


import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a : Union[str, Any]		=				logging.get_logger(__name__)





def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   SwinConfig.from_pretrained(
			    '''microsoft/swin-tiny-patch4-window7-224'''       ,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']  )
			__lowercase 					=   MaskFormerConfig(backbone_config=_UpperCamelCase  )

			__lowercase 					=   '''huggingface/label-files'''
			if "ade20k-full" in model_name:
						# this should be ok
						__lowercase 					=   8_47
						__lowercase 					=   '''maskformer-ade20k-full-id2label.json'''
			elif "ade" in model_name:
						# this should be ok
						__lowercase 					=   1_50
						__lowercase 					=   '''ade20k-id2label.json'''
			elif "coco-stuff" in model_name:
						# this should be ok
						__lowercase 					=   1_71
						__lowercase 					=   '''maskformer-coco-stuff-id2label.json'''
			elif "coco" in model_name:
						# TODO
						__lowercase 					=   1_33
						__lowercase 					=   '''coco-panoptic-id2label.json'''
			elif "cityscapes" in model_name:
						# this should be ok
						__lowercase 					=   19
						__lowercase 					=   '''cityscapes-id2label.json'''
			elif "vistas" in model_name:
						# this should be ok
						__lowercase 					=   65
						__lowercase 					=   '''mapillary-vistas-id2label.json'''

			__lowercase 					=   json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type='''dataset'''  )       ,			'''r'''  )  )
			__lowercase 					=   {int(_UpperCamelCase  ): v for k, v in idalabel.items()}

			return config





def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []
			# stem
			# fmt: off
			rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')  )
			rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')  )
			rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')  )
			rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')  )
			# stages
			for i in range(len(config.backbone_config.depths  )  ):
						for j in range(config.backbone_config.depths[i]  ):
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.attn.proj.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.norm2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')  )
									rename_keys.append((F'backbone.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')  )

						if i < 3:
									rename_keys.append((F'backbone.layers.{i}.downsample.reduction.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight')  )
									rename_keys.append((F'backbone.layers.{i}.downsample.norm.weight', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight')  )
									rename_keys.append((F'backbone.layers.{i}.downsample.norm.bias', F'model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias')  )
						rename_keys.append((F'backbone.norm{i}.weight', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.weight')  )
						rename_keys.append((F'backbone.norm{i}.bias', F'model.pixel_level_module.encoder.hidden_states_norms.{i}.bias')  )

			# FPN
			rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')  )
			rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')  )
			rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')  )
			for source_index, target_index in zip(range(3       ,			0       ,			-1  )       ,			range(0       ,			3  )  ):
						rename_keys.append((F'sem_seg_head.adapter_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight')  )
						rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight')  )
						rename_keys.append((F'sem_seg_head.adapter_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias')  )
						rename_keys.append((F'sem_seg_head.layer_{source_index}.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight')  )
						rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.weight', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight')  )
						rename_keys.append((F'sem_seg_head.layer_{source_index}.norm.bias', F'model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias')  )
			rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')  )
			rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')  )

			# Transformer decoder
			for idx in range(config.decoder_config.decoder_layers  ):
						# self-attention out projection
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias')  )
						# cross-attention out projection
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias')  )
						# MLP 1
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight', F'model.transformer_module.decoder.layers.{idx}.fc1.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias', F'model.transformer_module.decoder.layers.{idx}.fc1.bias')  )
						# MLP 2
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight', F'model.transformer_module.decoder.layers.{idx}.fc2.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias', F'model.transformer_module.decoder.layers.{idx}.fc2.bias')  )
						# layernorm 1 (self-attention layernorm)
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias', F'model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias')  )
						# layernorm 2 (cross-attention layernorm)
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias', F'model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias')  )
						# layernorm 3 (final layernorm)
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias', F'model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias')  )

			rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')  )
			rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')  )

			# heads on top
			rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')  )

			rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')  )
			rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')  )

			rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')  )
			rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')  )

			for i in range(3  ):
						rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.weight', F'mask_embedder.{i}.0.weight')  )
						rename_keys.append((F'sem_seg_head.predictor.mask_embed.layers.{i}.bias', F'mask_embedder.{i}.0.bias')  )
			# fmt: on

			return rename_keys





def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   dct.pop(_UpperCamelCase  )
			__lowercase 					=   val





def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
			for i in range(len(backbone_config.depths  )  ):
						__lowercase 					=   num_features[i]
						for j in range(backbone_config.depths[i]  ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									__lowercase 					=   state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'  )
									__lowercase 					=   state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.bias'  )
									# next, add query, keys and values (in that order) to the state dict
									__lowercase 					=   in_proj_weight[:dim, :]
									__lowercase 					=   in_proj_bias[: dim]
									__lowercase 					=   in_proj_weight[
									    dim : dim * 2, :
									]
									__lowercase 					=   in_proj_bias[
									    dim : dim * 2
									]
									__lowercase 					=   in_proj_weight[
									    -dim :, :
									]
									__lowercase 					=   in_proj_bias[-dim :]
									# fmt: on





def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   config.decoder_config.hidden_size
			for idx in range(config.decoder_config.decoder_layers  ):
						# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
						__lowercase 					=   state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'  )
						__lowercase 					=   state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'  )
						# next, add query, keys and values (in that order) to the state dict
						__lowercase 					=   in_proj_weight[: hidden_size, :]
						__lowercase 					=   in_proj_bias[:config.hidden_size]
						__lowercase 					=   in_proj_weight[hidden_size : hidden_size * 2, :]
						__lowercase 					=   in_proj_bias[hidden_size : hidden_size * 2]
						__lowercase 					=   in_proj_weight[-hidden_size :, :]
						__lowercase 					=   in_proj_bias[-hidden_size :]
						# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
						__lowercase 					=   state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'  )
						__lowercase 					=   state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'  )
						# next, add query, keys and values (in that order) to the state dict
						__lowercase 					=   in_proj_weight[: hidden_size, :]
						__lowercase 					=   in_proj_bias[:config.hidden_size]
						__lowercase 					=   in_proj_weight[hidden_size : hidden_size * 2, :]
						__lowercase 					=   in_proj_bias[hidden_size : hidden_size * 2]
						__lowercase 					=   in_proj_weight[-hidden_size :, :]
						__lowercase 					=   in_proj_bias[-hidden_size :]
			# fmt: on





def 		lowercase_  (     ):


			'''simple docstring'''







			__lowercase 					=   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
			__lowercase 					=   Image.open(requests.get(_UpperCamelCase       ,			stream=_UpperCamelCase  ).raw  )
			return im





@torch.no_grad()
def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase = False  ):


			'''simple docstring'''







			__lowercase 					=   get_maskformer_config(_UpperCamelCase  )

			# load original state_dict
			with open(_UpperCamelCase       ,			'''rb'''  ) as f:
						__lowercase 					=   pickle.load(_UpperCamelCase  )
			__lowercase 					=   data['''model''']

			# for name, param in state_dict.items():
			#     print(name, param.shape)

			# rename keys
			__lowercase 					=   create_rename_keys(_UpperCamelCase  )
			for src, dest in rename_keys:
						rename_key(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )
			read_in_swin_q_k_v(_UpperCamelCase       ,			config.backbone_config  )
			read_in_decoder_q_k_v(_UpperCamelCase       ,			_UpperCamelCase  )

			# update to torch tensors
			for key, value in state_dict.items():
						__lowercase 					=   torch.from_numpy(_UpperCamelCase  )

			# load 🤗 model
			__lowercase 					=   MaskFormerForInstanceSegmentation(_UpperCamelCase  )
			model.eval()

			for name, param in model.named_parameters():
						print(_UpperCamelCase       ,			param.shape  )

			__lowercase					, __lowercase 					=   model.load_state_dict(_UpperCamelCase       ,			strict=_UpperCamelCase  )
			assert missing_keys == [
			    "model.pixel_level_module.encoder.model.layernorm.weight",
			    "model.pixel_level_module.encoder.model.layernorm.bias",
			]
			assert len(_UpperCamelCase  ) == 0, F'Unexpected keys: {unexpected_keys}'

			# verify results
			__lowercase 					=   prepare_img()
			if "vistas" in model_name:
						__lowercase 					=   65
			elif "cityscapes" in model_name:
						__lowercase 					=   6_55_35
			else:
						__lowercase 					=   2_55
			__lowercase 					=   True if '''ade''' in model_name else False
			__lowercase 					=   MaskFormerImageProcessor(ignore_index=_UpperCamelCase       ,			reduce_labels=_UpperCamelCase  )

			__lowercase 					=   image_processor(_UpperCamelCase       ,			return_tensors='''pt'''  )

			__lowercase 					=   model(**_UpperCamelCase  )

			print('''Logits:'''       ,			outputs.class_queries_logits[0, :3, :3]  )

			if model_name == "maskformer-swin-tiny-ade":
						__lowercase 					=   torch.tensor(
						    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]  )
			assert torch.allclose(outputs.class_queries_logits[0, :3, :3]       ,			_UpperCamelCase       ,			atol=1E-4  )
			print('''Looks ok!'''  )

			if pytorch_dump_folder_path is not None:
						print(F'Saving model and image processor to {pytorch_dump_folder_path}'  )
						Path(_UpperCamelCase  ).mkdir(exist_ok=_UpperCamelCase  )
						model.save_pretrained(_UpperCamelCase  )
						image_processor.save_pretrained(_UpperCamelCase  )

			if push_to_hub:
						print('''Pushing model and image processor to the hub...'''  )
						model.push_to_hub(F'nielsr/{model_name}'  )
						image_processor.push_to_hub(F'nielsr/{model_name}'  )


if __name__ == "__main__":
	a : Union[str, Any]		=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default='''maskformer-swin-tiny-ade''',
	    type=str,
	    help=('''Name of the MaskFormer model you\'d like to convert''',),
	)
	parser.add_argument(
	    '''--checkpoint_path''',
	    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
	    type=str,
	    help='''Path to the original state dict (.pth file).''',
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
	)
	parser.add_argument(
	    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
	)

	a : int		=				parser.parse_args()
	convert_maskformer_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)






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


a : Union[str, Any]		=				logging.get_logger(__name__)


a : Union[str, Any]		=				{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

a : Union[str, Any]		=				{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

a : Dict		=				{
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}


@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 		lowercase_  (     ):


			'''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(_UpperCamelCase  )
									cs.append(2**8 + n  )
									n += 1
			__lowercase 					=   [chr(_UpperCamelCase  ) for n in cs]
			return dict(zip(_UpperCamelCase       ,			_UpperCamelCase  )  )


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







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





class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				VOCAB_FILES_NAMES
					__UpperCAmelCase            =				PRETRAINED_VOCAB_FILES_MAP
					__UpperCAmelCase            =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__UpperCAmelCase            =				["input_ids", "attention_mask"]






					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_="replace" ,       snake_case_="<s>" ,       snake_case_="</s>" ,       snake_case_="</s>" ,       snake_case_="<s>" ,       snake_case_="<unk>" ,       snake_case_="<pad>" ,       snake_case_="<mask>" ,       snake_case_=False ,       **snake_case_ ,       )   ->			Optional[int]:







								'''simple docstring'''

								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else bos_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else eos_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else sep_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else cls_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else unk_token
								__lowercase 					=   AddedToken(snake_case_ ,       lstrip=snake_case_ ,       rstrip=snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else pad_token

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

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

								with open(snake_case_ ,       encoding='''utf-8'''						) as vocab_handle:
											__lowercase 					=   json.load(snake_case_						)
								__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(snake_case_ ,       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(snake_case_ ,       range(len(snake_case_						)						)						)						)
								__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 A      (				self						)   ->			List[str]:







								'''simple docstring'''

								return len(self.encoder						)






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

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

								if not pairs:
											return token

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

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






					def A      (				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   []
								for token in re.findall(self.pat ,       snake_case_						):
											__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(snake_case_						).split(''' '''						)						)
								return bpe_tokens






					def A      (				self ,       snake_case_						)   ->			Any:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			Dict:







								'''simple docstring'''

								return self.decoder.get(snake_case_						)






					def A      (				self ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			Tuple[str]:







								'''simple docstring'''

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

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

								__lowercase 					=   0
								with open(snake_case_ ,       '''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 snake_case_						: 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(snake_case_						) + '''\n'''						)
														index += 1

								return vocab_file, merge_file






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = False						)   ->			List[int]:







								'''simple docstring'''

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

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






					def A      (				self ,       snake_case_ ,       snake_case_ = None						)   ->			List[int]:







								'''simple docstring'''

								__lowercase 					=   [self.sep_token_id]
								__lowercase 					=   [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 A      (				self ,       snake_case_ ,       snake_case_=False ,       **snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

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


