






'''simple docstring'''







import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)


def 						_UpperCamelCase							(     snake_case__							,			snake_case__=False							,			snake_case__=False							,			snake_case__="data2vec."							):
      __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"{hf_prefix}encoder.layer.{i}.layernorm_before.weight")							)
            rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_before.bias")							)
            rename_keys.append(
                (f"{prefix}blocks.{i}.attn.proj.weight", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.weight")							)
            rename_keys.append(
                (f"{prefix}blocks.{i}.attn.proj.bias", f"{hf_prefix}encoder.layer.{i}.attention.output.dense.bias")							)
            rename_keys.append(
                (f"{prefix}blocks.{i}.norm2.weight", f"{hf_prefix}encoder.layer.{i}.layernorm_after.weight")							)
            rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"{hf_prefix}encoder.layer.{i}.layernorm_after.bias")							)
            rename_keys.append(
                (f"{prefix}blocks.{i}.mlp.fc1.weight", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.weight")							)
            rename_keys.append(
                (f"{prefix}blocks.{i}.mlp.fc1.bias", f"{hf_prefix}encoder.layer.{i}.intermediate.dense.bias")							)
            rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"{hf_prefix}encoder.layer.{i}.output.dense.weight")							)
            rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"{hf_prefix}encoder.layer.{i}.output.dense.bias")							)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              (f"{prefix}cls_token", f"{hf_prefix}embeddings.cls_token"),
              (f"{prefix}patch_embed.proj.weight", f"{hf_prefix}embeddings.patch_embeddings.projection.weight"),
              (f"{prefix}patch_embed.proj.bias", f"{hf_prefix}embeddings.patch_embeddings.projection.bias"),
          ]							)

      if has_lm_head:
            # mask token + shared relative position bias + layernorm
            rename_keys.extend(
                [
                    ("mask_token", f"{hf_prefix}embeddings.mask_token"),
                    (
                        "rel_pos_bias.relative_position_bias_table",
                        f"{hf_prefix}encoder.relative_position_bias.relative_position_bias_table",
                    ),
                    (
                        "rel_pos_bias.relative_position_index",
                        f"{hf_prefix}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", f"{hf_prefix}pooler.layernorm.weight"),
                    ("fc_norm.bias", f"{hf_prefix}pooler.layernorm.bias"),
                    ("head.weight", "classifier.weight"),
                    ("head.bias", "classifier.bias"),
                ]							)

      return rename_keys


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							,			snake_case__=False							,			snake_case__=False							,			snake_case__="data2vec_vision."							):
      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 						_UpperCamelCase							(     ):
      __A		   =      argparse.ArgumentParser(
          "Convert Data2VecVision to HF for image classification and pretraining"							,			add_help=snake_case__							)
      parser.add_argument("--hf_checkpoint_name"							,			type=snake_case__							)
      parser.add_argument("--input_size"							,			default=224							,			type=snake_case__							,			help="images input size"							)
      parser.add_argument("--beit_checkpoint"							,			default=""							,			help="beit checkpoint"							)

      return parser.parse_args()


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							,			snake_case__							):
      def load_state_dict(snake_case__							,			snake_case__							,			snake_case__=""							,			snake_case__="relative_position_index"							):
            __A		   =      []
            __A		   =      []
            __A		   =      []
            # copy state_dict so _load_from_state_dict can modify it
            __A		   =      getattr(snake_case__							,			"_metadata"							,			snake_case__							)
            __A		   =      state_dict.copy()
            if metadata is not None:
                  __A		   =      metadata

            def load(snake_case__							,			snake_case__=""							):
                  __A		   =      {} if metadata is None else metadata.get(prefix[:-1]							,			{}							)
                  module._load_from_state_dict(
                      snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							,			snake_case__							)
                  for name, child in module._modules.items():
                        if child is not None:
                              load(snake_case__							,			prefix + name + "."							)

            load(snake_case__							,			prefix=snake_case__							)

            __A		   =      []
            __A		   =      []
            for key in missing_keys:
                  __A		   =      True
                  for ignore_key in ignore_missing.split("|"							):
                        if ignore_key in key:
                              __A		   =      False
                              break
                  if keep_flag:
                        warn_missing_keys.append(snake_case__							)
                  else:
                        ignore_missing_keys.append(snake_case__							)

            __A		   =      warn_missing_keys

            if len(snake_case__							) > 0:
                  print(
                      "Weights of {} not initialized from pretrained model: {}".format(
                          model.__class__.__name__							,			snake_case__							)							)
            if len(snake_case__							) > 0:
                  print("Weights from pretrained model not used in {}: {}".format(model.__class__.__name__							,			snake_case__							)							)
            if len(snake_case__							) > 0:
                  print(
                      "Ignored weights of {} not initialized from pretrained model: {}".format(
                          model.__class__.__name__							,			snake_case__							)							)
            if len(snake_case__							) > 0:
                  print("\n".join(snake_case__							)							)

      __A		   =      {
          "pretrained": False,
          "use_shared_rel_pos_bias": True,
          "use_abs_pos_emb": False,
          "init_values": 0.1,
      }

      if is_finetuned:
            model_kwargs.update(
                {
                    "num_classes": 1_000,
                    "use_mean_pooling": True,
                    "init_scale": 0.001,
                    "use_rel_pos_bias": True,
                }							)

      __A		   =      create_model(
          "beit_large_patch16_224" if is_large else "beit_base_patch16_224"							,			**snake_case__							,			)
      __A		   =      model.patch_embed.patch_size
      __A		   =      (args.input_size // patch_size[0], args.input_size // patch_size[1])
      __A		   =      torch.load(args.beit_checkpoint							,			map_location="cpu"							)

      print(f"Load ckpt from {args.beit_checkpoint}"							)
      __A		   =      None
      for model_key in ("model", "module"):
            if model_key in checkpoint:
                  __A		   =      checkpoint[model_key]
                  print(f"Load state_dict by model_key = {model_key}"							)
                  break

      __A		   =      list(checkpoint_model.keys()							)
      for key in all_keys:
            if "relative_position_index" in key:
                  checkpoint_model.pop(snake_case__							)

            if "relative_position_bias_table" in key:
                  __A		   =      checkpoint_model[key]
                  __A   ,				__A		   =      rel_pos_bias.size()
                  __A   ,				__A		   =      model.state_dict()[key].size()
                  __A		   =      model.patch_embed.patch_shape
                  if dst_patch_shape[0] != dst_patch_shape[1]:
                        raise NotImplementedError()

      load_state_dict(snake_case__							,			snake_case__							,			prefix=""							)

      return model


def 						_UpperCamelCase							(     ):
      __A		   =      get_args()

      __A		   =      "ft1k" in args.hf_checkpoint_name
      __A		   =      "large" in args.hf_checkpoint_name

      if is_finetuned:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
            # into this folder.
            import modeling_finetune  # noqa: F401
      else:
            # To convert Beit's data2vec_vision to HF you need to copy
            # https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
            # into this folder
            # IMPORTANT: Note that for now we've only converted the down-stream
            # model and not the full pretrained model. This means for the integration
            # test you need to add a `return x` after the following line:
            # https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
            # to make the integration test pass.
            import modeling_cyclical  # noqa: F401

      # 1. Create model config
      __A		   =      DataaVecVisionConfig()
      if is_finetuned:
            __A		   =      True
            __A		   =      False
            __A		   =      True
            __A		   =      1_000

            __A		   =      "huggingface/label-files"
            __A		   =      "imagenet-1k-id2label.json"
            __A		   =      json.load(open(hf_hub_download(snake_case__							,			snake_case__							,			repo_type="dataset"							)							,			"r"							)							)
            __A		   =      {int(snake_case__							): v for k, v in idalabel.items()}
            __A		   =      idalabel
            __A		   =      {v: k for k, v in idalabel.items()}
      else:
            __A		   =      False
            __A		   =      True
            __A		   =      False

      if is_large:
            __A		   =      1_024
            __A		   =      4_096
            __A		   =      24
            __A		   =      16

      # 2. Load Beit model
      __A		   =      load_beit_model(snake_case__							,			snake_case__							,			snake_case__							)
      orig_model.eval()

      # 3. Forward Beit model
      __A		   =      BeitImageProcessor(size=config.image_size							,			do_center_crop=snake_case__							)
      __A		   =      Image.open("../../../../tests/fixtures/tests_samples/COCO/000000039769.png"							)
      __A		   =      image_processor(images=snake_case__							,			return_tensors="pt"							)
      __A		   =      encoding["pixel_values"]

      __A		   =      (pixel_values,) if is_finetuned else (pixel_values, None)
      with torch.no_grad():
            __A		   =      orig_model(*snake_case__							)

      # 4. Load HF Data2VecVision model
      if is_finetuned:
            __A		   =      DataaVecVisionForImageClassification(snake_case__							)
            hf_model.eval()
            __A		   =      False
            __A		   =      "data2vec_vision."
      else:
            __A		   =      DataaVecVisionModel(snake_case__							)
            hf_model.eval()
            __A		   =      True
            __A		   =      ""

      __A		   =      create_rename_keys(snake_case__							,			hf_prefix=snake_case__							,			has_lm_head=snake_case__							)
      __A		   =      orig_model.state_dict()
      for src, dest in rename_keys:
            __A		   =      state_dict.pop(snake_case__							)
            __A		   =      val

      read_in_q_k_v(snake_case__							,			snake_case__							,			hf_prefix=snake_case__							,			has_lm_head=snake_case__							)
      __A   ,				__A		   =      hf_model.load_state_dict(snake_case__							,			strict=snake_case__							)
      print("HF missing"							,			snake_case__							)
      print("HF unexpected_keys"							,			snake_case__							)

      # 5. Forward HF Data2VecVision model
      with torch.no_grad():
            __A		   =      hf_model(snake_case__							)

      __A		   =      hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

      # 6. Compare
      __A		   =      torch.max(torch.abs(hf_output - orig_model_output							)							).item()

      print(f"max_absolute_diff = {max_absolute_diff}"							)
      __A		   =      torch.allclose(snake_case__							,			snake_case__							,			atol=1E-3							)
      print("Do both models output the same tensors?"							,			"🔥" if success else "💩"							)
      if not success:
            raise Exception("Something went wRoNg"							)

      # 7. Save
      print(f"Saving to {args.hf_checkpoint_name}"							)
      hf_model.save_pretrained(args.hf_checkpoint_name							)
      image_processor.save_pretrained(args.hf_checkpoint_name							)


if __name__ == "__main__":
       main()
       # Run the following to convert checkpoints
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_base.pt \
       #          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./pretrained_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large"
       #  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
       #          --beit_checkpoint ./finetuned_large.pt \
       #          --hf_checkpoint_name "./data2vec-vision-large-ft1k"










'''simple docstring'''







from __future__ import annotations

from queue import Queue






class 					a__    :
     '''simple docstring'''







     def __init__(self    :	Any					)  ->       None:
           __A		   =      {}






     def     _UpperCamelCase   (self    :	Optional[int]					)  ->       None:
           for i in self.vertices:
                 print(lowercase__				,				" : "				,				" -> ".join([str(lowercase__					) for j in self.vertices[i]]					)					)






     def     _UpperCamelCase   (self    :	Union[str, Any]				,				lowercase__    :	int				,				lowercase__    :	int					)  ->       None:
           if from_vertex in self.vertices:
                 self.vertices[from_vertex].append(lowercase__					)
           else:
                 __A		   =      [to_vertex]






     def     _UpperCamelCase   (self    :	Optional[int]				,				lowercase__    :	int					)  ->       set[int]:
           __A		   =      set()

           # create a first in first out queue to store all the vertices for BFS
           __A		   =      Queue()

           # mark the source node as visited and enqueue it
           visited.add(lowercase__					)
           queue.put(lowercase__					)

           while not queue.empty():
                 __A		   =      queue.get()

                 # loop through all adjacent vertex and enqueue it if not yet visited
                 for adjacent_vertex in self.vertices[vertex]:
                       if adjacent_vertex not in visited:
                             queue.put(lowercase__					)
                             visited.add(lowercase__					)
           return visited


if __name__ == "__main__":
       from doctest import testmod

       testmod(verbose=True)

       __A      :       Dict      =	Graph()
       g.add_edge(0, 1)
       g.add_edge(0, 2)
       g.add_edge(1, 2)
       g.add_edge(2, 0)
       g.add_edge(2, 3)
       g.add_edge(3, 3)

       g.print_graph()
       # 0  :  1 -> 2
       # 1  :  2
       # 2  :  0 -> 3
       # 3  :  3

       assert sorted(g.bfs(2)) == [0, 1, 2, 3]


