






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_     =			logging.get_logger(__name__)







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							int=False,    UpperCAmelCase						:							Any=False ):
				A									=   'backbone.' if is_semantic else ''

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

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

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

								A									=   gamma_a
								A									=   gamma_a

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

												A									=   table
												A									=   index







def   snake_case		(				UpperCAmelCase						:							Any,    UpperCAmelCase						:							Optional[int],    UpperCAmelCase						:							Any ):
				A									=   dct.pop(UpperCAmelCase )
				A									=   val







def   snake_case		(				):
				A									=   'http://images.cocodataset.org/val2017/000000039769.jpg'
				A									=   Image.open(requests.get(UpperCAmelCase,    stream=UpperCAmelCase ).raw )
				return im







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

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

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

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

				A									=   image_processor(images=UpperCAmelCase,    return_tensors='pt' )
				A									=   encoding['pixel_values']

				A									=   model(UpperCAmelCase )
				A									=   outputs.logits

				# verify logits
				A									=   torch.Size([1, 10_00] )
				if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k' ):
								A									=   torch.Size([1, 1_96, 81_92] )
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k' ):
								A									=   torch.Size([1, 1_96, 81_92] )
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k' ):
								A									=   torch.Size([1, 2_18_41] )
								A									=   torch.tensor([2.2_288, 2.4_671, 0.7_395] )
								A									=   23_97
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k' ):
								A									=   torch.Size([1, 2_18_41] )
								A									=   torch.tensor([1.6_881, -0.2_787, 0.5_901] )
								A									=   23_96
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k' ):
								A									=   torch.tensor([0.1_241, 0.0_798, -0.6_569] )
								A									=   2_85
				elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k' ):
								A									=   torch.tensor([-1.2_385, -1.0_987, -1.0_108] )
								A									=   2_81
				elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k' ):
								A									=   torch.tensor([-1.5_303, -0.9_484, -0.3_147] )
								A									=   7_61
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k' ):
								A									=   torch.tensor([0.4_610, -0.0_928, 0.2_086] )
								A									=   7_61
				elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k' ):
								A									=   torch.tensor([-0.4_804, 0.6_257, -0.1_837] )
								A									=   7_61
				elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k' ):
								A									=   torch.tensor([[-0.5_122, 0.5_117, -0.2_113]] )
								A									=   7_61
				elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k' ):
								A									=   torch.tensor([-0.3_062, 0.7_261, 0.4_852] )
								A									=   7_61
				elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k' ):
								A									=   (1, 1_50, 1_60, 1_60)
								A									=   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' ):
								A									=   (1, 1_50, 1_60, 1_60)
								A									=   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],    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_     =			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_     =			parser.parse_args()
					convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













import numpy as np







def   snake_case		(				UpperCAmelCase						:							Any ):
				A									=   np.exp(UpperCAmelCase )

				# Add up the all the exponentials
				A									=   np.sum(UpperCAmelCase )

				# Divide every exponent by the sum of all exponents
				A									=   exponent_vector / sum_of_exponents

				return softmax_vector


if __name__ == "__main__":
					print(softmax((0,)))





