




from manim import *







class 						lowercase							(   UpperCamelCase__		):

     def 					a__	(  self				)						->							Dict:
           _A							:     List[Any]			=						Rectangle(height=0.5		,     width=0.5				)
           _A							:     Optional[int]			=						Rectangle(height=0.46		,     width=0.46				).set_stroke(width=0				)

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

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

           _A							:     str			=						[mem.copy() for i in range(6				)]
           _A							:     int			=						VGroup(*_a				).arrange(_a		,     buff=0				)

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

           _A							:     List[str]			=						[]
           for i, rect in enumerate(_a				):
                 rect.set_stroke(_a				)
                 # target = fill.copy().set_fill(YELLOW, opacity=0.7)
                 # target.move_to(rect)
                 # self.add(target)

                 _A							:     str			=						Rectangle(height=0.46 / 4		,     width=0.46 / 3				).set_stroke(width=0.0				).set_fill(_a		,     opacity=0.7				)

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

           _A							:     Union[str, Any]			=						[mem.copy() for i in range(6				)]
           _A							:     str			=						VGroup(*_a				).arrange(_a		,     buff=0				)

           _A							:     List[str]			=						Text("""Loaded Checkpoint"""		,     font_size=24				)
           _A							:     Optional[int]			=						Group(_a		,     _a				).arrange(_a		,     aligned_edge=_a		,     buff=0.4				)
           checkpoint.move_to([3, 0.5, 0]				)

           _A							:     List[Any]			=						Square(side_length=2.2				)
           key.move_to([-5, 2, 0]				)

           _A							:     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(_a		,     _a				)

           _A							:     List[str]			=						MarkupText(
               F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''		,     font_size=18		,     )

           blue_text.next_to(_a		,     DOWN * 2.4		,     aligned_edge=key_text.get_left()				)

           _A							:     Optional[int]			=						MarkupText(
               F'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.'''		,     font_size=24		,     )
           step_a.move_to([2, 2, 0]				)
           self.play(Write(_a				)		,     Write(_a				)				)

           self.play(Write(_a		,     run_time=1				)		,     Create(_a		,     run_time=1				)				)

           _A							:     Optional[int]			=						[]
           _A							:     Dict			=						[]
           for i, rect in enumerate(_a				):
                 _A							:     int			=						fill.copy().set_fill(_a		,     opacity=0.7				)
                 target.move_to(_a				)
                 first_animations.append(GrowFromCenter(_a		,     run_time=1				)				)

                 _A							:     Optional[Any]			=						target.copy()
                 cpu_target.generate_target()
                 if i < 5:
                       cpu_target.target.move_to(cpu_left_col_base[i + 1]				)
                 else:
                       cpu_target.target.move_to(cpu_right_col_base[i - 5]				)
                 second_animations.append(MoveToTarget(_a		,     run_time=1.5				)				)

           self.play(*_a				)
           self.play(*_a				)
           self.wait()






'''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   :			List[Any]					  =    logging.get_logger(__name__)

a   :			Union[str, Any]					  =    torch.device("""cpu""")



def       __lowerCamelCase  (					)      ->			Any:
							UpperCAmelCase     :		Optional[Any]							     =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
							UpperCAmelCase     :		Dict							     =		Image.open(requests.get(_lowercase	,				stream=_lowercase		).raw		)
							return im



def       __lowerCamelCase  (					_lowercase		)      ->			Dict:
							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  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			str:
							UpperCAmelCase     :		Union[str, Any]							     =		dct.pop(_lowercase		)
							UpperCAmelCase     :		str							     =		val



def       __lowerCamelCase  (					_lowercase		)      ->			List[str]:
							UpperCAmelCase     :		Tuple							     =		[]
							for k in state_dict.keys():
														UpperCAmelCase     :		Dict							     =		k
														if ".pwconv" in k:
																					UpperCAmelCase     :		Union[str, Any]							     =		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     :		str							     =		k_new.replace(""".Proj."""	,				""".proj."""		)
														if "patch_embed" in k_new:
																					UpperCAmelCase     :		Dict							     =		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     :		Any							     =		"""swiftformer.encoder.network.""" + ls[1] + """.blocks.""" + ls[2] + """.""" + """.""".join(ls[3:]		)
																					else:
																												UpperCAmelCase     :		Any							     =		k_new.replace("""network"""	,				"""swiftformer.encoder.network"""		)
														rename_keys.append((k, k_new)		)
							return rename_keys



@torch.no_grad()
def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase		)      ->			Optional[int]:
							UpperCAmelCase     :		Optional[Any]							     =		SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							UpperCAmelCase     :		List[Any]							     =		1_0_0_0
							UpperCAmelCase     :		List[str]							     =		"""huggingface/label-files"""
							UpperCAmelCase     :		Tuple							     =		"""imagenet-1k-id2label.json"""
							UpperCAmelCase     :		Dict							     =		json.load(open(hf_hub_download(_lowercase	,				_lowercase	,				repo_type="""dataset"""		)	,				"""r"""		)		)
							UpperCAmelCase     :		Tuple							     =		{int(_lowercase		): v for k, v in idalabel.items()}
							UpperCAmelCase     :		Tuple							     =		idalabel
							UpperCAmelCase     :		List[Any]							     =		{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							     =		[4_8, 5_6, 1_1_2, 2_2_0]

							elif swiftformer_name == "swiftformer_s":
														UpperCAmelCase     :		str							     =		[3, 3, 9, 6]
														UpperCAmelCase     :		str							     =		[4_8, 6_4, 1_6_8, 2_2_4]

							elif swiftformer_name == "swiftformer_l1":
														UpperCAmelCase     :		List[Any]							     =		[4, 3, 1_0, 5]
														UpperCAmelCase     :		Union[str, Any]							     =		[4_8, 9_6, 1_9_2, 3_8_4]

							elif swiftformer_name == "swiftformer_l3":
														UpperCAmelCase     :		Any							     =		[4, 4, 1_2, 6]
														UpperCAmelCase     :		List[Any]							     =		[6_4, 1_2_8, 3_2_0, 5_1_2]

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

							UpperCAmelCase     :		Tuple							     =		create_rename_keys(_lowercase		)
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(_lowercase	,				_lowercase	,				_lowercase		)

							# load HuggingFace model
							UpperCAmelCase     :		str							     =		SwiftFormerForImageClassification(_lowercase		).eval()
							hf_model.load_state_dict(_lowercase		)

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

							# compare outputs from both models
							UpperCAmelCase     :		List[str]							     =		get_expected_output(_lowercase		)
							UpperCAmelCase     :		Dict							     =		hf_model(inputs["""pixel_values"""]		).logits

							assert hf_logits.shape == torch.Size([1, 1_0_0_0]		)
							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   :			Tuple					  =    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   :			Union[str, Any]					  =    parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

