


"""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  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase=False		,	__UpperCamelCase=False		,	__UpperCamelCase="data2vec."			):




  """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'{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  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase=False		,	__UpperCamelCase=False		,	__UpperCamelCase="data2vec_vision."			):




  """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    (   ):




  """simple docstring"""







  __A						      =			argparse.ArgumentParser(
      '''Convert Data2VecVision to HF for image classification and pretraining'''		,	add_help=__UpperCamelCase			)
  parser.add_argument('''--hf_checkpoint_name'''		,	type=__UpperCamelCase			)
  parser.add_argument('''--input_size'''		,	default=2_2_4		,	type=__UpperCamelCase		,	help='''images input size'''			)
  parser.add_argument('''--beit_checkpoint'''		,	default=''''''		,	help='''beit checkpoint'''			)

  return parser.parse_args()



def  lowerCAmelCase    (   __UpperCamelCase		,	__UpperCamelCase		,	__UpperCamelCase			):




  """simple docstring"""







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

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

    load(__UpperCamelCase		,	prefix=__UpperCamelCase			)

    __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(__UpperCamelCase			)
      else:
        ignore_missing_keys.append(__UpperCamelCase			)

    __A						      =			warn_missing_keys

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

  __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_0_0_0,
            '''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'''		,	**__UpperCamelCase		,	)
  __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(__UpperCamelCase			)

    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(__UpperCamelCase		,	__UpperCamelCase		,	prefix=''''''			)

  return model



def  lowerCAmelCase    (   ):




  """simple docstring"""







  __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_0_0_0

    __A						      =			'''huggingface/label-files'''
    __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()}
  else:
    __A						      =			False
    __A						      =			True
    __A						      =			False

  if is_large:
    __A						      =			1_0_2_4
    __A						      =			4_0_9_6
    __A						      =			2_4
    __A						      =			1_6

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

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

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

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

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

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

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

  __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(__UpperCamelCase		,	__UpperCamelCase		,	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 itertools import product



def  lowerCAmelCase    (   __UpperCamelCase = 1_5			):




  """simple docstring"""







  __A						      =			0.0
  __A						      =			42
  __A						      =			42
  __A						      =			42
  __A						      =			42
  __A						      =			42
  __A						      =			42

  for series in product(range(2			)		,	repeat=__UpperCamelCase			):
    __A						      =			series.count(1			)
    __A						      =			num_turns - num_blue
    if num_red >= num_blue:
      continue
    __A						      =			1.0
    for ind, col in enumerate(__UpperCamelCase		,	2			):
      if col == 0:
        prob *= (ind - 1) / ind
      else:
        prob *= 1 / ind

    total_prob += prob

  return int(1 / total_prob			)


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




