






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 			=							logging.get_logger(__name__)
def    a__    (	A		,					A=False		,					A=False       ):
   lowercase__    :						Optional[Any]       =			"""backbone.""" if is_semantic else """"""

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

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

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

      lowercase__    :						str       =			gamma_a
      lowercase__    :						Dict       =			gamma_a

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

         lowercase__    :						List[Any]       =			table
         lowercase__    :						int       =			index
def    a__    (	A		,					A		,					A       ):
   lowercase__    :						List[Any]       =			dct.pop(A       )
   lowercase__    :						Union[str, Any]       =			val
def    a__    (	):
   lowercase__    :						Optional[Any]       =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
   lowercase__    :						Union[str, Any]       =			Image.open(requests.get(A		,					stream=A       ).raw       )
   return im
@torch.no_grad()
def    a__    (	A		,					A       ):
   lowercase__    :						Dict       =			BeitConfig()
   lowercase__    :						List[str]       =			False
   lowercase__    :						Any       =			False
   lowercase__    :						int       =			"""huggingface/label-files"""
   # set config parameters based on URL
   if checkpoint_url[-9:-4] == "pt22k":
      # masked image modeling
      lowercase__    :						List[Any]       =			True
      lowercase__    :						Dict       =			True
      lowercase__    :						List[Any]       =			True
   elif checkpoint_url[-9:-4] == "ft22k":
      # intermediate fine-tuning on ImageNet-22k
      lowercase__    :						Optional[int]       =			True
      lowercase__    :						List[str]       =			21_841
      lowercase__    :						Optional[int]       =			"""imagenet-22k-id2label.json"""
      lowercase__    :						Tuple       =			json.load(open(hf_hub_download(A		,					A		,					repo_type="""dataset"""       )		,					"""r"""       )       )
      lowercase__    :						Optional[Any]       =			{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[9_205]
      del idalabel[15_027]
      lowercase__    :						Dict       =			idalabel
      lowercase__    :						int       =			{v: k for k, v in idalabel.items()}
   elif checkpoint_url[-8:-4] == "to1k":
      # fine-tuning on ImageNet-1k
      lowercase__    :						Tuple       =			True
      lowercase__    :						Union[str, Any]       =			1_000
      lowercase__    :						Optional[Any]       =			"""imagenet-1k-id2label.json"""
      lowercase__    :						str       =			json.load(open(hf_hub_download(A		,					A		,					repo_type="""dataset"""       )		,					"""r"""       )       )
      lowercase__    :						str       =			{int(A       ): v for k, v in idalabel.items()}
      lowercase__    :						Optional[Any]       =			idalabel
      lowercase__    :						str       =			{v: k for k, v in idalabel.items()}
      if "384" in checkpoint_url:
         lowercase__    :						Union[str, Any]       =			384
      if "512" in checkpoint_url:
         lowercase__    :						Optional[Any]       =			512
   elif "ade20k" in checkpoint_url:
      # fine-tuning
      lowercase__    :						Optional[int]       =			True
      lowercase__    :						Union[str, Any]       =			150
      lowercase__    :						List[Any]       =			"""ade20k-id2label.json"""
      lowercase__    :						Optional[Any]       =			json.load(open(hf_hub_download(A		,					A		,					repo_type="""dataset"""       )		,					"""r"""       )       )
      lowercase__    :						int       =			{int(A       ): v for k, v in idalabel.items()}
      lowercase__    :						int       =			idalabel
      lowercase__    :						Tuple       =			{v: k for k, v in idalabel.items()}
      lowercase__    :						List[str]       =			640
      lowercase__    :						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:
      lowercase__    :						Tuple       =			1_024
      lowercase__    :						Union[str, Any]       =			4_096
      lowercase__    :						Dict       =			24
      lowercase__    :						List[Any]       =			16
      if "ade20k" in checkpoint_url:
         lowercase__    :						List[str]       =			640
         lowercase__    :						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
   lowercase__    :						int       =			torch.hub.load_state_dict_from_url(A		,					map_location="""cpu"""		,					check_hash=A       )
   lowercase__    :						List[str]       =			state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

   lowercase__    :						Any       =			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():
         lowercase__    :						List[str]       =			state_dict.pop(A       )
         if key.startswith("""backbone.fpn"""       ):
            lowercase__    :						Union[str, Any]       =			key.replace("""backbone.fpn"""		,					"""fpn"""       )
         lowercase__    :						int       =			val

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

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

   lowercase__    :						Optional[Any]       =			image_processor(images=A		,					return_tensors="""pt"""       )
   lowercase__    :						List[str]       =			encoding["""pixel_values"""]

   lowercase__    :						str       =			model(A       )
   lowercase__    :						List[Any]       =			outputs.logits

   # verify logits
   lowercase__    :						str       =			torch.Size([1, 1_000]       )
   if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""       ):
      lowercase__    :						Any       =			torch.Size([1, 196, 8_192]       )
   elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""       ):
      lowercase__    :						Any       =			torch.Size([1, 196, 8_192]       )
   elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""       ):
      lowercase__    :						str       =			torch.Size([1, 21_841]       )
      lowercase__    :						List[str]       =			torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]       )
      lowercase__    :						List[str]       =			2_397
   elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""       ):
      lowercase__    :						Optional[Any]       =			torch.Size([1, 21_841]       )
      lowercase__    :						Tuple       =			torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]       )
      lowercase__    :						Optional[Any]       =			2_396
   elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""       ):
      lowercase__    :						List[Any]       =			torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]       )
      lowercase__    :						List[str]       =			285
   elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""       ):
      lowercase__    :						List[Any]       =			torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]       )
      lowercase__    :						int       =			281
   elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""       ):
      lowercase__    :						List[str]       =			torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]       )
      lowercase__    :						Tuple       =			761
   elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""       ):
      lowercase__    :						List[Any]       =			torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]       )
      lowercase__    :						Tuple       =			761
   elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""       ):
      lowercase__    :						List[Any]       =			torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]       )
      lowercase__    :						Optional[int]       =			761
   elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""       ):
      lowercase__    :						List[str]       =			torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]       )
      lowercase__    :						Optional[int]       =			761
   elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""       ):
      lowercase__    :						List[str]       =			torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]       )
      lowercase__    :						Optional[Any]       =			761
   elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""       ):
      lowercase__    :						Any       =			(1, 150, 160, 160)
      lowercase__    :						Union[str, Any]       =			torch.tensor(
          [
              [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
              [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
              [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
          ]       )
   elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""       ):
      lowercase__    :						Any       =			(1, 150, 160, 160)
      lowercase__    :						Union[str, Any]       =			torch.tensor(
          [
              [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
              [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
              [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
          ]       )
   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 			=							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 			=							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()


