

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							:						int     			=		logging.get_logger(__name__)







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=False					):
						SCREAMING_SNAKE_CASE_:				int							= "backbone." if is_semantic else ""

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

												SCREAMING_SNAKE_CASE_:				Optional[Any]							= in_proj_weight[
												    : config.hidden_size, :
												]
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= q_bias
												SCREAMING_SNAKE_CASE_:				Tuple							= in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												SCREAMING_SNAKE_CASE_:				List[Any]							= in_proj_weight[
												    -config.hidden_size :, :
												]
												SCREAMING_SNAKE_CASE_:				int							= v_bias

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

												SCREAMING_SNAKE_CASE_:				Tuple							= gamma_a
												SCREAMING_SNAKE_CASE_:				Dict							= gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		SCREAMING_SNAKE_CASE_:				Optional[Any]							= state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table"					)
																		SCREAMING_SNAKE_CASE_:				Optional[int]							= state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"					)

																		SCREAMING_SNAKE_CASE_:				Tuple							= table
																		SCREAMING_SNAKE_CASE_:				int							= index







def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Tuple							= dct.pop(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				int							= val







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				str							= "http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE_:				Optional[int]							= Image.open(requests.get(_UpperCAmelCase       ,			stream=_UpperCAmelCase					).raw					)
						return im







@torch.no_grad()
def     A_   (					_UpperCAmelCase       ,			_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				str							= BeitConfig()
						SCREAMING_SNAKE_CASE_:				List[str]							= False
						SCREAMING_SNAKE_CASE_:				int							= False
						SCREAMING_SNAKE_CASE_:				int							= "huggingface/label-files"
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												SCREAMING_SNAKE_CASE_:				Dict							= True
												SCREAMING_SNAKE_CASE_:				str							= True
												SCREAMING_SNAKE_CASE_:				List[Any]							= True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												SCREAMING_SNAKE_CASE_:				List[Any]							= True
												SCREAMING_SNAKE_CASE_:				Any							= 2_18_41
												SCREAMING_SNAKE_CASE_:				int							= "imagenet-22k-id2label.json"
												SCREAMING_SNAKE_CASE_:				str							= json.load(open(hf_hub_download(_UpperCAmelCase       ,			_UpperCAmelCase       ,			repo_type="dataset"					)       ,			"r"					)					)
												SCREAMING_SNAKE_CASE_:				int							= {int(_UpperCAmelCase					): 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[92_05]
												del idalabel[1_50_27]
												SCREAMING_SNAKE_CASE_:				Optional[int]							= idalabel
												SCREAMING_SNAKE_CASE_:				int							= {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= True
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= 10_00
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= "imagenet-1k-id2label.json"
												SCREAMING_SNAKE_CASE_:				Dict							= json.load(open(hf_hub_download(_UpperCAmelCase       ,			_UpperCAmelCase       ,			repo_type="dataset"					)       ,			"r"					)					)
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= {int(_UpperCAmelCase					): v for k, v in idalabel.items()}
												SCREAMING_SNAKE_CASE_:				List[Any]							= idalabel
												SCREAMING_SNAKE_CASE_:				int							= {v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		SCREAMING_SNAKE_CASE_:				Optional[int]							= 3_84
												if "512" in checkpoint_url:
																		SCREAMING_SNAKE_CASE_:				Optional[Any]							= 5_12
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												SCREAMING_SNAKE_CASE_:				Optional[int]							= True
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 1_50
												SCREAMING_SNAKE_CASE_:				int							= "ade20k-id2label.json"
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= json.load(open(hf_hub_download(_UpperCAmelCase       ,			_UpperCAmelCase       ,			repo_type="dataset"					)       ,			"r"					)					)
												SCREAMING_SNAKE_CASE_:				int							= {int(_UpperCAmelCase					): v for k, v in idalabel.items()}
												SCREAMING_SNAKE_CASE_:				List[Any]							= idalabel
												SCREAMING_SNAKE_CASE_:				Dict							= {v: k for k, v in idalabel.items()}
												SCREAMING_SNAKE_CASE_:				Dict							= 6_40
												SCREAMING_SNAKE_CASE_:				List[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:
												SCREAMING_SNAKE_CASE_:				str							= 10_24
												SCREAMING_SNAKE_CASE_:				List[Any]							= 40_96
												SCREAMING_SNAKE_CASE_:				Tuple							= 24
												SCREAMING_SNAKE_CASE_:				Any							= 16
												if "ade20k" in checkpoint_url:
																		SCREAMING_SNAKE_CASE_:				str							= 6_40
																		SCREAMING_SNAKE_CASE_:				Dict							= [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
						SCREAMING_SNAKE_CASE_:				Dict							= torch.hub.load_state_dict_from_url(_UpperCAmelCase       ,			map_location="cpu"       ,			check_hash=_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

						SCREAMING_SNAKE_CASE_:				Optional[int]							= create_rename_keys(_UpperCAmelCase       ,			has_lm_head=_UpperCAmelCase       ,			is_semantic=_UpperCAmelCase					)
						for src, dest in rename_keys:
												rename_key(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase					)
						read_in_q_k_v(_UpperCAmelCase       ,			_UpperCAmelCase       ,			has_lm_head=_UpperCAmelCase       ,			is_semantic=_UpperCAmelCase					)
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		SCREAMING_SNAKE_CASE_:				List[Any]							= state_dict.pop(_UpperCAmelCase					)
																		if key.startswith("backbone.fpn"					):
																								SCREAMING_SNAKE_CASE_:				Union[str, Any]							= key.replace("backbone.fpn"       ,			"fpn"					)
																		SCREAMING_SNAKE_CASE_:				Optional[Any]							= val

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

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

						SCREAMING_SNAKE_CASE_:				Tuple							= image_processor(images=_UpperCAmelCase       ,			return_tensors="pt"					)
						SCREAMING_SNAKE_CASE_:				str							= encoding["pixel_values"]

						SCREAMING_SNAKE_CASE_:				Dict							= model(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				Tuple							= outputs.logits

						# verify logits
						SCREAMING_SNAKE_CASE_:				Optional[int]							= torch.Size([1, 10_00]					)
						if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"					):
												SCREAMING_SNAKE_CASE_:				List[str]							= torch.Size([1, 1_96, 81_92]					)
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"					):
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= torch.Size([1, 1_96, 81_92]					)
						elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"					):
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= torch.Size([1, 2_18_41]					)
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]					)
												SCREAMING_SNAKE_CASE_:				str							= 23_97
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"					):
												SCREAMING_SNAKE_CASE_:				Any							= torch.Size([1, 2_18_41]					)
												SCREAMING_SNAKE_CASE_:				List[Any]							= torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]					)
												SCREAMING_SNAKE_CASE_:				str							= 23_96
						elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"					):
												SCREAMING_SNAKE_CASE_:				List[Any]							= torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]					)
												SCREAMING_SNAKE_CASE_:				Dict							= 2_85
						elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"					):
												SCREAMING_SNAKE_CASE_:				str							= torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]					)
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= 2_81
						elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"					):
												SCREAMING_SNAKE_CASE_:				Optional[int]							= torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]					)
												SCREAMING_SNAKE_CASE_:				List[str]							= 7_61
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"					):
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]					)
												SCREAMING_SNAKE_CASE_:				Any							= 7_61
						elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"					):
												SCREAMING_SNAKE_CASE_:				Tuple							= torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]					)
												SCREAMING_SNAKE_CASE_:				int							= 7_61
						elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"					):
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]					)
												SCREAMING_SNAKE_CASE_:				Optional[int]							= 7_61
						elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"					):
												SCREAMING_SNAKE_CASE_:				Optional[int]							= torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]					)
												SCREAMING_SNAKE_CASE_:				Any							= 7_61
						elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"					):
												SCREAMING_SNAKE_CASE_:				Tuple							= (1, 1_50, 1_60, 1_60)
												SCREAMING_SNAKE_CASE_:				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"					):
												SCREAMING_SNAKE_CASE_:				Tuple							= (1, 1_50, 1_60, 1_60)
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 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]       ,			_UpperCAmelCase       ,			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]       ,			_UpperCAmelCase       ,			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(_UpperCAmelCase					).mkdir(exist_ok=_UpperCAmelCase					)
						print(f"Saving model to {pytorch_dump_folder_path}"					)
						model.save_pretrained(_UpperCAmelCase					)
						print(f"Saving image processor to {pytorch_dump_folder_path}"					)
						image_processor.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
						lowerCAmelCase							:						Tuple     			=		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							:						List[str]     			=		parser.parse_args()
						convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= []
						SCREAMING_SNAKE_CASE_:				Any							= 1

						for i in range(len(_UpperCAmelCase					)					):
												if i + 1 < len(_UpperCAmelCase					) and text[i] == text[i + 1]:
																		count += 1
												else:
																		encoded.append((text[i], count)					)
																		SCREAMING_SNAKE_CASE_:				List[Any]							= 1

						return encoded







def     A_   (					_UpperCAmelCase					):
						return "".join(char * length for char, length in encoded					)


if __name__ == "__main__":
						from doctest import testmod

						testmod(name="""run_length_encode""", verbose=True)
						testmod(name="""run_length_decode""", verbose=True)






