
import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase	      = logging.get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    original_name.split('.'							)[0]
	lowercase       =    key.split('.'							)
	lowercase       =    int(key_list[key_list.index(__SCREAMING_SNAKE_CASE							) - 2]							)
	lowercase       =    int(key_list[key_list.index(__SCREAMING_SNAKE_CASE							) - 1]							)
	lowercase       =    orig_block_num - offset

	lowercase       =    key.replace(F'''{orig_block_num}.{layer_num}.{original_name}''' ,							F'''block.{new_block_num}.{layer_num}.{new_name}'''							)
	return key






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE							):
	lowercase       =    OrderedDict()
	lowercase  ,					lowercase       =    0, 0
	for key, value in state_dict.items():
		if key.startswith('network'							):
			lowercase       =    key.replace('network' ,							'poolformer.encoder'							)
		if "proj" in key:
			# Works for the first embedding as well as the internal embedding layers
			if key.endswith('bias'							) and "patch_embed" not in key:
				patch_emb_offset += 1
			lowercase       =    key[: key.find('proj'							)]
			lowercase       =    key.replace(__SCREAMING_SNAKE_CASE ,							F'''patch_embeddings.{total_embed_found}.'''							)
			lowercase       =    key.replace('proj' ,							'projection'							)
			if key.endswith('bias'							):
				total_embed_found += 1
		if "patch_embeddings" in key:
			lowercase       =    'poolformer.encoder.' + key
		if "mlp.fc1" in key:
			lowercase       =    replace_key_with_offset(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							'mlp.fc1' ,							'output.conv1'							)
		if "mlp.fc2" in key:
			lowercase       =    replace_key_with_offset(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							'mlp.fc2' ,							'output.conv2'							)
		if "norm1" in key:
			lowercase       =    replace_key_with_offset(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							'norm1' ,							'before_norm'							)
		if "norm2" in key:
			lowercase       =    replace_key_with_offset(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							'norm2' ,							'after_norm'							)
		if "layer_scale_1" in key:
			lowercase       =    replace_key_with_offset(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							'layer_scale_1' ,							'layer_scale_1'							)
		if "layer_scale_2" in key:
			lowercase       =    replace_key_with_offset(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							'layer_scale_2' ,							'layer_scale_2'							)
		if "head" in key:
			lowercase       =    key.replace('head' ,							'classifier'							)
		lowercase       =    value
	return new_state_dict






def 							UpperCAmelCase_		(							):
	lowercase       =    'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowercase       =    Image.open(requests.get(__SCREAMING_SNAKE_CASE ,							stream=__SCREAMING_SNAKE_CASE							).raw							)

	return image






@torch.no_grad()
def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    PoolFormerConfig()

	# set attributes based on model_name
	lowercase       =    'huggingface/label-files'
	lowercase       =    model_name[-3:]
	lowercase       =    1000
	lowercase       =    'imagenet-1k-id2label.json'
	lowercase       =    (1, 1000)

	# set config attributes
	lowercase       =    json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							repo_type='dataset'							) ,							'r'							)							)
	lowercase       =    {int(__SCREAMING_SNAKE_CASE							): v for k, v in idalabel.items()}
	lowercase       =    idalabel
	lowercase       =    {v: k for k, v in idalabel.items()}
	if size == "s12":
		lowercase       =    [2, 2, 6, 2]
		lowercase       =    [64, 128, 320, 512]
		lowercase       =    4.0
		lowercase       =    0.9
	elif size == "s24":
		lowercase       =    [4, 4, 12, 4]
		lowercase       =    [64, 128, 320, 512]
		lowercase       =    4.0
		lowercase       =    0.9
	elif size == "s36":
		lowercase       =    [6, 6, 18, 6]
		lowercase       =    [64, 128, 320, 512]
		lowercase       =    4.0
		lowercase       =    1e-6
		lowercase       =    0.9
	elif size == "m36":
		lowercase       =    [6, 6, 18, 6]
		lowercase       =    [96, 192, 384, 768]
		lowercase       =    4.0
		lowercase       =    1e-6
		lowercase       =    0.95
	elif size == "m48":
		lowercase       =    [8, 8, 24, 8]
		lowercase       =    [96, 192, 384, 768]
		lowercase       =    4.0
		lowercase       =    1e-6
		lowercase       =    0.95
	else:
		raise ValueError(F'''Size {size} not supported'''							)

	# load image processor
	lowercase       =    PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE							)

	# Prepare image
	lowercase       =    prepare_img()
	lowercase       =    image_processor(images=__SCREAMING_SNAKE_CASE ,							return_tensors='pt'							).pixel_values

	logger.info(F'''Converting model {model_name}...'''							)

	# load original state dict
	lowercase       =    torch.load(__SCREAMING_SNAKE_CASE ,							map_location=torch.device('cpu'							)							)

	# rename keys
	lowercase       =    rename_keys(__SCREAMING_SNAKE_CASE							)

	# create HuggingFace model and load state dict
	lowercase       =    PoolFormerForImageClassification(__SCREAMING_SNAKE_CASE							)
	model.load_state_dict(__SCREAMING_SNAKE_CASE							)
	model.eval()

	# Define image processor
	lowercase       =    PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE							)
	lowercase       =    image_processor(images=prepare_img() ,							return_tensors='pt'							).pixel_values

	# forward pass
	lowercase       =    model(__SCREAMING_SNAKE_CASE							)
	lowercase       =    outputs.logits

	# define expected logit slices for different models
	if size == "s12":
		lowercase       =    torch.tensor([-0.30_45, -0.67_58, -0.48_69]							)
	elif size == "s24":
		lowercase       =    torch.tensor([0.44_02, -0.13_74, -0.80_45]							)
	elif size == "s36":
		lowercase       =    torch.tensor([-0.60_80, -0.51_33, -0.58_98]							)
	elif size == "m36":
		lowercase       =    torch.tensor([0.39_52, 0.22_63, -1.26_68]							)
	elif size == "m48":
		lowercase       =    torch.tensor([0.11_67, -0.06_56, -0.34_23]							)
	else:
		raise ValueError(F'''Size {size} not supported'''							)

	# verify logits
	assert logits.shape == expected_shape
	assert torch.allclose(logits[0, :3] ,							__SCREAMING_SNAKE_CASE ,							atol=1e-2							)

	# finally, save model and image processor
	logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''							)
	Path(__SCREAMING_SNAKE_CASE							).mkdir(exist_ok=__SCREAMING_SNAKE_CASE							)
	model.save_pretrained(__SCREAMING_SNAKE_CASE							)
	print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
	image_processor.save_pretrained(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
	UpperCAmelCase	      = argparse.ArgumentParser()

	parser.add_argument(
	    '''--model_name''',
	    default='''poolformer_s12''',
	    type=str,
	    help='''Name of the model you\'d like to convert.''',
	)
	parser.add_argument(
	    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
	)
	UpperCAmelCase	      = parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



from manim import *







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    Rectangle(height=0.5		,    width=0.5       )
		lowercase       =    Rectangle(height=0.25		,    width=0.25       )
		lowercase       =    Rectangle(height=0.46		,    width=0.46       ).set_stroke(width=0       )

		lowercase       =    [mem.copy() for i in range(6       )]
		lowercase       =    [mem.copy() for i in range(6       )]
		lowercase       =    VGroup(*snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    VGroup(*snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    VGroup(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    Text('CPU'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		cpu.move_to([-2.5, -0.5, 0]       )
		self.add(snake_case       )

		lowercase       =    [mem.copy() for i in range(4       )]
		lowercase       =    VGroup(*snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    Text('GPU'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		gpu.move_to([-1, -1, 0]       )
		self.add(snake_case       )

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

		lowercase       =    Text('Model'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		model.move_to([3, -1.0, 0]       )
		self.add(snake_case       )

		lowercase       =    []
		lowercase       =    []
		lowercase       =    []

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

			lowercase       =    Rectangle(height=0.46 / 4		,    width=0.46 / 3       ).set_stroke(width=0.0       ).set_fill(snake_case		,    opacity=0.7       )

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

		self.add(*snake_case		,    *snake_case		,    *snake_case       )

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

		lowercase       =    Text('Loaded Checkpoint'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		checkpoint.move_to([3, 0.5, 0]       )

		self.add(snake_case       )

		lowercase       =    []
		lowercase       =    []

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

			lowercase       =    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(snake_case       )
		self.add(*snake_case		,    *snake_case       )

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

		lowercase       =    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(snake_case		,    snake_case       )

		lowercase       =    MarkupText(
		    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''		,    font_size=18		,    )

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

		lowercase       =    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]       )

		lowercase       =    [meta_mem.copy() for i in range(6       )]
		lowercase       =    [meta_mem.copy() for i in range(6       )]
		lowercase       =    VGroup(*snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    VGroup(*snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    VGroup(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0       )
		lowercase       =    Text('Disk'		,    font_size=24       )
		lowercase       =    Group(snake_case		,    snake_case       ).arrange(snake_case		,    buff=0.5		,    aligned_edge=snake_case       )
		disk.move_to([-4.0, -1.25, 0]       )
		self.play(Write(snake_case		,    run_time=3       )		,    Write(snake_case		,    run_time=1       )		,    Create(snake_case		,    run_time=1       )       )

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

		self.play(FadeOut(snake_case       )       )

		lowercase       =    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(snake_case		,    run_time=3       )       )

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

		self.wait()

