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    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__=False			,						lowerCAmelCase__=False			):
       lowerCAmelCase__  			=      'backbone.' if is_semantic else ''

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

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

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

              lowerCAmelCase__  			=      gamma_a
              lowerCAmelCase__  			=      gamma_a

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

                     lowerCAmelCase__  			=      table
                     lowerCAmelCase__  			=      index






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      dct.pop(lowerCAmelCase__			)
       lowerCAmelCase__  			=      val






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






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

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

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

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

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

       lowerCAmelCase__  			=      model(lowerCAmelCase__			)
       lowerCAmelCase__  			=      outputs.logits

       # verify logits
       lowerCAmelCase__  			=      torch.Size([1, 1_0_0_0]			)
       if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'			):
              lowerCAmelCase__  			=      torch.Size([1, 1_9_6, 8_1_9_2]			)
       elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'			):
              lowerCAmelCase__  			=      torch.Size([1, 1_9_6, 8_1_9_2]			)
       elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'			):
              lowerCAmelCase__  			=      torch.Size([1, 2_1_8_4_1]			)
              lowerCAmelCase__  			=      torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]			)
              lowerCAmelCase__  			=      2_3_9_7
       elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'			):
              lowerCAmelCase__  			=      torch.Size([1, 2_1_8_4_1]			)
              lowerCAmelCase__  			=      torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]			)
              lowerCAmelCase__  			=      2_3_9_6
       elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'			):
              lowerCAmelCase__  			=      torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]			)
              lowerCAmelCase__  			=      2_8_5
       elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'			):
              lowerCAmelCase__  			=      torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]			)
              lowerCAmelCase__  			=      2_8_1
       elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'			):
              lowerCAmelCase__  			=      torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]			)
              lowerCAmelCase__  			=      7_6_1
       elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'			):
              lowerCAmelCase__  			=      torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]			)
              lowerCAmelCase__  			=      7_6_1
       elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'			):
              lowerCAmelCase__  			=      torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]			)
              lowerCAmelCase__  			=      7_6_1
       elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'			):
              lowerCAmelCase__  			=      torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]			)
              lowerCAmelCase__  			=      7_6_1
       elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'			):
              lowerCAmelCase__  			=      torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]			)
              lowerCAmelCase__  			=      7_6_1
       elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'			):
              lowerCAmelCase__  			=      (1, 1_5_0, 1_6_0, 1_6_0)
              lowerCAmelCase__  			=      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'			):
              lowerCAmelCase__  			=      (1, 1_5_0, 1_6_0, 1_6_0)
              lowerCAmelCase__  			=      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__":
    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)

def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			):
       if target == 0:
              answer.append(path.copy()			)
       else:
              for index in range(lowerCAmelCase__			,						len(lowerCAmelCase__			)			):
                     if target >= candidates[index]:
                            path.append(candidates[index]			)
                            backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						target - candidates[index]			,						lowerCAmelCase__			)
                            path.pop(len(lowerCAmelCase__			) - 1			)






def    __lowerCamelCase	(		lowerCAmelCase__			,						lowerCAmelCase__			):
       lowerCAmelCase__  			=      []  # type: list[int]
       lowerCAmelCase__  			=      []  # type: list[int]
       backtrack(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						0			)
       return answer






def    __lowerCamelCase	(		):
       print(combination_sum([-8, 2.3, 0]			,						1			)			)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
