





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





def        a_				(							_lowercase       ,						_lowercase=False       ,						_lowercase=False   ):
						_UpperCamelCase			:					int     =    '''backbone.''' if is_semantic else ''''''

						_UpperCamelCase			:					str     =    []
						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        a_				(							_lowercase       ,						_lowercase       ,						_lowercase=False       ,						_lowercase=False   ):
						for i in range(config.num_hidden_layers   ):
												_UpperCamelCase			:					int     =    '''backbone.''' if is_semantic else ''''''
												# queries, keys and values
												_UpperCamelCase			:					Tuple     =    state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""   )
												_UpperCamelCase			:					Optional[int]     =    state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""   )
												_UpperCamelCase			:					int     =    state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""   )

												_UpperCamelCase			:					Dict     =    in_proj_weight[
												    : config.hidden_size, :
												]
												_UpperCamelCase			:					Tuple     =    q_bias
												_UpperCamelCase			:					List[Any]     =    in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												_UpperCamelCase			:					List[str]     =    in_proj_weight[
												    -config.hidden_size :, :
												]
												_UpperCamelCase			:					List[Any]     =    v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												_UpperCamelCase			:					List[Any]     =    state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""   )
												_UpperCamelCase			:					str     =    state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""   )

												_UpperCamelCase			:					Union[str, Any]     =    gamma_a
												_UpperCamelCase			:					List[str]     =    gamma_a

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

																		_UpperCamelCase			:					str     =    table
																		_UpperCamelCase			:					Optional[Any]     =    index





def        a_				(							_lowercase       ,						_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Optional[int]     =    dct.pop(_lowercase   )
						_UpperCamelCase			:					List[str]     =    val





def        a_				(							):
						_UpperCamelCase			:					Dict     =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						_UpperCamelCase			:					Dict     =    Image.open(requests.get(_lowercase       ,						stream=_lowercase   ).raw   )
						return im





@torch.no_grad()
def        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					int     =    BeitConfig()
						_UpperCamelCase			:					Optional[Any]     =    False
						_UpperCamelCase			:					Union[str, Any]     =    False
						_UpperCamelCase			:					str     =    '''huggingface/label-files'''
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												_UpperCamelCase			:					Any     =    True
												_UpperCamelCase			:					List[Any]     =    True
												_UpperCamelCase			:					Any     =    True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												_UpperCamelCase			:					List[Any]     =    True
												_UpperCamelCase			:					Optional[int]     =    2_1841
												_UpperCamelCase			:					int     =    '''imagenet-22k-id2label.json'''
												_UpperCamelCase			:					str     =    json.load(open(hf_hub_download(_lowercase       ,						_lowercase       ,						repo_type='''dataset'''   )       ,						'''r'''   )   )
												_UpperCamelCase			:					List[Any]     =    {int(_lowercase   ): 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[9205]
												del idalabel[1_5027]
												_UpperCamelCase			:					Optional[Any]     =    idalabel
												_UpperCamelCase			:					Union[str, Any]     =    {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												_UpperCamelCase			:					Union[str, Any]     =    True
												_UpperCamelCase			:					int     =    1000
												_UpperCamelCase			:					Optional[int]     =    '''imagenet-1k-id2label.json'''
												_UpperCamelCase			:					List[str]     =    json.load(open(hf_hub_download(_lowercase       ,						_lowercase       ,						repo_type='''dataset'''   )       ,						'''r'''   )   )
												_UpperCamelCase			:					int     =    {int(_lowercase   ): v for k, v in idalabel.items()}
												_UpperCamelCase			:					str     =    idalabel
												_UpperCamelCase			:					Optional[Any]     =    {v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		_UpperCamelCase			:					int     =    384
												if "512" in checkpoint_url:
																		_UpperCamelCase			:					Any     =    512
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												_UpperCamelCase			:					int     =    True
												_UpperCamelCase			:					Optional[Any]     =    150
												_UpperCamelCase			:					Dict     =    '''ade20k-id2label.json'''
												_UpperCamelCase			:					str     =    json.load(open(hf_hub_download(_lowercase       ,						_lowercase       ,						repo_type='''dataset'''   )       ,						'''r'''   )   )
												_UpperCamelCase			:					Union[str, Any]     =    {int(_lowercase   ): v for k, v in idalabel.items()}
												_UpperCamelCase			:					List[str]     =    idalabel
												_UpperCamelCase			:					Optional[Any]     =    {v: k for k, v in idalabel.items()}
												_UpperCamelCase			:					Optional[Any]     =    640
												_UpperCamelCase			:					Union[str, Any]     =    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:
												_UpperCamelCase			:					List[Any]     =    1024
												_UpperCamelCase			:					str     =    4096
												_UpperCamelCase			:					Dict     =    24
												_UpperCamelCase			:					Any     =    16
												if "ade20k" in checkpoint_url:
																		_UpperCamelCase			:					List[str]     =    640
																		_UpperCamelCase			:					List[Any]     =    [7, 11, 15, 23]
						else:
												raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''   )

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

						_UpperCamelCase			:					int     =    create_rename_keys(_lowercase       ,						has_lm_head=_lowercase       ,						is_semantic=_lowercase   )
						for src, dest in rename_keys:
												rename_key(_lowercase       ,						_lowercase       ,						_lowercase   )
						read_in_q_k_v(_lowercase       ,						_lowercase       ,						has_lm_head=_lowercase       ,						is_semantic=_lowercase   )
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		_UpperCamelCase			:					List[str]     =    state_dict.pop(_lowercase   )
																		if key.startswith('''backbone.fpn'''   ):
																								_UpperCamelCase			:					Dict     =    key.replace('''backbone.fpn'''       ,						'''fpn'''   )
																		_UpperCamelCase			:					Dict     =    val

    # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												_UpperCamelCase			:					List[str]     =    BeitForMaskedImageModeling(_lowercase   )
						elif "ade20k" in checkpoint_url:
												_UpperCamelCase			:					Tuple     =    BeitForSemanticSegmentation(_lowercase   )
						else:
												_UpperCamelCase			:					Tuple     =    BeitForImageClassification(_lowercase   )
						model.eval()
						model.load_state_dict(_lowercase   )

						# Check outputs on an image
						if is_semantic:
												_UpperCamelCase			:					Dict     =    BeitImageProcessor(size=config.image_size       ,						do_center_crop=_lowercase   )
												_UpperCamelCase			:					Tuple     =    load_dataset('''hf-internal-testing/fixtures_ade20k'''       ,						split='''test'''   )
												_UpperCamelCase			:					Union[str, Any]     =    Image.open(ds[0]['''file''']   )
						else:
												_UpperCamelCase			:					Optional[int]     =    BeitImageProcessor(
												    size=config.image_size       ,						resample=PILImageResampling.BILINEAR       ,						do_center_crop=_lowercase   )
												_UpperCamelCase			:					List[str]     =    prepare_img()

						_UpperCamelCase			:					str     =    image_processor(images=_lowercase       ,						return_tensors='''pt'''   )
						_UpperCamelCase			:					Any     =    encoding['''pixel_values''']

						_UpperCamelCase			:					Any     =    model(_lowercase   )
						_UpperCamelCase			:					Any     =    outputs.logits

						# verify logits
						_UpperCamelCase			:					Any     =    torch.Size([1, 1000]   )
						if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''   ):
												_UpperCamelCase			:					int     =    torch.Size([1, 196, 8192]   )
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''   ):
												_UpperCamelCase			:					str     =    torch.Size([1, 196, 8192]   )
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''   ):
												_UpperCamelCase			:					int     =    torch.Size([1, 2_1841]   )
												_UpperCamelCase			:					Any     =    torch.tensor([2.2288, 2.4671, 0.7395]   )
												_UpperCamelCase			:					Union[str, Any]     =    2397
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''   ):
												_UpperCamelCase			:					Dict     =    torch.Size([1, 2_1841]   )
												_UpperCamelCase			:					str     =    torch.tensor([1.6881, -0.2787, 0.5901]   )
												_UpperCamelCase			:					Any     =    2396
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''   ):
												_UpperCamelCase			:					Optional[Any]     =    torch.tensor([0.1241, 0.0798, -0.6569]   )
												_UpperCamelCase			:					Optional[int]     =    285
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''   ):
												_UpperCamelCase			:					List[str]     =    torch.tensor([-1.2385, -1.0987, -1.0108]   )
												_UpperCamelCase			:					Optional[Any]     =    281
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''   ):
												_UpperCamelCase			:					List[Any]     =    torch.tensor([-1.5303, -0.9484, -0.3147]   )
												_UpperCamelCase			:					Tuple     =    761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''   ):
												_UpperCamelCase			:					List[str]     =    torch.tensor([0.4610, -0.0928, 0.2086]   )
												_UpperCamelCase			:					Optional[Any]     =    761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''   ):
												_UpperCamelCase			:					Tuple     =    torch.tensor([-0.4804, 0.6257, -0.1837]   )
												_UpperCamelCase			:					Dict     =    761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''   ):
												_UpperCamelCase			:					Optional[int]     =    torch.tensor([[-0.5122, 0.5117, -0.2113]]   )
												_UpperCamelCase			:					List[Any]     =    761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''   ):
												_UpperCamelCase			:					Optional[int]     =    torch.tensor([-0.3062, 0.7261, 0.4852]   )
												_UpperCamelCase			:					Optional[int]     =    761
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''   ):
												_UpperCamelCase			:					Optional[int]     =    (1, 150, 160, 160)
												_UpperCamelCase			:					Union[str, Any]     =    torch.tensor(
												    [
												        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
												        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
												        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
												    ]   )
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''   ):
												_UpperCamelCase			:					List[str]     =    (1, 150, 160, 160)
												_UpperCamelCase			:					Tuple     =    torch.tensor(
												    [
												        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
												        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
												        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
												    ]   )
						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]       ,						_lowercase       ,						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]       ,						_lowercase       ,						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(_lowercase   ).mkdir(exist_ok=_lowercase   )
						print(F"""Saving model to {pytorch_dump_folder_path}"""   )
						model.save_pretrained(_lowercase   )
						print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
						image_processor.save_pretrained(_lowercase   )


if __name__ == "__main__":
						UpperCamelCase_           =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."""
						)
						UpperCamelCase_           =parser.parse_args()
						convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




def 							lowerCAmelCase(	__lowerCamelCase = 10**6						):

					if n <= 0:
										raise ValueError('Please enter an integer greater than 0'						)

					__a							=   list(range(n + 1						)						)
					for number in range(2      ,				n + 1						):
										if phi[number] == number:
															phi[number] -= 1
															for multiple in range(number * 2      ,				n + 1      ,				__lowerCamelCase						):
																				__a							=   (phi[multiple] // number) * (number - 1)

					__a							=   1
					for number in range(1      ,				n + 1						):
										if (answer / phi[answer]) < (number / phi[number]):
															__a							=   number

					return answer


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




