

'''simple docstring'''

import argparse
import json
from pathlib import Path

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

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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

def     lowerCamelCase__       ( A					,   A=False					,   A=False							)			->		List[Any]:
	snake_case							 =				'backbone.' if is_semantic else ''

	snake_case							 =				[]
	for i in range(config.num_hidden_layers							):
		# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
		rename_keys.append((F'''{prefix}blocks.{i}.norm1.weight''', F'''beit.encoder.layer.{i}.layernorm_before.weight''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.norm1.bias''', F'''beit.encoder.layer.{i}.layernorm_before.bias''')							)
		rename_keys.append(
		    (F'''{prefix}blocks.{i}.attn.proj.weight''', F'''beit.encoder.layer.{i}.attention.output.dense.weight''')							)
		rename_keys.append(
		    (F'''{prefix}blocks.{i}.attn.proj.bias''', F'''beit.encoder.layer.{i}.attention.output.dense.bias''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.norm2.weight''', F'''beit.encoder.layer.{i}.layernorm_after.weight''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.norm2.bias''', F'''beit.encoder.layer.{i}.layernorm_after.bias''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.weight''', F'''beit.encoder.layer.{i}.intermediate.dense.weight''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc1.bias''', F'''beit.encoder.layer.{i}.intermediate.dense.bias''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.weight''', F'''beit.encoder.layer.{i}.output.dense.weight''')							)
		rename_keys.append((F'''{prefix}blocks.{i}.mlp.fc2.bias''', F'''beit.encoder.layer.{i}.output.dense.bias''')							)

	# projection layer + position embeddings
	rename_keys.extend(
	    [
	        (F'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
	        (F'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
	        (F'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
	    ]							)

	if has_lm_head:
		# mask token + shared relative position bias + layernorm
		rename_keys.extend(
		    [
		        ('mask_token', 'beit.embeddings.mask_token'),
		        (
		            'rel_pos_bias.relative_position_bias_table',
		            'beit.encoder.relative_position_bias.relative_position_bias_table',
		        ),
		        (
		            'rel_pos_bias.relative_position_index',
		            'beit.encoder.relative_position_bias.relative_position_index',
		        ),
		        ('norm.weight', 'layernorm.weight'),
		        ('norm.bias', 'layernorm.bias'),
		    ]							)
	elif is_semantic:
		# semantic segmentation classification heads
		rename_keys.extend(
		    [
		        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
		        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
		        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
		        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
		    ]							)
	else:
		# layernorm + classification head
		rename_keys.extend(
		    [
		        ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
		        ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
		        ('head.weight', 'classifier.weight'),
		        ('head.bias', 'classifier.bias'),
		    ]							)

	return rename_keys

def     lowerCamelCase__       ( A					,   A					,   A=False					,   A=False							)			->		Optional[int]:







	for i in range(config.num_hidden_layers							):
		snake_case							 =				'backbone.' if is_semantic else ''
		# queries, keys and values
		snake_case							 =				state_dict.pop(F'''{prefix}blocks.{i}.attn.qkv.weight'''							)
		snake_case							 =				state_dict.pop(F'''{prefix}blocks.{i}.attn.q_bias'''							)
		snake_case							 =				state_dict.pop(F'''{prefix}blocks.{i}.attn.v_bias'''							)

		snake_case							 =				in_proj_weight[
		    : config.hidden_size, :
		]
		snake_case							 =				q_bias
		snake_case							 =				in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		snake_case							 =				in_proj_weight[
		    -config.hidden_size :, :
		]
		snake_case							 =				v_bias

		# gamma_1 and gamma_2
		# we call them lambda because otherwise they are renamed when using .from_pretrained
		snake_case							 =				state_dict.pop(F'''{prefix}blocks.{i}.gamma_1'''							)
		snake_case							 =				state_dict.pop(F'''{prefix}blocks.{i}.gamma_2'''							)

		snake_case							 =				gamma_a
		snake_case							 =				gamma_a

		# relative_position bias table + index
		if not has_lm_head:
			# each layer has its own relative position bias
			snake_case							 =				state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_bias_table'''							)
			snake_case							 =				state_dict.pop(F'''{prefix}blocks.{i}.attn.relative_position_index'''							)

			snake_case							 =				table
			snake_case							 =				index

def     lowerCamelCase__       ( A					,   A					,   A							)			->		List[str]:
	snake_case							 =				dct.pop(A							)
	snake_case							 =				val

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

@torch.no_grad()
def     lowerCamelCase__       ( A					,   A							)			->		int:
	snake_case							 =				BeitConfig()
	snake_case							 =				False
	snake_case							 =				False
	snake_case							 =				'huggingface/label-files'
	# set config parameters based on URL
	if checkpoint_url[-9:-4] == "pt22k":
		# masked image modeling
		snake_case							 =				True
		snake_case							 =				True
		snake_case							 =				True
	elif checkpoint_url[-9:-4] == "ft22k":
		# intermediate fine-tuning on ImageNet-22k
		snake_case							 =				True
		snake_case							 =				2_1_8_4_1
		snake_case							 =				'imagenet-22k-id2label.json'
		snake_case							 =				json.load(open(hf_hub_download(A					,   A					,   repo_type='dataset'							)					,   'r'							)							)
		snake_case							 =				{int(A							): v for k, v in idalabel.items()}
		# this dataset contains 21843 labels but the model only has 21841
		# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
		del idalabel[9_2_0_5]
		del idalabel[1_5_0_2_7]
		snake_case							 =				idalabel
		snake_case							 =				{v: k for k, v in idalabel.items()}
	elif checkpoint_url[-8:-4] == "to1k":
		# fine-tuning on ImageNet-1k
		snake_case							 =				True
		snake_case							 =				1_0_0_0
		snake_case							 =				'imagenet-1k-id2label.json'
		snake_case							 =				json.load(open(hf_hub_download(A					,   A					,   repo_type='dataset'							)					,   'r'							)							)
		snake_case							 =				{int(A							): v for k, v in idalabel.items()}
		snake_case							 =				idalabel
		snake_case							 =				{v: k for k, v in idalabel.items()}
		if "384" in checkpoint_url:
			snake_case							 =				3_8_4
		if "512" in checkpoint_url:
			snake_case							 =				5_1_2
	elif "ade20k" in checkpoint_url:
		# fine-tuning
		snake_case							 =				True
		snake_case							 =				1_5_0
		snake_case							 =				'ade20k-id2label.json'
		snake_case							 =				json.load(open(hf_hub_download(A					,   A					,   repo_type='dataset'							)					,   'r'							)							)
		snake_case							 =				{int(A							): v for k, v in idalabel.items()}
		snake_case							 =				idalabel
		snake_case							 =				{v: k for k, v in idalabel.items()}
		snake_case							 =				6_4_0
		snake_case							 =				True
	else:
		raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''							)

	# size of the architecture
	if "base" in checkpoint_url:
		pass
	elif "large" in checkpoint_url:
		snake_case							 =				1_0_2_4
		snake_case							 =				4_0_9_6
		snake_case							 =				2_4
		snake_case							 =				1_6
		if "ade20k" in checkpoint_url:
			snake_case							 =				6_4_0
			snake_case							 =				[7, 1_1, 1_5, 2_3]
	else:
		raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'							)

	# load state_dict of original model, remove and rename some keys
	snake_case							 =				torch.hub.load_state_dict_from_url(A					,   map_location='cpu'					,   check_hash=A							)
	snake_case							 =				state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

	snake_case							 =				create_rename_keys(A					,   has_lm_head=A					,   is_semantic=A							)
	for src, dest in rename_keys:
		rename_key(A					,   A					,   A							)
	read_in_q_k_v(A					,   A					,   has_lm_head=A					,   is_semantic=A							)
	if is_semantic:
		# add prefix to decoder keys
		for key, val in state_dict.copy().items():
			snake_case							 =				state_dict.pop(A							)
			if key.startswith('backbone.fpn'							):
				snake_case							 =				key.replace('backbone.fpn'					,   'fpn'							)
			snake_case							 =				val

    # load HuggingFace model
	if checkpoint_url[-9:-4] == "pt22k":
		snake_case							 =				BeitForMaskedImageModeling(A							)
	elif "ade20k" in checkpoint_url:
		snake_case							 =				BeitForSemanticSegmentation(A							)
	else:
		snake_case							 =				BeitForImageClassification(A							)
	model.eval()
	model.load_state_dict(A							)

	# Check outputs on an image
	if is_semantic:
		snake_case							 =				BeitImageProcessor(size=config.image_size					,   do_center_crop=A							)
		snake_case							 =				load_dataset('hf-internal-testing/fixtures_ade20k'					,   split='test'							)
		snake_case							 =				Image.open(ds[0]['file']							)
	else:
		snake_case							 =				BeitImageProcessor(
		    size=config.image_size					,   resample=PILImageResampling.BILINEAR					,   do_center_crop=A							)
		snake_case							 =				prepare_img()

	snake_case							 =				image_processor(images=A					,   return_tensors='pt'							)
	snake_case							 =				encoding['pixel_values']

	snake_case							 =				model(A							)
	snake_case							 =				outputs.logits

	# verify logits
	snake_case							 =				torch.Size([1, 1_0_0_0]							)
	if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'							):
		snake_case							 =				torch.Size([1, 1_9_6, 8_1_9_2]							)
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'							):
		snake_case							 =				torch.Size([1, 1_9_6, 8_1_9_2]							)
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'							):
		snake_case							 =				torch.Size([1, 2_1_8_4_1]							)
		snake_case							 =				torch.tensor([2.2_288, 2.4_671, 0.7_395]							)
		snake_case							 =				2_3_9_7
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'							):
		snake_case							 =				torch.Size([1, 2_1_8_4_1]							)
		snake_case							 =				torch.tensor([1.6_881, -0.2_787, 0.5_901]							)
		snake_case							 =				2_3_9_6
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'							):
		snake_case							 =				torch.tensor([0.1_241, 0.0_798, -0.6_569]							)
		snake_case							 =				2_8_5
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'							):
		snake_case							 =				torch.tensor([-1.2_385, -1.0_987, -1.0_108]							)
		snake_case							 =				2_8_1
	elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'							):
		snake_case							 =				torch.tensor([-1.5_303, -0.9_484, -0.3_147]							)
		snake_case							 =				7_6_1
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'							):
		snake_case							 =				torch.tensor([0.4_610, -0.0_928, 0.2_086]							)
		snake_case							 =				7_6_1
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'							):
		snake_case							 =				torch.tensor([-0.4_804, 0.6_257, -0.1_837]							)
		snake_case							 =				7_6_1
	elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'							):
		snake_case							 =				torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]							)
		snake_case							 =				7_6_1
	elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'							):
		snake_case							 =				torch.tensor([-0.3_062, 0.7_261, 0.4_852]							)
		snake_case							 =				7_6_1
	elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'							):
		snake_case							 =				(1, 1_5_0, 1_6_0, 1_6_0)
		snake_case							 =				torch.tensor(
		    [
		        [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
		        [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
		        [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
		    ]							)
	elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'							):
		snake_case							 =				(1, 1_5_0, 1_6_0, 1_6_0)
		snake_case							 =				torch.tensor(
		    [
		        [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
		        [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
		        [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
		    ]							)
	else:
		raise ValueError('Can\'t verify logits as model is not supported'							)

	assert logits.shape == expected_shape, "Shape of logits not as expected"
	if not has_lm_head:
		if is_semantic:
			assert torch.allclose(
			    logits[0, :3, :3, :3]					,   A					,   atol=1E-3							), "First elements of logits not as expected"
		else:
			print('Predicted class idx:'					,   logits.argmax(-1							).item()							)
			assert torch.allclose(
			    logits[0, :3]					,   A					,   atol=1E-3							), "First elements of logits not as expected"
			assert logits.argmax(-1							).item() == expected_class_idx, "Predicted class index not as expected"

	Path(A							).mkdir(exist_ok=A							)
	print(F'''Saving model 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_						   =			argparse.ArgumentParser()

						parser.add_argument(
						    "--checkpoint_url",
						    default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
						    type=str,
						    help="URL 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."
						)
						lowerCAmelCase_						   =			parser.parse_args()
						convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








'''simple docstring'''

import os

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

lowerCAmelCase_						   =			{"UserAgent": UserAgent().random}
lowerCAmelCase_						   =			"https://www.mywaifulist.moe/random"

def     __magic_name__       ( A					,   A							)			->		None:
	snake_case							 =				requests.get(A					,   headers=A							)
	with open(A					,   'wb'							) as file:
		file.write(image.content							)

def     __magic_name__       ( )			->		tuple[str, str, str]:
	snake_case							 =				BeautifulSoup(requests.get(A					,   headers=A							).text					,   'html.parser'							)
	snake_case							 =				soup.find('meta'					,   attrs={'property': 'og:title'}							).attrs['content']
	snake_case							 =				soup.find('meta'					,   attrs={'property': 'og:image'}							).attrs['content']
	snake_case							 =				soup.find('p'					,   id='description'							).get_text()
	snake_case , snake_case							 =				os.path.splitext(os.path.basename(A							)							)
	snake_case							 =				title.strip().replace(' '					,   '_'							)
	snake_case							 =				F'''{image_title}{image_extension}'''
	save_image(A					,   A							)
	return (title, description, image_title)


if __name__ == "__main__":
						lowerCAmelCase_   ,	lowerCAmelCase_   ,	lowerCAmelCase_						   =			random_anime_character()
						print(f"{title}\n\n{desc}\n\nImage saved : {image_title}")





