




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__  (							_A      ,      _A=False      ,      _A=False			):






  """simple docstring"""


  a_		    =  '''backbone.''' if is_semantic else ''''''

  a_		    =  []
  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__  (							_A      ,      _A      ,      _A=False      ,      _A=False			):






  """simple docstring"""


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

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

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

    a_		    =  gamma_a
    a_		    =  gamma_a

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

      a_		    =  table
      a_		    =  index






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  dct.pop(_UpperCAmelCase			)
  a_		    =  val






def 				UpperCAmelCase__  (							):






  """simple docstring"""


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






@torch.no_grad()
def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  BeitConfig()
  a_		    =  False
  a_		    =  False
  a_		    =  '''huggingface/label-files'''
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    a_		    =  True
    a_		    =  True
    a_		    =  True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    a_		    =  True
    a_		    =  21_841
    a_		    =  '''imagenet-22k-id2label.json'''
    a_		    =  json.load(open(hf_hub_download(_UpperCAmelCase      ,      _UpperCAmelCase      ,      repo_type='''dataset'''			)      ,      '''r'''			)			)
    a_		    =  {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[9_205]
    del idalabel[15_027]
    a_		    =  idalabel
    a_		    =  {v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    a_		    =  True
    a_		    =  1_000
    a_		    =  '''imagenet-1k-id2label.json'''
    a_		    =  json.load(open(hf_hub_download(_UpperCAmelCase      ,      _UpperCAmelCase      ,      repo_type='''dataset'''			)      ,      '''r'''			)			)
    a_		    =  {int(_UpperCAmelCase			): v for k, v in idalabel.items()}
    a_		    =  idalabel
    a_		    =  {v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      a_		    =  384
    if "512" in checkpoint_url:
      a_		    =  512
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    a_		    =  True
    a_		    =  150
    a_		    =  '''ade20k-id2label.json'''
    a_		    =  json.load(open(hf_hub_download(_UpperCAmelCase      ,      _UpperCAmelCase      ,      repo_type='''dataset'''			)      ,      '''r'''			)			)
    a_		    =  {int(_UpperCAmelCase			): v for k, v in idalabel.items()}
    a_		    =  idalabel
    a_		    =  {v: k for k, v in idalabel.items()}
    a_		    =  640
    a_		    =  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:
    a_		    =  1_024
    a_		    =  4_096
    a_		    =  24
    a_		    =  16
    if "ade20k" in checkpoint_url:
      a_		    =  640
      a_		    =  [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
  a_		    =  torch.hub.load_state_dict_from_url(_UpperCAmelCase      ,      map_location='''cpu'''      ,      check_hash=_UpperCAmelCase			)
  a_		    =  state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

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

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

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

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

  a_		    =  model(_UpperCAmelCase			)
  a_		    =  outputs.logits

  # verify logits
  a_		    =  torch.Size([1, 1_000]			)
  if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''			):
    a_		    =  torch.Size([1, 196, 8_192]			)
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''			):
    a_		    =  torch.Size([1, 196, 8_192]			)
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''			):
    a_		    =  torch.Size([1, 21_841]			)
    a_		    =  torch.tensor([2.22_88, 2.46_71, 0.73_95]			)
    a_		    =  2_397
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''			):
    a_		    =  torch.Size([1, 21_841]			)
    a_		    =  torch.tensor([1.68_81, -0.27_87, 0.59_01]			)
    a_		    =  2_396
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''			):
    a_		    =  torch.tensor([0.12_41, 0.07_98, -0.65_69]			)
    a_		    =  285
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''			):
    a_		    =  torch.tensor([-1.23_85, -1.09_87, -1.01_08]			)
    a_		    =  281
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''			):
    a_		    =  torch.tensor([-1.53_03, -0.94_84, -0.31_47]			)
    a_		    =  761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''			):
    a_		    =  torch.tensor([0.46_10, -0.09_28, 0.20_86]			)
    a_		    =  761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''			):
    a_		    =  torch.tensor([-0.48_04, 0.62_57, -0.18_37]			)
    a_		    =  761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''			):
    a_		    =  torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]			)
    a_		    =  761
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''			):
    a_		    =  torch.tensor([-0.30_62, 0.72_61, 0.48_52]			)
    a_		    =  761
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''			):
    a_		    =  (1, 150, 160, 160)
    a_		    =  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'''			):
    a_		    =  (1, 150, 160, 160)
    a_		    =  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]      ,      _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__":
  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)





import numpy as np

def 			a		( _UpperCAmelCase				:   str					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   List[Any]="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	Union[str, Any]						=      bin(int.from_bytes(text.encode(_UpperCAmelCase					,  _UpperCAmelCase		)					,  '''big'''		)		)[2:]
		return bits.zfill(8 * ((len(_UpperCAmelCase		) + 7) // 8)		)

def 			a		( _UpperCAmelCase				:   Dict					,  _UpperCAmelCase				:   Any="utf-8"					,  _UpperCAmelCase				:   str="surrogatepass"		):







		'''simple docstring'''





		__UpperCAmelCase       :	List[str]						=      int(_UpperCAmelCase					,  2		)
		return n.to_bytes((n.bit_length() + 7) // 8					,  '''big'''		).decode(_UpperCAmelCase					,  _UpperCAmelCase		) or "\0"

def 			a		( _UpperCAmelCase				:   List[Any]					,  _UpperCAmelCase				:   List[str]		):







		'''simple docstring'''





		if size_par + len(_UpperCAmelCase		) <= 2**size_par - (len(_UpperCAmelCase		) - 1):
				raise ValueError('''size of parity don\'t match with size of data'''		)

		__UpperCAmelCase       :	str						=      []
		__UpperCAmelCase       :	Any						=      []
		__UpperCAmelCase       :	Any						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		# sorted information data for the size of the output data
		__UpperCAmelCase       :	Optional[Any]						=      []
		# data position template + parity
		__UpperCAmelCase       :	Any						=      []
		# parity bit counter
		__UpperCAmelCase       :	Tuple						=      0
		# counter position of data bits
		__UpperCAmelCase       :	Optional[int]						=      0

		for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
								data_out_gab.append('''P'''		)
								__UpperCAmelCase       :	Union[str, Any]						=      qtd_bp + 1
						else:
								data_out_gab.append('''D'''		)
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data[cont_data]		)
						cont_data += 1
				else:
						data_ord.append(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a given parity
				__UpperCAmelCase       :	List[str]						=      0
				# counter to control the loop reading
				__UpperCAmelCase       :	str						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	Tuple						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	List[str]						=      '''0'''
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2		)

				qtd_bp += 1

		# Mount the message
		__UpperCAmelCase       :	int						=      0  # parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]		)		)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]		)

		return data_out

def 			a		( _UpperCAmelCase				:   List[str]					,  _UpperCAmelCase				:   str		):







		'''simple docstring'''





		__UpperCAmelCase       :	str						=      []
		# Parity bit counter
		__UpperCAmelCase       :	List[str]						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	int						=      0
		# list of parity received
		__UpperCAmelCase       :	int						=      []
		__UpperCAmelCase       :	List[Any]						=      []

		for x in range(1					,  len(_UpperCAmelCase		) + 1		):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
						data_out_gab.append('''P'''		)
						__UpperCAmelCase       :	Union[str, Any]						=      qtd_bp + 1
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_output.append(data[cont_data]		)
				else:
						parity_received.append(data[cont_data]		)
				cont_data += 1

		# -----------calculates the parity with the data
		__UpperCAmelCase       :	Optional[int]						=      []
		__UpperCAmelCase       :	Dict						=      []
		__UpperCAmelCase       :	Union[str, Any]						=      [bin(_UpperCAmelCase		)[2:] for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		)]

		#  sorted information data for the size of the output data
		__UpperCAmelCase       :	str						=      []
		# Data position feedback + parity
		__UpperCAmelCase       :	Any						=      []
		# Parity bit counter
		__UpperCAmelCase       :	int						=      0
		# Counter p data bit reading
		__UpperCAmelCase       :	List[Any]						=      0

		for x in range(1					,  size_par + len(_UpperCAmelCase		) + 1		):
				# Performs a template position of bits - who should be given,
				# and who should be parity
				if qtd_bp < size_par and (np.log(_UpperCAmelCase		) / np.log(2		)).is_integer():
						data_out_gab.append('''P'''		)
						__UpperCAmelCase       :	Dict						=      qtd_bp + 1
				else:
						data_out_gab.append('''D'''		)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data_output[cont_data]		)
						cont_data += 1
				else:
						data_ord.append(_UpperCAmelCase		)

    # Calculates parity
		__UpperCAmelCase       :	Union[str, Any]						=      0  # parity bit counter
		for bp in range(1					,  size_par + 1		):
				# Bit counter one for a certain parity
				__UpperCAmelCase       :	Union[str, Any]						=      0
				# Counter to control loop reading
				__UpperCAmelCase       :	Optional[Any]						=      0
				for x in data_ord:
						if x is not None:
								try:
										__UpperCAmelCase       :	int						=      (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										__UpperCAmelCase       :	int						=      '''0'''
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(str(cont_bo % 2		)		)

				qtd_bp += 1

		# Mount the message
		__UpperCAmelCase       :	Any						=      0  # Parity bit counter
		for x in range(0					,  size_par + len(_UpperCAmelCase		)		):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]		)		)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]		)

		__UpperCAmelCase       :	Optional[Any]						=      parity_received == parity
		return data_output, ack





