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

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_=False,	SCREAMING_SNAKE_CASE_=False     ):
					SCREAMING_SNAKE_CASE           =      'backbone.' if is_semantic else ''

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

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

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

										SCREAMING_SNAKE_CASE           =      gamma_a
										SCREAMING_SNAKE_CASE           =      gamma_a

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

															SCREAMING_SNAKE_CASE           =      table
															SCREAMING_SNAKE_CASE           =      index

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      dct.pop(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      val

def     UpperCamelCase_    (						):
					SCREAMING_SNAKE_CASE           =      'http://images.cocodataset.org/val2017/000000039769.jpg'
					SCREAMING_SNAKE_CASE           =      Image.open(requests.get(SCREAMING_SNAKE_CASE_,	stream=SCREAMING_SNAKE_CASE_     ).raw     )
					return im

@torch.no_grad()
def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      BeitConfig()
					SCREAMING_SNAKE_CASE           =      False
					SCREAMING_SNAKE_CASE           =      False
					SCREAMING_SNAKE_CASE           =      'huggingface/label-files'
					# set config parameters based on URL
					if checkpoint_url[-9:-4] == "pt22k":
										# masked image modeling
										SCREAMING_SNAKE_CASE           =      True
										SCREAMING_SNAKE_CASE           =      True
										SCREAMING_SNAKE_CASE           =      True
					elif checkpoint_url[-9:-4] == "ft22k":
										# intermediate fine-tuning on ImageNet-22k
										SCREAMING_SNAKE_CASE           =      True
										SCREAMING_SNAKE_CASE           =      2_1_8_4_1
										SCREAMING_SNAKE_CASE           =      'imagenet-22k-id2label.json'
										SCREAMING_SNAKE_CASE           =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	repo_type='dataset'     ),	'r'     )     )
										SCREAMING_SNAKE_CASE           =      {int(SCREAMING_SNAKE_CASE_     ): 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]
										SCREAMING_SNAKE_CASE           =      idalabel
										SCREAMING_SNAKE_CASE           =      {v: k for k, v in idalabel.items()}
					elif checkpoint_url[-8:-4] == "to1k":
										# fine-tuning on ImageNet-1k
										SCREAMING_SNAKE_CASE           =      True
										SCREAMING_SNAKE_CASE           =      1_0_0_0
										SCREAMING_SNAKE_CASE           =      'imagenet-1k-id2label.json'
										SCREAMING_SNAKE_CASE           =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	repo_type='dataset'     ),	'r'     )     )
										SCREAMING_SNAKE_CASE           =      {int(SCREAMING_SNAKE_CASE_     ): v for k, v in idalabel.items()}
										SCREAMING_SNAKE_CASE           =      idalabel
										SCREAMING_SNAKE_CASE           =      {v: k for k, v in idalabel.items()}
										if "384" in checkpoint_url:
															SCREAMING_SNAKE_CASE           =      3_8_4
										if "512" in checkpoint_url:
															SCREAMING_SNAKE_CASE           =      5_1_2
					elif "ade20k" in checkpoint_url:
										# fine-tuning
										SCREAMING_SNAKE_CASE           =      True
										SCREAMING_SNAKE_CASE           =      1_5_0
										SCREAMING_SNAKE_CASE           =      'ade20k-id2label.json'
										SCREAMING_SNAKE_CASE           =      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	repo_type='dataset'     ),	'r'     )     )
										SCREAMING_SNAKE_CASE           =      {int(SCREAMING_SNAKE_CASE_     ): v for k, v in idalabel.items()}
										SCREAMING_SNAKE_CASE           =      idalabel
										SCREAMING_SNAKE_CASE           =      {v: k for k, v in idalabel.items()}
										SCREAMING_SNAKE_CASE           =      6_4_0
										SCREAMING_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:
										SCREAMING_SNAKE_CASE           =      1_0_2_4
										SCREAMING_SNAKE_CASE           =      4_0_9_6
										SCREAMING_SNAKE_CASE           =      2_4
										SCREAMING_SNAKE_CASE           =      1_6
										if "ade20k" in checkpoint_url:
															SCREAMING_SNAKE_CASE           =      6_4_0
															SCREAMING_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
					SCREAMING_SNAKE_CASE           =      torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_,	map_location='cpu',	check_hash=SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

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

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

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

					SCREAMING_SNAKE_CASE           =      image_processor(images=SCREAMING_SNAKE_CASE_,	return_tensors='pt'     )
					SCREAMING_SNAKE_CASE           =      encoding['pixel_values']

					SCREAMING_SNAKE_CASE           =      model(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      outputs.logits

					# verify logits
					SCREAMING_SNAKE_CASE           =      torch.Size([1, 1_0_0_0]     )
					if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'     ):
										SCREAMING_SNAKE_CASE           =      torch.Size([1, 1_9_6, 8_1_9_2]     )
					elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'     ):
										SCREAMING_SNAKE_CASE           =      torch.Size([1, 1_9_6, 8_1_9_2]     )
					elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'     ):
										SCREAMING_SNAKE_CASE           =      torch.Size([1, 2_1_8_4_1]     )
										SCREAMING_SNAKE_CASE           =      torch.tensor([2.2_288, 2.4_671, 0.7_395]     )
										SCREAMING_SNAKE_CASE           =      2_3_9_7
					elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'     ):
										SCREAMING_SNAKE_CASE           =      torch.Size([1, 2_1_8_4_1]     )
										SCREAMING_SNAKE_CASE           =      torch.tensor([1.6_881, -0.2_787, 0.5_901]     )
										SCREAMING_SNAKE_CASE           =      2_3_9_6
					elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'     ):
										SCREAMING_SNAKE_CASE           =      torch.tensor([0.1_241, 0.0_798, -0.6_569]     )
										SCREAMING_SNAKE_CASE           =      2_8_5
					elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'     ):
										SCREAMING_SNAKE_CASE           =      torch.tensor([-1.2_385, -1.0_987, -1.0_108]     )
										SCREAMING_SNAKE_CASE           =      2_8_1
					elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'     ):
										SCREAMING_SNAKE_CASE           =      torch.tensor([-1.5_303, -0.9_484, -0.3_147]     )
										SCREAMING_SNAKE_CASE           =      7_6_1
					elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'     ):
										SCREAMING_SNAKE_CASE           =      torch.tensor([0.4_610, -0.0_928, 0.2_086]     )
										SCREAMING_SNAKE_CASE           =      7_6_1
					elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'     ):
										SCREAMING_SNAKE_CASE           =      torch.tensor([-0.4_804, 0.6_257, -0.1_837]     )
										SCREAMING_SNAKE_CASE           =      7_6_1
					elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'     ):
										SCREAMING_SNAKE_CASE           =      torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]     )
										SCREAMING_SNAKE_CASE           =      7_6_1
					elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'     ):
										SCREAMING_SNAKE_CASE           =      torch.tensor([-0.3_062, 0.7_261, 0.4_852]     )
										SCREAMING_SNAKE_CASE           =      7_6_1
					elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'     ):
										SCREAMING_SNAKE_CASE           =      (1, 1_5_0, 1_6_0, 1_6_0)
										SCREAMING_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'     ):
										SCREAMING_SNAKE_CASE           =      (1, 1_5_0, 1_6_0, 1_6_0)
										SCREAMING_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],	SCREAMING_SNAKE_CASE_,	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],	SCREAMING_SNAKE_CASE_,	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(SCREAMING_SNAKE_CASE_     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_     )
					print(f'''Saving model to {pytorch_dump_folder_path}'''     )
					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__":
	snake_case					  =						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.'
	)
	snake_case					  =						parser.parse_args()
	convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


"""simple docstring"""




from itertools import permutations

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_ = 5     ):
					if gon_side < 3 or gon_side > 5:
										raise ValueError('gon_side must be in the range [3, 5]'     )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 1,	0,	-1     )     )
					SCREAMING_SNAKE_CASE           =      list(range(gon_side + 2,	gon_side * 2 + 1     )     )

					for perm in permutations(small_numbers + big_numbers     ):
										SCREAMING_SNAKE_CASE           =      generate_gon_ring(SCREAMING_SNAKE_CASE_,	list(SCREAMING_SNAKE_CASE_     )     )
										if is_magic_gon(SCREAMING_SNAKE_CASE_     ):
															return int(''.join(str(SCREAMING_SNAKE_CASE_     ) for n in numbers     )     )

					SCREAMING_SNAKE_CASE           =      f'''Magic {gon_side}-gon ring is impossible'''
					raise ValueError(SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [0] * (gon_side * 3)
					SCREAMING_SNAKE_CASE           =      perm[0:3]
					perm.append(perm[1]     )

					SCREAMING_SNAKE_CASE           =      1 if gon_side < 5 else 2

					for i in range(1,	len(SCREAMING_SNAKE_CASE_     ) // 3 + magic_number     ):
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 1]
										SCREAMING_SNAKE_CASE           =      result[3 * i - 1]
										SCREAMING_SNAKE_CASE           =      perm[2 * i + 2]

					return result

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if len(SCREAMING_SNAKE_CASE_     ) % 3 != 0:
										raise ValueError('a gon ring should have a length that is a multiple of 3'     )

					if min(numbers[::3]     ) != numbers[0]:
										return False

					SCREAMING_SNAKE_CASE           =      sum(numbers[:3]     )

					return all(sum(numbers[i : i + 3]     ) == total for i in range(3,	len(SCREAMING_SNAKE_CASE_     ),	3     )     )


if __name__ == "__main__":
	print(solution())
