


'''simple docstring'''



import argparse
import json

import torch

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




def       _a      (		_lowercase   :      Optional[Any]     ,   _lowercase   :      int=1    ):





     '''simple docstring'''






     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      (		_lowercase   :      int     ,   _lowercase   :      Any=0    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Any    				=     []
     for old_item in old_list:
          __UpperCAmelCase      :					List[str]    				=     old_item
          __UpperCAmelCase      :					Tuple    				=     new_item.replace('''block.'''     ,   '''resnets.'''    )
          __UpperCAmelCase      :					List[Any]    				=     new_item.replace('''conv_shorcut'''     ,   '''conv1'''    )
          __UpperCAmelCase      :					List[str]    				=     new_item.replace('''in_shortcut'''     ,   '''conv_shortcut'''    )
          __UpperCAmelCase      :					List[Any]    				=     new_item.replace('''temb_proj'''     ,   '''time_emb_proj'''    )

          __UpperCAmelCase      :					Union[str, Any]    				=     shave_segments(_lowercase     ,   n_shave_prefix_segments=_lowercase    )

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

     return mapping




def       _a      (		_lowercase   :      int     ,   _lowercase   :      List[str]=0     ,   _lowercase   :      int=False    ):





     '''simple docstring'''






     __UpperCAmelCase      :					int    				=     []
     for old_item in old_list:
          __UpperCAmelCase      :					Optional[int]    				=     old_item

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

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

          __UpperCAmelCase      :					List[Any]    				=     shave_segments(_lowercase     ,   n_shave_prefix_segments=_lowercase    )
          mapping.append({'''old''': old_item, '''new''': new_item}    )

     return mapping




def       _a      (		_lowercase   :      Tuple     ,   _lowercase   :      int     ,   _lowercase   :      List[Any]     ,   _lowercase   :      List[str]=None     ,   _lowercase   :      Dict=None     ,   _lowercase   :      Dict=None    ):





     '''simple docstring'''






     assert isinstance(_lowercase     ,   _lowercase    ), "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():
               __UpperCAmelCase      :					Optional[Any]    				=     old_checkpoint[path]
               __UpperCAmelCase      :					List[Any]    				=     old_tensor.shape[0] // 3

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

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

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

               __UpperCAmelCase      :					Any    				=     query.reshape(_lowercase    ).squeeze()
               __UpperCAmelCase      :					Tuple    				=     key.reshape(_lowercase    ).squeeze()
               __UpperCAmelCase      :					Optional[Any]    				=     value.reshape(_lowercase    ).squeeze()

     for path in paths:
          __UpperCAmelCase      :					Tuple    				=     path['''new''']

          if attention_paths_to_split is not None and new_path in attention_paths_to_split:
               continue

          __UpperCAmelCase      :					Optional[int]    				=     new_path.replace('''down.'''     ,   '''down_blocks.'''    )
          __UpperCAmelCase      :					List[str]    				=     new_path.replace('''up.'''     ,   '''up_blocks.'''    )

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

          if "attentions" in new_path:
               __UpperCAmelCase      :					Optional[Any]    				=     old_checkpoint[path['''old''']].squeeze()
          else:
               __UpperCAmelCase      :					Tuple    				=     old_checkpoint[path['''old''']]




def       _a      (		_lowercase   :      List[Any]     ,   _lowercase   :      Optional[Any]    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Any    				=     {}

     __UpperCAmelCase      :					Optional[int]    				=     checkpoint['''temb.dense.0.weight''']
     __UpperCAmelCase      :					Any    				=     checkpoint['''temb.dense.0.bias''']
     __UpperCAmelCase      :					Any    				=     checkpoint['''temb.dense.1.weight''']
     __UpperCAmelCase      :					Union[str, Any]    				=     checkpoint['''temb.dense.1.bias''']

     __UpperCAmelCase      :					Tuple    				=     checkpoint['''norm_out.weight''']
     __UpperCAmelCase      :					Dict    				=     checkpoint['''norm_out.bias''']

     __UpperCAmelCase      :					Any    				=     checkpoint['''conv_in.weight''']
     __UpperCAmelCase      :					Tuple    				=     checkpoint['''conv_in.bias''']
     __UpperCAmelCase      :					Any    				=     checkpoint['''conv_out.weight''']
     __UpperCAmelCase      :					int    				=     checkpoint['''conv_out.bias''']

     __UpperCAmelCase      :					Optional[Any]    				=     len({'''.'''.join(layer.split('''.'''    )[:2]    ) for layer in checkpoint if '''down''' in layer}    )
     __UpperCAmelCase      :					int    				=     {
         layer_id: [key for key in checkpoint if F'down.{layer_id}' in key] for layer_id in range(_lowercase    )
     }

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

     for i in range(_lowercase    ):
          __UpperCAmelCase      :					Union[str, Any]    				=     (i - 1) // (config['''layers_per_block'''] + 1)

          if any('''downsample''' in layer for layer in down_blocks[i]    ):
               __UpperCAmelCase      :					Union[str, Any]    				=     checkpoint[
                   F'down.{i}.downsample.op.weight'
               ]
               __UpperCAmelCase      :					Union[str, 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]    ):
               __UpperCAmelCase      :					Tuple    				=     len(
                   {'''.'''.join(shave_segments(_lowercase     ,   2    ).split('''.'''    )[:2]    ) for layer in down_blocks[i] if '''block''' in layer}    )
               __UpperCAmelCase      :					Any    				=     {
                   layer_id: [key for key in down_blocks[i] if F'block.{layer_id}' in key]
                   for layer_id in range(_lowercase    )
               }

               if num_blocks > 0:
                    for j in range(config['''layers_per_block''']    ):
                         __UpperCAmelCase      :					Tuple    				=     renew_resnet_paths(blocks[j]    )
                         assign_to_checkpoint(_lowercase     ,   _lowercase     ,   _lowercase    )

          if any('''attn''' in layer for layer in down_blocks[i]    ):
               __UpperCAmelCase      :					List[str]    				=     len(
                   {'''.'''.join(shave_segments(_lowercase     ,   2    ).split('''.'''    )[:2]    ) for layer in down_blocks[i] if '''attn''' in layer}    )
               __UpperCAmelCase      :					Union[str, Any]    				=     {
                   layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
                   for layer_id in range(_lowercase    )
               }

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

     __UpperCAmelCase      :					Optional[Any]    				=     [key for key in checkpoint if '''mid.block_1''' in key]
     __UpperCAmelCase      :					Optional[int]    				=     [key for key in checkpoint if '''mid.block_2''' in key]
     __UpperCAmelCase      :					Tuple    				=     [key for key in checkpoint if '''mid.attn_1''' in key]

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

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

     __UpperCAmelCase      :					Dict    				=     renew_attention_paths(_lowercase     ,   in_mid=_lowercase    )
     assign_to_checkpoint(
         _lowercase     ,   _lowercase     ,   _lowercase     ,   additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''attn_1''', '''new''': '''attentions.0'''}]     ,   )

     for i in range(_lowercase    ):
          __UpperCAmelCase      :					List[str]    				=     num_up_blocks - 1 - i

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

          if any('''block''' in layer for layer in up_blocks[i]    ):
               __UpperCAmelCase      :					Optional[Any]    				=     len(
                   {'''.'''.join(shave_segments(_lowercase     ,   2    ).split('''.'''    )[:2]    ) for layer in up_blocks[i] if '''block''' in layer}    )
               __UpperCAmelCase      :					List[str]    				=     {
                   layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(_lowercase    )
               }

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

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

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

     __UpperCAmelCase      :					Tuple    				=     {k.replace('''mid_new_2'''     ,   '''mid_block'''    ): v for k, v in new_checkpoint.items()}
     return new_checkpoint




def       _a      (		_lowercase   :      Any     ,   _lowercase   :      int    ):





     '''simple docstring'''






     __UpperCAmelCase      :					Dict    				=     {}

     __UpperCAmelCase      :					Union[str, Any]    				=     checkpoint['''encoder.norm_out.weight''']
     __UpperCAmelCase      :					Optional[Any]    				=     checkpoint['''encoder.norm_out.bias''']

     __UpperCAmelCase      :					Optional[Any]    				=     checkpoint['''encoder.conv_in.weight''']
     __UpperCAmelCase      :					Tuple    				=     checkpoint['''encoder.conv_in.bias''']
     __UpperCAmelCase      :					List[Any]    				=     checkpoint['''encoder.conv_out.weight''']
     __UpperCAmelCase      :					Dict    				=     checkpoint['''encoder.conv_out.bias''']

     __UpperCAmelCase      :					List[Any]    				=     checkpoint['''decoder.norm_out.weight''']
     __UpperCAmelCase      :					int    				=     checkpoint['''decoder.norm_out.bias''']

     __UpperCAmelCase      :					List[Any]    				=     checkpoint['''decoder.conv_in.weight''']
     __UpperCAmelCase      :					Any    				=     checkpoint['''decoder.conv_in.bias''']
     __UpperCAmelCase      :					Optional[Any]    				=     checkpoint['''decoder.conv_out.weight''']
     __UpperCAmelCase      :					List[Any]    				=     checkpoint['''decoder.conv_out.bias''']

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

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

     for i in range(_lowercase    ):
          __UpperCAmelCase      :					int    				=     (i - 1) // (config['''layers_per_block'''] + 1)

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

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

               if num_blocks > 0:
                    for j in range(config['''layers_per_block''']    ):
                         __UpperCAmelCase      :					Dict    				=     renew_resnet_paths(blocks[j]    )
                         assign_to_checkpoint(_lowercase     ,   _lowercase     ,   _lowercase    )

          if any('''attn''' in layer for layer in down_blocks[i]    ):
               __UpperCAmelCase      :					Tuple    				=     len(
                   {'''.'''.join(shave_segments(_lowercase     ,   3    ).split('''.'''    )[:3]    ) for layer in down_blocks[i] if '''attn''' in layer}    )
               __UpperCAmelCase      :					Union[str, Any]    				=     {
                   layer_id: [key for key in down_blocks[i] if F'attn.{layer_id}' in key]
                   for layer_id in range(_lowercase    )
               }

               if num_attn > 0:
                    for j in range(config['''layers_per_block''']    ):
                         __UpperCAmelCase      :					int    				=     renew_attention_paths(attns[j]    )
                         assign_to_checkpoint(_lowercase     ,   _lowercase     ,   _lowercase     ,   config=_lowercase    )

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

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

     __UpperCAmelCase      :					Any    				=     renew_resnet_paths(_lowercase    )
     assign_to_checkpoint(
         _lowercase     ,   _lowercase     ,   _lowercase     ,   additional_replacements=[{'''old''': '''mid.''', '''new''': '''mid_new_2.'''}, {'''old''': '''block_2''', '''new''': '''resnets.1'''}]     ,   )

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

     for i in range(_lowercase    ):
          __UpperCAmelCase      :					Tuple    				=     num_up_blocks - 1 - i

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

          if any('''block''' in layer for layer in up_blocks[i]    ):
               __UpperCAmelCase      :					Tuple    				=     len(
                   {'''.'''.join(shave_segments(_lowercase     ,   3    ).split('''.'''    )[:3]    ) for layer in up_blocks[i] if '''block''' in layer}    )
               __UpperCAmelCase      :					List[str]    				=     {
                   layer_id: [key for key in up_blocks[i] if F'block.{layer_id}' in key] for layer_id in range(_lowercase    )
               }

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

          if any('''attn''' in layer for layer in up_blocks[i]    ):
               __UpperCAmelCase      :					int    				=     len(
                   {'''.'''.join(shave_segments(_lowercase     ,   3    ).split('''.'''    )[:3]    ) for layer in up_blocks[i] if '''attn''' in layer}    )
               __UpperCAmelCase      :					Tuple    				=     {
                   layer_id: [key for key in up_blocks[i] if F'attn.{layer_id}' in key] for layer_id in range(_lowercase    )
               }

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

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

     return new_checkpoint


if __name__ == "__main__":
      __UpperCAmelCase     :int												=  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     :str												=  parser.parse_args()
      __UpperCAmelCase     :Optional[Any]												=  torch.load(args.checkpoint_path)

      with open(args.config_file) as f:
            __UpperCAmelCase     :Optional[int]												=  json.loads(f.read())

      # unet case
      __UpperCAmelCase     :Union[str, Any]												=  {key.split(".")[0] for key in checkpoint.keys()}
      if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
            __UpperCAmelCase     :Dict												=  convert_vq_autoenc_checkpoint(checkpoint, config)
      else:
            __UpperCAmelCase     :Any												=  convert_ddpm_checkpoint(checkpoint, config)

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

      if config["_class_name"] == "VQModel":
            __UpperCAmelCase     :Any												=  VQModel(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      elif config["_class_name"] == "AutoencoderKL":
            __UpperCAmelCase     :Dict												=  AutoencoderKL(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      else:
            __UpperCAmelCase     :Dict												=  UNetaDModel(**config)
            model.load_state_dict(converted_checkpoint)

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

            __UpperCAmelCase     :Dict												=  DDPMPipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)



'''simple docstring'''



import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir



@dataclass
class       a							:


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					str



class       a							(						unittest.TestCase			):


     """simple docstring"""






     def 					lowerCamelCase__					(							self	:						str     )		->				Dict:
          __UpperCAmelCase      :					str    				=     get_tests_dir('''fixtures/test_sentencepiece.model'''     )
          __UpperCAmelCase      :					List[Any]    				=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''     )

          __UpperCAmelCase      :					Tuple    				=     FakeOriginalTokenizer(vocab_file=snake_case     )

          with warnings.catch_warnings(record=snake_case     ) as w:
               __UpperCAmelCase      :					Optional[int]    				=     SpmConverter(snake_case     )
          self.assertEqual(len(snake_case     )				,					0     )

          __UpperCAmelCase      :					str    				=     FakeOriginalTokenizer(vocab_file=snake_case     )

          with warnings.catch_warnings(record=snake_case     ) as w:
               __UpperCAmelCase      :					Optional[int]    				=     SpmConverter(snake_case     )
          self.assertEqual(len(snake_case     )				,					1     )

          self.assertIn(
              '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
              ''' which is not implemented in the fast tokenizers.'''				,					str(w[0].message     )				,					)