




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()
UpperCamelCase__		=						logging.get_logger(__name__)






def  snake_case (							A						,       A=False						,       A=False      )							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							       =							'backbone.' if is_semantic else ''

	lowerCamelCase_							:		int							       =							[]
	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  snake_case (							A						,       A						,       A=False						,       A=False      )							->							Dict:




	'''simple docstring'''


	for i in range(config.num_hidden_layers      ):
		lowerCamelCase_							:		Optional[int]							       =							'backbone.' if is_semantic else ''
		# queries, keys and values
		lowerCamelCase_							:		List[Any]							       =							state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"      )
		lowerCamelCase_							:		Dict							       =							state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"      )
		lowerCamelCase_							:		List[Any]							       =							state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"      )

		lowerCamelCase_							:		str							       =							in_proj_weight[
		    : config.hidden_size, :
		]
		lowerCamelCase_							:		Dict							       =							q_bias
		lowerCamelCase_							:		Tuple							       =							in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		lowerCamelCase_							:		Optional[int]							       =							in_proj_weight[
		    -config.hidden_size :, :
		]
		lowerCamelCase_							:		List[str]							       =							v_bias

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

		lowerCamelCase_							:		int							       =							gamma_a
		lowerCamelCase_							:		Optional[Any]							       =							gamma_a

		# relative_position bias table + index
		if not has_lm_head:
			# each layer has its own relative position bias
			lowerCamelCase_							:		Dict							       =							state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table"      )
			lowerCamelCase_							:		Union[str, Any]							       =							state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"      )

			lowerCamelCase_							:		List[str]							       =							table
			lowerCamelCase_							:		Tuple							       =							index






def  snake_case (							A						,       A						,       A      )							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							       =							dct.pop(__UpperCamelCase      )
	lowerCamelCase_							:		str							       =							val






def  snake_case (							)							->							Dict:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							       =							'http://images.cocodataset.org/val2017/000000039769.jpg'
	lowerCamelCase_							:		Any							       =							Image.open(requests.get(__UpperCamelCase						,       stream=__UpperCamelCase      ).raw      )
	return im






@torch.no_grad()
def  snake_case (							A						,       A      )							->							Union[str, Any]:




	'''simple docstring'''


	lowerCamelCase_							:		str							       =							BeitConfig()
	lowerCamelCase_							:		Optional[Any]							       =							False
	lowerCamelCase_							:		List[str]							       =							False
	lowerCamelCase_							:		str							       =							'huggingface/label-files'
	# set config parameters based on URL
	if checkpoint_url[-9:-4] == "pt22k":
		# masked image modeling
		lowerCamelCase_							:		str							       =							True
		lowerCamelCase_							:		int							       =							True
		lowerCamelCase_							:		Dict							       =							True
	elif checkpoint_url[-9:-4] == "ft22k":
		# intermediate fine-tuning on ImageNet-22k
		lowerCamelCase_							:		List[Any]							       =							True
		lowerCamelCase_							:		Union[str, Any]							       =							21841
		lowerCamelCase_							:		int							       =							'imagenet-22k-id2label.json'
		lowerCamelCase_							:		str							       =							json.load(open(hf_hub_download(__UpperCamelCase						,       __UpperCamelCase						,       repo_type='dataset'      )						,       'r'      )      )
		lowerCamelCase_							:		Any							       =							{int(__UpperCamelCase      ): 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[15027]
		lowerCamelCase_							:		Tuple							       =							idalabel
		lowerCamelCase_							:		List[str]							       =							{v: k for k, v in idalabel.items()}
	elif checkpoint_url[-8:-4] == "to1k":
		# fine-tuning on ImageNet-1k
		lowerCamelCase_							:		str							       =							True
		lowerCamelCase_							:		int							       =							1000
		lowerCamelCase_							:		str							       =							'imagenet-1k-id2label.json'
		lowerCamelCase_							:		Union[str, Any]							       =							json.load(open(hf_hub_download(__UpperCamelCase						,       __UpperCamelCase						,       repo_type='dataset'      )						,       'r'      )      )
		lowerCamelCase_							:		Optional[int]							       =							{int(__UpperCamelCase      ): v for k, v in idalabel.items()}
		lowerCamelCase_							:		str							       =							idalabel
		lowerCamelCase_							:		Optional[int]							       =							{v: k for k, v in idalabel.items()}
		if "384" in checkpoint_url:
			lowerCamelCase_							:		Optional[Any]							       =							384
		if "512" in checkpoint_url:
			lowerCamelCase_							:		Optional[Any]							       =							512
	elif "ade20k" in checkpoint_url:
		# fine-tuning
		lowerCamelCase_							:		str							       =							True
		lowerCamelCase_							:		Dict							       =							150
		lowerCamelCase_							:		Any							       =							'ade20k-id2label.json'
		lowerCamelCase_							:		List[Any]							       =							json.load(open(hf_hub_download(__UpperCamelCase						,       __UpperCamelCase						,       repo_type='dataset'      )						,       'r'      )      )
		lowerCamelCase_							:		str							       =							{int(__UpperCamelCase      ): v for k, v in idalabel.items()}
		lowerCamelCase_							:		Dict							       =							idalabel
		lowerCamelCase_							:		Any							       =							{v: k for k, v in idalabel.items()}
		lowerCamelCase_							:		Tuple							       =							640
		lowerCamelCase_							:		Dict							       =							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:
		lowerCamelCase_							:		Optional[Any]							       =							1024
		lowerCamelCase_							:		Any							       =							4096
		lowerCamelCase_							:		int							       =							24
		lowerCamelCase_							:		Any							       =							16
		if "ade20k" in checkpoint_url:
			lowerCamelCase_							:		Dict							       =							640
			lowerCamelCase_							:		Union[str, Any]							       =							[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
	lowerCamelCase_							:		str							       =							torch.hub.load_state_dict_from_url(__UpperCamelCase						,       map_location='cpu'						,       check_hash=__UpperCamelCase      )
	lowerCamelCase_							:		List[Any]							       =							state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

	lowerCamelCase_							:		int							       =							create_rename_keys(__UpperCamelCase						,       has_lm_head=__UpperCamelCase						,       is_semantic=__UpperCamelCase      )
	for src, dest in rename_keys:
		rename_key(__UpperCamelCase						,       __UpperCamelCase						,       __UpperCamelCase      )
	read_in_q_k_v(__UpperCamelCase						,       __UpperCamelCase						,       has_lm_head=__UpperCamelCase						,       is_semantic=__UpperCamelCase      )
	if is_semantic:
		# add prefix to decoder keys
		for key, val in state_dict.copy().items():
			lowerCamelCase_							:		Optional[int]							       =							state_dict.pop(__UpperCamelCase      )
			if key.startswith('backbone.fpn'      ):
				lowerCamelCase_							:		Optional[int]							       =							key.replace('backbone.fpn'						,       'fpn'      )
			lowerCamelCase_							:		List[Any]							       =							val

		# load HuggingFace model
	if checkpoint_url[-9:-4] == "pt22k":
		lowerCamelCase_							:		Tuple							       =							BeitForMaskedImageModeling(__UpperCamelCase      )
	elif "ade20k" in checkpoint_url:
		lowerCamelCase_							:		Union[str, Any]							       =							BeitForSemanticSegmentation(__UpperCamelCase      )
	else:
		lowerCamelCase_							:		Optional[int]							       =							BeitForImageClassification(__UpperCamelCase      )
	model.eval()
	model.load_state_dict(__UpperCamelCase      )

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

	lowerCamelCase_							:		Union[str, Any]							       =							image_processor(images=__UpperCamelCase						,       return_tensors='pt'      )
	lowerCamelCase_							:		List[str]							       =							encoding['pixel_values']

	lowerCamelCase_							:		List[str]							       =							model(__UpperCamelCase      )
	lowerCamelCase_							:		Optional[int]							       =							outputs.logits

	# verify logits
	lowerCamelCase_							:		int							       =							torch.Size([1, 1000]      )
	if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'      ):
		lowerCamelCase_							:		Any							       =							torch.Size([1, 196, 8192]      )
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'      ):
		lowerCamelCase_							:		Optional[Any]							       =							torch.Size([1, 196, 8192]      )
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'      ):
		lowerCamelCase_							:		int							       =							torch.Size([1, 21841]      )
		lowerCamelCase_							:		int							       =							torch.tensor([2.2288, 2.4671, 0.7395]      )
		lowerCamelCase_							:		Optional[Any]							       =							2397
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'      ):
		lowerCamelCase_							:		Tuple							       =							torch.Size([1, 21841]      )
		lowerCamelCase_							:		Union[str, Any]							       =							torch.tensor([1.6881, -0.2787, 0.5901]      )
		lowerCamelCase_							:		str							       =							2396
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'      ):
		lowerCamelCase_							:		List[str]							       =							torch.tensor([0.1241, 0.0798, -0.6569]      )
		lowerCamelCase_							:		Tuple							       =							285
	elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'      ):
		lowerCamelCase_							:		Dict							       =							torch.tensor([-1.2385, -1.0987, -1.0108]      )
		lowerCamelCase_							:		Dict							       =							281
	elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'      ):
		lowerCamelCase_							:		Optional[Any]							       =							torch.tensor([-1.5303, -0.9484, -0.3147]      )
		lowerCamelCase_							:		Dict							       =							761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'      ):
		lowerCamelCase_							:		List[Any]							       =							torch.tensor([0.4610, -0.0928, 0.2086]      )
		lowerCamelCase_							:		Any							       =							761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'      ):
		lowerCamelCase_							:		int							       =							torch.tensor([-0.4804, 0.6257, -0.1837]      )
		lowerCamelCase_							:		Tuple							       =							761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'      ):
		lowerCamelCase_							:		Tuple							       =							torch.tensor([[-0.5122, 0.5117, -0.2113]]      )
		lowerCamelCase_							:		Optional[Any]							       =							761
	elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'      ):
		lowerCamelCase_							:		str							       =							torch.tensor([-0.3062, 0.7261, 0.4852]      )
		lowerCamelCase_							:		List[Any]							       =							761
	elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'      ):
		lowerCamelCase_							:		Optional[int]							       =							(1, 150, 160, 160)
		lowerCamelCase_							:		Tuple							       =							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'      ):
		lowerCamelCase_							:		Optional[Any]							       =							(1, 150, 160, 160)
		lowerCamelCase_							:		List[str]							       =							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]						,       __UpperCamelCase						,       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]						,       __UpperCamelCase						,       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(__UpperCamelCase      ).mkdir(exist_ok=__UpperCamelCase      )
	print(f"Saving model to {pytorch_dump_folder_path}"      )
	model.save_pretrained(__UpperCamelCase      )
	print(f"Saving image processor to {pytorch_dump_folder_path}"      )
	image_processor.save_pretrained(__UpperCamelCase      )


if __name__ == "__main__":
			UpperCamelCase__		=						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.'
			)
			UpperCamelCase__		=						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 List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__		=						logging.get_logger(__name__)

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

UpperCamelCase__		=						{
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

UpperCamelCase__		=						{
    'roberta-base': 5_1_2,
    'roberta-large': 5_1_2,
    'roberta-large-mnli': 5_1_2,
    'distilroberta-base': 5_1_2,
    'roberta-base-openai-detector': 5_1_2,
    'roberta-large-openai-detector': 5_1_2,
}






@lru_cache()
def  snake_case (							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							       =							(
	    list(range(ord('!'      )						,       ord('~'      ) + 1      )      ) + list(range(ord('¡'      )						,       ord('¬'      ) + 1      )      ) + list(range(ord('®'      )						,       ord('ÿ'      ) + 1      )      )
	)
	lowerCamelCase_							:		Dict							       =							bs[:]
	lowerCamelCase_							:		Dict							       =							0
	for b in range(2**8      ):
		if b not in bs:
			bs.append(A      )
			cs.append(2**8 + n      )
			n += 1
	lowerCamelCase_							:		List[str]							       =							[chr(A      ) for n in cs]
	return dict(zip(A						,       A      )      )






def  snake_case (							A      )							->							int:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							       =							set()
	lowerCamelCase_							:		Optional[int]							       =							word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)      )
		lowerCamelCase_							:		Optional[Any]							       =							char
	return pairs



class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase  ):
					_lowerCamelCase		  =			VOCAB_FILES_NAMES
					_lowerCamelCase		  =			PRETRAINED_VOCAB_FILES_MAP
					_lowerCamelCase		  =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCamelCase		  =			["""input_ids""", """attention_mask"""]

					def __init__(    self			,	__lowercase			,	__lowercase			,	__lowercase="replace"			,	__lowercase="<s>"			,	__lowercase="</s>"			,	__lowercase="</s>"			,	__lowercase="<s>"			,	__lowercase="<unk>"			,	__lowercase="<pad>"			,	__lowercase="<mask>"			,	__lowercase=False			,	**__lowercase			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else bos_token
						lowerCamelCase_							:		Dict							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else eos_token
						lowerCamelCase_							:		Union[str, Any]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else sep_token
						lowerCamelCase_							:		Union[str, Any]							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else cls_token
						lowerCamelCase_							:		Any							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else unk_token
						lowerCamelCase_							:		Tuple							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						lowerCamelCase_							:		Dict							       =							AddedToken(__lowercase			,	lstrip=__lowercase			,	rstrip=__lowercase ) if isinstance(__lowercase			,	__lowercase ) else mask_token

						super().__init__(
						    errors=__lowercase			,	bos_token=__lowercase			,	eos_token=__lowercase			,	unk_token=__lowercase			,	sep_token=__lowercase			,	cls_token=__lowercase			,	pad_token=__lowercase			,	mask_token=__lowercase			,	add_prefix_space=__lowercase			,	**__lowercase			,	)

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

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

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return len(self.encoder )

					def      lowercase_							(    self ):







						'''simple docstring'''




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

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						if token in self.cache:
							return self.cache[token]
						lowerCamelCase_							:		Any							       =							tuple(__lowercase )
						lowerCamelCase_							:		Tuple							       =							get_pairs(__lowercase )

						if not pairs:
							return token

						while True:
							lowerCamelCase_							:		str							       =							min(__lowercase			,	key=lambda __lowercase : self.bpe_ranks.get(__lowercase			,	float('inf' ) ) )
							if bigram not in self.bpe_ranks:
								break
							lowerCamelCase_       ,      lowerCamelCase_							:		Dict							       =							bigram
							lowerCamelCase_							:		str							       =							[]
							lowerCamelCase_							:		int							       =							0
							while i < len(__lowercase ):
								try:
									lowerCamelCase_							:		Tuple							       =							word.index(__lowercase			,	__lowercase )
								except ValueError:
									new_word.extend(word[i:] )
									break
								else:
									new_word.extend(word[i:j] )
									lowerCamelCase_							:		Any							       =							j

								if word[i] == first and i < len(__lowercase ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
								else:
									new_word.append(word[i] )
									i += 1
							lowerCamelCase_							:		List[Any]							       =							tuple(__lowercase )
							lowerCamelCase_							:		Optional[Any]							       =							new_word
							if len(__lowercase ) == 1:
								break
							else:
								lowerCamelCase_							:		Optional[Any]							       =							get_pairs(__lowercase )
						lowerCamelCase_							:		Optional[int]							       =							' '.join(__lowercase )
						lowerCamelCase_							:		Union[str, Any]							       =							word
						return word

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							[]
						for token in re.findall(self.pat			,	__lowercase ):
							lowerCamelCase_							:		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(__lowercase ).split(' ' ) )
						return bpe_tokens

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




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

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return self.decoder.get(__lowercase )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							''.join(__lowercase )
						lowerCamelCase_							:		Tuple							       =							bytearray([self.byte_decoder[c] for c in text] ).decode('utf-8'			,	errors=self.errors )
						return text

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						if not os.path.isdir(__lowercase ):
							logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
							return
						lowerCamelCase_							:		List[str]							       =							os.path.join(
						    __lowercase			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
						lowerCamelCase_							:		Union[str, Any]							       =							os.path.join(
						    __lowercase			,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

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

						lowerCamelCase_							:		Dict							       =							0
						with open(__lowercase			,	'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 __lowercase : kv[1] ):
								if index != token_index:
									logger.warning(
									    f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
									    ' Please check that the tokenizer is not corrupted!' )
									lowerCamelCase_							:		Tuple							       =							token_index
								writer.write(' '.join(__lowercase ) + '\n' )
								index += 1

						return vocab_file, merge_file

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




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

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None			,	__lowercase = False ):







						'''simple docstring'''




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

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

					def      lowercase_							(    self			,	__lowercase			,	__lowercase = None ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							[self.sep_token_id]
						lowerCamelCase_							:		Any							       =							[self.cls_token_id]

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





					def      lowercase_							(    self			,	__lowercase			,	__lowercase=False			,	**__lowercase ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							kwargs.pop('add_prefix_space'			,	self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(__lowercase ) > 0 and not text[0].isspace()):
							lowerCamelCase_							:		List[Any]							       =							' ' + text
						return (text, kwargs)
