
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()
SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				str   ,							SCREAMING_SNAKE_CASE	:				Optional[int]=False   ,							SCREAMING_SNAKE_CASE	:				Union[str, Any]=False      )					->						Any:
				__lowercase								= 'backbone.' if is_semantic else ''

				__lowercase								= []
				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 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Union[str, Any]   ,							SCREAMING_SNAKE_CASE	:				List[str]   ,							SCREAMING_SNAKE_CASE	:				Optional[int]=False   ,							SCREAMING_SNAKE_CASE	:				int=False      )					->						List[str]:
				for i in range(config.num_hidden_layers      ):
								__lowercase								= 'backbone.' if is_semantic else ''
								# queries, keys and values
								__lowercase								= state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""      )
								__lowercase								= state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""      )
								__lowercase								= state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""      )

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

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

								__lowercase								= gamma_a
								__lowercase								= gamma_a

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

												__lowercase								= table
												__lowercase								= index




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Any   ,							SCREAMING_SNAKE_CASE	:				List[str]   ,							SCREAMING_SNAKE_CASE	:				Optional[int]      )					->						Any:
				__lowercase								= dct.pop(SCREAMING_SNAKE_CASE      )
				__lowercase								= val




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




@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				Optional[Any]   ,							SCREAMING_SNAKE_CASE	:				List[Any]      )					->						Union[str, Any]:
				__lowercase								= BeitConfig()
				__lowercase								= False
				__lowercase								= False
				__lowercase								= 'huggingface/label-files'
				# set config parameters based on URL
				if checkpoint_url[-9:-4] == "pt22k":
								# masked image modeling
								__lowercase								= True
								__lowercase								= True
								__lowercase								= True
				elif checkpoint_url[-9:-4] == "ft22k":
								# intermediate fine-tuning on ImageNet-22k
								__lowercase								= True
								__lowercase								= 21841
								__lowercase								= 'imagenet-22k-id2label.json'
								__lowercase								= json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							repo_type='dataset'      )   ,							'r'      )      )
								__lowercase								= {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[9205]
								del idalabel[15027]
								__lowercase								= idalabel
								__lowercase								= {v: k for k, v in idalabel.items()}
				elif checkpoint_url[-8:-4] == "to1k":
								# fine-tuning on ImageNet-1k
								__lowercase								= True
								__lowercase								= 1000
								__lowercase								= 'imagenet-1k-id2label.json'
								__lowercase								= json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							repo_type='dataset'      )   ,							'r'      )      )
								__lowercase								= {int(SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
								__lowercase								= idalabel
								__lowercase								= {v: k for k, v in idalabel.items()}
								if "384" in checkpoint_url:
												__lowercase								= 384
								if "512" in checkpoint_url:
												__lowercase								= 512
				elif "ade20k" in checkpoint_url:
								# fine-tuning
								__lowercase								= True
								__lowercase								= 150
								__lowercase								= 'ade20k-id2label.json'
								__lowercase								= json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE   ,							repo_type='dataset'      )   ,							'r'      )      )
								__lowercase								= {int(SCREAMING_SNAKE_CASE      ): v for k, v in idalabel.items()}
								__lowercase								= idalabel
								__lowercase								= {v: k for k, v in idalabel.items()}
								__lowercase								= 640
								__lowercase								= 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:
								__lowercase								= 1024
								__lowercase								= 4096
								__lowercase								= 24
								__lowercase								= 16
								if "ade20k" in checkpoint_url:
												__lowercase								= 640
												__lowercase								= [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
				__lowercase								= torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE   ,							map_location='cpu'   ,							check_hash=SCREAMING_SNAKE_CASE      )
				__lowercase								= state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

				__lowercase								= 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():
												__lowercase								= state_dict.pop(SCREAMING_SNAKE_CASE      )
												if key.startswith('backbone.fpn'      ):
																__lowercase								= key.replace('backbone.fpn'   ,							'fpn'      )
												__lowercase								= val

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

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

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

				__lowercase								= model(SCREAMING_SNAKE_CASE      )
				__lowercase								= outputs.logits

				# verify logits
				__lowercase								= torch.Size([1, 1000]      )
				if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'      ):
								__lowercase								= torch.Size([1, 196, 8192]      )
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'      ):
								__lowercase								= torch.Size([1, 196, 8192]      )
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'      ):
								__lowercase								= torch.Size([1, 21841]      )
								__lowercase								= torch.tensor([2.2_288, 2.4_671, 0.7_395]      )
								__lowercase								= 2397
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'      ):
								__lowercase								= torch.Size([1, 21841]      )
								__lowercase								= torch.tensor([1.6_881, -0.2_787, 0.5_901]      )
								__lowercase								= 2396
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'      ):
								__lowercase								= torch.tensor([0.1_241, 0.0_798, -0.6_569]      )
								__lowercase								= 285
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'      ):
								__lowercase								= torch.tensor([-1.2_385, -1.0_987, -1.0_108]      )
								__lowercase								= 281
				elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'      ):
								__lowercase								= torch.tensor([-1.5_303, -0.9_484, -0.3_147]      )
								__lowercase								= 761
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'      ):
								__lowercase								= torch.tensor([0.4_610, -0.0_928, 0.2_086]      )
								__lowercase								= 761
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'      ):
								__lowercase								= torch.tensor([-0.4_804, 0.6_257, -0.1_837]      )
								__lowercase								= 761
				elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'      ):
								__lowercase								= torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]      )
								__lowercase								= 761
				elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'      ):
								__lowercase								= torch.tensor([-0.3_062, 0.7_261, 0.4_852]      )
								__lowercase								= 761
				elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'      ):
								__lowercase								= (1, 150, 160, 160)
								__lowercase								= 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'      ):
								__lowercase								= (1, 150, 160, 160)
								__lowercase								= 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__":
							SCREAMING_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."""
							)
							SCREAMING_SNAKE_CASE__							=    parser.parse_args()
							convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







from __future__ import annotations

from enum import Enum
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Dict         =							24
							lowerCAmelCase__		:						Dict         =							21
							lowerCAmelCase__		:						Union[str, Any]         =							18
							lowerCAmelCase__		:						Dict         =							15
							lowerCAmelCase__		:						Any         =							12
							lowerCAmelCase__		:						Union[str, Any]         =							9
							lowerCAmelCase__		:						Any         =							6
							lowerCAmelCase__		:						Optional[Any]         =							3
							lowerCAmelCase__		:						Union[str, Any]         =							2
							lowerCAmelCase__		:						List[Any]         =							1
							lowerCAmelCase__		:						Optional[Any]         =							-1
							lowerCAmelCase__		:						Tuple         =							-2
							lowerCAmelCase__		:						Dict         =							-3
							lowerCAmelCase__		:						Union[str, Any]         =							-6
							lowerCAmelCase__		:						Any         =							-9
							lowerCAmelCase__		:						Union[str, Any]         =							-12
							lowerCAmelCase__		:						str         =							-15
							lowerCAmelCase__		:						Optional[int]         =							-18
							lowerCAmelCase__		:						Any         =							-21
							lowerCAmelCase__		:						Any         =							-24






class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							8
							lowerCAmelCase__		:						Union[str, Any]         =							7
							lowerCAmelCase__		:						Optional[Any]         =							6
							lowerCAmelCase__		:						Tuple         =							5
							lowerCAmelCase__		:						Optional[int]         =							4
							lowerCAmelCase__		:						Optional[int]         =							3
							lowerCAmelCase__		:						Optional[int]         =							2
							lowerCAmelCase__		:						Any         =							1






def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float   ,							SCREAMING_SNAKE_CASE	:				str | SIUnit   ,							SCREAMING_SNAKE_CASE	:				str | SIUnit   ,							)					->						float:
				if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								__lowercase								= SIUnit[known_prefix.lower()]
				if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								__lowercase								= SIUnit[unknown_prefix.lower()]
				__lowercase								= known_amount * (
				    10 ** (known_prefix.value - unknown_prefix.value)
				)
				return unknown_amount




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float   ,							SCREAMING_SNAKE_CASE	:				str | BinaryUnit   ,							SCREAMING_SNAKE_CASE	:				str | BinaryUnit   ,							)					->						float:
				if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								__lowercase								= BinaryUnit[known_prefix.lower()]
				if isinstance(SCREAMING_SNAKE_CASE   ,							SCREAMING_SNAKE_CASE      ):
								__lowercase								= BinaryUnit[unknown_prefix.lower()]
				__lowercase								= known_amount * (
				    2 ** ((known_prefix.value - unknown_prefix.value) * 10)
				)
				return unknown_amount


if __name__ == "__main__":
							import doctest

							doctest.testmod()





