






"""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  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase=False		,	__UpperCamelCase=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  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase=False		,	__UpperCamelCase=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  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







  __A						      =			dct.pop(__UpperCamelCase			)
  __A						      =			val



def  lowerCAmelCase    (   ):




  """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  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase			):




  """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						      =			2_1_8_4_1
    __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_2_0_5]
    del idalabel[1_5_0_2_7]
    __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_0_0_0
    __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						      =			3_8_4
    if "512" in checkpoint_url:
      __A						      =			5_1_2
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    __A						      =			True
    __A						      =			1_5_0
    __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						      =			6_4_0
    __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_0_2_4
    __A						      =			4_0_9_6
    __A						      =			2_4
    __A						      =			1_6
    if "ade20k" in checkpoint_url:
      __A						      =			6_4_0
      __A						      =			[7, 1_1, 1_5, 2_3]
  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_0_0_0]			)
  if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''			):
    __A						      =			torch.Size([1, 1_9_6, 8_1_9_2]			)
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''			):
    __A						      =			torch.Size([1, 1_9_6, 8_1_9_2]			)
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''			):
    __A						      =			torch.Size([1, 2_1_8_4_1]			)
    __A						      =			torch.tensor([2.2288, 2.4671, 0.7395]			)
    __A						      =			2_3_9_7
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''			):
    __A						      =			torch.Size([1, 2_1_8_4_1]			)
    __A						      =			torch.tensor([1.6881, -0.2787, 0.5901]			)
    __A						      =			2_3_9_6
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''			):
    __A						      =			torch.tensor([0.1241, 0.0798, -0.6569]			)
    __A						      =			2_8_5
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''			):
    __A						      =			torch.tensor([-1.2385, -1.0987, -1.0108]			)
    __A						      =			2_8_1
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''			):
    __A						      =			torch.tensor([-1.5303, -0.9484, -0.3147]			)
    __A						      =			7_6_1
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''			):
    __A						      =			torch.tensor([0.4610, -0.0928, 0.2086]			)
    __A						      =			7_6_1
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''			):
    __A						      =			torch.tensor([-0.4804, 0.6257, -0.1837]			)
    __A						      =			7_6_1
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''			):
    __A						      =			torch.tensor([[-0.5122, 0.5117, -0.2113]]			)
    __A						      =			7_6_1
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''			):
    __A						      =			torch.tensor([-0.3062, 0.7261, 0.4852]			)
    __A						      =			7_6_1
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''			):
    __A						      =			(1, 1_5_0, 1_6_0, 1_6_0)
    __A						      =			torch.tensor(
        [
            [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
            [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
            [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
        ]			)
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''			):
    __A						      =			(1, 1_5_0, 1_6_0, 1_6_0)
    __A						      =			torch.tensor(
        [
            [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
            [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
            [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
        ]			)
  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__":
       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)












"""simple docstring"""


import itertools
from pathlib import Path



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  __A						      =			[tuple(__UpperCamelCase			) for login in logins]

  __A						      =			{char for login in split_logins for char in login}

  for permutation in itertools.permutations(__UpperCamelCase			):
    __A						      =			True
    for login in logins:
      if not (
          permutation.index(login[0]			)
          < permutation.index(login[1]			)
          < permutation.index(login[2]			)
      ):
        __A						      =			False
        break

    if satisfied:
      return int(''''''.join(__UpperCamelCase			)			)

  raise Exception('''Unable to find the secret passcode'''			)



def  lowerCAmelCase    (   __UpperCamelCase = "keylog.txt"			):




  """simple docstring"""







  __A						      =			Path(__UpperCamelCase			).parent.joinpath(__UpperCamelCase			).read_text().splitlines()

  return find_secret_passcode(__UpperCamelCase			)


if __name__ == "__main__":
       print(F'''{solution() = }''')




