


"""simple docstring"""







import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


__lowercase    =				re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
__lowercase    =				10
__lowercase    =				256


def 					lowercase      (    A_	)->				Optional[MinHash]:
  '''simple docstring'''







  if len(A_	) < MIN_NUM_TOKENS:
    return None
  a						:		Optional[int]											=      MinHash(num_perm=A_	)
  for token in set(A_	):
    min_hash.update(token.encode()	)
  return min_hash


def 					lowercase      (    A_	)->				Set[str]:
  '''simple docstring'''







  return {t for t in NON_ALPHA.split(A_	) if len(t.strip()	) > 0}






class 						_A  :







    """simple docstring"""




    def __init__( self    :   List[str]    ,			*,
        __UpperCAmelCase    :   float = 0.85    ,			):
      a						:		str											=      duplication_jaccard_threshold
      a						:		List[Any]											=      NUM_PERM
      a						:		Tuple											=      MinHashLSH(threshold=self._duplication_jaccard_threshold    ,			num_perm=self._num_perm)

      a						:		Dict											=      defaultdict(__UpperCAmelCase)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   Tuple    ,			__UpperCAmelCase    :   MinHash):
      a						:		Tuple											=      self._index.query(__UpperCAmelCase)
      if code_key in self._index.keys:
        print(f'''Duplicate key {code_key}''')
        return

      self._index.insert(__UpperCAmelCase    ,			__UpperCAmelCase)
      if len(__UpperCAmelCase) > 0:
        for base_duplicate in close_duplicates:
          if base_duplicate in self._duplicate_clusters:
            self._duplicate_clusters[base_duplicate].add(__UpperCAmelCase)
            break
        else:
          self._duplicate_clusters[close_duplicates[0]].add(__UpperCAmelCase)






    def      __snake_case							( self    :   int):
      a						:		Dict											=      []
      for base, duplicates in self._duplicate_clusters.items():
        a						:		Any											=      [base] + list(__UpperCAmelCase)
        # reformat the cluster to be a list of dict
        a						:		Dict											=      [{"base_index": el[0], "repo_name": el[1], "path": el[2]} for el in cluster]
        duplicate_clusters.append(__UpperCAmelCase)
      return duplicate_clusters






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   List[str]):
      a						:		Optional[Any]											=      self.get_duplicate_clusters()
      with open(__UpperCAmelCase    ,			"w") as f:
        json.dump(__UpperCAmelCase    ,			__UpperCAmelCase)






def 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







  a						:		int											=      element
  a						:		str											=      get_min_hash([t for t in NON_ALPHA.split(data["content"]	) if len(t.strip()	) > 0]	)
  if min_hash is not None:
    return (index, data["repo_name"], data["path"]), min_hash


def 					lowercase      (    A_	)->				List[str]:
  '''simple docstring'''







  with mp.Pool() as pool:
    for data in pool.imap_unordered(
        _compute_min_hash  ,       ThreadedIterator(A_  ,       max_queue_size=10_000	)  ,       chunksize=100  ,       ):
      if data is not None:
        yield data


def 					lowercase      (    A_  ,       A_	)->				Any:
  '''simple docstring'''







  a						:		List[Any]											=      DuplicationIndex(duplication_jaccard_threshold=A_	)

  for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(A_	)	)  ,       max_queue_size=100	)	):
    di.add(A_  ,       A_	)

  # Returns a List[Cluster] where Cluster is List[str] with the filenames.
  return di.get_duplicate_clusters()


def 					lowercase      (    A_  ,       A_	)->				float:
  '''simple docstring'''







  a						:		str											=      get_tokens(A_	)
  a						:		Optional[Any]											=      get_tokens(A_	)
  return len(tokensa & tokensa	) / len(tokensa | tokensa	)


__lowercase    =				None


def 					lowercase      (    A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Any											=      []
  for elementa in cluster:
    a						:		Tuple											=      _shared_dataset[elementa["base_index"]]["content"]
    for elementa in extremes:
      a						:		int											=      _shared_dataset[elementa["base_index"]]["content"]
      if jaccard_similarity(A_  ,       A_	) >= jaccard_threshold:
        elementa["copies"] += 1
        break
    else:
      a						:		Any											=      1
      extremes.append(A_	)
  return extremes


def 					lowercase      (    A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  global _shared_dataset
  a						:		Optional[Any]											=      dataset
  a						:		Any											=      []
  a						:		int											=      partial(_find_cluster_extremes_shared  ,       jaccard_threshold=A_	)
  with mp.Pool() as pool:
    for extremes in tqdm(
        pool.imap_unordered(
            A_  ,       A_  ,       )  ,       total=len(A_	)  ,       ):
      extremes_list.append(A_	)
  return extremes_list







def 					lowercase      (    A_  ,       A_ = 0.8_5	)->				Tuple[Type[Dataset], List[List[Dict]]]:
  '''simple docstring'''







  a						:		Optional[Any]											=      make_duplicate_clusters(A_  ,       A_	)
  a						:		int											=      {x["base_index"] for cluster in duplicate_clusters for x in cluster}
  a						:		int											=      {}
  a						:		Union[str, Any]											=      find_extremes(A_  ,       A_  ,       A_	)
  for extremes in extremes_clusters:
    for element in extremes:
      a						:		str											=      element
  a						:		Dict											=      duplicate_indices - set(extreme_dict.keys()	)
  a						:		str											=      dataset.filter(lambda A_  ,       A_	: idx not in remove_indices  ,       with_indices=A_	)

  # update duplicate_clusters
  for cluster in duplicate_clusters:
    for element in cluster:
      a						:		Dict											=      element["base_index"] in extreme_dict
      if element["is_extreme"]:
        a						:		Union[str, Any]											=      extreme_dict[element["base_index"]]["copies"]

  print(F'''Original dataset size: {len(A_	)}'''	)
  print(F'''Number of duplicate clusters: {len(A_	)}'''	)
  print(F'''Files in duplicate cluster: {len(A_	)}'''	)
  print(F'''Unique files in duplicate cluster: {len(A_	)}'''	)
  print(F'''Filtered dataset size: {len(A_	)}'''	)

  return ds_filter, duplicate_clusters










"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase    =				logging.get_logger(__name__)


def 					lowercase      (    A_  ,       A_=False	)->				int:
  '''simple docstring'''







  a						:		List[str]											=      []

  # fmt: off
  # stem:
  rename_keys.append(("cls_token", "vit.embeddings.cls_token")	)
  rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings")	)

  rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight")	)
  rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias")	)

  # backbone
  rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight")	)
  rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight")	)
  rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias")	)

  for stage_idx in range(len(config.backbone_config.depths	)	):
    for layer_idx in range(config.backbone_config.depths[stage_idx]	):
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''')	)
      rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''')	)

    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''')	)
    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''')	)
    rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''')	)

  # transformer encoder
  for i in range(config.num_hidden_layers	):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
    rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
    rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
    rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
    rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
    rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
    rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
    rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
    rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''')	)
    rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''')	)

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ("norm.weight", "layernorm.weight"),
            ("norm.bias", "layernorm.bias"),
            ("pre_logits.fc.weight", "pooler.dense.weight"),
            ("pre_logits.fc.bias", "pooler.dense.bias"),
        ]	)

    # if just the base model, we should remove "vit" from all keys that start with "vit"
    a						:		Tuple											=      [(pair[0], pair[1][4:]) if pair[1].startswith("vit"	) else pair for pair in rename_keys]
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("norm.weight", "vit.layernorm.weight"),
            ("norm.bias", "vit.layernorm.bias"),
            ("head.weight", "classifier.weight"),
            ("head.bias", "classifier.bias"),
        ]	)
  # fmt: on

  return rename_keys


def 					lowercase      (    A_  ,       A_  ,       A_=False	)->				Optional[Any]:
  '''simple docstring'''







  for i in range(config.num_hidden_layers	):
    if base_model:
      a						:		Tuple											=      ""
    else:
      a						:		Dict											=      "vit."
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    a						:		str											=      state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''	)
    a						:		List[Any]											=      state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''	)
    # next, add query, keys and values (in that order) to the state dict
    a						:		Optional[int]											=      in_proj_weight[
        : config.hidden_size, :
    ]
    a						:		Tuple											=      in_proj_bias[: config.hidden_size]
    a						:		List[Any]											=      in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    a						:		Optional[int]											=      in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    a						:		int											=      in_proj_weight[
        -config.hidden_size :, :
    ]
    a						:		int											=      in_proj_bias[-config.hidden_size :]


def 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







  a						:		Dict											=      ["head.weight", "head.bias"]
  for k in ignore_keys:
    state_dict.pop(A_  ,       A_	)


def 					lowercase      (    A_  ,       A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







  a						:		List[Any]											=      dct.pop(A_	)
  a						:		str											=      val


def 					lowercase      (    )->				List[Any]:
  '''simple docstring'''







  a						:		Any											=      "http://images.cocodataset.org/val2017/000000039769.jpg"
  a						:		Tuple											=      Image.open(requests.get(A_  ,       stream=A_	).raw	)
  return im


@torch.no_grad()
def 					lowercase      (    A_  ,       A_  ,       A_=False	)->				Union[str, Any]:
  '''simple docstring'''







  a						:		Optional[Any]											=      BitConfig(
      global_padding="same"  ,       layer_type="bottleneck"  ,       depths=(3, 4, 9)  ,       out_features=["stage3"]  ,       embedding_dynamic_padding=A_  ,       )
  a						:		Union[str, Any]											=      ViTHybridConfig(backbone_config=A_  ,       image_size=384  ,       num_labels=1_000	)
  a						:		Optional[Any]											=      False

  # load original model from timm
  a						:		Any											=      timm.create_model(A_  ,       pretrained=A_	)
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  a						:		Optional[Any]											=      timm_model.state_dict()
  if base_model:
    remove_classification_head_(A_	)
  a						:		int											=      create_rename_keys(A_  ,       A_	)
  for src, dest in rename_keys:
    rename_key(A_  ,       A_  ,       A_	)
  read_in_q_k_v(A_  ,       A_  ,       A_	)

  a						:		Union[str, Any]											=      "huggingface/label-files"
  a						:		Optional[int]											=      "imagenet-1k-id2label.json"
  a						:		str											=      json.load(open(hf_hub_download(A_  ,       A_  ,       repo_type="dataset"	)  ,       "r"	)	)
  a						:		Optional[Any]											=      {int(A_	): v for k, v in idalabel.items()}
  a						:		str											=      idalabel
  a						:		Any											=      {v: k for k, v in idalabel.items()}

  # load HuggingFace model
  if vit_name[-5:] == "in21k":
    a						:		List[Any]											=      ViTHybridModel(A_	).eval()
  else:
    a						:		Optional[int]											=      ViTHybridForImageClassification(A_	).eval()
  model.load_state_dict(A_	)

  # create image processor
  a						:		Tuple											=      create_transform(**resolve_data_config({}  ,       model=A_	)	)
  a						:		List[Any]											=      transform.transforms

  a						:		int											=      {
      "bilinear": PILImageResampling.BILINEAR,
      "bicubic": PILImageResampling.BICUBIC,
      "nearest": PILImageResampling.NEAREST,
  }

  a						:		str											=      ViTHybridImageProcessor(
      do_resize=A_  ,       size={"shortest_edge": timm_transforms[0].size}  ,       resample=pillow_resamplings[timm_transforms[0].interpolation.value]  ,       do_center_crop=A_  ,       crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}  ,       do_normalize=A_  ,       image_mean=timm_transforms[-1].mean.tolist()  ,       image_std=timm_transforms[-1].std.tolist()  ,       )

  a						:		List[Any]											=      prepare_img()
  a						:		Optional[Any]											=      transform(A_	).unsqueeze(0	)
  a						:		str											=      processor(A_  ,       return_tensors="pt"	).pixel_values

  # verify pixel values
  assert torch.allclose(A_  ,       A_	)

  # verify logits
  with torch.no_grad():
    a						:		Dict											=      model(A_	)
    a						:		Tuple											=      outputs.logits

  print("Predicted class:"  ,       logits.argmax(-1	).item()	)
  if base_model:
    a						:		str											=      timm_model.forward_features(A_	)
    assert timm_pooled_output.shape == outputs.pooler_output.shape
    assert torch.allclose(A_  ,       outputs.pooler_output  ,       atol=1e-3	)
  else:
    a						:		int											=      timm_model(A_	)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(A_  ,       outputs.logits  ,       atol=1e-3	)
  print("Looks ok!"	)

  if pytorch_dump_folder_path is not None:
    Path(A_	).mkdir(exist_ok=A_	)
    print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''	)
    model.save_pretrained(A_	)
    print(F'''Saving processor to {pytorch_dump_folder_path}'''	)
    processor.save_pretrained(A_	)

  if push_to_hub:
    print(F'''Pushing model and processor to the hub {vit_name}'''	)
    model.push_to_hub(F'''ybelkada/{vit_name}'''	)
    processor.push_to_hub(F'''ybelkada/{vit_name}'''	)


if __name__ == "__main__":
 __lowercase    =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--vit_name""",
     default="""vit_base_r50_s16_384""",
     type=str,
     help="""Name of the hybrid ViT timm model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
 )
 parser.add_argument(
     """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
 )

 __lowercase    =				parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






