code: 
  
  
  '''simple docstring'''
  
  import argparse
  import json
  from pathlib import Path
  
  import requests
  import torch
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from PIL import Image
  
  from transformers import (
      BeitConfig,
      BeitForImageClassification,
      BeitForMaskedImageModeling,
      BeitForSemanticSegmentation,
      BeitImageProcessor,
  )
  from transformers.image_utils import PILImageResampling
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase							:     Optional[int]								=			logging.get_logger(__name__)
  
  def _SCREAMING_SNAKE_CASE   (A						,				A=False						,				A=False ) -> List[str]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				'''backbone.''' if is_semantic else ''''''
  
         lowercase__       =				[]
         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"beit.encoder.layer.{i}.layernorm_before.weight") )
                rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"beit.encoder.layer.{i}.layernorm_before.bias") )
                rename_keys.append(
                    (f"{prefix}blocks.{i}.attn.proj.weight", f"beit.encoder.layer.{i}.attention.output.dense.weight") )
                rename_keys.append(
                    (f"{prefix}blocks.{i}.attn.proj.bias", f"beit.encoder.layer.{i}.attention.output.dense.bias") )
                rename_keys.append((f"{prefix}blocks.{i}.norm2.weight", f"beit.encoder.layer.{i}.layernorm_after.weight") )
                rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"beit.encoder.layer.{i}.layernorm_after.bias") )
                rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.weight", f"beit.encoder.layer.{i}.intermediate.dense.weight") )
                rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.bias", f"beit.encoder.layer.{i}.intermediate.dense.bias") )
                rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"beit.encoder.layer.{i}.output.dense.weight") )
                rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"beit.encoder.layer.{i}.output.dense.bias") )
  
         # projection layer + position embeddings
         rename_keys.extend(
             [
                 (f"{prefix}cls_token", '''beit.embeddings.cls_token'''),
                 (f"{prefix}patch_embed.proj.weight", '''beit.embeddings.patch_embeddings.projection.weight'''),
                 (f"{prefix}patch_embed.proj.bias", '''beit.embeddings.patch_embeddings.projection.bias'''),
             ] )
  
         if has_lm_head:
                # mask token + shared relative position bias + layernorm
                rename_keys.extend(
                    [
                        ('''mask_token''', '''beit.embeddings.mask_token'''),
                        (
                            '''rel_pos_bias.relative_position_bias_table''',
                            '''beit.encoder.relative_position_bias.relative_position_bias_table''',
                        ),
                        (
                            '''rel_pos_bias.relative_position_index''',
                            '''beit.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''', '''beit.pooler.layernorm.weight'''),
                        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                        ('''head.weight''', '''classifier.weight'''),
                        ('''head.bias''', '''classifier.bias'''),
                    ] )
  
         return rename_keys
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A=False						,				A=False ) -> Tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         for i in range(config.num_hidden_layers ):
                lowercase__       =				'''backbone.''' if is_semantic else ''''''
                # queries, keys and values
                lowercase__       =				state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight" )
                lowercase__       =				state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias" )
                lowercase__       =				state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias" )
  
                lowercase__       =				in_proj_weight[
                    : config.hidden_size, :
                ]
                lowercase__       =				q_bias
                lowercase__       =				in_proj_weight[
                    config.hidden_size : config.hidden_size * 2, :
                ]
                lowercase__       =				in_proj_weight[
                    -config.hidden_size :, :
                ]
                lowercase__       =				v_bias
  
                # gamma_1 and gamma_2
                # we call them lambda because otherwise they are renamed when using .from_pretrained
                lowercase__       =				state_dict.pop(f"{prefix}blocks.{i}.gamma_1" )
                lowercase__       =				state_dict.pop(f"{prefix}blocks.{i}.gamma_2" )
  
                lowercase__       =				gamma_a
                lowercase__       =				gamma_a
  
                # relative_position bias table + index
                if not has_lm_head:
                       # each layer has its own relative position bias
                       lowercase__       =				state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table" )
                       lowercase__       =				state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index" )
  
                       lowercase__       =				table
                       lowercase__       =				index
  
  def _SCREAMING_SNAKE_CASE   (A						,				A						,				A ) -> str:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				dct.pop(A )
         lowercase__       =				val
  
  def _SCREAMING_SNAKE_CASE   () -> List[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
         lowercase__       =				Image.open(requests.get(A						,				stream=A ).raw )
         return im
  
  @torch.no_grad()
  def _SCREAMING_SNAKE_CASE   (A						,				A ) -> Optional[Any]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				BeitConfig()
         lowercase__       =				False
         lowercase__       =				False
         lowercase__       =				'''huggingface/label-files'''
         # set config parameters based on URL
         if checkpoint_url[-9:-4] == "pt22k":
                # masked image modeling
                lowercase__       =				True
                lowercase__       =				True
                lowercase__       =				True
         elif checkpoint_url[-9:-4] == "ft22k":
                # intermediate fine-tuning on ImageNet-22k
                lowercase__       =				True
                lowercase__       =				21_841
                lowercase__       =				'''imagenet-22k-id2label.json'''
                lowercase__       =				json.load(open(hf_hub_download(A						,				A						,				repo_type='''dataset''' )						,				'''r''' ) )
                lowercase__       =				{int(A ): v for k, v in idalabel.items()}
                # this dataset contains 21843 labels but the model only has 21841
                # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
                del idalabel[9_205]
                del idalabel[15_027]
                lowercase__       =				idalabel
                lowercase__       =				{v: k for k, v in idalabel.items()}
         elif checkpoint_url[-8:-4] == "to1k":
                # fine-tuning on ImageNet-1k
                lowercase__       =				True
                lowercase__       =				1_000
                lowercase__       =				'''imagenet-1k-id2label.json'''
                lowercase__       =				json.load(open(hf_hub_download(A						,				A						,				repo_type='''dataset''' )						,				'''r''' ) )
                lowercase__       =				{int(A ): v for k, v in idalabel.items()}
                lowercase__       =				idalabel
                lowercase__       =				{v: k for k, v in idalabel.items()}
                if "384" in checkpoint_url:
                       lowercase__       =				384
                if "512" in checkpoint_url:
                       lowercase__       =				512
         elif "ade20k" in checkpoint_url:
                # fine-tuning
                lowercase__       =				True
                lowercase__       =				150
                lowercase__       =				'''ade20k-id2label.json'''
                lowercase__       =				json.load(open(hf_hub_download(A						,				A						,				repo_type='''dataset''' )						,				'''r''' ) )
                lowercase__       =				{int(A ): v for k, v in idalabel.items()}
                lowercase__       =				idalabel
                lowercase__       =				{v: k for k, v in idalabel.items()}
                lowercase__       =				640
                lowercase__       =				True
         else:
                raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\'''' )
  
         # size of the architecture
         if "base" in checkpoint_url:
                pass
         elif "large" in checkpoint_url:
                lowercase__       =				1_024
                lowercase__       =				4_096
                lowercase__       =				24
                lowercase__       =				16
                if "ade20k" in checkpoint_url:
                       lowercase__       =				640
                       lowercase__       =				[7, 11, 15, 23]
         else:
                raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL''' )
  
         # load state_dict of original model, remove and rename some keys
         lowercase__       =				torch.hub.load_state_dict_from_url(A						,				map_location='''cpu'''						,				check_hash=A )
         lowercase__       =				state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']
  
         lowercase__       =				create_rename_keys(A						,				has_lm_head=A						,				is_semantic=A )
         for src, dest in rename_keys:
                rename_key(A						,				A						,				A )
         read_in_q_k_v(A						,				A						,				has_lm_head=A						,				is_semantic=A )
         if is_semantic:
                # add prefix to decoder keys
                for key, val in state_dict.copy().items():
                       lowercase__       =				state_dict.pop(A )
                       if key.startswith('''backbone.fpn''' ):
                              lowercase__       =				key.replace('''backbone.fpn'''						,				'''fpn''' )
                       lowercase__       =				val
  
      # load HuggingFace model
         if checkpoint_url[-9:-4] == "pt22k":
                lowercase__       =				BeitForMaskedImageModeling(A )
         elif "ade20k" in checkpoint_url:
                lowercase__       =				BeitForSemanticSegmentation(A )
         else:
                lowercase__       =				BeitForImageClassification(A )
         model.eval()
         model.load_state_dict(A )
  
         # Check outputs on an image
         if is_semantic:
                lowercase__       =				BeitImageProcessor(size=config.image_size						,				do_center_crop=A )
                lowercase__       =				load_dataset('''hf-internal-testing/fixtures_ade20k'''						,				split='''test''' )
                lowercase__       =				Image.open(ds[0]['''file'''] )
         else:
                lowercase__       =				BeitImageProcessor(
                    size=config.image_size						,				resample=PILImageResampling.BILINEAR						,				do_center_crop=A )
                lowercase__       =				prepare_img()
  
         lowercase__       =				image_processor(images=A						,				return_tensors='''pt''' )
         lowercase__       =				encoding['''pixel_values''']
  
         lowercase__       =				model(A )
         lowercase__       =				outputs.logits
  
         # verify logits
         lowercase__       =				torch.Size([1, 1_000] )
         if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k''' ):
                lowercase__       =				torch.Size([1, 196, 8_192] )
         elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k''' ):
                lowercase__       =				torch.Size([1, 196, 8_192] )
         elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k''' ):
                lowercase__       =				torch.Size([1, 21_841] )
                lowercase__       =				torch.tensor([2.2_288, 2.4_671, 0.7_395] )
                lowercase__       =				2_397
         elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k''' ):
                lowercase__       =				torch.Size([1, 21_841] )
                lowercase__       =				torch.tensor([1.6_881, -0.2_787, 0.5_901] )
                lowercase__       =				2_396
         elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k''' ):
                lowercase__       =				torch.tensor([0.1_241, 0.0_798, -0.6_569] )
                lowercase__       =				285
         elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k''' ):
                lowercase__       =				torch.tensor([-1.2_385, -1.0_987, -1.0_108] )
                lowercase__       =				281
         elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k''' ):
                lowercase__       =				torch.tensor([-1.5_303, -0.9_484, -0.3_147] )
                lowercase__       =				761
         elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k''' ):
                lowercase__       =				torch.tensor([0.4_610, -0.0_928, 0.2_086] )
                lowercase__       =				761
         elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k''' ):
                lowercase__       =				torch.tensor([-0.4_804, 0.6_257, -0.1_837] )
                lowercase__       =				761
         elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k''' ):
                lowercase__       =				torch.tensor([[-0.5_122, 0.5_117, -0.2_113]] )
                lowercase__       =				761
         elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k''' ):
                lowercase__       =				torch.tensor([-0.3_062, 0.7_261, 0.4_852] )
                lowercase__       =				761
         elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k''' ):
                lowercase__       =				(1, 150, 160, 160)
                lowercase__       =				torch.tensor(
                    [
                        [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
                        [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
                        [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
                    ] )
         elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k''' ):
                lowercase__       =				(1, 150, 160, 160)
                lowercase__       =				torch.tensor(
                    [
                        [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
                        [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
                        [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
                    ] )
         else:
                raise ValueError('''Can\'t verify logits as model is not supported''' )
  
         assert logits.shape == expected_shape, "Shape of logits not as expected"
         if not has_lm_head:
                if is_semantic:
                       assert torch.allclose(
                           logits[0, :3, :3, :3]						,				A						,				atol=1E-3 ), "First elements of logits not as expected"
                else:
                       print('''Predicted class idx:'''						,				logits.argmax(-1 ).item() )
                       assert torch.allclose(
                           logits[0, :3]						,				A						,				atol=1E-3 ), "First elements of logits not as expected"
                       assert logits.argmax(-1 ).item() == expected_class_idx, "Predicted class index not as expected"
  
         Path(A ).mkdir(exist_ok=A )
         print(f"Saving model to {pytorch_dump_folder_path}" )
         model.save_pretrained(A )
         print(f"Saving image processor to {pytorch_dump_folder_path}" )
         image_processor.save_pretrained(A )
  
  
  if __name__ == "__main__":
   lowerCamelCase							:     Optional[int]								=			argparse.ArgumentParser()
  
   parser.add_argument(
       '--checkpoint_url',
       default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
       type=str,
       help='URL to the original PyTorch checkpoint (.pth file).',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
   )
   lowerCamelCase							:     Dict								=			parser.parse_args()
   convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  
  
  
  
  
style_context: 
  
  
  '''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
  
  
  lowerCamelCase							:     Union[str, Any]								=			logging.get_logger(__name__)
  
  lowerCamelCase							:     Tuple								=			{
      'vocab_file': 'vocab.json',
      'merges_file': 'merges.txt',
  }
  
  lowerCamelCase							:     Tuple								=			{
      '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',
      },
  }
  
  lowerCamelCase							:     Optional[int]								=			{
      'Salesforce/codegen-350M-mono': 2_048,
  }
  
  @lru_cache()
  def _SCREAMING_SNAKE_CASE   () -> Optional[int]:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				(
             list(range(ord('''!''' )						,				ord('''~''' ) + 1 ) ) + list(range(ord('''¡''' )						,				ord('''¬''' ) + 1 ) ) + list(range(ord('''®''' )						,				ord('''ÿ''' ) + 1 ) )
         )
         lowercase__       =				bs[:]
         lowercase__       =				0
         for b in range(2**8 ):
                if b not in bs:
                       bs.append(A )
                       cs.append(2**8 + n )
                       n += 1
         lowercase__       =				[chr(A ) for n in cs]
         return dict(zip(A						,				A ) )
  
  def _SCREAMING_SNAKE_CASE   (A ) -> Tuple:
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         lowercase__       =				set()
         lowercase__       =				word[0]
         for char in word[1:]:
                pairs.add((prev_char, char) )
                lowercase__       =				char
         return pairs
  
  class 						__lowerCAmelCase		(lowercase_  ):
  
  
  
  
  
        '''simple docstring'''
  
  
  
  
  
  
        lowerCAmelCase__      :  int						      =							VOCAB_FILES_NAMES
        lowerCAmelCase__      :  Union[str, Any]						      =							PRETRAINED_VOCAB_FILES_MAP
        lowerCAmelCase__      :  Optional[int]						      =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        lowerCAmelCase__      :  Tuple						      =							["""input_ids""", """attention_mask"""]
  
  
  
        def __init__(self					:					List[Any]     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					Optional[int]="replace"     ,		UpperCamelCase					:					Optional[Any]="<|endoftext|>"     ,		UpperCamelCase					:					str="<|endoftext|>"     ,		UpperCamelCase					:					Optional[int]="<|endoftext|>"     ,		UpperCamelCase					:					Optional[int]=None     ,		UpperCamelCase					:					str=False     ,		UpperCamelCase					:					Any=False     ,		**UpperCamelCase					:					Dict     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else bos_token
               lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else eos_token
               lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else unk_token
               lowercase__       =				AddedToken(UpperCamelCase     ,		lstrip=UpperCamelCase     ,		rstrip=UpperCamelCase				) if isinstance(UpperCamelCase     ,		UpperCamelCase				) else pad_token
               super().__init__(
                   errors=UpperCamelCase     ,		unk_token=UpperCamelCase     ,		bos_token=UpperCamelCase     ,		eos_token=UpperCamelCase     ,		pad_token=UpperCamelCase     ,		add_prefix_space=UpperCamelCase     ,		add_bos_token=UpperCamelCase     ,		**UpperCamelCase     ,		)
               lowercase__       =				add_bos_token
  
               with open(UpperCamelCase     ,		encoding='''utf-8'''				) as vocab_handle:
                      lowercase__       =				json.load(UpperCamelCase				)
               lowercase__       =				{v: k for k, v in self.encoder.items()}
               lowercase__       =				errors  # how to handle errors in decoding
               lowercase__       =				bytes_to_unicode()
               lowercase__       =				{v: k for k, v in self.byte_encoder.items()}
               with open(UpperCamelCase     ,		encoding='''utf-8'''				) as merges_handle:
                      lowercase__       =				merges_handle.read().split('''\n'''				)[1:-1]
               lowercase__       =				[tuple(merge.split()				) for merge in bpe_merges]
               lowercase__       =				dict(zip(UpperCamelCase     ,		range(len(UpperCamelCase				)				)				)				)
               lowercase__       =				{}
               lowercase__       =				add_prefix_space
  
               # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
               lowercase__       =				re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''				)
  
  
  
        @property
        def    UpperCamelCase__	(self					:					Any				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return len(self.encoder				)
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return dict(self.encoder     ,		**self.added_tokens_encoder				)
  
  
  
        def    UpperCamelCase__	(self					:					Union[str, Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if token in self.cache:
                      return self.cache[token]
               lowercase__       =				tuple(UpperCamelCase				)
               lowercase__       =				get_pairs(UpperCamelCase				)
  
               if not pairs:
                      return token
  
               while True:
                      lowercase__       =				min(UpperCamelCase     ,		key=lambda UpperCamelCase				: self.bpe_ranks.get(UpperCamelCase     ,		float('''inf'''				)				)				)
                      if bigram not in self.bpe_ranks:
                             break
                      lowercase__    ,lowercase__       =				bigram
                      lowercase__       =				[]
                      lowercase__       =				0
                      while i < len(UpperCamelCase				):
                             try:
                                    lowercase__       =				word.index(UpperCamelCase     ,		UpperCamelCase				)
                             except ValueError:
                                    new_word.extend(word[i:]				)
                                    break
                             else:
                                    new_word.extend(word[i:j]				)
                                    lowercase__       =				j
  
                             if word[i] == first and i < len(UpperCamelCase				) - 1 and word[i + 1] == second:
                                    new_word.append(first + second				)
                                    i += 2
                             else:
                                    new_word.append(word[i]				)
                                    i += 1
                      lowercase__       =				tuple(UpperCamelCase				)
                      lowercase__       =				new_word
                      if len(UpperCamelCase				) == 1:
                             break
                      else:
                             lowercase__       =				get_pairs(UpperCamelCase				)
               lowercase__       =				''' '''.join(UpperCamelCase				)
               lowercase__       =				word
               return word
  
  
  
        def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					str=None				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if self.add_bos_token:
                      lowercase__       =				[self.bos_token_id]
               else:
                      lowercase__       =				[]
  
               lowercase__       =				bos_token_ids + token_ids_a
  
               if token_ids_a is None:
                      return output
  
               return output + bos_token_ids + token_ids_a
  
  
  
        def    UpperCamelCase__	(self					:					Optional[Any]     ,		UpperCamelCase					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				[]
               for token in re.findall(self.pat     ,		UpperCamelCase				):
                      lowercase__       =				''''''.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(UpperCamelCase				).split(''' '''				)				)
               return bpe_tokens
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					str				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self.encoder.get(UpperCamelCase     ,		self.encoder.get(self.unk_token				)				)
  
  
  
        def    UpperCamelCase__	(self					:					Any     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               return self.decoder.get(UpperCamelCase				)
  
  
  
        def    UpperCamelCase__	(self					:					List[Any]     ,		UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				''''''.join(UpperCamelCase				)
               lowercase__       =				bytearray([self.byte_decoder[c] for c in text]				).decode('''utf-8'''     ,		errors=self.errors				)
               return text
  
  
  
        def    UpperCamelCase__	(self					:					int     ,		UpperCamelCase					:					str     ,		UpperCamelCase					:					Optional[str] = None				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               if not os.path.isdir(UpperCamelCase				):
                      logger.error(f"Vocabulary path ({save_directory}) should be a directory"				)
                      return
               lowercase__       =				os.path.join(
                   UpperCamelCase     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']				)
               lowercase__       =				os.path.join(
                   UpperCamelCase     ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']				)
  
               with open(UpperCamelCase     ,		'''w'''     ,		encoding='''utf-8'''				) as f:
                      f.write(json.dumps(self.encoder     ,		indent=2     ,		sort_keys=UpperCamelCase     ,		ensure_ascii=UpperCamelCase				) + '''\n'''				)
  
               lowercase__       =				0
               with open(UpperCamelCase     ,		'''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 UpperCamelCase				: 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!'''				)
                                    lowercase__       =				token_index
                             writer.write(''' '''.join(UpperCamelCase				) + '''\n'''				)
                             index += 1
  
               return vocab_file, merge_file
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					List[Any]     ,		UpperCamelCase					:					int=False     ,		**UpperCamelCase					:					Optional[int]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				kwargs.pop('''add_prefix_space'''     ,		self.add_prefix_space				)
               if is_split_into_words or add_prefix_space:
                      lowercase__       =				''' ''' + text
               return (text, kwargs)
  
  
  
        def    UpperCamelCase__	(self					:					Tuple     ,		UpperCamelCase					:					Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]     ,		UpperCamelCase					:					bool = False     ,		UpperCamelCase					:					bool = None     ,		UpperCamelCase					:					Optional[List[str]] = None     ,		**UpperCamelCase					:					Optional[Any]     ,		):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               lowercase__       =				super()._decode(
                   token_ids=UpperCamelCase     ,		skip_special_tokens=UpperCamelCase     ,		clean_up_tokenization_spaces=UpperCamelCase     ,		**UpperCamelCase     ,		)
  
               if truncate_before_pattern is not None and len(UpperCamelCase				) > 0:
                      lowercase__       =				self.truncate(UpperCamelCase     ,		UpperCamelCase				)
  
               return decoded_text
  
  
  
  
  
  
        def    UpperCamelCase__	(self					:					str     ,		UpperCamelCase					:					List[str]     ,		UpperCamelCase					:					Union[str, Any]				):
  
               '''simple docstring'''
  
  
  
  
  
  
  
               def find_re(UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Dict     ,		UpperCamelCase					:					Optional[Any]				):
                      lowercase__       =				pattern.search(UpperCamelCase     ,		UpperCamelCase				)
                      return m.start() if m else -1
  
               lowercase__       =				[re.compile(UpperCamelCase     ,		re.MULTILINE				) for pattern in truncate_before_pattern]
  
               lowercase__       =				list(re.finditer('''^print'''     ,		UpperCamelCase     ,		re.MULTILINE				)				)
  
               if len(UpperCamelCase				) > 1:
                      lowercase__       =				completion[: prints[1].start()]
  
               lowercase__       =				list(re.finditer('''^def'''     ,		UpperCamelCase     ,		re.MULTILINE				)				)
  
               if len(UpperCamelCase				) > 1:
                      lowercase__       =				completion[: defs[1].start()]
  
               lowercase__       =				0
  
               lowercase__       =				[
                   pos for pos in [find_re(UpperCamelCase     ,		UpperCamelCase     ,		UpperCamelCase				) for terminal in terminals] if pos != -1
               ]
  
               if len(UpperCamelCase				) > 0:
                      return completion[: min(UpperCamelCase				)]
               else:
                      return completion
  
  
  
  
  