code: 
  
  
  
  
  
  
  import os
  from typing import Optional, Union
  
  import numpy as np
  from huggingface_hub import hf_hub_download
  
  from ... import AutoTokenizer
  from ...utils import logging
  
  
  lowerCamelCase										=  '''block_records.npy'''
  
  
  lowerCamelCase										=  logging.get_logger(__name__)
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      import tensorflow.compat.va as tf
  
      _lowercase     =	tf.data.TFRecordDataset(_lowercase,      buffer_size=512 * 1024 * 1024      )
      _lowercase     =	blocks_dataset.batch(_lowercase,      drop_remainder=_lowercase      )
      _lowercase     =	next(blocks_dataset.take(1      ).as_numpy_iterator()      )
  
      return np_record
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Dict   ,       snake_case__:	Optional[Any]   ,       snake_case__:	List[Any]   ,       snake_case__:	List[Any]=2   ,       snake_case__:	Optional[int]=1_000   ,       snake_case__:	List[str]=100   ,       snake_case__:	Union[str, Any]=100_000   ,       )  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         from scann.scann_ops.py.scann_ops_pybind import builder as Builder
  
         _lowercase     =	Builder(db=snake_case__   ,       num_neighbors=snake_case__   ,       distance_measure='dot_product'	)
         _lowercase     =	builder.tree(
             num_leaves=snake_case__   ,       num_leaves_to_search=snake_case__   ,       training_sample_size=snake_case__	)
         _lowercase     =	builder.score_ah(dimensions_per_block=snake_case__	)
  
         _lowercase     =	builder.build()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]   ,       snake_case__:	int	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         _lowercase ,    _lowercase     =	self.searcher.search_batched(question_projection.detach().cpu()	)
         return retrieved_block_ids.astype('int64'	)
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Optional[Any]   ,       snake_case__:	Tuple   ,       snake_case__:	Dict	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         super().__init__()
         _lowercase     =	block_records
         _lowercase     =	tokenizer
  
     def __call__(    self:	Optional[int]   ,       snake_case__:	Tuple   ,       snake_case__:	Union[str, Any]   ,       snake_case__:	str   ,       snake_case__:	Union[str, Any]=None   ,       snake_case__:	int="pt"	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	np.take(self.block_records   ,       indices=snake_case__   ,       axis=0	)
  
         _lowercase     =	self.tokenizer.decode(question_input_ids[0]   ,       skip_special_tokens=snake_case__	)
  
         _lowercase     =	[]
         _lowercase     =	[]
         for retrieved_block in retrieved_blocks:
             text.append(snake_case__	)
             text_pair.append(retrieved_block.decode()	)
  
         _lowercase     =	self.tokenizer(
             snake_case__   ,       snake_case__   ,       padding=snake_case__   ,       truncation=snake_case__   ,       return_special_tokens_mask=snake_case__   ,       max_length=snake_case__	)
         _lowercase     =	concat_inputs.convert_to_tensors(snake_case__	)
  
         if answer_ids is not None:
             return self.block_has_answer(snake_case__   ,       snake_case__	) + (concat_inputs_tensors,)
         else:
             return (None, None, None, concat_inputs_tensors)
  
     @classmethod
     def 			SCREAMING_SNAKE_CASE__				(    cls:	List[Any]   ,       snake_case__:	Optional[Union[str, os.PathLike]]   ,       *snake_case__:	Optional[Any]   ,       **snake_case__:	int	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         if os.path.isdir(snake_case__	):
             _lowercase     =	os.path.join(snake_case__   ,       _REALM_BLOCK_RECORDS_FILENAME	)
         else:
             _lowercase     =	hf_hub_download(
                 repo_id=snake_case__   ,       filename=_REALM_BLOCK_RECORDS_FILENAME   ,       **snake_case__	)
         _lowercase     =	np.load(snake_case__   ,       allow_pickle=snake_case__	)
  
         _lowercase     =	AutoTokenizer.from_pretrained(snake_case__   ,       *snake_case__   ,       **snake_case__	)
  
         return cls(snake_case__   ,       snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	Tuple	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         np.save(os.path.join(snake_case__   ,       _REALM_BLOCK_RECORDS_FILENAME	)   ,       self.block_records	)
         # save tokenizer
         self.tokenizer.save_pretrained(snake_case__	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int   ,       snake_case__:	Optional[Any]   ,       snake_case__:	int	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	[]
         _lowercase     =	[]
         _lowercase     =	[]
         _lowercase     =	0
  
         for input_id in concat_inputs.input_ids:
             _lowercase     =	input_id.tolist()
             # Check answers between two [SEP] tokens
             _lowercase     =	input_id_list.index(self.tokenizer.sep_token_id	)
             _lowercase     =	first_sep_idx + 1 + input_id_list[first_sep_idx + 1 :].index(self.tokenizer.sep_token_id	)
  
             start_pos.append([]	)
             end_pos.append([]	)
             for answer in answer_ids:
                 for idx in range(first_sep_idx + 1   ,       snake_case__	):
                     if answer[0] == input_id_list[idx]:
                         if input_id_list[idx : idx + len(snake_case__	)] == answer:
                             start_pos[-1].append(snake_case__	)
                             end_pos[-1].append(idx + len(snake_case__	) - 1	)
  
             if len(start_pos[-1]	) == 0:
                 has_answers.append(snake_case__	)
             else:
                 has_answers.append(snake_case__	)
                 if len(start_pos[-1]	) > max_answers:
                     _lowercase     =	len(start_pos[-1]	)
  
          # Pad -1 to max_answers
         for start_pos_, end_pos_ in zip(snake_case__   ,       snake_case__	):
             if len(start_pos_	) < max_answers:
                 _lowercase     =	[-1] * (max_answers - len(start_pos_	))
                 start_pos_ += padded
                 end_pos_ += padded
         return has_answers, start_pos, end_pos
  
style_context: 
  
  
  
  
  
  
  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      ):
  
  
      '''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      (   _lowercase,      _lowercase,      _lowercase=False,      _lowercase=False      ):
  
  
      '''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      (   _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	dct.pop(_lowercase      )
      _lowercase     =	val
  def        UpperCAmelCase      (   ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	'http://images.cocodataset.org/val2017/000000039769.jpg'
      _lowercase     =	Image.open(requests.get(_lowercase,      stream=_lowercase      ).raw      )
      return im
  @torch.no_grad()
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''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(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{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]
          _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(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): 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(_lowercase,      _lowercase,      repo_type='dataset'      ),      'r'      )      )
          _lowercase     =	{int(_lowercase      ): 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(_lowercase,      map_location='cpu',      check_hash=_lowercase      )
      _lowercase     =	state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']
  
      _lowercase     =	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():
              _lowercase     =	state_dict.pop(_lowercase      )
              if key.startswith('backbone.fpn'      ):
                  _lowercase     =	key.replace('backbone.fpn',      'fpn'      )
              _lowercase     =	val
  
      # load HuggingFace model
      if checkpoint_url[-9:-4] == "pt22k":
          _lowercase     =	BeitForMaskedImageModeling(_lowercase      )
      elif "ade20k" in checkpoint_url:
          _lowercase     =	BeitForSemanticSegmentation(_lowercase      )
      else:
          _lowercase     =	BeitForImageClassification(_lowercase      )
      model.eval()
      model.load_state_dict(_lowercase      )
  
      # Check outputs on an image
      if is_semantic:
          _lowercase     =	BeitImageProcessor(size=config.image_size,      do_center_crop=_lowercase      )
          _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=_lowercase      )
          _lowercase     =	prepare_img()
  
      _lowercase     =	image_processor(images=_lowercase,      return_tensors='pt'      )
      _lowercase     =	encoding['pixel_values']
  
      _lowercase     =	model(_lowercase      )
      _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([2.2_288, 2.4_671, 0.7_395]      )
          _lowercase     =	2397
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'      ):
          _lowercase     =	torch.Size([1, 21841]      )
          _lowercase     =	torch.tensor([1.6_881, -0.2_787, 0.5_901]      )
          _lowercase     =	2396
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'      ):
          _lowercase     =	torch.tensor([0.1_241, 0.0_798, -0.6_569]      )
          _lowercase     =	285
      elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-1.2_385, -1.0_987, -1.0_108]      )
          _lowercase     =	281
      elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-1.5_303, -0.9_484, -0.3_147]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'      ):
          _lowercase     =	torch.tensor([0.4_610, -0.0_928, 0.2_086]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-0.4_804, 0.6_257, -0.1_837]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'      ):
          _lowercase     =	torch.tensor([-0.3_062, 0.7_261, 0.4_852]      )
          _lowercase     =	761
      elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'      ):
          _lowercase     =	(1, 150, 160, 160)
          _lowercase     =	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'      ):
          _lowercase     =	(1, 150, 160, 160)
          _lowercase     =	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],      _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)
  