


'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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




def 	_A  (       A							,A=False							)  ->				int:
	lowercase						:		Optional[Any]   					=		[]
	for i in range(config.num_hidden_layers							):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')							)
		rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')							)
		rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')							)
		rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')							)
		rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')							)
		rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')							)
		rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')							)
		rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')							)
		rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')							)
		rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')							)

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        ("cls_token", "deit.embeddings.cls_token"),
	        ("dist_token", "deit.embeddings.distillation_token"),
	        ("patch_embed.proj.weight", "deit.embeddings.patch_embeddings.projection.weight"),
	        ("patch_embed.proj.bias", "deit.embeddings.patch_embeddings.projection.bias"),
	        ("pos_embed", "deit.embeddings.position_embeddings"),
	    ]							)

	if base_model:
		# layernorm + pooler
		rename_keys.extend(
		    [
		        ("norm.weight", "layernorm.weight"),
		        ("norm.bias", "layernorm.bias"),
		        ("pre_logits.fc.weight", "pooler.dense.weight"),
		        ("pre_logits.fc.bias", "pooler.dense.bias"),
		    ]							)

		# if just the base model, we should remove "deit" from all keys that start with "deit"
		lowercase						:		int   					=		[(pair[0], pair[1][4:]) if pair[1].startswith("deit"							) else pair for pair in rename_keys]
	else:
		# layernorm + classification heads
		rename_keys.extend(
		    [
		        ("norm.weight", "deit.layernorm.weight"),
		        ("norm.bias", "deit.layernorm.bias"),
		        ("head.weight", "cls_classifier.weight"),
		        ("head.bias", "cls_classifier.bias"),
		        ("head_dist.weight", "distillation_classifier.weight"),
		        ("head_dist.bias", "distillation_classifier.bias"),
		    ]							)

	return rename_keys




def 	_A  (       A							,A							,A=False							)  ->				Union[str, Any]:
	for i in range(config.num_hidden_layers							):
		if base_model:
			lowercase						:		List[str]   					=		""
		else:
			lowercase						:		Any   					=		"deit."
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		lowercase						:		List[str]   					=		state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''							)
		lowercase						:		Dict   					=		state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''							)
		# next, add query, keys and values (in that order) to the state dict
		lowercase						:		Optional[int]   					=		in_proj_weight[
		    : config.hidden_size, :
		]
		lowercase						:		Optional[Any]   					=		in_proj_bias[: config.hidden_size]
		lowercase						:		Any   					=		in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowercase						:		Optional[Any]   					=		in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		lowercase						:		Tuple   					=		in_proj_weight[
		    -config.hidden_size :, :
		]
		lowercase						:		Dict   					=		in_proj_bias[-config.hidden_size :]




def 	_A  (       A							,A							,A							)  ->				Any:
	lowercase						:		Tuple   					=		dct.pop(A							)
	lowercase						:		Tuple   					=		val




def 	_A  (       )  ->				List[str]:
	lowercase						:		Any   					=		"http://images.cocodataset.org/val2017/000000039769.jpg"
	lowercase						:		List[Any]   					=		Image.open(requests.get(A							,stream=A							).raw							)
	return im




@torch.no_grad()
def 	_A  (       A							,A							)  ->				str:
	lowercase						:		Tuple   					=		DeiTConfig()
	# all deit models have fine-tuned heads
	lowercase						:		List[str]   					=		False
	# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
	lowercase						:		List[str]   					=		1_0_0_0
	lowercase						:		List[Any]   					=		"huggingface/label-files"
	lowercase						:		Optional[Any]   					=		"imagenet-1k-id2label.json"
	lowercase						:		Any   					=		json.load(open(hf_hub_download(A							,A							,repo_type="dataset"							)							,"r"							)							)
	lowercase						:		Optional[Any]   					=		{int(A							): v for k, v in idalabel.items()}
	lowercase						:		Optional[int]   					=		idalabel
	lowercase						:		Optional[Any]   					=		{v: k for k, v in idalabel.items()}
	lowercase						:		Optional[Any]   					=		int(deit_name[-6:-4]							)
	lowercase						:		Any   					=		int(deit_name[-3:]							)
	# size of the architecture
	if deit_name[9:].startswith("tiny"							):
		lowercase						:		List[str]   					=		1_9_2
		lowercase						:		Optional[int]   					=		7_6_8
		lowercase						:		Optional[Any]   					=		1_2
		lowercase						:		Optional[int]   					=		3
	elif deit_name[9:].startswith("small"							):
		lowercase						:		Optional[Any]   					=		3_8_4
		lowercase						:		List[Any]   					=		1_5_3_6
		lowercase						:		int   					=		1_2
		lowercase						:		List[str]   					=		6
	if deit_name[9:].startswith("base"							):
		pass
	elif deit_name[4:].startswith("large"							):
		lowercase						:		Any   					=		1_0_2_4
		lowercase						:		List[Any]   					=		4_0_9_6
		lowercase						:		Optional[int]   					=		2_4
		lowercase						:		Optional[int]   					=		1_6

	# load original model from timm
	lowercase						:		Tuple   					=		timm.create_model(A							,pretrained=A							)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	lowercase						:		Union[str, Any]   					=		timm_model.state_dict()
	lowercase						:		List[str]   					=		create_rename_keys(A							,A							)
	for src, dest in rename_keys:
		rename_key(A							,A							,A							)
	read_in_q_k_v(A							,A							,A							)

	# load HuggingFace model
	lowercase						:		Optional[Any]   					=		DeiTForImageClassificationWithTeacher(A							).eval()
	model.load_state_dict(A							)

	# Check outputs on an image, prepared by DeiTImageProcessor
	lowercase						:		Union[str, Any]   					=		int(
	    (2_5_6 / 2_2_4) * config.image_size							)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
	lowercase						:		int   					=		DeiTImageProcessor(size=A							,crop_size=config.image_size							)
	lowercase						:		Union[str, Any]   					=		image_processor(images=prepare_img()							,return_tensors="pt"							)
	lowercase						:		Optional[Any]   					=		encoding["pixel_values"]
	lowercase						:		Tuple   					=		model(A							)

	lowercase						:		Dict   					=		timm_model(A							)
	assert timm_logits.shape == outputs.logits.shape
	assert torch.allclose(A							,outputs.logits							,atol=1e-3							)

	Path(A							).mkdir(exist_ok=A							)
	print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''							)
	model.save_pretrained(A							)
	print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
	image_processor.save_pretrained(A							)


if __name__ == "__main__":
					lowerCAmelCase   : str             =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--deit_name""",
					    default="""vit_deit_base_distilled_patch16_224""",
					    type=str,
					    help="""Name of the DeiT timm model you'd like to convert.""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
					)

					lowerCAmelCase   : List[str]             =				parser.parse_args()
					convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






'''simple docstring'''
from manim import *




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		List[Any]   					=		Rectangle(height=0.5							,      width=0.5							)
					lowercase						:		str   					=		Rectangle(height=0.25							,      width=0.25							)
					lowercase						:		Union[str, Any]   					=		Rectangle(height=0.46							,      width=0.46							).set_stroke(width=0							)

					lowercase						:		List[str]   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Any   					=		[mem.copy() for i in range(6							)]
					lowercase						:		List[str]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		List[Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		List[Any]   					=		VGroup(a_							,      a_							).arrange(a_							,      buff=0							)
					lowercase						:		Union[str, Any]   					=		Text("CPU"							,      font_size=2_4							)
					lowercase						:		List[Any]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					cpu.move_to([-2.5, -0.5, 0]							)
					self.add(a_							)

					lowercase						:		List[Any]   					=		[mem.copy() for i in range(4							)]
					lowercase						:		Union[str, Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Dict   					=		Text("GPU"							,      font_size=2_4							)
					lowercase						:		Tuple   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					gpu.move_to([-1, -1, 0]							)
					self.add(a_							)

					lowercase						:		Tuple   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Optional[int]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)

					lowercase						:		Any   					=		Text("Model"							,      font_size=2_4							)
					lowercase						:		str   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					model.move_to([3, -1.0, 0]							)
					self.add(a_							)

					lowercase						:		Dict   					=		[]
					lowercase						:		Tuple   					=		[]
					lowercase						:		List[Any]   					=		[]

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

						lowercase						:		Tuple   					=		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(model_cpu_arr[0]							,      direction=a_							,      buff=0.0							)
						else:
							cpu_target.next_to(model_cpu_arr[i - 1]							,      direction=a_							,      buff=0.0							)
						self.add(a_							)
						model_cpu_arr.append(a_							)

					self.add(*a_							,      *a_							,      *a_							)

					lowercase						:		Any   					=		[mem.copy() for i in range(6							)]
					lowercase						:		Dict   					=		VGroup(*a_							).arrange(a_							,      buff=0							)

					lowercase						:		List[str]   					=		Text("Loaded Checkpoint"							,      font_size=2_4							)
					lowercase						:		Optional[Any]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					checkpoint.move_to([3, 0.5, 0]							)

					self.add(a_							)

					lowercase						:		Any   					=		[]
					lowercase						:		int   					=		[]

					for i, rect in enumerate(a_							):
						lowercase						:		str   					=		fill.copy().set_fill(a_							,      opacity=0.7							)
						target.move_to(a_							)
						ckpt_arr.append(a_							)

						lowercase						:		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(a_							)
					self.add(*a_							,      *a_							)

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

					lowercase						:		str   					=		MarkupText(
					    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''							,      font_size=1_8							,      )

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

					self.add(a_							,      a_							)

					lowercase						:		Any   					=		MarkupText(
					    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''							,      font_size=1_8							,      )

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

					lowercase						:		List[Any]   					=		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=2_4							,      )
					step_a.move_to([2, 2, 0]							)

					lowercase						:		Any   					=		[meta_mem.copy() for i in range(6							)]
					lowercase						:		Dict   					=		[meta_mem.copy() for i in range(6							)]
					lowercase						:		Union[str, Any]   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Dict   					=		VGroup(*a_							).arrange(a_							,      buff=0							)
					lowercase						:		Any   					=		VGroup(a_							,      a_							).arrange(a_							,      buff=0							)
					lowercase						:		Optional[Any]   					=		Text("Disk"							,      font_size=2_4							)
					lowercase						:		List[str]   					=		Group(a_							,      a_							).arrange(a_							,      buff=0.5							,      aligned_edge=a_							)
					disk.move_to([-4.0, -1.25, 0]							)
					self.play(Write(a_							,      run_time=3							)							,      Write(a_							,      run_time=1							)							,      Create(a_							,      run_time=1							)							)

					lowercase						:		Optional[Any]   					=		[]
					for i, rect in enumerate(a_							):
						lowercase						:		int   					=		rect.copy()
						target.generate_target()
						target.target.move_to(disk_left_col_base[i]							).scale(0.5							)
						animations.append(MoveToTarget(a_							,      run_time=1.5							)							)
					self.play(*a_							)

					self.play(FadeOut(a_							)							)

					lowercase						:		List[Any]   					=		MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.'''							,      font_size=2_4							)
					step_a.move_to([2, 2, 0]							)

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

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

					self.wait()


