


'''simple docstring'''


import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCamelCase          =     logging.get_logger()







@dataclass
class        __magic_name__  :

					"""simple docstring"""






					lowerCamelCase__						=							42
					lowerCamelCase__						=							field(default_factory=lowerCAmelCase   )
					lowerCamelCase__						=							field(default_factory=lowerCAmelCase   )
					lowerCamelCase__						=							field(default_factory=lowerCAmelCase   )


					def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


										'''simple docstring'''





										__A  :					List[str]						     =       len(list(m.modules()		)		) == 1 or isinstance(lowerCamelCase ,    nn.Convad		) or isinstance(lowerCamelCase ,    nn.BatchNormad		)
										if has_not_submodules:
															self.traced.append(lowerCamelCase		)
															__A  :					Union[str, Any]						     =       m


					def __call__(      self ,    lowerCamelCase		):


										'''simple docstring'''





										for name, m in self.module.named_modules():
															self.handles.append(m.register_forward_hook(partial(self._forward_hook ,    name=lowerCamelCase		)		)		)
										self.module(lowerCamelCase		)
										[x.remove() for x in self.handles]
										return self


					@property
					def lowerCAmelCase__     (      self		):


										'''simple docstring'''





										return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()		)		) > 0}







class        __magic_name__  (					nn.Module   ):

					"""simple docstring"""






					def __init__(      self ,    lowerCamelCase		):


										'''simple docstring'''





										super().__init__()

										__A  :					List[Tuple[str, nn.Module]]						     =       []
										# - get the stem
										feature_blocks.append(("conv1", model.stem)		)
										# - get all the feature blocks
										for k, v in model.trunk_output.named_children():
															assert k.startswith("block"		), f"Unexpected layer name {k}"
															__A  :					Union[str, Any]						     =       len(lowerCamelCase		) + 1
															feature_blocks.append((f"res{block_index}", v)		)

										__A  :					str						     =       nn.ModuleDict(lowerCamelCase		)


					def lowerCAmelCase__     (      self ,    lowerCamelCase		):


										'''simple docstring'''





										return get_trunk_forward_outputs(
										    lowerCamelCase ,    out_feat_keys=lowerCamelCase ,    feature_blocks=self._feature_blocks ,    )







class        __magic_name__  (					lowerCAmelCase   ):

					"""simple docstring"""






					def lowerCAmelCase__     (      self		):


										'''simple docstring'''





										return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




					'''simple docstring'''





					__A  :					Dict						     =       RegNetConfig(depths=[2, 7, 17, 1]					,				hidden_sizes=[8, 8, 8, 8]					,				groups_width=8       )
					if "in1k" in model_name:
										__A  :					Optional[int]						     =       RegNetForImageClassification(SCREAMING_SNAKE_CASE       )
					else:
										__A  :					List[Any]						     =       RegNetModel(SCREAMING_SNAKE_CASE       )
					# create from model (with small weights)
					__A  :					Tuple						     =       FakeRegNetVisslWrapper(
					    RegNet(FakeRegNetParams(depth=27					,				group_width=1010					,				w_a=1744					,				w_a=620.83					,				w_m=2.52       )       )       )

					with torch.no_grad():
										__A  :					str						     =       from_model.eval()
										__A  :					Optional[int]						     =       our_model.eval()

										__A  :					str						     =       torch.randn((1, 3, 32, 32)       )
										# trace both
										__A  :					Any						     =       Tracker(SCREAMING_SNAKE_CASE       )
										__A  :					Optional[Any]						     =       dest_tracker(SCREAMING_SNAKE_CASE       ).parametrized

										pprint(dest_tracker.nameamodule       )
										__A  :					Optional[int]						     =       Tracker(SCREAMING_SNAKE_CASE       )
										__A  :					Any						     =       src_tracker(SCREAMING_SNAKE_CASE       ).parametrized

					# convert the keys -> module dict to keys -> params
					def to_params_dict(SCREAMING_SNAKE_CASE       ):
										__A  :					Union[str, Any]						     =       OrderedDict()
										for name, module in dict_with_modules.items():
															for param_name, param in module.state_dict().items():
																				__A  :					Optional[int]						     =       param
										return params_dict

					__A  :					Union[str, Any]						     =       {}

					__A  :					Tuple						     =       to_params_dict(SCREAMING_SNAKE_CASE       )
					__A  :					Union[str, Any]						     =       to_params_dict(SCREAMING_SNAKE_CASE       )

					for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()					,				dst_state_dict.items()       ):
										__A  :					str						     =       dest_key
										logger.info(f"{src_key} -> {dest_key}"       )
					# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
					if "in1k" in model_name:
										__A  :					Union[str, Any]						     =       "classifier.1.weight"
										__A  :					str						     =       "classifier.1.bias"

					return from_to_ours_keys


def 		_lowercase   (SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE = None					,				SCREAMING_SNAKE_CASE = True       ):




					'''simple docstring'''





					__A  :					Any						     =       "imagenet-1k-id2label.json"
					__A  :					Union[str, Any]						     =       1000

					__A  :					List[Any]						     =       "huggingface/label-files"
					__A  :					str						     =       num_labels
					__A  :					Optional[int]						     =       json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				repo_type="dataset"       )       )					,				"r"       )       )
					__A  :					int						     =       {int(SCREAMING_SNAKE_CASE       ): v for k, v in idalabel.items()}

					__A  :					Optional[int]						     =       idalabel
					__A  :					Dict						     =       {v: k for k, v in idalabel.items()}

					__A  :					Any						     =       partial(SCREAMING_SNAKE_CASE					,				num_labels=SCREAMING_SNAKE_CASE					,				idalabel=SCREAMING_SNAKE_CASE					,				labelaid=SCREAMING_SNAKE_CASE       )

					__A  :					str						     =       {
					    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]					,				hidden_sizes=[2020, 4040, 1_1110, 2_8280]					,				groups_width=1010       ),
					    # finetuned on imagenet
					    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
					        depths=[2, 7, 17, 1]					,				hidden_sizes=[2020, 4040, 1_1110, 2_8280]					,				groups_width=1010       ),
					}

					# add seer weights logic
					def load_using_classy_vision(SCREAMING_SNAKE_CASE       ) -> Tuple[Dict, Dict]:
										__A  :					Optional[Any]						     =       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE					,				model_dir=str(SCREAMING_SNAKE_CASE       )					,				map_location="cpu"       )
										# check if we have a head, if yes add it
										__A  :					Any						     =       files["classy_state_dict"]["base_model"]["model"]
										return model_state_dict["trunk"], model_state_dict["heads"]

					__A  :					List[str]						     =       {
					    "regnet-y-10b-seer": partial(
					        SCREAMING_SNAKE_CASE					,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"					,				),
					    "regnet-y-10b-seer-in1k": partial(
					        SCREAMING_SNAKE_CASE					,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"					,				),
					}

					__A  :					Optional[Any]						     =       get_from_to_our_keys(SCREAMING_SNAKE_CASE       )

					if not (save_directory / f"{model_name}.pth").exists():
										logger.info("Loading original state_dict."       )
										__A  :					Optional[int]						     =       names_to_from_model[model_name]()
										__A  :					Optional[Any]						     =       from_state_dict_trunk
										if "in1k" in model_name:
															# add the head
															__A  :					List[str]						     =       {**from_state_dict_trunk, **from_state_dict_head}
										logger.info("Done!"       )

										__A  :					Optional[int]						     =       {}

										__A  :					List[str]						     =       list(from_state_dict.keys()       )
										__A  :					Any						     =       r"\.block.-part."
										# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
										for key in from_state_dict.keys():
															# remove the weird "block[0,1]-part" from the key
															__A  :					List[Any]						     =       re.sub(SCREAMING_SNAKE_CASE					,				""					,				SCREAMING_SNAKE_CASE       )
															# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
															__A  :					Tuple						     =       from_to_ours_keys[src_key]
															# store the parameter with our key
															__A  :					Any						     =       from_state_dict[key]
															not_used_keys.remove(SCREAMING_SNAKE_CASE       )
										# check that all keys have been updated
										assert len(SCREAMING_SNAKE_CASE       ) == 0, f"Some keys where not used {','.join(SCREAMING_SNAKE_CASE       )}"

										logger.info(f"The following keys were not used: {','.join(SCREAMING_SNAKE_CASE       )}"       )

										# save our state dict to disk
										torch.save(SCREAMING_SNAKE_CASE					,				save_directory / f"{model_name}.pth"       )

										del converted_state_dict
					else:
										logger.info("The state_dict was already stored on disk."       )
					if push_to_hub:
										logger.info(f"Token is {os.environ['HF_TOKEN']}"       )
										logger.info("Loading our model."       )
										# create our model
										__A  :					int						     =       names_to_config[model_name]
										__A  :					Dict						     =       RegNetModel
										if "in1k" in model_name:
															__A  :					str						     =       RegNetForImageClassification
										__A  :					List[str]						     =       our_model_func(SCREAMING_SNAKE_CASE       )
										# place our model to the meta device (so remove all the weights)
										our_model.to(torch.device("meta"       )       )
										logger.info("Loading state_dict in our model."       )
										# load state dict
										__A  :					Union[str, Any]						     =       our_model.state_dict().keys()
										PreTrainedModel._load_pretrained_model_low_mem(
										    SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE					,				[save_directory / f"{model_name}.pth"]       )
										logger.info("Finally, pushing!"       )
										# push it to hub
										our_model.push_to_hub(
										    repo_path_or_name=save_directory / model_name					,				commit_message="Add model"					,				output_dir=save_directory / model_name					,				)
										__A  :					str						     =       384
										# we can use the convnext one
										__A  :					Tuple						     =       AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"					,				size=SCREAMING_SNAKE_CASE       )
										image_processor.push_to_hub(
										    repo_path_or_name=save_directory / model_name					,				commit_message="Add image processor"					,				output_dir=save_directory / model_name					,				)


if __name__ == "__main__":
						_UpperCamelCase          =     argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--model_name""",
						    default=None,
						    type=str,
						    help=(
						        """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
						        """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
						    ),
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""",
						    default=None,
						    type=Path,
						    required=True,
						    help="""Path to the output PyTorch model directory.""",
						)
						parser.add_argument(
						    """--push_to_hub""",
						    default=True,
						    type=bool,
						    required=False,
						    help="""If True, push model and image processor to the hub.""",
						)

						_UpperCamelCase          =     parser.parse_args()

						_UpperCamelCase          =     args.pytorch_dump_folder_path
						pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
						convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





'''simple docstring'''


import json
import os
import re
import unicodedata
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging


_UpperCamelCase          =     logging.get_logger(__name__)

_UpperCamelCase          =     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_UpperCamelCase          =     {
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
}

_UpperCamelCase          =     {
    """openai-gpt""": 512,
}


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					List[str]						     =       text.strip()
     if not text:
          return []
     __A  :					Any						     =       text.split()
     return tokens







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     def __init__(      self ,    lowerCamelCase=True ,    lowerCamelCase=None ,    lowerCamelCase=True ,    lowerCamelCase=None ,    lowerCamelCase=True ,    ):


          '''simple docstring'''





          if never_split is None:
               __A  :					List[Any]						     =       []
          __A  :					str						     =       do_lower_case
          __A  :					str						     =       set(lowerCamelCase		)
          __A  :					Tuple						     =       tokenize_chinese_chars
          __A  :					Any						     =       strip_accents
          __A  :					Dict						     =       do_split_on_punc


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=None		):


          '''simple docstring'''





          __A  :					str						     =       self.never_split.union(set(lowerCamelCase		)		) if never_split else self.never_split
          __A  :					Optional[Any]						     =       self._clean_text(lowerCamelCase		)

          # This was added on November 1st, 2018 for the multilingual and Chinese
          # models. This is also applied to the English models now, but it doesn't
          # matter since the English models were not trained on any Chinese data
          # and generally don't have any Chinese data in them (there are Chinese
          # characters in the vocabulary because Wikipedia does have some Chinese
          # words in the English Wikipedia.).
          if self.tokenize_chinese_chars:
               __A  :					Tuple						     =       self._tokenize_chinese_chars(lowerCamelCase		)
          # prevents treating the same character with different unicode codepoints as different characters
          __A  :					str						     =       unicodedata.normalize("NFC" ,    lowerCamelCase		)
          __A  :					Tuple						     =       whitespace_tokenize(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       []
          for token in orig_tokens:
               if token not in never_split:
                    if self.do_lower_case:
                         __A  :					str						     =       token.lower()
                         if self.strip_accents is not False:
                              __A  :					str						     =       self._run_strip_accents(lowerCamelCase		)
                    elif self.strip_accents:
                         __A  :					Tuple						     =       self._run_strip_accents(lowerCamelCase		)
               split_tokens.extend(self._run_split_on_punc(lowerCamelCase ,    lowerCamelCase		)		)

          __A  :					Tuple						     =       whitespace_tokenize(" ".join(lowerCamelCase		)		)
          return output_tokens


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Tuple						     =       unicodedata.normalize("NFD" ,    lowerCamelCase		)
          __A  :					Tuple						     =       []
          for char in text:
               __A  :					Dict						     =       unicodedata.category(lowerCamelCase		)
               if cat == "Mn":
                    continue
               output.append(lowerCamelCase		)
          return "".join(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=None		):


          '''simple docstring'''





          if not self.do_split_on_punc or (never_split is not None and text in never_split):
               return [text]
          __A  :					int						     =       list(lowerCamelCase		)
          __A  :					int						     =       0
          __A  :					int						     =       True
          __A  :					Tuple						     =       []
          while i < len(lowerCamelCase		):
               __A  :					str						     =       chars[i]
               if _is_punctuation(lowerCamelCase		):
                    output.append([char]		)
                    __A  :					Optional[int]						     =       True
               else:
                    if start_new_word:
                         output.append([]		)
                    __A  :					List[str]						     =       False
                    output[-1].append(lowerCamelCase		)
               i += 1

          return ["".join(lowerCamelCase		) for x in output]


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					int						     =       []
          for char in text:
               __A  :					Union[str, Any]						     =       ord(lowerCamelCase		)
               if self._is_chinese_char(lowerCamelCase		):
                    output.append(" "		)
                    output.append(lowerCamelCase		)
                    output.append(" "		)
               else:
                    output.append(lowerCamelCase		)
          return "".join(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          if (
              (cp >= 0x4E00 and cp <= 0x9FFF)
              or (cp >= 0x3400 and cp <= 0x4DBF)  #
              or (cp >= 0x2_0000 and cp <= 0x2_A6DF)  #
              or (cp >= 0x2_A700 and cp <= 0x2_B73F)  #
              or (cp >= 0x2_B740 and cp <= 0x2_B81F)  #
              or (cp >= 0x2_B820 and cp <= 0x2_CEAF)  #
              or (cp >= 0xF900 and cp <= 0xFAFF)
              or (cp >= 0x2_F800 and cp <= 0x2_FA1F)  #
          ):  #
               return True

          return False


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[str]						     =       []
          for char in text:
               __A  :					Dict						     =       ord(lowerCamelCase		)
               if cp == 0 or cp == 0xFFFD or _is_control(lowerCamelCase		):
                    continue
               if _is_whitespace(lowerCamelCase		):
                    output.append(" "		)
               else:
                    output.append(lowerCamelCase		)
          return "".join(lowerCamelCase		)


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Dict						     =       set()
     __A  :					Tuple						     =       word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)       )
          __A  :					str						     =       char
     return pairs


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Dict						     =       text.replace("—"					,				"-"       )
     __A  :					Optional[int]						     =       text.replace("–"					,				"-"       )
     __A  :					Dict						     =       text.replace("―"					,				"-"       )
     __A  :					Optional[int]						     =       text.replace("…"					,				"..."       )
     __A  :					int						     =       text.replace("´"					,				"'"       )
     __A  :					List[Any]						     =       re.sub(r"(-+|~+|!+|\"+|;+|\?+|\++|,+|\)+|\(+|\\+|\/+|\*+|\[+|\]+|}+|{+|\|+|_+)"					,				r" \1 "					,				SCREAMING_SNAKE_CASE       )
     __A  :					Union[str, Any]						     =       re.sub(r"\s*\n\s*"					,				" \n "					,				SCREAMING_SNAKE_CASE       )
     __A  :					List[Any]						     =       re.sub(r"[^\S\n]+"					,				" "					,				SCREAMING_SNAKE_CASE       )
     return text.strip()







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							VOCAB_FILES_NAMES
     lowerCamelCase__						=							PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__						=							['input_ids', 'attention_mask']


     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase="<unk>" ,    **lowerCamelCase		):


          '''simple docstring'''





          super().__init__(unk_token=lowerCamelCase ,    **lowerCamelCase		)

          try:
               import ftfy
               from spacy.lang.en import English

               __A  :					Union[str, Any]						     =       English()
               __A  :					Tuple						     =       _nlp.tokenizer
               __A  :					Any						     =       ftfy.fix_text
          except ImportError:
               logger.warning("ftfy or spacy is not installed using BERT BasicTokenizer instead of SpaCy & ftfy."		)
               __A  :					List[Any]						     =       BasicTokenizer(do_lower_case=lowerCamelCase		)
               __A  :					Any						     =       None

          with open(lowerCamelCase ,    encoding="utf-8"		) as vocab_handle:
               __A  :					int						     =       json.load(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       {v: k for k, v in self.encoder.items()}
          with open(lowerCamelCase ,    encoding="utf-8"		) as merges_handle:
               __A  :					Tuple						     =       merges_handle.read().split("\n"		)[1:-1]
          __A  :					List[Any]						     =       [tuple(merge.split()		) for merge in merges]
          __A  :					Optional[int]						     =       dict(zip(lowerCamelCase ,    range(len(lowerCamelCase		)		)		)		)
          __A  :					Union[str, Any]						     =       {}


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return True


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return len(self.encoder		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       tuple(token[:-1]		) + (token[-1] + "</w>",)
          if token in self.cache:
               return self.cache[token]
          __A  :					Optional[Any]						     =       get_pairs(lowerCamelCase		)

          if not pairs:
               return token + "</w>"

          while True:
               __A  :					List[Any]						     =       min(lowerCamelCase ,    key=lambda lowerCamelCase		: self.bpe_ranks.get(lowerCamelCase ,    float("inf"		)		)		)
               if bigram not in self.bpe_ranks:
                    break
               __A     ,__A  :					Tuple						     =       bigram
               __A  :					List[Any]						     =       []
               __A  :					Optional[int]						     =       0
               while i < len(lowerCamelCase		):
                    try:
                         __A  :					Dict						     =       word.index(lowerCamelCase ,    lowerCamelCase		)
                    except ValueError:
                         new_word.extend(word[i:]		)
                         break
                    else:
                         new_word.extend(word[i:j]		)
                         __A  :					int						     =       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
               __A  :					List[str]						     =       tuple(lowerCamelCase		)
               __A  :					int						     =       new_word
               if len(lowerCamelCase		) == 1:
                    break
               else:
                    __A  :					List[str]						     =       get_pairs(lowerCamelCase		)
          __A  :					Tuple						     =       " ".join(lowerCamelCase		)
          if word == "\n  </w>":
               __A  :					List[str]						     =       "\n</w>"
          __A  :					List[Any]						     =       word
          return word


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Dict						     =       []
          if self.fix_text is None:
               # Using BERT's BasicTokenizer
               __A  :					Tuple						     =       self.nlp.tokenize(lowerCamelCase		)
               for token in text:
                    split_tokens.extend(list(self.bpe(lowerCamelCase		).split(" "		)		)		)
          else:
               # Using SpaCy & ftfy (original tokenization process of OpenAI GPT)
               __A  :					List[Any]						     =       self.nlp(text_standardize(self.fix_text(lowerCamelCase		)		)		)
               for token in text:
                    split_tokens.extend(list(self.bpe(token.text.lower()		).split(" "		)		)		)
          return split_tokens


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





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


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.decoder.get(lowerCamelCase ,    self.unk_token		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Any						     =       "".join(lowerCamelCase		).replace("</w>" ,    " "		).strip()
          return out_string


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





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

          __A  :					Tuple						     =       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!"		)
                         __A  :					Tuple						     =       token_index
                    writer.write(" ".join(lowerCamelCase		) + "\n"		)
                    index += 1

          return vocab_file, merge_file

