


'''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()
lowerCamelCase__    	=							logging.get_logger(__name__)

lowerCamelCase__    	=							torch.device('cpu')






def  __lowerCAmelCase ():
		_UpperCAmelCase		:       int								=      "http://images.cocodataset.org/val2017/000000039769.jpg"
		_UpperCAmelCase		:       Tuple								=      Image.open(requests.get(__lowerCAmelCase      , stream=__lowerCAmelCase   ).raw   )
		return im






def  __lowerCAmelCase (__lowerCAmelCase   ):
		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  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Tuple								=      dct.pop(__lowerCAmelCase   )
		_UpperCAmelCase		:       Dict								=      val






def  __lowerCAmelCase (__lowerCAmelCase   ):
		_UpperCAmelCase		:       Optional[int]								=      []
		for k in state_dict.keys():
				_UpperCAmelCase		:       Union[str, Any]								=      k
				if ".pwconv" in k:
						_UpperCAmelCase		:       List[str]								=      k_new.replace(".pwconv"      , ".point_wise_conv"   )
				if ".dwconv" in k:
						_UpperCAmelCase		:       Dict								=      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		:       Any								=      k_new.replace("patch_embed"      , "swiftformer.patch_embed.patch_embedding"   )
				if "network" in k_new:
						_UpperCAmelCase		:       Optional[Any]								=      k_new.split("."   )
						if ls[2].isdigit():
								_UpperCAmelCase		:       Optional[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  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       List[Any]								=      SwiftFormerConfig()

		# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
		_UpperCAmelCase		:       Dict								=      1_000
		_UpperCAmelCase		:       List[Any]								=      "huggingface/label-files"
		_UpperCAmelCase		:       str								=      "imagenet-1k-id2label.json"
		_UpperCAmelCase		:       Optional[int]								=      json.load(open(hf_hub_download(__lowerCAmelCase      , __lowerCAmelCase      , repo_type="dataset"   )      , "r"   )   )
		_UpperCAmelCase		:       Any								=      {int(__lowerCAmelCase   ): v for k, v in idalabel.items()}
		_UpperCAmelCase		:       Any								=      idalabel
		_UpperCAmelCase		:       List[str]								=      {v: k for k, v in idalabel.items()}

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

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

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

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

		# load state_dict of original model, remove and rename some keys
		if original_ckpt:
				if original_ckpt.startswith("https"   ):
						_UpperCAmelCase		:       Tuple								=      torch.hub.load_state_dict_from_url(__lowerCAmelCase      , map_location="cpu"      , check_hash=__lowerCAmelCase   )
				else:
						_UpperCAmelCase		:       Union[str, Any]								=      torch.load(__lowerCAmelCase      , map_location="cpu"   )
		_UpperCAmelCase		:       Optional[Any]								=      checkpoint

		_UpperCAmelCase		:       int								=      create_rename_keys(__lowerCAmelCase   )
		for rename_key_src, rename_key_dest in rename_keys:
				rename_key(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   )

		# load HuggingFace model
		_UpperCAmelCase		:       Any								=      SwiftFormerForImageClassification(__lowerCAmelCase   ).eval()
		hf_model.load_state_dict(__lowerCAmelCase   )

		# prepare test inputs
		_UpperCAmelCase		:       Optional[int]								=      prepare_img()
		_UpperCAmelCase		:       Dict								=      ViTImageProcessor.from_pretrained("preprocessor_config"   )
		_UpperCAmelCase		:       Optional[Any]								=      processor(images=__lowerCAmelCase      , return_tensors="pt"   )

		# compare outputs from both models
		_UpperCAmelCase		:       Any								=      get_expected_output(__lowerCAmelCase   )
		_UpperCAmelCase		:       List[Any]								=      hf_model(inputs["pixel_values"]   ).logits

		assert hf_logits.shape == torch.Size([1, 1_000]   )
		assert torch.allclose(hf_logits[0, 0:5]      , __lowerCAmelCase      , atol=1e-3   )

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


if __name__ == "__main__":
						lowerCamelCase__    	=							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.')

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







'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCamelCase__    	=     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
lowerCamelCase__    	=     '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'






class 	lowerCAmelCase__			(			unittest.TestCase	):




			def 							lowerCAmelCase__    (		self  :     Any )   ->Any:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[int]								=      tempfile.mkdtemp()
					os.makedirs(os.path.join(self.diffusers_dir	,  "schedulers/" ) )
					_UpperCAmelCase		:       Optional[Any]								=      self.diffusers_dir
					shutil.copy(
					    os.path.join(lowerCamelCase__	,  "src/diffusers/schedulers/scheduling_ddpm.py" )	,  os.path.join(self.diffusers_dir	,  "schedulers/scheduling_ddpm.py" )	,  )




			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      "src/diffusers"
					shutil.rmtree(self.diffusers_dir )




			def 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Any=None )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Union[str, Any]								=      comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
					if overwrite_result is not None:
							_UpperCAmelCase		:       Tuple								=      comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
					_UpperCAmelCase		:       Optional[Any]								=      black.Mode(target_versions={black.TargetVersion.PYaa}	,  line_length=1_19 )
					_UpperCAmelCase		:       Tuple								=      black.format_str(lowerCamelCase__	,  mode=lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[int]								=      os.path.join(self.diffusers_dir	,  "new_code.py" )
					with open(lowerCamelCase__	,  "w"	,  newline="\n" ) as f:
							f.write(lowerCamelCase__ )
					if overwrite_result is None:
							self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase__ ) ) == 0 )
					else:
							check_copies.is_copy_consistent(f.name	,  overwrite=lowerCamelCase__ )
							with open(lowerCamelCase__	,  "r" ) as f:
									self.assertTrue(f.read()	,  lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     str )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput" )
					self.assertEqual(lowerCamelCase__	,  lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Optional[int]:



					'''simple docstring'''







					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"	,  "DDPMSchedulerOutput"	,  REFERENCE_CODE + "\n"	,  )

					# With no empty line at the end
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"	,  "DDPMSchedulerOutput"	,  lowerCamelCase__	,  )

					# Copy consistency with rename
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"	,  "TestSchedulerOutput"	,  re.sub("DDPM"	,  "Test"	,  lowerCamelCase__ )	,  )

					# Copy consistency with a really long name
					_UpperCAmelCase		:       int								=      "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
					self.check_copy_consistency(
					    F"""# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}"""	,  F"""{long_class_name}SchedulerOutput"""	,  re.sub("Bert"	,  lowerCamelCase__	,  lowerCamelCase__ )	,  )

					# Copy consistency with overwrite
					self.check_copy_consistency(
					    "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"	,  "TestSchedulerOutput"	,  lowerCamelCase__	,  overwrite_result=re.sub("DDPM"	,  "Test"	,  lowerCamelCase__ )	,  )



