





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_  							=				logging.get_logger(__name__)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			List[Any]:
    UpperCamelCase__							:       List[str]      =   SwinConfig.from_pretrained(
        '''microsoft/swin-tiny-patch4-window7-224'''	,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']   )
    UpperCamelCase__							:       Tuple      =   MaskFormerConfig(backbone_config=__UpperCAmelCase   )

    UpperCamelCase__							:       Tuple      =   '''huggingface/label-files'''
    if "ade20k-full" in model_name:
        # this should be ok
        UpperCamelCase__							:       List[Any]      =   847
        UpperCamelCase__							:       str      =   '''maskformer-ade20k-full-id2label.json'''
    elif "ade" in model_name:
        # this should be ok
        UpperCamelCase__							:       Union[str, Any]      =   150
        UpperCamelCase__							:       Dict      =   '''ade20k-id2label.json'''
    elif "coco-stuff" in model_name:
        # this should be ok
        UpperCamelCase__							:       Union[str, Any]      =   171
        UpperCamelCase__							:       Optional[Any]      =   '''maskformer-coco-stuff-id2label.json'''
    elif "coco" in model_name:
        # TODO
        UpperCamelCase__							:       str      =   133
        UpperCamelCase__							:       List[Any]      =   '''coco-panoptic-id2label.json'''
    elif "cityscapes" in model_name:
        # this should be ok
        UpperCamelCase__							:       str      =   19
        UpperCamelCase__							:       Union[str, Any]      =   '''cityscapes-id2label.json'''
    elif "vistas" in model_name:
        # this should be ok
        UpperCamelCase__							:       Any      =   65
        UpperCamelCase__							:       Optional[int]      =   '''mapillary-vistas-id2label.json'''

    UpperCamelCase__							:       Union[str, Any]      =   json.load(open(hf_hub_download(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )	,     '''r'''   )   )
    UpperCamelCase__							:       Optional[Any]      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}

    return config




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]   )   ->			Optional[Any]:
    UpperCamelCase__							:       Union[str, Any]      =   []
    # 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 	lowerCAmelCase_					(						__UpperCAmelCase:    int	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    Optional[Any]   )   ->			int:
    UpperCamelCase__							:       List[Any]      =   dct.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Tuple      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    str   )   ->			List[str]:
    UpperCamelCase__							:       Optional[Any]      =   [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
    for i in range(len(backbone_config.depths   )   ):
        UpperCamelCase__							:       List[str]      =   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__							:       Any      =   state_dict.pop(f"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"   )
            UpperCamelCase__							:       str      =   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__							:       Any      =   in_proj_weight[:dim, :]
            UpperCamelCase__							:       Tuple      =   in_proj_bias[: dim]
            UpperCamelCase__							:       List[str]      =   in_proj_weight[
                dim : dim * 2, :
            ]
            UpperCamelCase__							:       List[Any]      =   in_proj_bias[
                dim : dim * 2
            ]
            UpperCamelCase__							:       Dict      =   in_proj_weight[
                -dim :, :
            ]
            UpperCamelCase__							:       Optional[Any]      =   in_proj_bias[-dim :]
            # fmt: on




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[str]   )   ->			Any:
    # fmt: off
    UpperCamelCase__							:       List[Any]      =   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__							:       Union[str, Any]      =   state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"   )
        UpperCamelCase__							:       str      =   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__							:       List[Any]      =   in_proj_weight[: hidden_size, :]
        UpperCamelCase__							:       Dict      =   in_proj_bias[:config.hidden_size]
        UpperCamelCase__							:       Optional[Any]      =   in_proj_weight[hidden_size : hidden_size * 2, :]
        UpperCamelCase__							:       int      =   in_proj_bias[hidden_size : hidden_size * 2]
        UpperCamelCase__							:       int      =   in_proj_weight[-hidden_size :, :]
        UpperCamelCase__							:       Union[str, Any]      =   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__							:       Tuple      =   state_dict.pop(f"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"   )
        UpperCamelCase__							:       Optional[Any]      =   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__							:       Optional[int]      =   in_proj_weight[: hidden_size, :]
        UpperCamelCase__							:       List[Any]      =   in_proj_bias[:config.hidden_size]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[hidden_size : hidden_size * 2, :]
        UpperCamelCase__							:       Dict      =   in_proj_bias[hidden_size : hidden_size * 2]
        UpperCamelCase__							:       Any      =   in_proj_weight[-hidden_size :, :]
        UpperCamelCase__							:       int      =   in_proj_bias[-hidden_size :]
    # fmt: on




def 	lowerCAmelCase_					(						)   ->			torch.Tensor:
    UpperCamelCase__							:       int      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Tuple      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )
    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    bool = False   )   ->			Dict:
    UpperCamelCase__							:       Tuple      =   get_maskformer_config(__UpperCAmelCase   )

    # load original state_dict
    with open(__UpperCAmelCase	,     '''rb'''   ) as f:
        UpperCamelCase__							:       Tuple      =   pickle.load(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   data['''model''']

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

    # rename keys
    UpperCamelCase__							:       List[Any]      =   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():
        UpperCamelCase__							:       Union[str, Any]      =   torch.from_numpy(__UpperCAmelCase   )

    # load 🤗 model
    UpperCamelCase__							:       Any      =   MaskFormerForInstanceSegmentation(__UpperCAmelCase   )
    model.eval()

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

    UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   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
    UpperCamelCase__							:       Any      =   prepare_img()
    if "vistas" in model_name:
        UpperCamelCase__							:       List[str]      =   65
    elif "cityscapes" in model_name:
        UpperCamelCase__							:       List[Any]      =   6_5535
    else:
        UpperCamelCase__							:       List[str]      =   255
    UpperCamelCase__							:       Dict      =   True if '''ade''' in model_name else False
    UpperCamelCase__							:       Optional[Any]      =   MaskFormerImageProcessor(ignore_index=__UpperCAmelCase	,     reduce_labels=__UpperCAmelCase   )

    UpperCamelCase__							:       Tuple      =   image_processor(__UpperCAmelCase	,     return_tensors='''pt'''   )

    UpperCamelCase__							:       List[Any]      =   model(**__UpperCAmelCase   )

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

    if model_name == "maskformer-swin-tiny-ade":
        UpperCamelCase__							:       Union[str, Any]      =   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__":
  UpperCAmelCase_  							=				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_  							=				parser.parse_args()
  convert_maskformer_checkpoint(
      args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )








import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

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 tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

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',
    },
    'tokenizer_file': {
        'Salesforce/codegen-350M-mono': (
            'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'
        ),
    },
}

UpperCAmelCase_  							=				{
    'Salesforce/codegen-350M-mono': 2048,
}






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Union[str, Any]     	=	VOCAB_FILES_NAMES
   a		:							List[str]     	=	PRETRAINED_VOCAB_FILES_MAP
   a		:							Optional[int]     	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a		:							List[Any]     	=	["input_ids", "attention_mask"]
   a		:							Any     	=	CodeGenTokenizer




   def __init__(					self,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	__magic_name__="<|endoftext|>",	__magic_name__="<|endoftext|>",	__magic_name__="<|endoftext|>",	__magic_name__=False,	**__magic_name__,	)				->   Optional[int]:






       """simple docstring"""
       super().__init__(
           __magic_name__,	__magic_name__,	tokenizer_file=__magic_name__,	unk_token=__magic_name__,	bos_token=__magic_name__,	eos_token=__magic_name__,	add_prefix_space=__magic_name__,	**__magic_name__,	)

       if kwargs.pop('''add_bos_token''',	__magic_name__							):
           UpperCamelCase__							:       List[Any]      =   kwargs.pop('''name_or_path''',	''''''							)
           raise ValueError(
               '''Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token.'''
               '''Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n'''
               f"`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"
               f"`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"
               '''This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005.'''
               ''' so that the fast tokenizer works correctly.'''							)

       UpperCamelCase__							:       Dict      =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
       if pre_tok_state.get('''add_prefix_space''',	__magic_name__							) != add_prefix_space:
           UpperCamelCase__							:       List[str]      =   getattr(__magic_name__,	pre_tok_state.pop('''type'''							)							)
           UpperCamelCase__							:       int      =   add_prefix_space
           UpperCamelCase__							:       List[str]      =   pre_tok_class(**__magic_name__							)

       UpperCamelCase__							:       List[Any]      =   add_prefix_space




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   kwargs.get('''is_split_into_words''',	__magic_name__							)
       assert self.add_prefix_space or not is_split_into_words, (
           f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   kwargs.get('''is_split_into_words''',	__magic_name__							)

       assert self.add_prefix_space or not is_split_into_words, (
           f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Tuple[str]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self._tokenizer.model.save(__magic_name__,	name=__magic_name__							)
       return tuple(__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = False,	__magic_name__ = None,	__magic_name__ = None,	**__magic_name__,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   super().decode(
           token_ids=__magic_name__,	skip_special_tokens=__magic_name__,	clean_up_tokenization_spaces=__magic_name__,	**__magic_name__,	)

       if truncate_before_pattern is not None and len(__magic_name__							) > 0:
           UpperCamelCase__							:       str      =   self.truncate(__magic_name__,	__magic_name__							)

       return decoded_text




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       def find_re(__magic_name__,	__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Dict      =   pattern.search(__magic_name__,	__magic_name__							)
           return m.start() if m else -1

       UpperCamelCase__							:       Union[str, Any]      =   [re.compile(__magic_name__,	re.MULTILINE							) for pattern in truncate_before_pattern]

       UpperCamelCase__							:       List[Any]      =   list(re.finditer('''^print''',	__magic_name__,	re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:       Tuple      =   completion[: prints[1].start()]

       UpperCamelCase__							:       Optional[int]      =   list(re.finditer('''^def''',	__magic_name__,	re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:       Optional[Any]      =   completion[: defs[1].start()]

       UpperCamelCase__							:       str      =   0

       UpperCamelCase__							:       Tuple      =   [
           pos for pos in [find_re(__magic_name__,	__magic_name__,	__magic_name__							) for terminal in terminals] if pos != -1
       ]

       if len(__magic_name__							) > 0:
           return completion[: min(__magic_name__							)]
       else:
           return completion

