



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







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[int]					     =					"""backbone.""" if is_semantic else """"""

						lowerCAmelCase	:   Tuple					     =					[]
						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  ):


						'''simple docstring'''




						for i in range(config.num_hidden_layers  ):
												lowerCAmelCase	:   List[str]					     =					"""backbone.""" if is_semantic else """"""
												# queries, keys and values
												lowerCAmelCase	:   List[Any]					     =					state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""  )
												lowerCAmelCase	:   Any					     =					state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""  )
												lowerCAmelCase	:   Any					     =					state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""  )

												lowerCAmelCase	:   int					     =					in_proj_weight[
												    : config.hidden_size, :
												]
												lowerCAmelCase	:   Optional[int]					     =					q_bias
												lowerCAmelCase	:   str					     =					in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												lowerCAmelCase	:   int					     =					in_proj_weight[
												    -config.hidden_size :, :
												]
												lowerCAmelCase	:   List[str]					     =					v_bias

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

												lowerCAmelCase	:   int					     =					gamma_a
												lowerCAmelCase	:   List[Any]					     =					gamma_a

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

																		lowerCAmelCase	:   Optional[int]					     =					table
																		lowerCAmelCase	:   Optional[int]					     =					index







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[Any]					     =					dct.pop(lowercase__  )
						lowerCAmelCase	:   Tuple					     =					val







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowerCAmelCase	:   Any					     =					Image.open(requests.get(lowercase__			,stream=lowercase__  ).raw  )
						return im







@torch.no_grad()
def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[Any]					     =					BeitConfig()
						lowerCAmelCase	:   List[Any]					     =					False
						lowerCAmelCase	:   List[str]					     =					False
						lowerCAmelCase	:   Optional[int]					     =					"""huggingface/label-files"""
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												lowerCAmelCase	:   Any					     =					True
												lowerCAmelCase	:   str					     =					True
												lowerCAmelCase	:   str					     =					True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												lowerCAmelCase	:   Union[str, Any]					     =					True
												lowerCAmelCase	:   str					     =					2_1_8_4_1
												lowerCAmelCase	:   Tuple					     =					"""imagenet-22k-id2label.json"""
												lowerCAmelCase	:   List[str]					     =					json.load(open(hf_hub_download(lowercase__			,lowercase__			,repo_type="""dataset"""  )			,"""r"""  )  )
												lowerCAmelCase	:   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[9_2_0_5]
												del idalabel[1_5_0_2_7]
												lowerCAmelCase	:   Optional[Any]					     =					idalabel
												lowerCAmelCase	:   int					     =					{v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												lowerCAmelCase	:   Optional[Any]					     =					True
												lowerCAmelCase	:   List[Any]					     =					1_0_0_0
												lowerCAmelCase	:   Optional[int]					     =					"""imagenet-1k-id2label.json"""
												lowerCAmelCase	:   Dict					     =					json.load(open(hf_hub_download(lowercase__			,lowercase__			,repo_type="""dataset"""  )			,"""r"""  )  )
												lowerCAmelCase	:   str					     =					{int(lowercase__  ): v for k, v in idalabel.items()}
												lowerCAmelCase	:   Optional[int]					     =					idalabel
												lowerCAmelCase	:   List[str]					     =					{v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		lowerCAmelCase	:   int					     =					3_8_4
												if "512" in checkpoint_url:
																		lowerCAmelCase	:   str					     =					5_1_2
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												lowerCAmelCase	:   Union[str, Any]					     =					True
												lowerCAmelCase	:   Any					     =					1_5_0
												lowerCAmelCase	:   Optional[Any]					     =					"""ade20k-id2label.json"""
												lowerCAmelCase	:   Union[str, Any]					     =					json.load(open(hf_hub_download(lowercase__			,lowercase__			,repo_type="""dataset"""  )			,"""r"""  )  )
												lowerCAmelCase	:   Dict					     =					{int(lowercase__  ): v for k, v in idalabel.items()}
												lowerCAmelCase	:   Optional[int]					     =					idalabel
												lowerCAmelCase	:   List[Any]					     =					{v: k for k, v in idalabel.items()}
												lowerCAmelCase	:   int					     =					6_4_0
												lowerCAmelCase	:   Dict					     =					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:
												lowerCAmelCase	:   Tuple					     =					1_0_2_4
												lowerCAmelCase	:   Union[str, Any]					     =					4_0_9_6
												lowerCAmelCase	:   Optional[Any]					     =					2_4
												lowerCAmelCase	:   List[Any]					     =					1_6
												if "ade20k" in checkpoint_url:
																		lowerCAmelCase	:   Tuple					     =					6_4_0
																		lowerCAmelCase	:   List[Any]					     =					[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
						lowerCAmelCase	:   Tuple					     =					torch.hub.load_state_dict_from_url(lowercase__			,map_location="""cpu"""			,check_hash=lowercase__  )
						lowerCAmelCase	:   Dict					     =					state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

						lowerCAmelCase	:   Any					     =					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():
																		lowerCAmelCase	:   Dict					     =					state_dict.pop(lowercase__  )
																		if key.startswith("""backbone.fpn"""  ):
																								lowerCAmelCase	:   Dict					     =					key.replace("""backbone.fpn"""			,"""fpn"""  )
																		lowerCAmelCase	:   int					     =					val

																		 # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												lowerCAmelCase	:   Optional[Any]					     =					BeitForMaskedImageModeling(lowercase__  )
						elif "ade20k" in checkpoint_url:
												lowerCAmelCase	:   Tuple					     =					BeitForSemanticSegmentation(lowercase__  )
						else:
												lowerCAmelCase	:   Dict					     =					BeitForImageClassification(lowercase__  )
						model.eval()
						model.load_state_dict(lowercase__  )

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

						lowerCAmelCase	:   str					     =					image_processor(images=lowercase__			,return_tensors="""pt"""  )
						lowerCAmelCase	:   Optional[int]					     =					encoding["""pixel_values"""]

						lowerCAmelCase	:   Union[str, Any]					     =					model(lowercase__  )
						lowerCAmelCase	:   Optional[Any]					     =					outputs.logits

						# verify logits
						lowerCAmelCase	:   Tuple					     =					torch.Size([1, 1_0_0_0]  )
						if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""  ):
												lowerCAmelCase	:   List[Any]					     =					torch.Size([1, 1_9_6, 8_1_9_2]  )
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""  ):
												lowerCAmelCase	:   str					     =					torch.Size([1, 1_9_6, 8_1_9_2]  )
						elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""  ):
												lowerCAmelCase	:   Tuple					     =					torch.Size([1, 2_1_8_4_1]  )
												lowerCAmelCase	:   Union[str, Any]					     =					torch.tensor([2.2288, 2.4671, 0.7395]  )
												lowerCAmelCase	:   Optional[Any]					     =					2_3_9_7
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""  ):
												lowerCAmelCase	:   str					     =					torch.Size([1, 2_1_8_4_1]  )
												lowerCAmelCase	:   Any					     =					torch.tensor([1.6881, -0.2787, 0.5901]  )
												lowerCAmelCase	:   List[Any]					     =					2_3_9_6
						elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""  ):
												lowerCAmelCase	:   Optional[Any]					     =					torch.tensor([0.1241, 0.0798, -0.6569]  )
												lowerCAmelCase	:   Tuple					     =					2_8_5
						elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""  ):
												lowerCAmelCase	:   Any					     =					torch.tensor([-1.2385, -1.0987, -1.0108]  )
												lowerCAmelCase	:   Tuple					     =					2_8_1
						elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""  ):
												lowerCAmelCase	:   Optional[int]					     =					torch.tensor([-1.5303, -0.9484, -0.3147]  )
												lowerCAmelCase	:   List[Any]					     =					7_6_1
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""  ):
												lowerCAmelCase	:   Optional[int]					     =					torch.tensor([0.4610, -0.0928, 0.2086]  )
												lowerCAmelCase	:   Optional[Any]					     =					7_6_1
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""  ):
												lowerCAmelCase	:   Dict					     =					torch.tensor([-0.4804, 0.6257, -0.1837]  )
												lowerCAmelCase	:   Optional[int]					     =					7_6_1
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""  ):
												lowerCAmelCase	:   List[str]					     =					torch.tensor([[-0.5122, 0.5117, -0.2113]]  )
												lowerCAmelCase	:   str					     =					7_6_1
						elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""  ):
												lowerCAmelCase	:   Tuple					     =					torch.tensor([-0.3062, 0.7261, 0.4852]  )
												lowerCAmelCase	:   Optional[int]					     =					7_6_1
						elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""  ):
												lowerCAmelCase	:   Union[str, Any]					     =					(1, 1_5_0, 1_6_0, 1_6_0)
												lowerCAmelCase	:   List[str]					     =					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"""  ):
												lowerCAmelCase	:   Optional[Any]					     =					(1, 1_5_0, 1_6_0, 1_6_0)
												lowerCAmelCase	:   List[Any]					     =					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__":
		lowerCAmelCase					:				Optional[Any]   	=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					:				int   	=parser.parse_args()
		convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









'''simple docstring'''





def  lowerCamelCase_  (     lowercase__):
 return int(lowercase__) if x - int(lowercase__) <= 0 else int(lowercase__) + 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()

