


"""simple docstring"""






import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__=1				)     ->     List[str]:
   if n_shave_prefix_segments >= 0:
      return ".".join(path.split('.'				)[n_shave_prefix_segments:]				)
   else:
      return ".".join(path.split('.'				)[:n_shave_prefix_segments]				)




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__=0				)     ->     List[str]:
   a   :	Any						=    []
   for old_item in old_list:
      a   :	Optional[Any]						=    old_item
      a   :	Any						=    new_item.replace('block.'       ,   'resnets.'				)
      a   :	Optional[Any]						=    new_item.replace('conv_shorcut'       ,   'conv1'				)
      a   :	Union[str, Any]						=    new_item.replace('in_shortcut'       ,   'conv_shortcut'				)
      a   :	int						=    new_item.replace('temb_proj'       ,   'time_emb_proj'				)

      a   :	List[Any]						=    shave_segments(UpperCAmelCase__       ,   n_shave_prefix_segments=UpperCAmelCase__				)

      mapping.append({'old': old_item, 'new': new_item}				)

   return mapping




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__=0       ,   UpperCAmelCase__=False				)     ->     List[Any]:
   a   :	Dict						=    []
   for old_item in old_list:
      a   :	Optional[int]						=    old_item

      # In `model.mid`, the layer is called `attn`.
      if not in_mid:
         a   :	Optional[Any]						=    new_item.replace('attn'       ,   'attentions'				)
      a   :	int						=    new_item.replace('.k.'       ,   '.key.'				)
      a   :	int						=    new_item.replace('.v.'       ,   '.value.'				)
      a   :	Dict						=    new_item.replace('.q.'       ,   '.query.'				)

      a   :	int						=    new_item.replace('proj_out'       ,   'proj_attn'				)
      a   :	Optional[Any]						=    new_item.replace('norm'       ,   'group_norm'				)

      a   :	int						=    shave_segments(UpperCAmelCase__       ,   n_shave_prefix_segments=UpperCAmelCase__				)
      mapping.append({'old': old_item, 'new': new_item}				)

   return mapping




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__=None       ,   UpperCAmelCase__=None       ,   UpperCAmelCase__=None				)     ->     int:
   assert isinstance(UpperCAmelCase__       ,   UpperCAmelCase__				), "Paths should be a list of dicts containing 'old' and 'new' keys."

   if attention_paths_to_split is not None:
      if config is None:
         raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'				)

      for path, path_map in attention_paths_to_split.items():
         a   :	Tuple						=    old_checkpoint[path]
         a   :	Dict						=    old_tensor.shape[0] // 3

         a   :	List[Any]						=    (-1, channels) if len(old_tensor.shape				) == 3 else (-1)

         a   :	str						=    old_tensor.shape[0] // config.get('num_head_channels'       ,   1				) // 3

         a   :	int						=    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]				)
         a	, a	, a   :	Tuple						=    old_tensor.split(channels // num_heads       ,   dim=1				)

         a   :	str						=    query.reshape(UpperCAmelCase__				).squeeze()
         a   :	int						=    key.reshape(UpperCAmelCase__				).squeeze()
         a   :	Optional[int]						=    value.reshape(UpperCAmelCase__				).squeeze()

   for path in paths:
      a   :	Optional[int]						=    path['new']

      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
         continue

      a   :	Union[str, Any]						=    new_path.replace('down.'       ,   'down_blocks.'				)
      a   :	List[str]						=    new_path.replace('up.'       ,   'up_blocks.'				)

      if additional_replacements is not None:
         for replacement in additional_replacements:
            a   :	List[Any]						=    new_path.replace(replacement['old']       ,   replacement['new']				)

      if "attentions" in new_path:
         a   :	Optional[int]						=    old_checkpoint[path['old']].squeeze()
      else:
         a   :	List[str]						=    old_checkpoint[path['old']]




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Optional[Any]:
   a   :	Tuple						=    {}

   a   :	Any						=    checkpoint['temb.dense.0.weight']
   a   :	Union[str, Any]						=    checkpoint['temb.dense.0.bias']
   a   :	Tuple						=    checkpoint['temb.dense.1.weight']
   a   :	List[str]						=    checkpoint['temb.dense.1.bias']

   a   :	str						=    checkpoint['norm_out.weight']
   a   :	Optional[Any]						=    checkpoint['norm_out.bias']

   a   :	str						=    checkpoint['conv_in.weight']
   a   :	Dict						=    checkpoint['conv_in.bias']
   a   :	str						=    checkpoint['conv_out.weight']
   a   :	str						=    checkpoint['conv_out.bias']

   a   :	str						=    len({'.'.join(layer.split('.'				)[:2]				) for layer in checkpoint if 'down' in layer}				)
   a   :	Union[str, Any]						=    {
       layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(UpperCAmelCase__				)
   }

   a   :	List[Any]						=    len({'.'.join(layer.split('.'				)[:2]				) for layer in checkpoint if 'up' in layer}				)
   a   :	List[Any]						=    {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(UpperCAmelCase__				)}

   for i in range(UpperCAmelCase__				):
      a   :	Optional[Any]						=    (i - 1) // (config['layers_per_block'] + 1)

      if any('downsample' in layer for layer in down_blocks[i]				):
         a   :	int						=    checkpoint[
             F'down.{i}.downsample.op.weight'
         ]
         a   :	Any						=    checkpoint[F'down.{i}.downsample.op.bias']
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
      #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

      if any('block' in layer for layer in down_blocks[i]				):
         a   :	Optional[int]						=    len(
             {'.'.join(shave_segments(UpperCAmelCase__       ,   2				).split('.'				)[:2]				) for layer in down_blocks[i] if 'block' in layer}				)
         a   :	List[str]						=    {
             layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
             for layer_id in range(UpperCAmelCase__				)
         }

         if num_blocks > 0:
            for j in range(config['layers_per_block']				):
               a   :	Optional[int]						=    renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)

      if any('attn' in layer for layer in down_blocks[i]				):
         a   :	Optional[int]						=    len(
             {'.'.join(shave_segments(UpperCAmelCase__       ,   2				).split('.'				)[:2]				) for layer in down_blocks[i] if 'attn' in layer}				)
         a   :	Optional[int]						=    {
             layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
             for layer_id in range(UpperCAmelCase__				)
         }

         if num_attn > 0:
            for j in range(config['layers_per_block']				):
               a   :	Optional[Any]						=    renew_attention_paths(attns[j]				)
               assign_to_checkpoint(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   config=UpperCAmelCase__				)

   a   :	str						=    [key for key in checkpoint if 'mid.block_1' in key]
   a   :	Optional[Any]						=    [key for key in checkpoint if 'mid.block_2' in key]
   a   :	str						=    [key for key in checkpoint if 'mid.attn_1' in key]

   # Mid new 2
   a   :	str						=    renew_resnet_paths(UpperCAmelCase__				)
   assign_to_checkpoint(
       UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]       ,   )

   a   :	str						=    renew_resnet_paths(UpperCAmelCase__				)
   assign_to_checkpoint(
       UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,   )

   a   :	List[str]						=    renew_attention_paths(UpperCAmelCase__       ,   in_mid=UpperCAmelCase__				)
   assign_to_checkpoint(
       UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,   )

   for i in range(UpperCAmelCase__				):
      a   :	List[str]						=    num_up_blocks - 1 - i

      if any('upsample' in layer for layer in up_blocks[i]				):
         a   :	int						=    checkpoint[
             F'up.{i}.upsample.conv.weight'
         ]
         a   :	Optional[int]						=    checkpoint[F'up.{i}.upsample.conv.bias']

      if any('block' in layer for layer in up_blocks[i]				):
         a   :	Tuple						=    len(
             {'.'.join(shave_segments(UpperCAmelCase__       ,   2				).split('.'				)[:2]				) for layer in up_blocks[i] if 'block' in layer}				)
         a   :	Any						=    {
             layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(UpperCAmelCase__				)
         }

         if num_blocks > 0:
            for j in range(config['layers_per_block'] + 1				):
               a   :	List[str]						=    {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
               a   :	List[str]						=    renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   additional_replacements=[replace_indices]				)

      if any('attn' in layer for layer in up_blocks[i]				):
         a   :	Union[str, Any]						=    len(
             {'.'.join(shave_segments(UpperCAmelCase__       ,   2				).split('.'				)[:2]				) for layer in up_blocks[i] if 'attn' in layer}				)
         a   :	Optional[int]						=    {
             layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(UpperCAmelCase__				)
         }

         if num_attn > 0:
            for j in range(config['layers_per_block'] + 1				):
               a   :	List[str]						=    {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
               a   :	Tuple						=    renew_attention_paths(attns[j]				)
               assign_to_checkpoint(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   additional_replacements=[replace_indices]				)

   a   :	List[Any]						=    {k.replace('mid_new_2'       ,   'mid_block'				): v for k, v in new_checkpoint.items()}
   return new_checkpoint




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     List[Any]:
   a   :	Optional[Any]						=    {}

   a   :	List[str]						=    checkpoint['encoder.norm_out.weight']
   a   :	List[str]						=    checkpoint['encoder.norm_out.bias']

   a   :	Any						=    checkpoint['encoder.conv_in.weight']
   a   :	Optional[Any]						=    checkpoint['encoder.conv_in.bias']
   a   :	List[Any]						=    checkpoint['encoder.conv_out.weight']
   a   :	int						=    checkpoint['encoder.conv_out.bias']

   a   :	str						=    checkpoint['decoder.norm_out.weight']
   a   :	Union[str, Any]						=    checkpoint['decoder.norm_out.bias']

   a   :	int						=    checkpoint['decoder.conv_in.weight']
   a   :	Dict						=    checkpoint['decoder.conv_in.bias']
   a   :	Optional[int]						=    checkpoint['decoder.conv_out.weight']
   a   :	Any						=    checkpoint['decoder.conv_out.bias']

   a   :	int						=    len({'.'.join(layer.split('.'				)[:3]				) for layer in checkpoint if 'down' in layer}				)
   a   :	Optional[Any]						=    {
       layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(UpperCAmelCase__				)
   }

   a   :	Optional[int]						=    len({'.'.join(layer.split('.'				)[:3]				) for layer in checkpoint if 'up' in layer}				)
   a   :	Any						=    {layer_id: [key for key in checkpoint if F'up.{layer_id}' in key] for layer_id in range(UpperCAmelCase__				)}

   for i in range(UpperCAmelCase__				):
      a   :	List[Any]						=    (i - 1) // (config['layers_per_block'] + 1)

      if any('downsample' in layer for layer in down_blocks[i]				):
         a   :	List[str]						=    checkpoint[
             F'encoder.down.{i}.downsample.conv.weight'
         ]
         a   :	int						=    checkpoint[
             F'encoder.down.{i}.downsample.conv.bias'
         ]

      if any('block' in layer for layer in down_blocks[i]				):
         a   :	int						=    len(
             {'.'.join(shave_segments(UpperCAmelCase__       ,   3				).split('.'				)[:3]				) for layer in down_blocks[i] if 'block' in layer}				)
         a   :	Any						=    {
             layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
             for layer_id in range(UpperCAmelCase__				)
         }

         if num_blocks > 0:
            for j in range(config['layers_per_block']				):
               a   :	Optional[int]						=    renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)

      if any('attn' in layer for layer in down_blocks[i]				):
         a   :	List[str]						=    len(
             {'.'.join(shave_segments(UpperCAmelCase__       ,   3				).split('.'				)[:3]				) for layer in down_blocks[i] if 'attn' in layer}				)
         a   :	Dict						=    {
             layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
             for layer_id in range(UpperCAmelCase__				)
         }

         if num_attn > 0:
            for j in range(config['layers_per_block']				):
               a   :	List[Any]						=    renew_attention_paths(attns[j]				)
               assign_to_checkpoint(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   config=UpperCAmelCase__				)

   a   :	List[str]						=    [key for key in checkpoint if 'mid.block_1' in key]
   a   :	List[Any]						=    [key for key in checkpoint if 'mid.block_2' in key]
   a   :	List[str]						=    [key for key in checkpoint if 'mid.attn_1' in key]

   # Mid new 2
   a   :	Optional[Any]						=    renew_resnet_paths(UpperCAmelCase__				)
   assign_to_checkpoint(
       UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]       ,   )

   a   :	int						=    renew_resnet_paths(UpperCAmelCase__				)
   assign_to_checkpoint(
       UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]       ,   )

   a   :	Optional[int]						=    renew_attention_paths(UpperCAmelCase__       ,   in_mid=UpperCAmelCase__				)
   assign_to_checkpoint(
       UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]       ,   )

   for i in range(UpperCAmelCase__				):
      a   :	Union[str, Any]						=    num_up_blocks - 1 - i

      if any('upsample' in layer for layer in up_blocks[i]				):
         a   :	Optional[Any]						=    checkpoint[
             F'decoder.up.{i}.upsample.conv.weight'
         ]
         a   :	Dict						=    checkpoint[
             F'decoder.up.{i}.upsample.conv.bias'
         ]

      if any('block' in layer for layer in up_blocks[i]				):
         a   :	List[Any]						=    len(
             {'.'.join(shave_segments(UpperCAmelCase__       ,   3				).split('.'				)[:3]				) for layer in up_blocks[i] if 'block' in layer}				)
         a   :	Optional[int]						=    {
             layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(UpperCAmelCase__				)
         }

         if num_blocks > 0:
            for j in range(config['layers_per_block'] + 1				):
               a   :	Optional[Any]						=    {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
               a   :	str						=    renew_resnet_paths(blocks[j]				)
               assign_to_checkpoint(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   additional_replacements=[replace_indices]				)

      if any('attn' in layer for layer in up_blocks[i]				):
         a   :	List[Any]						=    len(
             {'.'.join(shave_segments(UpperCAmelCase__       ,   3				).split('.'				)[:3]				) for layer in up_blocks[i] if 'attn' in layer}				)
         a   :	Optional[Any]						=    {
             layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(UpperCAmelCase__				)
         }

         if num_attn > 0:
            for j in range(config['layers_per_block'] + 1				):
               a   :	int						=    {'old': F'up_blocks.{i}', 'new': F'up_blocks.{block_id}'}
               a   :	Dict						=    renew_attention_paths(attns[j]				)
               assign_to_checkpoint(UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   additional_replacements=[replace_indices]				)

   a   :	Tuple						=    {k.replace('mid_new_2'       ,   'mid_block'				): v for k, v in new_checkpoint.items()}
   a   :	Optional[Any]						=    checkpoint['quant_conv.weight']
   a   :	Any						=    checkpoint['quant_conv.bias']
   if "quantize.embedding.weight" in checkpoint:
      a   :	Dict						=    checkpoint['quantize.embedding.weight']
   a   :	int						=    checkpoint['post_quant_conv.weight']
   a   :	Optional[Any]						=    checkpoint['post_quant_conv.bias']

   return new_checkpoint


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__				:  str						= argparse.ArgumentParser()

 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )

 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the architecture.",
 )

 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

 SCREAMING_SNAKE_CASE__				:  List[str]						= parser.parse_args()
 SCREAMING_SNAKE_CASE__				:  Tuple						= torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  SCREAMING_SNAKE_CASE__				:  Dict						= json.loads(f.read())

 # unet case
 SCREAMING_SNAKE_CASE__				:  Optional[int]						= {key.split(".")[0] for key in checkpoint.keys()}
 if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
  SCREAMING_SNAKE_CASE__				:  Optional[int]						= convert_vq_autoenc_checkpoint(checkpoint, config)
 else:
  SCREAMING_SNAKE_CASE__				:  Optional[int]						= convert_ddpm_checkpoint(checkpoint, config)

 if "ddpm" in config:
  del config["ddpm"]

 if config["_class_name"] == "VQModel":
  SCREAMING_SNAKE_CASE__				:  Union[str, Any]						= VQModel(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 elif config["_class_name"] == "AutoencoderKL":
  SCREAMING_SNAKE_CASE__				:  Optional[int]						= AutoencoderKL(**config)
  model.load_state_dict(converted_checkpoint)
  model.save_pretrained(args.dump_path)
 else:
  SCREAMING_SNAKE_CASE__				:  str						= UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  SCREAMING_SNAKE_CASE__				:  Any						= DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

  SCREAMING_SNAKE_CASE__				:  str						= DDPMPipeline(unet=model, scheduler=scheduler)
  pipe.save_pretrained(args.dump_path)









"""simple docstring"""






import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer


class 	A_       (     tf.keras.layers.Layer   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = "longest"  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = 5_12  ,					__UpperCAmelCase = None  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = True  ,					__UpperCAmelCase = True  ,					**__UpperCAmelCase  ,					)				->					Union[str, Any]:
     super().__init__()
     if use_fast_bert_tokenizer:
        a   :	int						=    FastBertTokenizer(
            __UpperCAmelCase  ,					token_out_type=tf.intaa  ,					lower_case_nfd_strip_accents=__UpperCAmelCase  ,					**__UpperCAmelCase						)
     else:
        a   :	Optional[Any]						=    tf.lookup.StaticVocabularyTable(
            tf.lookup.KeyValueTensorInitializer(
                keys=__UpperCAmelCase  ,					key_dtype=tf.string  ,					values=tf.range(tf.size(__UpperCAmelCase  ,					out_type=tf.intaa						)  ,					dtype=tf.intaa						)  ,					value_dtype=tf.intaa  ,					)  ,					num_oov_buckets=1  ,					)
        a   :	str						=    BertTokenizerLayer(
            __UpperCAmelCase  ,					token_out_type=tf.intaa  ,					lower_case=__UpperCAmelCase  ,					**__UpperCAmelCase						)

     a   :	str						=    vocab_list
     a   :	Dict						=    do_lower_case
     a   :	Dict						=    cls_token_id or vocab_list.index('[CLS]'						)
     a   :	Tuple						=    sep_token_id or vocab_list.index('[SEP]'						)
     a   :	Optional[Any]						=    pad_token_id or vocab_list.index('[PAD]'						)
     a   :	Tuple						=    ShrinkLongestTrimmer(max_length - 3  ,					axis=1						)  # Allow room for special tokens
     a   :	Union[str, Any]						=    max_length
     a   :	Tuple						=    padding
     a   :	List[Any]						=    truncation
     a   :	Union[str, Any]						=    pad_to_multiple_of
     a   :	List[Any]						=    return_token_type_ids
     a   :	Optional[int]						=    return_attention_mask
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					**__UpperCAmelCase						)				->					Tuple:  # noqa: F821
     a   :	List[Any]						=    kwargs.pop('do_lower_case'  ,					__UpperCAmelCase						)
     a   :	str						=    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
     a   :	List[Any]						=    kwargs.pop('cls_token_id'  ,					__UpperCAmelCase						)
     a   :	int						=    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
     a   :	Tuple						=    kwargs.pop('sep_token_id'  ,					__UpperCAmelCase						)
     a   :	Optional[int]						=    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
     a   :	List[Any]						=    kwargs.pop('pad_token_id'  ,					__UpperCAmelCase						)
     a   :	Any						=    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

     a   :	Optional[Any]						=    tokenizer.get_vocab()
     a   :	Optional[Any]						=    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]  ,					key=lambda __UpperCAmelCase						: x[1]						)
     a   :	Optional[Any]						=    [entry[0] for entry in vocab]
     return cls(
         vocab_list=__UpperCAmelCase  ,					do_lower_case=__UpperCAmelCase  ,					cls_token_id=__UpperCAmelCase  ,					sep_token_id=__UpperCAmelCase  ,					pad_token_id=__UpperCAmelCase  ,					**__UpperCAmelCase  ,					)
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)				->					Tuple:
     try:
        a   :	Dict						=    BertTokenizer.from_pretrained(__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)
     except:  # noqa: E722
        from .tokenization_bert_fast import BertTokenizerFast

        a   :	Tuple						=    BertTokenizerFast.from_pretrained(__UpperCAmelCase  ,					*__UpperCAmelCase  ,					**__UpperCAmelCase						)
     return cls.from_tokenizer(__UpperCAmelCase  ,					**__UpperCAmelCase						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase						)				->					List[Any]:
     if self.do_lower_case:
        a   :	int						=    case_fold_utfa(__UpperCAmelCase						)
     a   :	Union[str, Any]						=    self.tf_tokenizer.tokenize(__UpperCAmelCase						)
     return tokens.merge_dims(1  ,					-1						)
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					__UpperCAmelCase=None  ,					)				->					Optional[int]:
     if padding is None:
        a   :	List[Any]						=    self.padding
     if padding not in ("longest", "max_length"):
        raise ValueError('Padding must be either \'longest\' or \'max_length\'!'						)
     if max_length is not None and text_pair is not None:
        # Because we have to instantiate a Trimmer to do it properly
        raise ValueError('max_length cannot be overridden at call time when truncating paired texts!'						)
     if max_length is None:
        a   :	Any						=    self.max_length
     if truncation is None:
        a   :	Any						=    self.truncation
     if pad_to_multiple_of is None:
        a   :	Optional[int]						=    self.pad_to_multiple_of
     if return_token_type_ids is None:
        a   :	str						=    self.return_token_type_ids
     if return_attention_mask is None:
        a   :	Dict						=    self.return_attention_mask
     if not isinstance(__UpperCAmelCase  ,					tf.Tensor						):
        a   :	List[str]						=    tf.convert_to_tensor(__UpperCAmelCase						)
     if text_pair is not None and not isinstance(__UpperCAmelCase  ,					tf.Tensor						):
        a   :	Any						=    tf.convert_to_tensor(__UpperCAmelCase						)
     if text_pair is not None:
        if text.shape.rank > 1:
           raise ValueError('text argument should not be multidimensional when a text pair is supplied!'						)
        if text_pair.shape.rank > 1:
           raise ValueError('text_pair should not be multidimensional!'						)
     if text.shape.rank == 2:
        a	, a   :	Union[str, Any]						=    text[:, 0], text[:, 1]
     a   :	Union[str, Any]						=    self.unpaired_tokenize(__UpperCAmelCase						)
     if text_pair is None:  # Unpaired text
        if truncation:
           a   :	List[Any]						=    text[:, : max_length - 2]  # Allow room for special tokens
        a	, a   :	Dict						=    combine_segments(
            (text,)  ,					start_of_sequence_id=self.cls_token_id  ,					end_of_segment_id=self.sep_token_id						)
     else:  # Paired text
        a   :	Optional[Any]						=    self.unpaired_tokenize(__UpperCAmelCase						)
        if truncation:
           a	, a   :	List[str]						=    self.paired_trimmer.trim([text, text_pair]						)
        a	, a   :	List[Any]						=    combine_segments(
            (text, text_pair)  ,					start_of_sequence_id=self.cls_token_id  ,					end_of_segment_id=self.sep_token_id						)
     if padding == "longest":
        a   :	List[Any]						=    input_ids.bounding_shape(axis=1						)
        if pad_to_multiple_of is not None:
           # No ceiling division in tensorflow, so we negate floordiv instead
           a   :	Optional[int]						=    pad_to_multiple_of * (-tf.math.floordiv(-pad_length  ,					__UpperCAmelCase						))
     else:
        a   :	Any						=    max_length

     a	, a   :	Any						=    pad_model_inputs(__UpperCAmelCase  ,					max_seq_length=__UpperCAmelCase  ,					pad_value=self.pad_token_id						)
     a   :	Optional[Any]						=    {'input_ids': input_ids}
     if return_attention_mask:
        a   :	Tuple						=    attention_mask
     if return_token_type_ids:
        a	, a   :	int						=    pad_model_inputs(
            __UpperCAmelCase  ,					max_seq_length=__UpperCAmelCase  ,					pad_value=self.pad_token_id						)
        a   :	Union[str, Any]						=    token_type_ids
     return output






  def 							lowercase_     (						self						)				->					List[str]:
     return {
         "vocab_list": self.vocab_list,
         "do_lower_case": self.do_lower_case,
         "cls_token_id": self.cls_token_id,
         "sep_token_id": self.sep_token_id,
         "pad_token_id": self.pad_token_id,
     }





