


"""simple docstring"""







import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				'▁'

__lowercase    =				{'vocab_file': 'prophetnet.tokenizer'}

__lowercase    =				{
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

__lowercase    =				{
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

__lowercase    =				{
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}


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







  a						:		int											=      collections.OrderedDict()
  with open(snake_case_  ,       "r"  ,       encoding="utf-8"	) as reader:
    a						:		Optional[int]											=      reader.readlines()
  for index, token in enumerate(snake_case_	):
    a						:		str											=      token.rstrip("\n"	)
    a						:		str											=      index
  return vocab







class 						_A  (  _lowerCAmelCase  ):







    """simple docstring"""




    UpperCAmelCase	:							int     =	VOCAB_FILES_NAMES
    UpperCAmelCase	:							Dict     =	PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase	:							List[Any]     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase	:							int     =	["input_ids", "attention_mask"]






    def __init__( self    :   str    ,			__UpperCAmelCase    :   Any    ,			__UpperCAmelCase    :   Dict="[SEP]"    ,			__UpperCAmelCase    :   Optional[int]="[SEP]"    ,			__UpperCAmelCase    :   Optional[int]="[SEP]"    ,			__UpperCAmelCase    :   List[str]="[UNK]"    ,			__UpperCAmelCase    :   Union[str, Any]="[PAD]"    ,			__UpperCAmelCase    :   str="[CLS]"    ,			__UpperCAmelCase    :   Optional[Any]="[MASK]"    ,			__UpperCAmelCase    :   Optional[Dict[str, Any]] = None    ,			**__UpperCAmelCase    :   Tuple    ,			):
      a						:		List[Any]											=      {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=_lowercase    ,			eos_token=_lowercase    ,			sep_token=_lowercase    ,			unk_token=_lowercase    ,			pad_token=_lowercase    ,			cls_token=_lowercase    ,			mask_token=_lowercase    ,			sp_model_kwargs=self.sp_model_kwargs    ,			**_lowercase    ,			)

      try:
        import sentencepiece as spm
      except ImportError:
        logger.warning(
            "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
            " pip install sentencepiece")
        raise

      a						:		str											=      spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(str(_lowercase))
      a						:		str											=      vocab_file

      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

      # put special tokens and [unused] tokens into the vocab
      a						:		str											=      {"[PAD]": 0, "[CLS]": 1, "[SEP]": 2, "[UNK]": 3, "[MASK]": 4}

      for i in range(10):
        a						:		Tuple											=      f'''[unused{i}]'''
        a						:		Dict											=      5 + i

      # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
      a						:		int											=      12
      a						:		List[str]											=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
      for k in self.fairseq_tokens_to_ids.keys():
        self.unique_no_split_tokens.append(_lowercase)






    def __getstate__( self    :   Dict):
      a						:		str											=      self.__dict__.copy()
      a						:		Any											=      None
      return state






    def __setstate__( self    :   List[Any]    ,			__UpperCAmelCase    :   List[Any]):
      a						:		int											=      d
      try:
        import sentencepiece as spm
      except ImportError:
        logger.warning(
            "You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"
            " pip install sentencepiece")
        raise

      # for backward compatibility
      if not hasattr(self    ,			"sp_model_kwargs"):
        a						:		str											=      {}

      a						:		Tuple											=      spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(self.vocab_file)






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   List[int]    ,			__UpperCAmelCase    :   Optional[List[int]] = None    ,			__UpperCAmelCase    :   bool = False):

      if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=_lowercase    ,			token_ids_a=_lowercase    ,			already_has_special_tokens=_lowercase)

      if token_ids_a is None:
        return ([0] * len(_lowercase)) + [1]
      return ([0] * len(_lowercase)) + [1] + ([0] * len(_lowercase)) + [1]






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   List[int]    ,			__UpperCAmelCase    :   Optional[List[int]] = None):
      a						:		Dict											=      [self.sep_token_id]

      if token_ids_a is None:
        return len(token_ids_a + sep) * [0]
      return len(token_ids_a + sep + sep + token_ids_a + sep) * [0]






    @property
    def      __snake_case							( self    :   Optional[Any]):
      return len(self.sp_model) + self.fairseq_offset






    def      __snake_case							( self    :   str):
      a						:		Any											=      {self.convert_ids_to_tokens(_lowercase): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   str):
      return self.sp_model.encode(_lowercase    ,			out_type=_lowercase)






    def      __snake_case							( self    :   Optional[Any]    ,			__UpperCAmelCase    :   Optional[Any]):
      if token in self.fairseq_tokens_to_ids:
        return self.fairseq_tokens_to_ids[token]
      a						:		Optional[int]											=      self.sp_model.PieceToId(_lowercase)

      # Need to return unknown token if the SP model returned 0
      return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   List[str]):
      if index in self.fairseq_ids_to_tokens:
        return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(index - self.fairseq_offset)






    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   Any):
      a						:		List[str]											=      "".join(_lowercase).replace(_lowercase    ,			" ").strip()
      return out_string






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   Optional[str] = None):
      if not os.path.isdir(_lowercase):
        logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
        return
      a						:		Any											=      os.path.join(
          _lowercase    ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

      if os.path.abspath(self.vocab_file) != os.path.abspath(_lowercase) and os.path.isfile(self.vocab_file):
        copyfile(self.vocab_file    ,			_lowercase)
      elif not os.path.isfile(self.vocab_file):
        with open(_lowercase    ,			"wb") as fi:
          a						:		Tuple											=      self.sp_model.serialized_model_proto()
          fi.write(_lowercase)

      return (out_vocab_file,)






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   List[int]    ,			__UpperCAmelCase    :   Optional[List[int]] = None):

      if token_ids_a is None:
        return token_ids_a + [self.sep_token_id]
      a						:		Optional[Any]											=      [self.sep_token_id]
      return token_ids_a + sep + token_ids_a + sep










"""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)






