in zip(lowercase							,				lowercase					):
      assert rowa == rowa
@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							str       =	tmp_path / "cache"
   lowerCAmelCase_:							Optional[int]       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Optional[Any]       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=2					).write()

   lowerCAmelCase_:							Optional[Any]       =	iter_sql_file(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	iter_sql_file(lowercase					)

   for rowa, rowa in zip(lowercase							,				lowercase					):
      assert rowa == rowa







@require_sqlalchemy
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   lowerCAmelCase_:							Union[str, Any]       =	tmp_path / "cache"
   lowerCAmelCase_:							int       =	os.path.join(lowercase							,				"tmp.sql"					)
   lowerCAmelCase_:							Any       =	SqlDatasetReader("dataset"							,				"sqlite:///" + sqlite_path							,				cache_dir=lowercase					).read()
   with pytest.raises(lowercase					):
      SqlDatasetWriter(lowercase							,				"dataset"							,				"sqlite:///" + output_sqlite_path							,				num_proc=0					).write()