



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__						(							):
				"""simple docstring"""







				__a       =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				__a       =		Image.open(requests.get(_SCREAMING_SNAKE_CASE				,     stream=_SCREAMING_SNAKE_CASE    ).raw    )
				return im

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]    ):
				"""simple docstring"""







				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01]    )

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01]    )

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02]    )

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02]    )

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Tuple				,     _SCREAMING_SNAKE_CASE       :			Optional[Any]    ):
				"""simple docstring"""







				__a       =		dct.pop(_SCREAMING_SNAKE_CASE    )
				__a       =		val

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







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

@torch.no_grad()
def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		SwiftFormerConfig()

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

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

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

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

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

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

				__a       =		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
				__a       =		SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE    ).eval()
				hf_model.load_state_dict(_SCREAMING_SNAKE_CASE    )

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

				# compare outputs from both models
				__a       =		get_expected_output(_SCREAMING_SNAKE_CASE    )
				__a       =		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__":
						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)





from __future__ import annotations

from collections import Counter
from random import random






class    SCREAMING_SNAKE_CASE			:





							def __init__(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		Dict      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		{}





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		str      ,				__lowercase		:		str      ,				__lowercase		:		float	):

											'''simple docstring'''
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											if nodea not in self.connections:
															self.add_node(__lowercase	)
											__a       =		probability





							def 						UpperCamelCase_					(   self		:		Union[str, Any]	):

											'''simple docstring'''
											return list(self.connections	)







							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		0
											__a       =		random()

											for dest in self.connections[node]:
															current_probability += self.connections[node][dest]
															if current_probability > random_value:
																			return dest
											return ""



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str				,     _SCREAMING_SNAKE_CASE       :			list[tuple[str, str, float]]				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				__a       =		MarkovChainGraphUndirectedUnweighted()

				for nodea, nodea, probability in transitions:
								graph.add_transition_probability(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				__a       =		Counter(graph.get_nodes()    )
				__a       =		start

				for _ in range(_SCREAMING_SNAKE_CASE    ):
								__a       =		graph.transition(_SCREAMING_SNAKE_CASE    )
								visited[node] += 1

				return visited


if __name__ == "__main__":
						import doctest

						doctest.testmod()
