




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__				:     Any			       =							logging.get_logger(__name__)

def       UpperCAmelCase_							(   __UpperCAmelCase		:			int    ,    __UpperCAmelCase		:			List[Any]=False    ,    __UpperCAmelCase		:			List[Any]=False							)      ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE_					=    'backbone.' if is_semantic else ''

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

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

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

            SCREAMING_SNAKE_CASE_					=    gamma_a
            SCREAMING_SNAKE_CASE_					=    gamma_a

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

                  SCREAMING_SNAKE_CASE_					=    table
                  SCREAMING_SNAKE_CASE_					=    index

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]    ,    __UpperCAmelCase		:			Tuple    ,    __UpperCAmelCase		:			int							)      ->				Optional[int]:
      SCREAMING_SNAKE_CASE_					=    dct.pop(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    val

def       UpperCAmelCase_							(   )      ->				str:
      SCREAMING_SNAKE_CASE_					=    'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_					=    Image.open(requests.get(__UpperCAmelCase    ,    stream=__UpperCAmelCase							).raw							)
      return im

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

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

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

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

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

      SCREAMING_SNAKE_CASE_					=    model(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_					=    outputs.logits

      # verify logits
      SCREAMING_SNAKE_CASE_					=    torch.Size([1, 10_00]							)
      if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'							):
            SCREAMING_SNAKE_CASE_					=    torch.Size([1, 1_96, 81_92]							)
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'							):
            SCREAMING_SNAKE_CASE_					=    torch.Size([1, 1_96, 81_92]							)
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'							):
            SCREAMING_SNAKE_CASE_					=    torch.Size([1, 2_18_41]							)
            SCREAMING_SNAKE_CASE_					=    torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]							)
            SCREAMING_SNAKE_CASE_					=    23_97
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'							):
            SCREAMING_SNAKE_CASE_					=    torch.Size([1, 2_18_41]							)
            SCREAMING_SNAKE_CASE_					=    torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]							)
            SCREAMING_SNAKE_CASE_					=    23_96
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'							):
            SCREAMING_SNAKE_CASE_					=    torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]							)
            SCREAMING_SNAKE_CASE_					=    2_85
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'							):
            SCREAMING_SNAKE_CASE_					=    torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]							)
            SCREAMING_SNAKE_CASE_					=    2_81
      elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'							):
            SCREAMING_SNAKE_CASE_					=    torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]							)
            SCREAMING_SNAKE_CASE_					=    7_61
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'							):
            SCREAMING_SNAKE_CASE_					=    torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]							)
            SCREAMING_SNAKE_CASE_					=    7_61
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'							):
            SCREAMING_SNAKE_CASE_					=    torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]							)
            SCREAMING_SNAKE_CASE_					=    7_61
      elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'							):
            SCREAMING_SNAKE_CASE_					=    torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]							)
            SCREAMING_SNAKE_CASE_					=    7_61
      elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'							):
            SCREAMING_SNAKE_CASE_					=    torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]							)
            SCREAMING_SNAKE_CASE_					=    7_61
      elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'							):
            SCREAMING_SNAKE_CASE_					=    (1, 1_50, 1_60, 1_60)
            SCREAMING_SNAKE_CASE_					=    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_					=    (1, 1_50, 1_60, 1_60)
            SCREAMING_SNAKE_CASE_					=    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__				:     int			       =							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)





import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


lowerCamelCase__				:     Optional[Any]			       =							logging.get_logger(__name__)





class  lowerCamelCase_							(   _SCREAMING_SNAKE_CASE      ):


 '''simple docstring'''







 def __init__(							self						:	Dict       ,       *_lowerCAmelCase						:	str       ,       **_lowerCAmelCase						:	List[str]		):
       warnings.warn(
           'The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use DetrImageProcessor instead.'       ,       _lowerCAmelCase       ,       )
       super().__init__(*_lowerCAmelCase       ,       **_lowerCAmelCase		)