

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 				A_					(							a   ,				a=False   ,				a=False					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       'backbone.' if is_semantic else ''

      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       []
      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_					(							a   ,				a   ,				a=False   ,				a=False					):

      """simple docstring"""



      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_			:					List[Any]          =       state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"					)
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"					)
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"					)

            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       in_proj_weight[
                : config.hidden_size, :
            ]
            SCREAMING_SNAKE_CASE_			:					int          =       q_bias
            SCREAMING_SNAKE_CASE_			:					int          =       in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE_			:					List[str]          =       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[Any]          =       state_dict.pop(f"{prefix}blocks.{i}.gamma_2"					)

            SCREAMING_SNAKE_CASE_			:					Tuple          =       gamma_a
            SCREAMING_SNAKE_CASE_			:					Tuple          =       gamma_a

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

                  SCREAMING_SNAKE_CASE_			:					List[Any]          =       table
                  SCREAMING_SNAKE_CASE_			:					Any          =       index

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					int          =       dct.pop(a					)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       val

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       'http://images.cocodataset.org/val2017/000000039769.jpg'
      SCREAMING_SNAKE_CASE_			:					str          =       Image.open(requests.get(a   ,				stream=a					).raw					)
      return im

@torch.no_grad()
def 				A_					(							a   ,				a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[Any]          =       BeitConfig()
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       False
      SCREAMING_SNAKE_CASE_			:					Dict          =       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_			:					Any          =       True
            SCREAMING_SNAKE_CASE_			:					Tuple          =       True
            SCREAMING_SNAKE_CASE_			:					int          =       True
      elif checkpoint_url[-9:-4] == "ft22k":
            # intermediate fine-tuning on ImageNet-22k
            SCREAMING_SNAKE_CASE_			:					List[str]          =       True
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       2_1_8_4_1
            SCREAMING_SNAKE_CASE_			:					Tuple          =       'imagenet-22k-id2label.json'
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       json.load(open(hf_hub_download(a   ,				a   ,				repo_type='dataset'					)   ,				'r'					)					)
            SCREAMING_SNAKE_CASE_			:					Tuple          =       {int(a					): 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]
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       idalabel
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       {v: k for k, v in idalabel.items()}
      elif checkpoint_url[-8:-4] == "to1k":
            # fine-tuning on ImageNet-1k
            SCREAMING_SNAKE_CASE_			:					Any          =       True
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       1_0_0_0
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       'imagenet-1k-id2label.json'
            SCREAMING_SNAKE_CASE_			:					List[str]          =       json.load(open(hf_hub_download(a   ,				a   ,				repo_type='dataset'					)   ,				'r'					)					)
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       {int(a					): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       idalabel
            SCREAMING_SNAKE_CASE_			:					Dict          =       {v: k for k, v in idalabel.items()}
            if "384" in checkpoint_url:
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       3_8_4
            if "512" in checkpoint_url:
                  SCREAMING_SNAKE_CASE_			:					Optional[int]          =       5_1_2
      elif "ade20k" in checkpoint_url:
            # fine-tuning
            SCREAMING_SNAKE_CASE_			:					str          =       True
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       1_5_0
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       'ade20k-id2label.json'
            SCREAMING_SNAKE_CASE_			:					Tuple          =       json.load(open(hf_hub_download(a   ,				a   ,				repo_type='dataset'					)   ,				'r'					)					)
            SCREAMING_SNAKE_CASE_			:					Dict          =       {int(a					): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       idalabel
            SCREAMING_SNAKE_CASE_			:					Tuple          =       {v: k for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE_			:					List[str]          =       6_4_0
            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_			:					List[Any]          =       1_0_2_4
            SCREAMING_SNAKE_CASE_			:					List[str]          =       4_0_9_6
            SCREAMING_SNAKE_CASE_			:					int          =       2_4
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       1_6
            if "ade20k" in checkpoint_url:
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       6_4_0
                  SCREAMING_SNAKE_CASE_			:					int          =       [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
      SCREAMING_SNAKE_CASE_			:					Tuple          =       torch.hub.load_state_dict_from_url(a   ,				map_location='cpu'   ,				check_hash=a					)
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

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

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

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

      SCREAMING_SNAKE_CASE_			:					Dict          =       image_processor(images=a   ,				return_tensors='pt'					)
      SCREAMING_SNAKE_CASE_			:					Tuple          =       encoding['pixel_values']

      SCREAMING_SNAKE_CASE_			:					List[Any]          =       model(a					)
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       outputs.logits

      # verify logits
      SCREAMING_SNAKE_CASE_			:					int          =       torch.Size([1, 1_0_0_0]					)
      if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'					):
            SCREAMING_SNAKE_CASE_			:					List[str]          =       torch.Size([1, 1_9_6, 8_1_9_2]					)
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'					):
            SCREAMING_SNAKE_CASE_			:					Dict          =       torch.Size([1, 1_9_6, 8_1_9_2]					)
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'					):
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       torch.Size([1, 2_1_8_4_1]					)
            SCREAMING_SNAKE_CASE_			:					int          =       torch.tensor([2.22_88, 2.46_71, 0.73_95]					)
            SCREAMING_SNAKE_CASE_			:					Dict          =       2_3_9_7
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'					):
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       torch.Size([1, 2_1_8_4_1]					)
            SCREAMING_SNAKE_CASE_			:					str          =       torch.tensor([1.68_81, -0.27_87, 0.59_01]					)
            SCREAMING_SNAKE_CASE_			:					Any          =       2_3_9_6
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'					):
            SCREAMING_SNAKE_CASE_			:					Dict          =       torch.tensor([0.12_41, 0.07_98, -0.65_69]					)
            SCREAMING_SNAKE_CASE_			:					str          =       2_8_5
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'					):
            SCREAMING_SNAKE_CASE_			:					Dict          =       torch.tensor([-1.23_85, -1.09_87, -1.01_08]					)
            SCREAMING_SNAKE_CASE_			:					str          =       2_8_1
      elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'					):
            SCREAMING_SNAKE_CASE_			:					str          =       torch.tensor([-1.53_03, -0.94_84, -0.31_47]					)
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       7_6_1
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'					):
            SCREAMING_SNAKE_CASE_			:					Any          =       torch.tensor([0.46_10, -0.09_28, 0.20_86]					)
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       7_6_1
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'					):
            SCREAMING_SNAKE_CASE_			:					int          =       torch.tensor([-0.48_04, 0.62_57, -0.18_37]					)
            SCREAMING_SNAKE_CASE_			:					int          =       7_6_1
      elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'					):
            SCREAMING_SNAKE_CASE_			:					Any          =       torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]					)
            SCREAMING_SNAKE_CASE_			:					List[Any]          =       7_6_1
      elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'					):
            SCREAMING_SNAKE_CASE_			:					str          =       torch.tensor([-0.30_62, 0.72_61, 0.48_52]					)
            SCREAMING_SNAKE_CASE_			:					List[str]          =       7_6_1
      elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'					):
            SCREAMING_SNAKE_CASE_			:					Any          =       (1, 1_5_0, 1_6_0, 1_6_0)
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       torch.tensor(
                [
                    [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
                    [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
                    [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
                ]					)
      elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'					):
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       (1, 1_5_0, 1_6_0, 1_6_0)
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       torch.tensor(
                [
                    [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
                    [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
                    [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
                ]					)
      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]   ,				a   ,				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]   ,				a   ,				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(a					).mkdir(exist_ok=a					)
      print(f"Saving model to {pytorch_dump_folder_path}"					)
      model.save_pretrained(a					)
      print(f"Saving image processor to {pytorch_dump_folder_path}"					)
      image_processor.save_pretrained(a					)


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









from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase       :				Tuple         =					{'tokenization_phobert': ['PhobertTokenizer']}


if TYPE_CHECKING:
    from .tokenization_phobert import PhobertTokenizer

else:
    import sys

    lowerCAmelCase       :				Optional[int]         =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






