


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()
UpperCAmelCase__        =				logging.get_logger(__name__)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case=False	,		__snake_case=False		)	->     List[str]:







 """simple docstring"""


 _lowercase				 ='''backbone.''' if is_semantic else ''''''

 _lowercase				 =[]
 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_   (				__snake_case	,		__snake_case	,		__snake_case=False	,		__snake_case=False		)	->     List[str]:







 """simple docstring"""


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

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

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

  _lowercase				 =gamma_a
  _lowercase				 =gamma_a

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

   _lowercase				 =table
   _lowercase				 =index
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case		)	->     List[str]:







 """simple docstring"""


 _lowercase				 =dct.pop(__snake_case		)
 _lowercase				 =val
def 							UpperCAmelCase_   (				)	->     Any:







 """simple docstring"""


 _lowercase				 ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
 _lowercase				 =Image.open(requests.get(__snake_case	,		stream=__snake_case		).raw		)
 return im
@torch.no_grad()
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     Union[str, Any]:







 """simple docstring"""


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

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

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

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

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

 _lowercase				 =model(__snake_case		)
 _lowercase				 =outputs.logits

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


if __name__ == "__main__":
  UpperCAmelCase__        =				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.'''
  )
  UpperCAmelCase__        =				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


UpperCAmelCase__        =				logging.get_logger(__name__)

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







class  lowerCamelCase__  (			lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      =     '''rembert'''





     def __init__(self						,	UpperCAmelCase=2_5_0_3_0_0						,	UpperCAmelCase=1_1_5_2						,	UpperCAmelCase=3_2						,	UpperCAmelCase=1_8						,	UpperCAmelCase=2_5_6						,	UpperCAmelCase=1_6_6_4						,	UpperCAmelCase=4_6_0_8						,	UpperCAmelCase="gelu"						,	UpperCAmelCase=0.0						,	UpperCAmelCase=0.0						,	UpperCAmelCase=0.1						,	UpperCAmelCase=5_1_2						,	UpperCAmelCase=2						,	UpperCAmelCase=0.02						,	UpperCAmelCase=1e-12						,	UpperCAmelCase=True						,	UpperCAmelCase=0						,	UpperCAmelCase=3_1_2						,	UpperCAmelCase=3_1_3						,	**UpperCAmelCase						,	)	->  Tuple:
      super().__init__(pad_token_id=UpperCAmelCase						,	bos_token_id=UpperCAmelCase						,	eos_token_id=UpperCAmelCase						,	**UpperCAmelCase			)

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







class  lowerCamelCase__  (			lowerCAmelCase):





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





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

