"""simple docstring"""
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()
lowercase__   			=      logging.get_logger(__name__)







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=False						,	_SCREAMING_SNAKE_CASE=False  )      ->List[str]:
  a__:	Optional[int]     				=      'backbone.' if is_semantic else ''

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

    a__:	Tuple     				=      in_proj_weight[
        : config.hidden_size, :
    ]
    a__:	Dict     				=      q_bias
    a__:	Optional[int]     				=      in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    a__:	int     				=      in_proj_weight[
        -config.hidden_size :, :
    ]
    a__:	List[str]     				=      v_bias

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

    a__:	List[Any]     				=      gamma_a
    a__:	str     				=      gamma_a

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

      a__:	Union[str, Any]     				=      table
      a__:	Any     				=      index







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->List[Any]:
  a__:	str     				=      dct.pop(_SCREAMING_SNAKE_CASE  )
  a__:	Tuple     				=      val







def 		__a  (      )      ->Any:
  a__:	Tuple     				=      'http://images.cocodataset.org/val2017/000000039769.jpg'
  a__:	int     				=      Image.open(requests.get(_SCREAMING_SNAKE_CASE						,	stream=_SCREAMING_SNAKE_CASE  ).raw  )
  return im







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

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

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

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

  a__:	Tuple     				=      image_processor(images=_SCREAMING_SNAKE_CASE						,	return_tensors='pt'  )
  a__:	int     				=      encoding['pixel_values']

  a__:	str     				=      model(_SCREAMING_SNAKE_CASE  )
  a__:	Union[str, Any]     				=      outputs.logits

  # verify logits
  a__:	Union[str, Any]     				=      torch.Size([1, 1000]  )
  if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'  ):
    a__:	Dict     				=      torch.Size([1, 196, 8192]  )
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'  ):
    a__:	str     				=      torch.Size([1, 196, 8192]  )
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'  ):
    a__:	List[Any]     				=      torch.Size([1, 21841]  )
    a__:	int     				=      torch.tensor([2.2_288, 2.4_671, 0.7_395]  )
    a__:	List[Any]     				=      2397
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'  ):
    a__:	int     				=      torch.Size([1, 21841]  )
    a__:	Dict     				=      torch.tensor([1.6_881, -0.2_787, 0.5_901]  )
    a__:	Tuple     				=      2396
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'  ):
    a__:	List[Any]     				=      torch.tensor([0.1_241, 0.0_798, -0.6_569]  )
    a__:	Tuple     				=      285
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'  ):
    a__:	int     				=      torch.tensor([-1.2_385, -1.0_987, -1.0_108]  )
    a__:	str     				=      281
  elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'  ):
    a__:	int     				=      torch.tensor([-1.5_303, -0.9_484, -0.3_147]  )
    a__:	Dict     				=      761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'  ):
    a__:	Dict     				=      torch.tensor([0.4_610, -0.0_928, 0.2_086]  )
    a__:	Any     				=      761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'  ):
    a__:	List[str]     				=      torch.tensor([-0.4_804, 0.6_257, -0.1_837]  )
    a__:	str     				=      761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'  ):
    a__:	Dict     				=      torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]  )
    a__:	int     				=      761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'  ):
    a__:	Optional[Any]     				=      torch.tensor([-0.3_062, 0.7_261, 0.4_852]  )
    a__:	Union[str, Any]     				=      761
  elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'  ):
    a__:	Optional[int]     				=      (1, 150, 160, 160)
    a__:	Union[str, Any]     				=      torch.tensor(
        [
            [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
            [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
            [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
        ]  )
  elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'  ):
    a__:	List[Any]     				=      (1, 150, 160, 160)
    a__:	str     				=      torch.tensor(
        [
            [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
            [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
            [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
        ]  )
  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]						,	_SCREAMING_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]						,	_SCREAMING_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(_SCREAMING_SNAKE_CASE  ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE  )
  print(F'Saving model to {pytorch_dump_folder_path}'  )
  model.save_pretrained(_SCREAMING_SNAKE_CASE  )
  print(F'Saving image processor to {pytorch_dump_folder_path}'  )
  image_processor.save_pretrained(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
 lowercase__   			=      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.'
 )
 lowercase__   			=      parser.parse_args()
 convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







import json
import logging
import os
import subprocess
from argparse import ArgumentParser


__A       =logging.getLogger(__name__)

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	Dict						=      ArgumentParser()
		__UpperCAmelCase				,   __UpperCAmelCase       :	int						=      parser.parse_known_args()
		for arg in unknown:
				if arg.startswith(('''-''', '''--''')		):
						parser.add_argument(arg.split('''='''		)[0]		)

		return parser.parse_args()

def 			a		( ):







		'''simple docstring'''





		__UpperCAmelCase       :	List[Any]						=      parse_args()
		__UpperCAmelCase       :	Tuple						=      88_88
		__UpperCAmelCase       :	Any						=      int(os.environ['''SM_NUM_GPUS''']		)
		__UpperCAmelCase       :	Optional[int]						=      json.loads(os.environ['''SM_HOSTS''']		)
		__UpperCAmelCase       :	int						=      len(_UpperCAmelCase		)
		__UpperCAmelCase       :	Optional[Any]						=      os.environ['''SM_CURRENT_HOST''']
		__UpperCAmelCase       :	List[str]						=      hosts.index(_UpperCAmelCase		)
		__UpperCAmelCase       :	Dict						=      '''INFO'''

		if num_nodes > 1:
				__UpperCAmelCase       :	Tuple						=      f'python -m torch.distributed.launch \\n                --nnodes={num_nodes}  \\n                --node_rank={rank}  \\n                --nproc_per_node={num_gpus}  \\n                --master_addr={hosts[0]}  \\n                --master_port={port} \\n                ./run_glue.py \\n                {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		else:
				__UpperCAmelCase       :	Dict						=      f'python -m torch.distributed.launch \\n            --nproc_per_node={num_gpus}  \\n            ./run_glue.py \\n            {"".join(["nested fstring" for parameter,value in args.__dict__.items()]		)}'
		try:
				subprocess.run(_UpperCAmelCase					,  shell=_UpperCAmelCase		)
		except Exception as e:
				logger.info(_UpperCAmelCase		)


if __name__ == "__main__":
				main()





