
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()
_UpperCAmelCase      :				int          = logging.get_logger(__name__)


def    A (      lowercase				)     ->						List[str]:
		'''simple docstring'''

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

		UpperCamelCase						=  'huggingface/label-files'
		if "ade20k-full" in model_name:
				# this should be ok
				UpperCamelCase						=  847
				UpperCamelCase						=  'maskformer-ade20k-full-id2label.json'
		elif "ade" in model_name:
				# this should be ok
				UpperCamelCase						=  150
				UpperCamelCase						=  'ade20k-id2label.json'
		elif "coco-stuff" in model_name:
				# this should be ok
				UpperCamelCase						=  171
				UpperCamelCase						=  'maskformer-coco-stuff-id2label.json'
		elif "coco" in model_name:
				# TODO
				UpperCamelCase						=  133
				UpperCamelCase						=  'coco-panoptic-id2label.json'
		elif "cityscapes" in model_name:
				# this should be ok
				UpperCamelCase						=  19
				UpperCamelCase						=  'cityscapes-id2label.json'
		elif "vistas" in model_name:
				# this should be ok
				UpperCamelCase						=  65
				UpperCamelCase						=  'mapillary-vistas-id2label.json'

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

		return config


def    A (      lowercase				)     ->						Any:
		'''simple docstring'''

		UpperCamelCase						=  []
		# 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    A (      lowercase					, lowercase					, lowercase				)     ->						List[Any]:
		'''simple docstring'''

		UpperCamelCase						=  dct.pop(__snake_case				)
		UpperCamelCase						=  val


def    A (      lowercase					, lowercase				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  [int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
		for i in range(len(backbone_config.depths				)				):
				UpperCamelCase						=  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)
						UpperCamelCase						=  state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''				)
						UpperCamelCase						=  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
						UpperCamelCase						=  in_proj_weight[:dim, :]
						UpperCamelCase						=  in_proj_bias[: dim]
						UpperCamelCase						=  in_proj_weight[
						    dim : dim * 2, :
						]
						UpperCamelCase						=  in_proj_bias[
						    dim : dim * 2
						]
						UpperCamelCase						=  in_proj_weight[
						    -dim :, :
						]
						UpperCamelCase						=  in_proj_bias[-dim :]
						# fmt: on


def    A (      lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  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)
				UpperCamelCase						=  state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''				)
				UpperCamelCase						=  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
				UpperCamelCase						=  in_proj_weight[: hidden_size, :]
				UpperCamelCase						=  in_proj_bias[:config.hidden_size]
				UpperCamelCase						=  in_proj_weight[hidden_size : hidden_size * 2, :]
				UpperCamelCase						=  in_proj_bias[hidden_size : hidden_size * 2]
				UpperCamelCase						=  in_proj_weight[-hidden_size :, :]
				UpperCamelCase						=  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)
				UpperCamelCase						=  state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''				)
				UpperCamelCase						=  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
				UpperCamelCase						=  in_proj_weight[: hidden_size, :]
				UpperCamelCase						=  in_proj_bias[:config.hidden_size]
				UpperCamelCase						=  in_proj_weight[hidden_size : hidden_size * 2, :]
				UpperCamelCase						=  in_proj_bias[hidden_size : hidden_size * 2]
				UpperCamelCase						=  in_proj_weight[-hidden_size :, :]
				UpperCamelCase						=  in_proj_bias[-hidden_size :]
		# fmt: on


def    A (      )     ->						torch.Tensor:
		'''simple docstring'''

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


@torch.no_grad()
def    A (      lowercase					, lowercase					, lowercase					, lowercase = False				)     ->						Tuple:
		'''simple docstring'''

		UpperCamelCase						=  get_maskformer_config(__snake_case				)

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

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

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

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

		# load 🤗 model
		UpperCamelCase						=  MaskFormerForInstanceSegmentation(__snake_case				)
		model.eval()

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

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

		# verify results
		UpperCamelCase						=  prepare_img()
		if "vistas" in model_name:
				UpperCamelCase						=  65
		elif "cityscapes" in model_name:
				UpperCamelCase						=  65_535
		else:
				UpperCamelCase						=  255
		UpperCamelCase						=  True if 'ade' in model_name else False
		UpperCamelCase						=  MaskFormerImageProcessor(ignore_index=__snake_case					, reduce_labels=__snake_case				)

		UpperCamelCase						=  image_processor(__snake_case					, return_tensors='pt'				)

		UpperCamelCase						=  model(**__snake_case				)

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

		if model_name == "maskformer-swin-tiny-ade":
				UpperCamelCase						=  torch.tensor(
				    [[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]]				)
		assert torch.allclose(outputs.class_queries_logits[0, :3, :3]					, __snake_case					, 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(__snake_case				).mkdir(exist_ok=__snake_case				)
				model.save_pretrained(__snake_case				)
				image_processor.save_pretrained(__snake_case				)

		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__":
					_UpperCAmelCase      :				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."
					)

					_UpperCAmelCase      :				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 typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCAmelCase      :				Dict          = logging.get_logger(__name__)


_UpperCAmelCase      :				Optional[Any]          = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

_UpperCAmelCase      :				str          = {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

_UpperCAmelCase      :				List[str]          = {"facebook/blenderbot_small-90M": 512}


def    A (      lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

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

		UpperCamelCase						=  set(lowercase				)
		return pairs


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : Optional[Any]			  =       VOCAB_FILES_NAMES
		__lowercase : Tuple			  =       PRETRAINED_VOCAB_FILES_MAP
		__lowercase : List[Any]			  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase : Any			  =       ["input_ids", "attention_mask"]



		def __init__(				self       ,   A_       ,   A_       ,   A_="__start__"       ,   A_="__end__"       ,   A_="__unk__"       ,   A_="__null__"       ,   **A_       ,   )						->      List[Any]:
				"""simple docstring"""
				super().__init__(unk_token=A_       ,   bos_token=A_       ,   eos_token=A_       ,   pad_token=A_       ,   **A_ )

				with open(A_       ,   encoding='utf-8' ) as vocab_handle:
						UpperCamelCase						=  json.load(A_ )
				UpperCamelCase						=  {v: k for k, v in self.encoder.items()}
				with open(A_       ,   encoding='utf-8' ) as merges_handle:
						UpperCamelCase						=  merges_handle.read().split('\n' )[1:-1]
				UpperCamelCase						=  [tuple(merge.split() ) for merge in merges]
				UpperCamelCase						=  dict(zip(A_       ,   range(len(A_ ) ) ) )
				UpperCamelCase						=  {}



		@property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return len(self.encoder )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				return dict(self.encoder       ,   **self.added_tokens_encoder )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				if token in self.cache:
						return self.cache[token]
				UpperCamelCase						=  re.sub('([.,!?()])'       ,   r' \1'       ,   A_ )
				UpperCamelCase						=  re.sub('(\')'       ,   r' \1 '       ,   A_ )
				UpperCamelCase						=  re.sub(r'\s{2,}'       ,   ' '       ,   A_ )
				if "\n" in token:
						UpperCamelCase						=  token.replace('\n'       ,   ' __newln__' )

				UpperCamelCase						=  token.split(' ' )
				UpperCamelCase						=  []
				for token in tokens:
						if not len(A_ ):
								continue

						UpperCamelCase						=  token.lower()
						UpperCamelCase						=  tuple(A_ )
						UpperCamelCase						=  tuple(list(word[:-1] ) + [word[-1] + '</w>'] )
						UpperCamelCase						=  get_pairs(A_ )

						if not pairs:
								words.append(A_ )
								continue

						while True:
								UpperCamelCase						=  min(A_       ,   key=lambda A_ : self.bpe_ranks.get(A_       ,   float('inf' ) ) )
								if bigram not in self.bpe_ranks:
										break
								UpperCamelCase			,						UpperCamelCase						=  bigram
								UpperCamelCase						=  []
								UpperCamelCase						=  0

								while i < len(A_ ):
										try:
												UpperCamelCase						=  word.index(A_       ,   A_ )
												new_word.extend(word[i:j] )
												UpperCamelCase						=  j
										except ValueError:
												new_word.extend(word[i:] )
												break

										if word[i] == first and i < len(A_ ) - 1 and word[i + 1] == second:
												new_word.append(first + second )
												i += 2
										else:
												new_word.append(word[i] )
												i += 1
								UpperCamelCase						=  tuple(A_ )
								UpperCamelCase						=  new_word
								if len(A_ ) == 1:
										break
								else:
										UpperCamelCase						=  get_pairs(A_ )
						UpperCamelCase						=  '@@ '.join(A_ )
						UpperCamelCase						=  word[:-4]

						UpperCamelCase						=  word
						words.append(A_ )
				return " ".join(A_ )



		def __UpperCamelCase				(				self       ,   A_ )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  []

				UpperCamelCase						=  re.findall(r'\S+\n?'       ,   A_ )

				for token in words:
						split_tokens.extend(list(self.bpe(A_ ).split(' ' ) ) )
				return split_tokens



		def __UpperCamelCase				(				self       ,   A_ )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  token.lower()
				return self.encoder.get(A_       ,   self.encoder.get(self.unk_token ) )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				return self.decoder.get(A_       ,   self.unk_token )



		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  ' '.join(A_ ).replace('@@ '       ,   '' ).strip()
				return out_string



		def __UpperCamelCase				(				self       ,   A_       ,   A_ = None )						->      Tuple[str]:
				"""simple docstring"""
				if not os.path.isdir(A_ ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
						return
				UpperCamelCase						=  os.path.join(
				    A_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
				UpperCamelCase						=  os.path.join(
				    A_       ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file'] )

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

				UpperCamelCase						=  0
				with open(A_       ,   '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 A_ : 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						=  token_index
								writer.write(' '.join(A_ ) + '\n' )
								index += 1

				return vocab_file, merge_file
