'''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     snake_case__							(      _A: Any				,       _A: Dict=False				,       _A: str=False				,       _A: Any="data2vec."			)   ->     int:

  '''simple docstring'''


  lowerCAmelCase        =  """backbone.""" if is_semantic else """"""

  lowerCAmelCase        =  []
  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     snake_case__							(      _A: Optional[Any]				,       _A: Dict				,       _A: Optional[int]=False				,       _A: int=False				,       _A: int="data2vec_vision."			)   ->     Union[str, Any]:

  '''simple docstring'''


  for i in range(config.num_hidden_layers			):
    lowerCAmelCase        =  """backbone.""" if is_semantic else """"""
    # queries, keys and values
    lowerCAmelCase        =  state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"			)
    lowerCAmelCase        =  state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"			)
    lowerCAmelCase        =  state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"			)

    lowerCAmelCase        =  in_proj_weight[
        : config.hidden_size, :
    ]
    lowerCAmelCase        =  q_bias
    lowerCAmelCase        =  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    lowerCAmelCase        =  in_proj_weight[
        -config.hidden_size :, :
    ]
    lowerCAmelCase        =  v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    lowerCAmelCase        =  state_dict.pop(f"{prefix}blocks.{i}.gamma_1"			)
    lowerCAmelCase        =  state_dict.pop(f"{prefix}blocks.{i}.gamma_2"			)

    lowerCAmelCase        =  gamma_a
    lowerCAmelCase        =  gamma_a

    # relative_position bias table + index
    if not has_lm_head:
      # each layer has its own relative position bias
      lowerCAmelCase        =  state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table"			)
      lowerCAmelCase        =  state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"			)

      lowerCAmelCase        =  table
      lowerCAmelCase        =  index






def     snake_case__							(      )   ->     Dict:

  '''simple docstring'''


  lowerCAmelCase        =  argparse.ArgumentParser(
      """Convert Data2VecVision to HF for image classification and pretraining"""				,       add_help=_A			)
  parser.add_argument("""--hf_checkpoint_name"""				,       type=_A			)
  parser.add_argument("""--input_size"""				,       default=224				,       type=_A				,       help="""images input size"""			)
  parser.add_argument("""--beit_checkpoint"""				,       default=""""""				,       help="""beit checkpoint"""			)

  return parser.parse_args()






def     snake_case__							(      _A: str				,       _A: Tuple				,       _A: Tuple			)   ->     List[str]:

  '''simple docstring'''


  def load_state_dict(_A: List[str]				,       _A: Any				,       _A: List[str]=""				,       _A: Optional[int]="relative_position_index"			):
    lowerCAmelCase        =  []
    lowerCAmelCase        =  []
    lowerCAmelCase        =  []
    # copy state_dict so _load_from_state_dict can modify it
    lowerCAmelCase        =  getattr(_A				,       """_metadata"""				,       _A			)
    lowerCAmelCase        =  state_dict.copy()
    if metadata is not None:
      lowerCAmelCase        =  metadata

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

    load(_A				,       prefix=_A			)

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

    lowerCAmelCase        =  warn_missing_keys

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

  lowerCAmelCase        =  {
      """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""": 1000,
            """use_mean_pooling""": True,
            """init_scale""": 0.001,
            """use_rel_pos_bias""": True,
        }			)

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

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

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

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

  load_state_dict(_A				,       _A				,       prefix=""""""			)

  return model






def     snake_case__							(      )   ->     Optional[Any]:

  '''simple docstring'''


  lowerCAmelCase        =  get_args()

  lowerCAmelCase        =  """ft1k""" in args.hf_checkpoint_name
  lowerCAmelCase        =  """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
  lowerCAmelCase        =  DataaVecVisionConfig()
  if is_finetuned:
    lowerCAmelCase        =  True
    lowerCAmelCase        =  False
    lowerCAmelCase        =  True
    lowerCAmelCase        =  1000

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

  if is_large:
    lowerCAmelCase        =  1024
    lowerCAmelCase        =  4096
    lowerCAmelCase        =  24
    lowerCAmelCase        =  16

  # 2. Load Beit model
  lowerCAmelCase        =  load_beit_model(_A				,       _A				,       _A			)
  orig_model.eval()

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

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

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

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

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

  # 5. Forward HF Data2VecVision model
  with torch.no_grad():
    lowerCAmelCase        =  hf_model(_A			)

  lowerCAmelCase        =  hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

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

  print(f"max_absolute_diff = {max_absolute_diff}"			)
  lowerCAmelCase        =  torch.allclose(_A				,       _A				,       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'''




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np
import regex as re

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
						if is_torch_available():
												import torch
						if is_tf_available():
												import tensorflow as tf

from ...tokenization_utils import AddedToken, PreTrainedTokenizer


__lowercase				   =  logging.get_logger(__name__)

__lowercase				   =  {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

__lowercase				   =  {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
}

__lowercase				   =  {
    '''Salesforce/codegen-350M-mono''': 2_0_4_8,
}






@lru_cache()
def     snake_case__							(      )   ->     int:

		'''simple docstring'''


		lowerCAmelCase        =  (
		    list(range(ord("""!"""			)				,       ord("""~"""			) + 1			)			) + list(range(ord("""¡"""			)				,       ord("""¬"""			) + 1			)			) + list(range(ord("""®"""			)				,       ord("""ÿ"""			) + 1			)			)
		)
		lowerCAmelCase        =  bs[:]
		lowerCAmelCase        =  0
		for b in range(2**8			):
				if b not in bs:
						bs.append(_A			)
						cs.append(2**8 + n			)
						n += 1
		lowerCAmelCase        =  [chr(_A			) for n in cs]
		return dict(zip(_A				,       _A			)			)






def     snake_case__							(      _A: int			)   ->     int:

		'''simple docstring'''


		lowerCAmelCase        =  set()
		lowerCAmelCase        =  word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)			)
				lowerCAmelCase        =  char
		return pairs






class     a__( lowerCAmelCase__     ):


			'''simple docstring'''






			UpperCAmelCase_      :					Tuple								=	VOCAB_FILES_NAMES
			UpperCAmelCase_      :					List[str]								=	PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_      :					Any								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_      :					str								=	['''input_ids''', '''attention_mask''']



			def __init__(       self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase="replace"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase="<|endoftext|>"	,   __lowerCAmelCase=None	,   __lowerCAmelCase=False	,   __lowerCAmelCase=False	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else bos_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else eos_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else unk_token
					lowerCAmelCase        =  AddedToken(__lowerCAmelCase	,   lstrip=__lowerCAmelCase	,   rstrip=__lowerCAmelCase) if isinstance(__lowerCAmelCase	,   __lowerCAmelCase) else pad_token
					super().__init__(
					    errors=__lowerCAmelCase	,   unk_token=__lowerCAmelCase	,   bos_token=__lowerCAmelCase	,   eos_token=__lowerCAmelCase	,   pad_token=__lowerCAmelCase	,   add_prefix_space=__lowerCAmelCase	,   add_bos_token=__lowerCAmelCase	,   **__lowerCAmelCase	,   )
					lowerCAmelCase        =  add_bos_token

					with open(__lowerCAmelCase	,   encoding="""utf-8""") as vocab_handle:
							lowerCAmelCase        =  json.load(__lowerCAmelCase)
					lowerCAmelCase        =  {v: k for k, v in self.encoder.items()}
					lowerCAmelCase        =  errors  # how to handle errors in decoding
					lowerCAmelCase        =  bytes_to_unicode()
					lowerCAmelCase        =  {v: k for k, v in self.byte_encoder.items()}
					with open(__lowerCAmelCase	,   encoding="""utf-8""") as merges_handle:
							lowerCAmelCase        =  merges_handle.read().split("""\n""")[1:-1]
					lowerCAmelCase        =  [tuple(merge.split()) for merge in bpe_merges]
					lowerCAmelCase        =  dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase))))
					lowerCAmelCase        =  {}
					lowerCAmelCase        =  add_prefix_space

					# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
					lowerCAmelCase        =  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")



			@property
			def 							a_	(       self):




					"""simple docstring"""


					return len(self.encoder)



			def 							a_	(       self):




					"""simple docstring"""


					return dict(self.encoder	,   **self.added_tokens_encoder)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					if token in self.cache:
							return self.cache[token]
					lowerCAmelCase        =  tuple(__lowerCAmelCase)
					lowerCAmelCase        =  get_pairs(__lowerCAmelCase)

					if not pairs:
							return token

					while True:
							lowerCAmelCase        =  min(__lowerCAmelCase	,   key=lambda __lowerCAmelCase: self.bpe_ranks.get(__lowerCAmelCase	,   float("""inf""")))
							if bigram not in self.bpe_ranks:
									break
							lowerCAmelCase ,					lowerCAmelCase        =  bigram
							lowerCAmelCase        =  []
							lowerCAmelCase        =  0
							while i < len(__lowerCAmelCase):
									try:
											lowerCAmelCase        =  word.index(__lowerCAmelCase	,   __lowerCAmelCase)
									except ValueError:
											new_word.extend(word[i:])
											break
									else:
											new_word.extend(word[i:j])
											lowerCAmelCase        =  j

									if word[i] == first and i < len(__lowerCAmelCase) - 1 and word[i + 1] == second:
											new_word.append(first + second)
											i += 2
									else:
											new_word.append(word[i])
											i += 1
							lowerCAmelCase        =  tuple(__lowerCAmelCase)
							lowerCAmelCase        =  new_word
							if len(__lowerCAmelCase) == 1:
									break
							else:
									lowerCAmelCase        =  get_pairs(__lowerCAmelCase)
					lowerCAmelCase        =  """ """.join(__lowerCAmelCase)
					lowerCAmelCase        =  word
					return word



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=None):




					"""simple docstring"""


					if self.add_bos_token:
							lowerCAmelCase        =  [self.bos_token_id]
					else:
							lowerCAmelCase        =  []

					lowerCAmelCase        =  bos_token_ids + token_ids_a

					if token_ids_a is None:
							return output

					return output + bos_token_ids + token_ids_a



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  []
					for token in re.findall(self.pat	,   __lowerCAmelCase):
							lowerCAmelCase        =  """""".join(
							    self.byte_encoder[b] for b in token.encode("""utf-8"""))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__lowerCAmelCase).split(""" """))
					return bpe_tokens



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return self.encoder.get(__lowerCAmelCase	,   self.encoder.get(self.unk_token))



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					return self.decoder.get(__lowerCAmelCase)



			def 							a_	(       self	,   __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  """""".join(__lowerCAmelCase)
					lowerCAmelCase        =  bytearray([self.byte_decoder[c] for c in text]).decode("""utf-8"""	,   errors=self.errors)
					return text



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = None):




					"""simple docstring"""


					if not os.path.isdir(__lowerCAmelCase):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory")
							return
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
					lowerCAmelCase        =  os.path.join(
					    __lowerCAmelCase	,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""])

					with open(__lowerCAmelCase	,   """w"""	,   encoding="""utf-8""") as f:
							f.write(json.dumps(self.encoder	,   indent=2	,   sort_keys=__lowerCAmelCase	,   ensure_ascii=__lowerCAmelCase) + """\n""")

					lowerCAmelCase        =  0
					with open(__lowerCAmelCase	,   """w"""	,   encoding="""utf-8""") as writer:
							writer.write("""#version: 0.2\n""")
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,   key=lambda __lowerCAmelCase: kv[1]):
									if index != token_index:
											logger.warning(
											    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
											    """ Please check that the tokenizer is not corrupted!""")
											lowerCAmelCase        =  token_index
									writer.write(""" """.join(__lowerCAmelCase) + """\n""")
									index += 1

					return vocab_file, merge_file



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase=False	,   **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase        =  kwargs.pop("""add_prefix_space"""	,   self.add_prefix_space)
					if is_split_into_words or add_prefix_space:
							lowerCAmelCase        =  """ """ + text
					return (text, kwargs)



			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase = False	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   ):




					"""simple docstring"""


					lowerCAmelCase        =  super()._decode(
					    token_ids=__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase	,   **__lowerCAmelCase	,   )

					if truncate_before_pattern is not None and len(__lowerCAmelCase) > 0:
							lowerCAmelCase        =  self.truncate(__lowerCAmelCase	,   __lowerCAmelCase)

					return decoded_text







			def 							a_	(       self	,   __lowerCAmelCase	,   __lowerCAmelCase):




					"""simple docstring"""


					def find_re(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase):
							lowerCAmelCase        =  pattern.search(__lowerCAmelCase	,   __lowerCAmelCase)
							return m.start() if m else -1

					lowerCAmelCase        =  [re.compile(__lowerCAmelCase	,   re.MULTILINE) for pattern in truncate_before_pattern]

					lowerCAmelCase        =  list(re.finditer("""^print"""	,   __lowerCAmelCase	,   re.MULTILINE))

					if len(__lowerCAmelCase) > 1:
							lowerCAmelCase        =  completion[: prints[1].start()]

					lowerCAmelCase        =  list(re.finditer("""^def"""	,   __lowerCAmelCase	,   re.MULTILINE))

					if len(__lowerCAmelCase) > 1:
							lowerCAmelCase        =  completion[: defs[1].start()]

					lowerCAmelCase        =  0

					lowerCAmelCase        =  [
					    pos for pos in [find_re(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase) for terminal in terminals] if pos != -1
					]

					if len(__lowerCAmelCase) > 0:
							return completion[: min(__lowerCAmelCase)]
					else:
							return completion




