






'''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()
a__  : Any				=	logging.get_logger(__name__)







def _UpperCamelCase	(					__A				,      __A=False				,      __A=False   )							->		List[str]:


      '''simple docstring'''



      UpperCamelCase__  =    "backbone." if is_semantic else ""

      UpperCamelCase__  =    []
      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   )							->		Tuple:


      '''simple docstring'''



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

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

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

            UpperCamelCase__  =    gamma_a
            UpperCamelCase__  =    gamma_a

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

                  UpperCamelCase__  =    table
                  UpperCamelCase__  =    index







def _UpperCamelCase	(					__A				,      __A				,      __A   )							->		List[Any]:


      '''simple docstring'''



      UpperCamelCase__  =    dct.pop(__A   )
      UpperCamelCase__  =    val







def _UpperCamelCase	(					)							->		Union[str, Any]:


      '''simple docstring'''



      UpperCamelCase__  =    "http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCamelCase__  =    Image.open(requests.get(__A				,      stream=__A   ).raw   )
      return im







@torch.no_grad()
def _UpperCamelCase	(					__A				,      __A   )							->		Optional[int]:


      '''simple docstring'''



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

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

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

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

      UpperCamelCase__  =    image_processor(images=__A				,      return_tensors="pt"   )
      UpperCamelCase__  =    encoding["pixel_values"]

      UpperCamelCase__  =    model(__A   )
      UpperCamelCase__  =    outputs.logits

      # verify logits
      UpperCamelCase__  =    torch.Size([1, 1000]   )
      if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"   ):
            UpperCamelCase__  =    torch.Size([1, 196, 8192]   )
      elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"   ):
            UpperCamelCase__  =    torch.Size([1, 196, 8192]   )
      elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"   ):
            UpperCamelCase__  =    torch.Size([1, 21841]   )
            UpperCamelCase__  =    torch.tensor([2.2288, 2.4671, 0.7395]   )
            UpperCamelCase__  =    2397
      elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"   ):
            UpperCamelCase__  =    torch.Size([1, 21841]   )
            UpperCamelCase__  =    torch.tensor([1.6881, -0.2787, 0.5901]   )
            UpperCamelCase__  =    2396
      elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"   ):
            UpperCamelCase__  =    torch.tensor([0.1241, 0.0798, -0.6569]   )
            UpperCamelCase__  =    285
      elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"   ):
            UpperCamelCase__  =    torch.tensor([-1.2385, -1.0987, -1.0108]   )
            UpperCamelCase__  =    281
      elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"   ):
            UpperCamelCase__  =    torch.tensor([-1.5303, -0.9484, -0.3147]   )
            UpperCamelCase__  =    761
      elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"   ):
            UpperCamelCase__  =    torch.tensor([0.4610, -0.0928, 0.2086]   )
            UpperCamelCase__  =    761
      elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"   ):
            UpperCamelCase__  =    torch.tensor([-0.4804, 0.6257, -0.1837]   )
            UpperCamelCase__  =    761
      elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"   ):
            UpperCamelCase__  =    torch.tensor([[-0.5122, 0.5117, -0.2113]]   )
            UpperCamelCase__  =    761
      elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"   ):
            UpperCamelCase__  =    torch.tensor([-0.3062, 0.7261, 0.4852]   )
            UpperCamelCase__  =    761
      elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"   ):
            UpperCamelCase__  =    (1, 150, 160, 160)
            UpperCamelCase__  =    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"   ):
            UpperCamelCase__  =    (1, 150, 160, 160)
            UpperCamelCase__  =    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]				,      __A				,      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]				,      __A				,      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(__A   ).mkdir(exist_ok=__A   )
      print(F'''Saving model to {pytorch_dump_folder_path}'''   )
      model.save_pretrained(__A   )
      print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
      image_processor.save_pretrained(__A   )


if __name__ == "__main__":
  a__  : List[str]				=	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.'
  )
  a__  : Any				=	parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









'''simple docstring'''







import math







def _UpperCamelCase	(					__A   )							->		bool:


      '''simple docstring'''



      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5				,      int(math.sqrt(__A   ) + 1   )				,      6   ):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True







def _UpperCamelCase	(					__A = 1000				,      __A = 1000   )							->		int:


      '''simple docstring'''



      UpperCamelCase__  =    [0, 0, 0]  # length, a, b
      for a in range((a_limit * -1) + 1				,      __A   ):
            for b in range(2				,      __A   ):
                  if is_prime(__A   ):
                        UpperCamelCase__  =    0
                        UpperCamelCase__  =    0
                        while is_prime((n**2) + (a * n) + b   ):
                              count += 1
                              n += 1
                        if count > longest[0]:
                              UpperCamelCase__  =    [count, a, b]
      UpperCamelCase__  =    longest[1] * longest[2]
      return ans


if __name__ == "__main__":
  print(solution(1_0_0_0, 1_0_0_0))





