


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			:			List[str]			      =					logging.get_logger(__name__)


def   a						(				SCREAMING_SNAKE_CASE_   :		int				,      SCREAMING_SNAKE_CASE_   :		str=False				,      SCREAMING_SNAKE_CASE_   :		List[str]=False							):






       """simple docstring"""





       UpperCamelCase     :						Optional[Any] 						=		"""backbone.""" if is_semantic else """"""

       UpperCamelCase     :						Tuple 						=		[]
       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_   :		Any				,      SCREAMING_SNAKE_CASE_   :		str				,      SCREAMING_SNAKE_CASE_   :		Optional[Any]=False				,      SCREAMING_SNAKE_CASE_   :		Any=False							):






       """simple docstring"""





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

              UpperCamelCase     :						Optional[int] 						=		in_proj_weight[
                  : config.hidden_size, :
              ]
              UpperCamelCase     :						Union[str, Any] 						=		q_bias
              UpperCamelCase     :						Dict 						=		in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              UpperCamelCase     :						List[Any] 						=		in_proj_weight[
                  -config.hidden_size :, :
              ]
              UpperCamelCase     :						Union[str, Any] 						=		v_bias

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

              UpperCamelCase     :						str 						=		gamma_a
              UpperCamelCase     :						Dict 						=		gamma_a

              # relative_position bias table + index
              if not has_lm_head:
                     # each layer has its own relative position bias
                     UpperCamelCase     :						Dict 						=		state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""							)
                     UpperCamelCase     :						List[str] 						=		state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""							)

                     UpperCamelCase     :						List[Any] 						=		table
                     UpperCamelCase     :						int 						=		index


def   a						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Optional[int]				,      SCREAMING_SNAKE_CASE_   :		Dict							):






       """simple docstring"""





       UpperCamelCase     :						List[Any] 						=		dct.pop(SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						Union[str, Any] 						=		val


def   a						(				):






       """simple docstring"""





       UpperCamelCase     :						Optional[Any] 						=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       UpperCamelCase     :						Union[str, Any] 						=		Image.open(requests.get(SCREAMING_SNAKE_CASE_				,      stream=SCREAMING_SNAKE_CASE_							).raw							)
       return im


@torch.no_grad()
def   a						(				SCREAMING_SNAKE_CASE_   :		Any				,      SCREAMING_SNAKE_CASE_   :		Any							):






       """simple docstring"""





       UpperCamelCase     :						Dict 						=		BeitConfig()
       UpperCamelCase     :						List[str] 						=		False
       UpperCamelCase     :						Any 						=		False
       UpperCamelCase     :						int 						=		"""huggingface/label-files"""
       # set config parameters based on URL
       if checkpoint_url[-9:-4] == "pt22k":
              # masked image modeling
              UpperCamelCase     :						List[Any] 						=		True
              UpperCamelCase     :						Dict 						=		True
              UpperCamelCase     :						List[Any] 						=		True
       elif checkpoint_url[-9:-4] == "ft22k":
              # intermediate fine-tuning on ImageNet-22k
              UpperCamelCase     :						Optional[int] 						=		True
              UpperCamelCase     :						List[str] 						=		2_1_8_4_1
              UpperCamelCase     :						Optional[int] 						=		"""imagenet-22k-id2label.json"""
              UpperCamelCase     :						Tuple 						=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      repo_type='''dataset'''							)				,      '''r'''							)							)
              UpperCamelCase     :						Optional[Any] 						=		{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[9_2_0_5]
              del idalabel[1_5_0_2_7]
              UpperCamelCase     :						Dict 						=		idalabel
              UpperCamelCase     :						int 						=		{v: k for k, v in idalabel.items()}
       elif checkpoint_url[-8:-4] == "to1k":
              # fine-tuning on ImageNet-1k
              UpperCamelCase     :						Tuple 						=		True
              UpperCamelCase     :						Union[str, Any] 						=		1_0_0_0
              UpperCamelCase     :						Optional[Any] 						=		"""imagenet-1k-id2label.json"""
              UpperCamelCase     :						str 						=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      repo_type='''dataset'''							)				,      '''r'''							)							)
              UpperCamelCase     :						str 						=		{int(SCREAMING_SNAKE_CASE_							): v for k, v in idalabel.items()}
              UpperCamelCase     :						Optional[Any] 						=		idalabel
              UpperCamelCase     :						str 						=		{v: k for k, v in idalabel.items()}
              if "384" in checkpoint_url:
                     UpperCamelCase     :						Union[str, Any] 						=		3_8_4
              if "512" in checkpoint_url:
                     UpperCamelCase     :						Optional[Any] 						=		5_1_2
       elif "ade20k" in checkpoint_url:
              # fine-tuning
              UpperCamelCase     :						Optional[int] 						=		True
              UpperCamelCase     :						Union[str, Any] 						=		1_5_0
              UpperCamelCase     :						List[Any] 						=		"""ade20k-id2label.json"""
              UpperCamelCase     :						Optional[Any] 						=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_				,      SCREAMING_SNAKE_CASE_				,      repo_type='''dataset'''							)				,      '''r'''							)							)
              UpperCamelCase     :						int 						=		{int(SCREAMING_SNAKE_CASE_							): v for k, v in idalabel.items()}
              UpperCamelCase     :						int 						=		idalabel
              UpperCamelCase     :						Tuple 						=		{v: k for k, v in idalabel.items()}
              UpperCamelCase     :						List[str] 						=		6_4_0
              UpperCamelCase     :						List[str] 						=		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     :						Tuple 						=		1_0_2_4
              UpperCamelCase     :						Union[str, Any] 						=		4_0_9_6
              UpperCamelCase     :						Dict 						=		2_4
              UpperCamelCase     :						List[Any] 						=		1_6
              if "ade20k" in checkpoint_url:
                     UpperCamelCase     :						List[str] 						=		6_4_0
                     UpperCamelCase     :						str 						=		[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
       UpperCamelCase     :						int 						=		torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_				,      map_location='''cpu'''				,      check_hash=SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						List[str] 						=		state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

       UpperCamelCase     :						Any 						=		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():
                     UpperCamelCase     :						List[str] 						=		state_dict.pop(SCREAMING_SNAKE_CASE_							)
                     if key.startswith('''backbone.fpn'''							):
                            UpperCamelCase     :						Union[str, Any] 						=		key.replace('''backbone.fpn'''				,      '''fpn'''							)
                     UpperCamelCase     :						int 						=		val

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

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

       UpperCamelCase     :						Optional[Any] 						=		image_processor(images=SCREAMING_SNAKE_CASE_				,      return_tensors='''pt'''							)
       UpperCamelCase     :						List[str] 						=		encoding["""pixel_values"""]

       UpperCamelCase     :						str 						=		model(SCREAMING_SNAKE_CASE_							)
       UpperCamelCase     :						List[Any] 						=		outputs.logits

       # verify logits
       UpperCamelCase     :						str 						=		torch.Size([1, 1_0_0_0]							)
       if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''							):
              UpperCamelCase     :						Any 						=		torch.Size([1, 1_9_6, 8_1_9_2]							)
       elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''							):
              UpperCamelCase     :						Any 						=		torch.Size([1, 1_9_6, 8_1_9_2]							)
       elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''							):
              UpperCamelCase     :						str 						=		torch.Size([1, 2_1_8_4_1]							)
              UpperCamelCase     :						List[str] 						=		torch.tensor([2.2288, 2.4671, 0.7395]							)
              UpperCamelCase     :						List[str] 						=		2_3_9_7
       elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''							):
              UpperCamelCase     :						Optional[Any] 						=		torch.Size([1, 2_1_8_4_1]							)
              UpperCamelCase     :						Tuple 						=		torch.tensor([1.6881, -0.2787, 0.5901]							)
              UpperCamelCase     :						Optional[Any] 						=		2_3_9_6
       elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''							):
              UpperCamelCase     :						List[Any] 						=		torch.tensor([0.1241, 0.0798, -0.6569]							)
              UpperCamelCase     :						List[str] 						=		2_8_5
       elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''							):
              UpperCamelCase     :						List[Any] 						=		torch.tensor([-1.2385, -1.0987, -1.0108]							)
              UpperCamelCase     :						int 						=		2_8_1
       elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''							):
              UpperCamelCase     :						List[str] 						=		torch.tensor([-1.5303, -0.9484, -0.3147]							)
              UpperCamelCase     :						Tuple 						=		7_6_1
       elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''							):
              UpperCamelCase     :						List[Any] 						=		torch.tensor([0.4610, -0.0928, 0.2086]							)
              UpperCamelCase     :						Tuple 						=		7_6_1
       elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''							):
              UpperCamelCase     :						List[Any] 						=		torch.tensor([-0.4804, 0.6257, -0.1837]							)
              UpperCamelCase     :						Optional[int] 						=		7_6_1
       elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''							):
              UpperCamelCase     :						List[str] 						=		torch.tensor([[-0.5122, 0.5117, -0.2113]]							)
              UpperCamelCase     :						Optional[int] 						=		7_6_1
       elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''							):
              UpperCamelCase     :						List[str] 						=		torch.tensor([-0.3062, 0.7261, 0.4852]							)
              UpperCamelCase     :						Optional[Any] 						=		7_6_1
       elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''							):
              UpperCamelCase     :						Any 						=		(1, 1_5_0, 1_6_0, 1_6_0)
              UpperCamelCase     :						Union[str, Any] 						=		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     :						Any 						=		(1, 1_5_0, 1_6_0, 1_6_0)
              UpperCamelCase     :						Union[str, Any] 						=		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]				,      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__":
  __UpperCAmelCase			:			Optional[int]			      =					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			:			List[Any]			      =					parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
a 			=							[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
def    a__    (	A       ):
   return [int(A       ) for c in F'''{ruleset:08}'''[:8]]
def    a__    (	A		,					A		,					A       ):
   lowercase__    :						List[Any]       =			len(cells[0]       )  # 31
   lowercase__    :						int       =			[]
   for i in range(A       ):
      # Get the neighbors of each cell
      # Handle neighbours outside bounds by using 0 as their value
      lowercase__    :						Optional[Any]       =			0 if i == 0 else cells[time][i - 1]
      lowercase__    :						Dict       =			0 if i == population - 1 else cells[time][i + 1]
      # Define a new cell and add it to the new generation
      lowercase__    :						str       =			7 - int(F'''{left_neighbor}{cells[time][i]}{right_neighbor}'''		,					2       )
      next_generation.append(rule[situation]       )
   return next_generation
def    a__    (	A       ):
   lowercase__    :						Tuple       =			Image.new("""RGB"""		,					(len(cells[0]       ), len(A       ))       )
   lowercase__    :						Optional[Any]       =			img.load()
   # Generates image
   for w in range(img.width       ):
      for h in range(img.height       ):
         lowercase__    :						Union[str, Any]       =			255 - int(255 * cells[h][w]       )
         lowercase__    :						int       =			(color, color, color)
   return img


if __name__ == "__main__":
 a 			=							bin(int(input('Rule:\n').strip()))[2:]
 a 			=							format_ruleset(int(rule_num))
 for time in range(16):
  CELLS.append(new_generation(CELLS, rule, time))
 a 			=							generate_image(CELLS)
 # Uncomment to save the image
 # img.save(f"rule_{rule_num}.png")
 img.show()


