






"""simple docstring"""


import argparse
import json

import torch

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




def 					a			(  UpperCamelCase_				:					int  ,						UpperCamelCase_				:					Tuple=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_				:					Union[str, Any]  ,						UpperCamelCase_				:					str=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_				:					Optional[int]  ,						UpperCamelCase_				:					Dict=0  ,						UpperCamelCase_				:					Dict=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_				:					Tuple  ,						UpperCamelCase_				:					Union[str, Any]  ,						UpperCamelCase_				:					List[Any]  ,						UpperCamelCase_				:					Optional[Any]=None  ,						UpperCamelCase_				:					Optional[Any]=None  ,						UpperCamelCase_				:					Tuple=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						:							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_				:					Any  ,						UpperCamelCase_				:					Union[str, Any]		)	->		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_				:					Tuple  ,						UpperCamelCase_				:					int		)	->		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__":
   _UpperCamelCase :    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.""")

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

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

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

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

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

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

      _UpperCamelCase :    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,
     }





