

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    UpperCamelCase(					lowercase_			,					lowercase_=False			,					lowercase_=False							)	->				Dict:





  '''simple docstring'''





  snake_case_       						=      """backbone.""" if is_semantic else """"""

  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(					lowercase_			,					lowercase_			,					lowercase_=False			,					lowercase_=False							)	->				Optional[Any]:





  '''simple docstring'''





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

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

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

    snake_case_       						=      gamma_a
    snake_case_       						=      gamma_a

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

      snake_case_       						=      table
      snake_case_       						=      index



def    UpperCamelCase(					lowercase_			,					lowercase_			,					lowercase_							)	->				Dict:





  '''simple docstring'''





  snake_case_       						=      dct.pop(lowercase_							)
  snake_case_       						=      val



def    UpperCamelCase(					)	->				Tuple:





  '''simple docstring'''





  snake_case_       						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
  snake_case_       						=      Image.open(requests.get(lowercase_			,					stream=lowercase_							).raw							)
  return im



@torch.no_grad()
def    UpperCamelCase(					lowercase_			,					lowercase_							)	->				List[Any]:





  '''simple docstring'''





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

  snake_case_       						=      create_rename_keys(lowercase_			,					has_lm_head=lowercase_			,					is_semantic=lowercase_							)
  for src, dest in rename_keys:
    rename_key(lowercase_			,					lowercase_			,					lowercase_							)
  read_in_q_k_v(lowercase_			,					lowercase_			,					has_lm_head=lowercase_			,					is_semantic=lowercase_							)
  if is_semantic:
    # add prefix to decoder keys
    for key, val in state_dict.copy().items():
      snake_case_       						=      state_dict.pop(lowercase_							)
      if key.startswith("""backbone.fpn"""							):
        snake_case_       						=      key.replace("""backbone.fpn"""			,					"""fpn"""							)
      snake_case_       						=      val

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

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

  snake_case_       						=      image_processor(images=lowercase_			,					return_tensors="""pt"""							)
  snake_case_       						=      encoding["""pixel_values"""]

  snake_case_       						=      model(lowercase_							)
  snake_case_       						=      outputs.logits

  # verify logits
  snake_case_       						=      torch.Size([1, 1000]							)
  if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""							):
    snake_case_       						=      torch.Size([1, 196, 8192]							)
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""							):
    snake_case_       						=      torch.Size([1, 196, 8192]							)
  elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""							):
    snake_case_       						=      torch.Size([1, 21841]							)
    snake_case_       						=      torch.tensor([2.22_88, 2.46_71, 0.73_95]							)
    snake_case_       						=      2397
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""							):
    snake_case_       						=      torch.Size([1, 21841]							)
    snake_case_       						=      torch.tensor([1.68_81, -0.27_87, 0.59_01]							)
    snake_case_       						=      2396
  elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""							):
    snake_case_       						=      torch.tensor([0.12_41, 0.07_98, -0.65_69]							)
    snake_case_       						=      285
  elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""							):
    snake_case_       						=      torch.tensor([-1.23_85, -1.09_87, -1.01_08]							)
    snake_case_       						=      281
  elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""							):
    snake_case_       						=      torch.tensor([-1.53_03, -0.94_84, -0.31_47]							)
    snake_case_       						=      761
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""							):
    snake_case_       						=      torch.tensor([0.46_10, -0.09_28, 0.20_86]							)
    snake_case_       						=      761
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""							):
    snake_case_       						=      torch.tensor([-0.48_04, 0.62_57, -0.18_37]							)
    snake_case_       						=      761
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""							):
    snake_case_       						=      torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]							)
    snake_case_       						=      761
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""							):
    snake_case_       						=      torch.tensor([-0.30_62, 0.72_61, 0.48_52]							)
    snake_case_       						=      761
  elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""							):
    snake_case_       						=      (1, 150, 160, 160)
    snake_case_       						=      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"""							):
    snake_case_       						=      (1, 150, 160, 160)
    snake_case_       						=      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]			,					lowercase_			,					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]			,					lowercase_			,					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(lowercase_							).mkdir(exist_ok=lowercase_							)
  print(f'''Saving model to {pytorch_dump_folder_path}'''							)
  model.save_pretrained(lowercase_							)
  print(f'''Saving image processor to {pytorch_dump_folder_path}'''							)
  image_processor.save_pretrained(lowercase_							)


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)


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_    =					logging.get_logger(__name__)

lowerCamelCase_    =					{
    '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/config.json''',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					'rembert'







      def __init__(     self							,  lowerCamelCase=250300							,  lowerCamelCase=1152							,  lowerCamelCase=32							,  lowerCamelCase=18							,  lowerCamelCase=256							,  lowerCamelCase=1664							,  lowerCamelCase=4608							,  lowerCamelCase="gelu"							,  lowerCamelCase=0.0							,  lowerCamelCase=0.0							,  lowerCamelCase=0.1							,  lowerCamelCase=512							,  lowerCamelCase=2							,  lowerCamelCase=0.02							,  lowerCamelCase=1e-12							,  lowerCamelCase=True							,  lowerCamelCase=0							,  lowerCamelCase=312							,  lowerCamelCase=313							,  **lowerCamelCase							,  )						->       Union[str, Any]:
        super().__init__(pad_token_id=lowerCamelCase							,  bos_token_id=lowerCamelCase							,  eos_token_id=lowerCamelCase							,  **lowerCamelCase   )

        snake_case_       						=      vocab_size
        snake_case_       						=      input_embedding_size
        snake_case_       						=      output_embedding_size
        snake_case_       						=      max_position_embeddings
        snake_case_       						=      hidden_size
        snake_case_       						=      num_hidden_layers
        snake_case_       						=      num_attention_heads
        snake_case_       						=      intermediate_size
        snake_case_       						=      hidden_act
        snake_case_       						=      hidden_dropout_prob
        snake_case_       						=      attention_probs_dropout_prob
        snake_case_       						=      classifier_dropout_prob
        snake_case_       						=      initializer_range
        snake_case_       						=      type_vocab_size
        snake_case_       						=      layer_norm_eps
        snake_case_       						=      use_cache
        snake_case_       						=      False







class  __lowerCamelCase				(	__snake_case	):







      @property
      def 	lowerCAmelCase_    (     self   )						->       Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
          snake_case_       						=      {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
          snake_case_       						=      {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
                ("""token_type_ids""", dynamic_axis),
            ]   )







      @property
      def 	lowerCAmelCase_    (     self   )						->       float:
        return 1e-4