




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__     :     Tuple		    =       logging.get_logger(__name__)

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

def lowercase    (  )			->							List[Any]:


			'''simple docstring'''







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

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Tuple:


			'''simple docstring'''







			if swiftformer_name == "swiftformer_xs":
						return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1] )

			elif swiftformer_name == "swiftformer_s":
						return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1] )

			elif swiftformer_name == "swiftformer_l1":
						return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2] )

			elif swiftformer_name == "swiftformer_l3":
						return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2] )

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Optional[int]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						dct.pop(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE_								=						val

def lowercase    (  SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







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

@torch.no_grad()
def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						SwiftFormerConfig()

			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			SCREAMING_SNAKE_CASE_								=						10_00
			SCREAMING_SNAKE_CASE_								=						'huggingface/label-files'
			SCREAMING_SNAKE_CASE_								=						'imagenet-1k-id2label.json'
			SCREAMING_SNAKE_CASE_								=						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    repo_type='dataset' )       ,    'r' ) )
			SCREAMING_SNAKE_CASE_								=						{int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
			SCREAMING_SNAKE_CASE_								=						idalabel
			SCREAMING_SNAKE_CASE_								=						{v: k for k, v in idalabel.items()}

			# size of the architecture
			if swiftformer_name == "swiftformer_xs":
						SCREAMING_SNAKE_CASE_								=						[3, 3, 6, 4]
						SCREAMING_SNAKE_CASE_								=						[48, 56, 1_12, 2_20]

			elif swiftformer_name == "swiftformer_s":
						SCREAMING_SNAKE_CASE_								=						[3, 3, 9, 6]
						SCREAMING_SNAKE_CASE_								=						[48, 64, 1_68, 2_24]

			elif swiftformer_name == "swiftformer_l1":
						SCREAMING_SNAKE_CASE_								=						[4, 3, 10, 5]
						SCREAMING_SNAKE_CASE_								=						[48, 96, 1_92, 3_84]

			elif swiftformer_name == "swiftformer_l3":
						SCREAMING_SNAKE_CASE_								=						[4, 4, 12, 6]
						SCREAMING_SNAKE_CASE_								=						[64, 1_28, 3_20, 5_12]

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

			SCREAMING_SNAKE_CASE_								=						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
			SCREAMING_SNAKE_CASE_								=						SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE ).eval()
			hf_model.load_state_dict(SCREAMING_SNAKE_CASE )

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

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

			assert hf_logits.shape == torch.Size([1, 10_00] )
			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__     :     Optional[int]		    =       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__     :     Any		    =       parser.parse_args()
				convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
				import torch

if is_torch_tpu_available(check_device=False):
				import torch_xla.core.xla_model as xm


SCREAMING_SNAKE_CASE__     :     int		    =       logging.get_logger(__name__)



@dataclass
class       a_       (     SCREAMING_SNAKE_CASE__     ):
	A    							= [
	    '''no_inference''',
	    '''no_cuda''',
	    '''no_tpu''',
	    '''no_speed''',
	    '''no_memory''',
	    '''no_env_print''',
	    '''no_multi_process''',
	]





	def __init__(							self      ,     **SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				for deprecated_arg in self.deprecated_args:
							if deprecated_arg in kwargs:
										SCREAMING_SNAKE_CASE_								=						deprecated_arg[3:]
										setattr(self      ,     SCREAMING_SNAKE_CASE      ,     not kwargs.pop(SCREAMING_SNAKE_CASE      )      )
										logger.warning(
										    f'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
										    f' {positive_arg}={kwargs[positive_arg]}'      )

				SCREAMING_SNAKE_CASE_								=						kwargs.pop('torchscript'      ,     self.torchscript      )
				SCREAMING_SNAKE_CASE_								=						kwargs.pop('torch_xla_tpu_print_metrics'      ,     self.torch_xla_tpu_print_metrics      )
				SCREAMING_SNAKE_CASE_								=						kwargs.pop('fp16_opt_level'      ,     self.fpaa_opt_level      )
				super().__init__(**SCREAMING_SNAKE_CASE      )

	A    							= field(default=SCREAMING_SNAKE_CASE__  ,    metadata={'''help''': '''Trace the models using torchscript'''}     )
	A    							= field(default=SCREAMING_SNAKE_CASE__  ,    metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}     )
	A    							= field(
	    default='''O1'''  ,    metadata={
	        '''help''': (
	            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
	            '''See details at https://nvidia.github.io/apex/amp.html'''
	        )
	    }  ,    )





	@cached_property
	def 						A_(							self      )  ->					Tuple["torch.device", int]:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				logger.info('PyTorch: setting up devices'      )
				if not self.cuda:
							SCREAMING_SNAKE_CASE_								=						torch.device('cpu'      )
							SCREAMING_SNAKE_CASE_								=						0
				elif is_torch_tpu_available():
							SCREAMING_SNAKE_CASE_								=						xm.xla_device()
							SCREAMING_SNAKE_CASE_								=						0
				else:
							SCREAMING_SNAKE_CASE_								=						torch.device('cuda' if torch.cuda.is_available() else 'cpu'      )
							SCREAMING_SNAKE_CASE_								=						torch.cuda.device_count()
				return device, n_gpu





	@property
	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				return is_torch_tpu_available() and self.tpu





	@property
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				# TODO(PVP): currently only single GPU is supported
				return torch.cuda.current_device()





	@property
	def 						A_(							self      )  ->					"torch.device":




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				return self._setup_devices[0]





	@property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				requires_backends(self      ,     ['torch']      )
				return self._setup_devices[1]





	@property
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				return self.n_gpu > 0
