
"""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()
A   =       logging.get_logger(__name__)

A   =       torch.device("""cpu""")

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


			"""simple docstring"""
			__UpperCAmelCase	: Dict		=					"http://images.cocodataset.org/val2017/000000039769.jpg"
			__UpperCAmelCase	: Dict		=					Image.open(requests.get(__UpperCamelCase				,       stream=__UpperCamelCase							).raw							)
			return im

def 					_UpperCamelCase    (			UpperCamelCase							)			->			List[Any]:


			"""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 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			int:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[Any]		=					dct.pop(__UpperCamelCase							)
			__UpperCAmelCase	: Tuple		=					val

def 					_UpperCamelCase    (			UpperCamelCase							)			->			str:


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

@torch.no_grad()
def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase				,       UpperCamelCase							)			->			Any:


			"""simple docstring"""
			__UpperCAmelCase	: Optional[int]		=					SwiftFormerConfig()

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

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

			elif swiftformer_name == "swiftformer_s":
						__UpperCAmelCase	: Union[str, Any]		=					[3, 3, 9, 6]
						__UpperCAmelCase	: Tuple		=					[48, 64, 168, 224]

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

			elif swiftformer_name == "swiftformer_l3":
						__UpperCAmelCase	: List[Any]		=					[4, 4, 12, 6]
						__UpperCAmelCase	: Optional[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	: Union[str, Any]		=					torch.hub.load_state_dict_from_url(__UpperCamelCase				,       map_location="cpu"				,       check_hash=__UpperCamelCase							)
						else:
									__UpperCAmelCase	: List[str]		=					torch.load(__UpperCamelCase				,       map_location="cpu"							)
			__UpperCAmelCase	: int		=					checkpoint

			__UpperCAmelCase	: Any		=					create_rename_keys(__UpperCamelCase							)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(__UpperCamelCase				,       __UpperCamelCase				,       __UpperCamelCase							)

			# load HuggingFace model
			__UpperCAmelCase	: Optional[Any]		=					SwiftFormerForImageClassification(__UpperCamelCase							).eval()
			hf_model.load_state_dict(__UpperCamelCase							)

			# prepare test inputs
			__UpperCAmelCase	: Tuple		=					prepare_img()
			__UpperCAmelCase	: Union[str, Any]		=					ViTImageProcessor.from_pretrained("preprocessor_config"							)
			__UpperCAmelCase	: Dict		=					processor(images=__UpperCamelCase				,       return_tensors="pt"							)

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

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

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


if __name__ == "__main__":
					A   =       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   =       parser.parse_args()
					convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








"""simple docstring"""





from manim import *



class      a__				(	__magic_name__   ):


			def       a_     (      self      :    str):


						"""simple docstring"""




						__UpperCAmelCase	: List[str]		=					Rectangle(height=0.5      ,  width=0.5)
						__UpperCAmelCase	: List[str]		=					Rectangle(height=0.25      ,  width=0.25)
						__UpperCAmelCase	: Dict		=					Rectangle(height=0.46      ,  width=0.46).set_stroke(width=0)

						__UpperCAmelCase	: List[str]		=					[mem.copy() for i in range(6)]
						__UpperCAmelCase	: Optional[int]		=					[mem.copy() for i in range(6)]
						__UpperCAmelCase	: List[str]		=					VGroup(*UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0)
						__UpperCAmelCase	: List[Any]		=					VGroup(*UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0)
						__UpperCAmelCase	: Optional[int]		=					VGroup(UpperCamelCase_      ,  UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0)
						__UpperCAmelCase	: Dict		=					Text("CPU"      ,  font_size=24)
						__UpperCAmelCase	: int		=					Group(UpperCamelCase_      ,  UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0.5      ,  aligned_edge=UpperCamelCase_)
						cpu.move_to([-2.5, -0.5, 0])
						self.add(UpperCamelCase_)

						__UpperCAmelCase	: List[str]		=					[mem.copy() for i in range(4)]
						__UpperCAmelCase	: List[Any]		=					VGroup(*UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0)
						__UpperCAmelCase	: Tuple		=					Text("GPU"      ,  font_size=24)
						__UpperCAmelCase	: Tuple		=					Group(UpperCamelCase_      ,  UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0.5      ,  aligned_edge=UpperCamelCase_)
						gpu.move_to([-1, -1, 0])
						self.add(UpperCamelCase_)

						__UpperCAmelCase	: Any		=					[mem.copy() for i in range(6)]
						__UpperCAmelCase	: int		=					VGroup(*UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0)

						__UpperCAmelCase	: Union[str, Any]		=					Text("Model"      ,  font_size=24)
						__UpperCAmelCase	: Optional[Any]		=					Group(UpperCamelCase_      ,  UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0.5      ,  aligned_edge=UpperCamelCase_)
						model.move_to([3, -1.0, 0])
						self.add(UpperCamelCase_)

						__UpperCAmelCase	: str		=					[]
						__UpperCAmelCase	: Tuple		=					[]
						__UpperCAmelCase	: Any		=					[]

						for i, rect in enumerate(UpperCamelCase_):
									rect.set_stroke(UpperCamelCase_)

									__UpperCAmelCase	: Optional[int]		=					Rectangle(height=0.46 / 4      ,  width=0.46 / 3).set_stroke(width=0.0).set_fill(UpperCamelCase_      ,  opacity=0.7)

									if i == 0:
												cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT)      ,  buff=0.02      ,  direction=UpperCamelCase_)
												cpu_target.set_x(cpu_target.get_x() + 0.1)
									elif i == 3:
												cpu_target.next_to(model_cpu_arr[0]      ,  direction=UpperCamelCase_      ,  buff=0.0)
									else:
												cpu_target.next_to(model_cpu_arr[i - 1]      ,  direction=UpperCamelCase_      ,  buff=0.0)
									self.add(UpperCamelCase_)
									model_cpu_arr.append(UpperCamelCase_)

						self.add(*UpperCamelCase_      ,  *UpperCamelCase_      ,  *UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					[mem.copy() for i in range(6)]
						__UpperCAmelCase	: Dict		=					VGroup(*UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0)

						__UpperCAmelCase	: Tuple		=					Text("Loaded Checkpoint"      ,  font_size=24)
						__UpperCAmelCase	: Any		=					Group(UpperCamelCase_      ,  UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0.5      ,  aligned_edge=UpperCamelCase_)
						checkpoint.move_to([3, 0.5, 0])

						self.add(UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					[]
						__UpperCAmelCase	: int		=					[]

						for i, rect in enumerate(UpperCamelCase_):
									__UpperCAmelCase	: Optional[Any]		=					fill.copy().set_fill(UpperCamelCase_      ,  opacity=0.7)
									target.move_to(UpperCamelCase_)
									ckpt_arr.append(UpperCamelCase_)

									__UpperCAmelCase	: Optional[Any]		=					target.copy()
									if i < 5:
												cpu_target.move_to(cpu_left_col_base[i + 1])
									else:
												cpu_target.move_to(cpu_right_col_base[i - 5])
									ckpt_cpu_arr.append(UpperCamelCase_)
						self.add(*UpperCamelCase_      ,  *UpperCamelCase_)

						__UpperCAmelCase	: str		=					Square(side_length=2.2)
						key.move_to([-5, 2, 0])

						__UpperCAmelCase	: int		=					MarkupText(
						    F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"      ,  font_size=18      ,  )

						key_text.move_to([-5, 2.4, 0])

						self.add(UpperCamelCase_      ,  UpperCamelCase_)

						__UpperCAmelCase	: Optional[Any]		=					MarkupText(
						    F"<span fgcolor='{BLUE}'>●</span> Checkpoint"      ,  font_size=18      ,  )

						blue_text.next_to(UpperCamelCase_      ,  DOWN * 2.4      ,  aligned_edge=key_text.get_left())
						self.add(UpperCamelCase_)

						__UpperCAmelCase	: Dict		=					MarkupText(
						    F"Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device."      ,  font_size=24      ,  )
						step_a.move_to([2, 2, 0])

						__UpperCAmelCase	: List[Any]		=					[meta_mem.copy() for i in range(6)]
						__UpperCAmelCase	: Optional[int]		=					[meta_mem.copy() for i in range(6)]
						__UpperCAmelCase	: int		=					VGroup(*UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0)
						__UpperCAmelCase	: Union[str, Any]		=					VGroup(*UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0)
						__UpperCAmelCase	: Tuple		=					VGroup(UpperCamelCase_      ,  UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0)
						__UpperCAmelCase	: Optional[int]		=					Text("Disk"      ,  font_size=24)
						__UpperCAmelCase	: str		=					Group(UpperCamelCase_      ,  UpperCamelCase_).arrange(UpperCamelCase_      ,  buff=0.5      ,  aligned_edge=UpperCamelCase_)
						disk.move_to([-4.0, -1.25, 0])
						self.play(Write(UpperCamelCase_      ,  run_time=3)      ,  Write(UpperCamelCase_      ,  run_time=1)      ,  Create(UpperCamelCase_      ,  run_time=1))

						__UpperCAmelCase	: str		=					[]
						for i, rect in enumerate(UpperCamelCase_):
									__UpperCAmelCase	: Union[str, Any]		=					rect.copy()
									target.generate_target()
									target.target.move_to(disk_left_col_base[i]).scale(0.5)
									animations.append(MoveToTarget(UpperCamelCase_      ,  run_time=1.5))
						self.play(*UpperCamelCase_)

						self.play(FadeOut(UpperCamelCase_))

						__UpperCAmelCase	: Union[str, Any]		=					MarkupText(F"Then, the checkpoint is removed from memory\nthrough garbage collection."      ,  font_size=24)
						step_a.move_to([2, 2, 0])

						self.play(Write(UpperCamelCase_      ,  run_time=3))

						self.play(
						    FadeOut(UpperCamelCase_      ,  UpperCamelCase_      ,  *UpperCamelCase_      ,  *UpperCamelCase_)      ,  )

						self.wait()


