



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


def 			snake_case_					( _lowerCAmelCase  :						str       ,				_lowerCAmelCase  :						Optional[int]=False       ,				_lowerCAmelCase  :						Union[str, Any]=False  )				->       Any:
							UpperCAmelCase			:	Tuple										=		'''backbone.''' if is_semantic else ''''''

							UpperCAmelCase			:	Any										=		[]
							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 			snake_case_					( _lowerCAmelCase  :						Union[str, Any]       ,				_lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						Optional[int]=False       ,				_lowerCAmelCase  :						int=False  )				->       List[str]:
							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			:	List[str]										=		state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""  )
														UpperCAmelCase			:	Optional[Any]										=		state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""  )

														UpperCAmelCase			:	Any										=		in_proj_weight[
														    : config.hidden_size, :
														]
														UpperCAmelCase			:	str										=		q_bias
														UpperCAmelCase			:	Union[str, Any]										=		in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCAmelCase			:	List[str]										=		in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCAmelCase			:	int										=		v_bias

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

														UpperCAmelCase			:	List[str]										=		gamma_a
														UpperCAmelCase			:	Optional[int]										=		gamma_a

														# relative_position bias table + index
														if not has_lm_head:
																					# each layer has its own relative position bias
																					UpperCAmelCase			:	Union[str, Any]										=		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			:	List[str]										=		table
																					UpperCAmelCase			:	Optional[int]										=		index


def 			snake_case_					( _lowerCAmelCase  :						Any       ,				_lowerCAmelCase  :						List[str]       ,				_lowerCAmelCase  :						Optional[int]  )				->       Any:
							UpperCAmelCase			:	List[str]										=		dct.pop(_lowerCAmelCase  )
							UpperCAmelCase			:	Optional[int]										=		val


def 			snake_case_					( )				->       Union[str, Any]:
							UpperCAmelCase			:	Any										=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase			:	List[str]										=		Image.open(requests.get(_lowerCAmelCase       ,				stream=_lowerCAmelCase  ).raw  )
							return im


@torch.no_grad()
def 			snake_case_					( _lowerCAmelCase  :						Optional[Any]       ,				_lowerCAmelCase  :						List[Any]  )				->       Union[str, Any]:
							UpperCAmelCase			:	Dict										=		BeitConfig()
							UpperCAmelCase			:	int										=		False
							UpperCAmelCase			:	int										=		False
							UpperCAmelCase			:	Optional[Any]										=		'''huggingface/label-files'''
							# set config parameters based on URL
							if checkpoint_url[-9:-4] == "pt22k":
														# masked image modeling
														UpperCAmelCase			:	Union[str, Any]										=		True
														UpperCAmelCase			:	List[str]										=		True
														UpperCAmelCase			:	List[str]										=		True
							elif checkpoint_url[-9:-4] == "ft22k":
														# intermediate fine-tuning on ImageNet-22k
														UpperCAmelCase			:	Optional[Any]										=		True
														UpperCAmelCase			:	Union[str, Any]										=		21841
														UpperCAmelCase			:	int										=		'''imagenet-22k-id2label.json'''
														UpperCAmelCase			:	Optional[Any]										=		json.load(open(hf_hub_download(_lowerCAmelCase       ,				_lowerCAmelCase       ,				repo_type='''dataset'''  )       ,				'''r'''  )  )
														UpperCAmelCase			:	Optional[Any]										=		{int(_lowerCAmelCase  ): 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[15027]
														UpperCAmelCase			:	Union[str, Any]										=		idalabel
														UpperCAmelCase			:	Any										=		{v: k for k, v in idalabel.items()}
							elif checkpoint_url[-8:-4] == "to1k":
														# fine-tuning on ImageNet-1k
														UpperCAmelCase			:	Tuple										=		True
														UpperCAmelCase			:	Any										=		1000
														UpperCAmelCase			:	Union[str, Any]										=		'''imagenet-1k-id2label.json'''
														UpperCAmelCase			:	Optional[int]										=		json.load(open(hf_hub_download(_lowerCAmelCase       ,				_lowerCAmelCase       ,				repo_type='''dataset'''  )       ,				'''r'''  )  )
														UpperCAmelCase			:	Tuple										=		{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
														UpperCAmelCase			:	List[Any]										=		idalabel
														UpperCAmelCase			:	Dict										=		{v: k for k, v in idalabel.items()}
														if "384" in checkpoint_url:
																					UpperCAmelCase			:	Dict										=		384
														if "512" in checkpoint_url:
																					UpperCAmelCase			:	int										=		512
							elif "ade20k" in checkpoint_url:
														# fine-tuning
														UpperCAmelCase			:	str										=		True
														UpperCAmelCase			:	List[str]										=		150
														UpperCAmelCase			:	Optional[int]										=		'''ade20k-id2label.json'''
														UpperCAmelCase			:	List[Any]										=		json.load(open(hf_hub_download(_lowerCAmelCase       ,				_lowerCAmelCase       ,				repo_type='''dataset'''  )       ,				'''r'''  )  )
														UpperCAmelCase			:	Any										=		{int(_lowerCAmelCase  ): v for k, v in idalabel.items()}
														UpperCAmelCase			:	Any										=		idalabel
														UpperCAmelCase			:	Tuple										=		{v: k for k, v in idalabel.items()}
														UpperCAmelCase			:	Optional[int]										=		640
														UpperCAmelCase			:	Tuple										=		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			:	int										=		1024
														UpperCAmelCase			:	List[Any]										=		4096
														UpperCAmelCase			:	List[Any]										=		24
														UpperCAmelCase			:	int										=		16
														if "ade20k" in checkpoint_url:
																					UpperCAmelCase			:	List[str]										=		640
																					UpperCAmelCase			:	Optional[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			:	int										=		torch.hub.load_state_dict_from_url(_lowerCAmelCase       ,				map_location='''cpu'''       ,				check_hash=_lowerCAmelCase  )
							UpperCAmelCase			:	Optional[int]										=		state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

							UpperCAmelCase			:	Dict										=		create_rename_keys(_lowerCAmelCase       ,				has_lm_head=_lowerCAmelCase       ,				is_semantic=_lowerCAmelCase  )
							for src, dest in rename_keys:
														rename_key(_lowerCAmelCase       ,				_lowerCAmelCase       ,				_lowerCAmelCase  )
							read_in_q_k_v(_lowerCAmelCase       ,				_lowerCAmelCase       ,				has_lm_head=_lowerCAmelCase       ,				is_semantic=_lowerCAmelCase  )
							if is_semantic:
														# add prefix to decoder keys
														for key, val in state_dict.copy().items():
																					UpperCAmelCase			:	int										=		state_dict.pop(_lowerCAmelCase  )
																					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			:	int										=		BeitForMaskedImageModeling(_lowerCAmelCase  )
							elif "ade20k" in checkpoint_url:
														UpperCAmelCase			:	List[Any]										=		BeitForSemanticSegmentation(_lowerCAmelCase  )
							else:
														UpperCAmelCase			:	str										=		BeitForImageClassification(_lowerCAmelCase  )
							model.eval()
							model.load_state_dict(_lowerCAmelCase  )

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

							UpperCAmelCase			:	str										=		image_processor(images=_lowerCAmelCase       ,				return_tensors='''pt'''  )
							UpperCAmelCase			:	Union[str, Any]										=		encoding['''pixel_values''']

							UpperCAmelCase			:	Dict										=		model(_lowerCAmelCase  )
							UpperCAmelCase			:	str										=		outputs.logits

							# verify logits
							UpperCAmelCase			:	Dict										=		torch.Size([1, 1000]  )
							if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''  ):
														UpperCAmelCase			:	Union[str, Any]										=		torch.Size([1, 196, 8192]  )
							elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''  ):
														UpperCAmelCase			:	List[str]										=		torch.Size([1, 196, 8192]  )
							elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''  ):
														UpperCAmelCase			:	Union[str, Any]										=		torch.Size([1, 21841]  )
														UpperCAmelCase			:	List[Any]										=		torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]  )
														UpperCAmelCase			:	List[Any]										=		2397
							elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''  ):
														UpperCAmelCase			:	Dict										=		torch.Size([1, 21841]  )
														UpperCAmelCase			:	List[str]										=		torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]  )
														UpperCAmelCase			:	Union[str, Any]										=		2396
							elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''  ):
														UpperCAmelCase			:	Any										=		torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]  )
														UpperCAmelCase			:	str										=		285
							elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''  ):
														UpperCAmelCase			:	Any										=		torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]  )
														UpperCAmelCase			:	Tuple										=		281
							elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''  ):
														UpperCAmelCase			:	Tuple										=		torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]  )
														UpperCAmelCase			:	Union[str, Any]										=		761
							elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''  ):
														UpperCAmelCase			:	List[str]										=		torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]  )
														UpperCAmelCase			:	Optional[Any]										=		761
							elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''  ):
														UpperCAmelCase			:	Optional[int]										=		torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]  )
														UpperCAmelCase			:	Optional[int]										=		761
							elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''  ):
														UpperCAmelCase			:	Optional[Any]										=		torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]  )
														UpperCAmelCase			:	Tuple										=		761
							elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''  ):
														UpperCAmelCase			:	Tuple										=		torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]  )
														UpperCAmelCase			:	Dict										=		761
							elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''  ):
														UpperCAmelCase			:	str										=		(1, 150, 160, 160)
														UpperCAmelCase			:	List[Any]										=		torch.tensor(
														    [
														        [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
														        [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
														        [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
														    ]  )
							elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''  ):
														UpperCAmelCase			:	Optional[int]										=		(1, 150, 160, 160)
														UpperCAmelCase			:	str										=		torch.tensor(
														    [
														        [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
														        [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
														        [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
														    ]  )
							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]       ,				_lowerCAmelCase       ,				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]       ,				_lowerCAmelCase       ,				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(_lowerCAmelCase  ).mkdir(exist_ok=_lowerCAmelCase  )
							print(f"""Saving model to {pytorch_dump_folder_path}"""  )
							model.save_pretrained(_lowerCAmelCase  )
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""  )
							image_processor.save_pretrained(_lowerCAmelCase  )


if __name__ == "__main__":
						UpperCamelCase__:       str							     =      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__:       Union[str, Any]							     =      parser.parse_args()
						convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










'''simple docstring'''






from __future__ import annotations


def 			snake_case_					( _lowerCAmelCase  :						list[int]       ,				_lowerCAmelCase  :						int  )				->       list[int]:
							UpperCAmelCase			:	dict[int, int]										=		{}
							for index, val in enumerate(_lowerCAmelCase  ):
														UpperCAmelCase			:	Tuple										=		target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														UpperCAmelCase			:	Any										=		index
							return []


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						print(F"{two_sum([2, 7, 11, 15], 9) = }")





