


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()
snake_case_  :	Dict							  =							logging.get_logger(__name__)







def   lowerCamelCase(      a__		,a__=False		,a__=False):
				_SCREAMING_SNAKE_CASE	     ='''backbone.''' if is_semantic else ''''''

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

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

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

								_SCREAMING_SNAKE_CASE	     =gamma_a
								_SCREAMING_SNAKE_CASE	     =gamma_a

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

												_SCREAMING_SNAKE_CASE	     =table
												_SCREAMING_SNAKE_CASE	     =index







def   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =dct.pop(a__)
				_SCREAMING_SNAKE_CASE	     =val







def   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_SCREAMING_SNAKE_CASE	     =Image.open(requests.get(a__		,stream=a__).raw)
				return im







@torch.no_grad()
def   lowerCamelCase(      a__		,a__):
				_SCREAMING_SNAKE_CASE	     =BeitConfig()
				_SCREAMING_SNAKE_CASE	     =False
				_SCREAMING_SNAKE_CASE	     =False
				_SCREAMING_SNAKE_CASE	     ='''huggingface/label-files'''
				# set config parameters based on URL
				if checkpoint_url[-9:-4] == "pt22k":
								# masked image modeling
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =True
				elif checkpoint_url[-9:-4] == "ft22k":
								# intermediate fine-tuning on ImageNet-22k
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =2_1841
								_SCREAMING_SNAKE_CASE	     ='''imagenet-22k-id2label.json'''
								_SCREAMING_SNAKE_CASE	     =json.load(open(hf_hub_download(a__		,a__		,repo_type='''dataset''')		,'''r'''))
								_SCREAMING_SNAKE_CASE	     ={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[9205]
								del idalabel[1_5027]
								_SCREAMING_SNAKE_CASE	     =idalabel
								_SCREAMING_SNAKE_CASE	     ={v: k for k, v in idalabel.items()}
				elif checkpoint_url[-8:-4] == "to1k":
								# fine-tuning on ImageNet-1k
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =1000
								_SCREAMING_SNAKE_CASE	     ='''imagenet-1k-id2label.json'''
								_SCREAMING_SNAKE_CASE	     =json.load(open(hf_hub_download(a__		,a__		,repo_type='''dataset''')		,'''r'''))
								_SCREAMING_SNAKE_CASE	     ={int(a__): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE	     =idalabel
								_SCREAMING_SNAKE_CASE	     ={v: k for k, v in idalabel.items()}
								if "384" in checkpoint_url:
												_SCREAMING_SNAKE_CASE	     =384
								if "512" in checkpoint_url:
												_SCREAMING_SNAKE_CASE	     =512
				elif "ade20k" in checkpoint_url:
								# fine-tuning
								_SCREAMING_SNAKE_CASE	     =True
								_SCREAMING_SNAKE_CASE	     =150
								_SCREAMING_SNAKE_CASE	     ='''ade20k-id2label.json'''
								_SCREAMING_SNAKE_CASE	     =json.load(open(hf_hub_download(a__		,a__		,repo_type='''dataset''')		,'''r'''))
								_SCREAMING_SNAKE_CASE	     ={int(a__): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE	     =idalabel
								_SCREAMING_SNAKE_CASE	     ={v: k for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE	     =640
								_SCREAMING_SNAKE_CASE	     =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:
								_SCREAMING_SNAKE_CASE	     =1024
								_SCREAMING_SNAKE_CASE	     =4096
								_SCREAMING_SNAKE_CASE	     =24
								_SCREAMING_SNAKE_CASE	     =16
								if "ade20k" in checkpoint_url:
												_SCREAMING_SNAKE_CASE	     =640
												_SCREAMING_SNAKE_CASE	     =[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
				_SCREAMING_SNAKE_CASE	     =torch.hub.load_state_dict_from_url(a__		,map_location='''cpu'''		,check_hash=a__)
				_SCREAMING_SNAKE_CASE	     =state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

				_SCREAMING_SNAKE_CASE	     =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():
												_SCREAMING_SNAKE_CASE	     =state_dict.pop(a__)
												if key.startswith('''backbone.fpn'''):
																_SCREAMING_SNAKE_CASE	     =key.replace('''backbone.fpn'''		,'''fpn''')
												_SCREAMING_SNAKE_CASE	     =val

    # load HuggingFace model
				if checkpoint_url[-9:-4] == "pt22k":
								_SCREAMING_SNAKE_CASE	     =BeitForMaskedImageModeling(a__)
				elif "ade20k" in checkpoint_url:
								_SCREAMING_SNAKE_CASE	     =BeitForSemanticSegmentation(a__)
				else:
								_SCREAMING_SNAKE_CASE	     =BeitForImageClassification(a__)
				model.eval()
				model.load_state_dict(a__)

				# Check outputs on an image
				if is_semantic:
								_SCREAMING_SNAKE_CASE	     =BeitImageProcessor(size=config.image_size		,do_center_crop=a__)
								_SCREAMING_SNAKE_CASE	     =load_dataset('''hf-internal-testing/fixtures_ade20k'''		,split='''test''')
								_SCREAMING_SNAKE_CASE	     =Image.open(ds[0]['''file'''])
				else:
								_SCREAMING_SNAKE_CASE	     =BeitImageProcessor(
								    size=config.image_size		,resample=PILImageResampling.BILINEAR		,do_center_crop=a__)
								_SCREAMING_SNAKE_CASE	     =prepare_img()

				_SCREAMING_SNAKE_CASE	     =image_processor(images=a__		,return_tensors='''pt''')
				_SCREAMING_SNAKE_CASE	     =encoding['''pixel_values''']

				_SCREAMING_SNAKE_CASE	     =model(a__)
				_SCREAMING_SNAKE_CASE	     =outputs.logits

				# verify logits
				_SCREAMING_SNAKE_CASE	     =torch.Size([1, 1000])
				if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 196, 8192])
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 196, 8192])
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 2_1841])
								_SCREAMING_SNAKE_CASE	     =torch.tensor([2.2288, 2.4671, 0.7395])
								_SCREAMING_SNAKE_CASE	     =2397
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''):
								_SCREAMING_SNAKE_CASE	     =torch.Size([1, 2_1841])
								_SCREAMING_SNAKE_CASE	     =torch.tensor([1.6881, -0.2787, 0.5901])
								_SCREAMING_SNAKE_CASE	     =2396
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([0.1241, 0.0798, -0.6569])
								_SCREAMING_SNAKE_CASE	     =285
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-1.2385, -1.0987, -1.0108])
								_SCREAMING_SNAKE_CASE	     =281
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-1.5303, -0.9484, -0.3147])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([0.4610, -0.0928, 0.2086])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-0.4804, 0.6257, -0.1837])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([[-0.5122, 0.5117, -0.2113]])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''):
								_SCREAMING_SNAKE_CASE	     =torch.tensor([-0.3062, 0.7261, 0.4852])
								_SCREAMING_SNAKE_CASE	     =761
				elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''):
								_SCREAMING_SNAKE_CASE	     =(1, 150, 160, 160)
								_SCREAMING_SNAKE_CASE	     =torch.tensor(
								    [
								        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
								        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
								        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
								    ])
				elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''):
								_SCREAMING_SNAKE_CASE	     =(1, 150, 160, 160)
								_SCREAMING_SNAKE_CASE	     =torch.tensor(
								    [
								        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
								        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
								        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
								    ])
				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__":
							snake_case_  :	Any							  =							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.'''
							)
							snake_case_  :	int							  =							parser.parse_args()
							convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
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


UpperCAmelCase							= logging.get_logger(__name__)

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

UpperCAmelCase							= {
    '''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''',
    },
}

UpperCAmelCase							= {
    '''Salesforce/codegen-350M-mono''': 20_48,
}


@lru_cache()
def SCREAMING_SNAKE_CASE (   ):
       _UpperCamelCase   :				Any       =     (
           list(range(ord('!' )						,  ord('~' ) + 1 ) ) + list(range(ord('¡' )						,  ord('¬' ) + 1 ) ) + list(range(ord('®' )						,  ord('ÿ' ) + 1 ) )
       )
       _UpperCamelCase   :				int       =     bs[:]
       _UpperCamelCase   :				Any       =     0
       for b in range(2**8 ):
              if b not in bs:
                     bs.append(lowercase_ )
                     cs.append(2**8 + n )
                     n += 1
       _UpperCamelCase   :				str       =     [chr(lowercase_ ) for n in cs]
       return dict(zip(lowercase_						,  lowercase_ ) )


def SCREAMING_SNAKE_CASE (   lowercase_:		int ):
       _UpperCamelCase   :				List[str]       =     set()
       _UpperCamelCase   :				Optional[int]       =     word[0]
       for char in word[1:]:
              pairs.add((prev_char, char) )
              _UpperCamelCase   :				Optional[Any]       =     char
       return pairs





class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Union[str, Any] 			=       VOCAB_FILES_NAMES
 _lowerCamelCase  :		Optional[int] 			=       PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  :		List[str] 			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  :		Optional[int] 			=       ['input_ids', 'attention_mask']

 def __init__(	self				:						Union[str, Any]				,	lowerCamelCase				:						Tuple				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Optional[Any]="replace"				,	lowerCamelCase				:						int="<|endoftext|>"				,	lowerCamelCase				:						Dict="<|endoftext|>"				,	lowerCamelCase				:						Dict="<|endoftext|>"				,	lowerCamelCase				:						str=None				,	lowerCamelCase				:						List[Any]=False				,	lowerCamelCase				:						Optional[int]=False				,	**lowerCamelCase				:						Optional[Any]				,	)       ->							int:
        _UpperCamelCase   :				int       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else bos_token
        _UpperCamelCase   :				Any       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else eos_token
        _UpperCamelCase   :				Dict       =     AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase     ) if isinstance(lowerCamelCase				,	lowerCamelCase     ) else unk_token
        _UpperCamelCase   :				Dict       =     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				,	)
        _UpperCamelCase   :				List[str]       =     add_bos_token

        with open(lowerCamelCase				,	encoding='utf-8'     ) as vocab_handle:
               _UpperCamelCase   :				Union[str, Any]       =     json.load(lowerCamelCase     )
        _UpperCamelCase   :				int       =     {v: k for k, v in self.encoder.items()}
        _UpperCamelCase   :				Optional[int]       =     errors  # how to handle errors in decoding
        _UpperCamelCase   :				Dict       =     bytes_to_unicode()
        _UpperCamelCase   :				Optional[Any]       =     {v: k for k, v in self.byte_encoder.items()}
        with open(lowerCamelCase				,	encoding='utf-8'     ) as merges_handle:
               _UpperCamelCase   :				Dict       =     merges_handle.read().split('\n'     )[1:-1]
        _UpperCamelCase   :				int       =     [tuple(merge.split()     ) for merge in bpe_merges]
        _UpperCamelCase   :				Optional[Any]       =     dict(zip(lowerCamelCase				,	range(len(lowerCamelCase     )     )     )     )
        _UpperCamelCase   :				List[Any]       =     {}
        _UpperCamelCase   :				Any       =     add_prefix_space

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

 @property
 def     __snake_case    (	self				:						Optional[Any]     )       ->							Union[str, Any]:
        return len(self.encoder     )

 def     __snake_case    (	self				:						Union[str, Any]     )       ->							Optional[Any]:
        return dict(self.encoder				,	**self.added_tokens_encoder     )

 def     __snake_case    (	self				:						int				,	lowerCamelCase				:						Dict     )       ->							Union[str, Any]:
        if token in self.cache:
               return self.cache[token]
        _UpperCamelCase   :				Dict       =     tuple(lowerCamelCase     )
        _UpperCamelCase   :				Union[str, Any]       =     get_pairs(lowerCamelCase     )

        if not pairs:
               return token

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

 def     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Any=None     )       ->							Any:
        if self.add_bos_token:
               _UpperCamelCase   :				List[str]       =     [self.bos_token_id]
        else:
               _UpperCamelCase   :				List[str]       =     []

        _UpperCamelCase   :				Union[str, Any]       =     bos_token_ids + token_ids_a

        if token_ids_a is None:
               return output

        return output + bos_token_ids + token_ids_a

 def     __snake_case    (	self				:						Union[str, Any]				,	lowerCamelCase				:						Union[str, Any]     )       ->							str:
        _UpperCamelCase   :				Tuple       =     []
        for token in re.findall(self.pat				,	lowerCamelCase     ):
               _UpperCamelCase   :				int       =     ''.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     __snake_case    (	self				:						int				,	lowerCamelCase				:						Any     )       ->							List[str]:
        return self.encoder.get(lowerCamelCase				,	self.encoder.get(self.unk_token     )     )

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						List[Any]     )       ->							List[str]:
        return self.decoder.get(lowerCamelCase     )

 def     __snake_case    (	self				:						Optional[int]				,	lowerCamelCase				:						Optional[Any]     )       ->							List[Any]:
        _UpperCamelCase   :				Union[str, Any]       =     ''.join(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     bytearray([self.byte_decoder[c] for c in text]     ).decode('utf-8'				,	errors=self.errors     )
        return text

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

        _UpperCamelCase   :				Any       =     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!'     )
                             _UpperCamelCase   :				Dict       =     token_index
                      writer.write(' '.join(lowerCamelCase     ) + '\n'     )
                      index += 1

        return vocab_file, merge_file

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						Optional[int]				,	lowerCamelCase				:						int=False				,	**lowerCamelCase				:						Any     )       ->							Tuple:
        _UpperCamelCase   :				Optional[int]       =     kwargs.pop('add_prefix_space'				,	self.add_prefix_space     )
        if is_split_into_words or add_prefix_space:
               _UpperCamelCase   :				Union[str, Any]       =     ' ' + text
        return (text, kwargs)

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]				,	lowerCamelCase				:						bool = False				,	lowerCamelCase				:						bool = None				,	lowerCamelCase				:						Optional[List[str]] = None				,	**lowerCamelCase				:						Union[str, Any]				,	)       ->							str:
        _UpperCamelCase   :				List[str]       =     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:
               _UpperCamelCase   :				Union[str, Any]       =     self.truncate(lowerCamelCase				,	lowerCamelCase     )

        return decoded_text

 def     __snake_case    (	self				:						Any				,	lowerCamelCase				:						Dict				,	lowerCamelCase				:						Optional[int]     )       ->							Any:
        def find_re(lowerCamelCase				:						int				,	lowerCamelCase				:						int				,	lowerCamelCase				:						List[Any]     ):
               _UpperCamelCase   :				Dict       =     pattern.search(lowerCamelCase				,	lowerCamelCase     )
               return m.start() if m else -1

        _UpperCamelCase   :				int       =     [re.compile(lowerCamelCase				,	re.MULTILINE     ) for pattern in truncate_before_pattern]

        _UpperCamelCase   :				Union[str, Any]       =     list(re.finditer('^print'				,	lowerCamelCase				,	re.MULTILINE     )     )

        if len(lowerCamelCase     ) > 1:
               _UpperCamelCase   :				List[Any]       =     completion[: prints[1].start()]

        _UpperCamelCase   :				Dict       =     list(re.finditer('^def'				,	lowerCamelCase				,	re.MULTILINE     )     )

        if len(lowerCamelCase     ) > 1:
               _UpperCamelCase   :				Tuple       =     completion[: defs[1].start()]

        _UpperCamelCase   :				Optional[Any]       =     0

        _UpperCamelCase   :				int       =     [
            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



