



import argparse
import json

import torch

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


def     UpperCamelCase      (					__lowerCamelCase  :				List[Any]       ,      __lowerCamelCase  :				Union[str, Any]=1					):
       if n_shave_prefix_segments >= 0:
              return ".".join(path.split("."					)[n_shave_prefix_segments:]					)
       else:
              return ".".join(path.split("."					)[:n_shave_prefix_segments]					)


def     UpperCamelCase      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				List[str]=0					):
       snake_case :     int           =   []
       for old_item in old_list:
              snake_case :     Dict           =   old_item
              snake_case :     Optional[Any]           =   new_item.replace("block."       ,      "resnets."					)
              snake_case :     str           =   new_item.replace("conv_shorcut"       ,      "conv1"					)
              snake_case :     List[str]           =   new_item.replace("in_shortcut"       ,      "conv_shortcut"					)
              snake_case :     str           =   new_item.replace("temb_proj"       ,      "time_emb_proj"					)

              snake_case :     Dict           =   shave_segments(_SCREAMING_SNAKE_CASE       ,      n_shave_prefix_segments=_SCREAMING_SNAKE_CASE					)

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

       return mapping


def     UpperCamelCase      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				Union[str, Any]=0       ,      __lowerCamelCase  :				Optional[int]=False					):
       snake_case :     List[str]           =   []
       for old_item in old_list:
              snake_case :     Optional[int]           =   old_item

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

              snake_case :     Optional[Any]           =   new_item.replace("proj_out"       ,      "proj_attn"					)
              snake_case :     List[Any]           =   new_item.replace("norm"       ,      "group_norm"					)

              snake_case :     str           =   shave_segments(_SCREAMING_SNAKE_CASE       ,      n_shave_prefix_segments=_SCREAMING_SNAKE_CASE					)
              mapping.append({"old": old_item, "new": new_item}					)

       return mapping


def     UpperCamelCase      (					__lowerCamelCase  :				int       ,      __lowerCamelCase  :				List[Any]       ,      __lowerCamelCase  :				str       ,      __lowerCamelCase  :				int=None       ,      __lowerCamelCase  :				Tuple=None       ,      __lowerCamelCase  :				str=None					):
       assert isinstance(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE					), "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():
                     snake_case :     Optional[int]           =   old_checkpoint[path]
                     snake_case :     str           =   old_tensor.shape[0] // 3

                     snake_case :     Tuple           =   (-1, channels) if len(old_tensor.shape					) == 3 else (-1)

                     snake_case :     Optional[Any]           =   old_tensor.shape[0] // config.get("num_head_channels"       ,      1					) // 3

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

                     snake_case :     List[str]           =   query.reshape(_SCREAMING_SNAKE_CASE					).squeeze()
                     snake_case :     List[Any]           =   key.reshape(_SCREAMING_SNAKE_CASE					).squeeze()
                     snake_case :     Optional[Any]           =   value.reshape(_SCREAMING_SNAKE_CASE					).squeeze()

       for path in paths:
              snake_case :     Union[str, Any]           =   path['new']

              if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                     continue

              snake_case :     Optional[Any]           =   new_path.replace("down."       ,      "down_blocks."					)
              snake_case :     Tuple           =   new_path.replace("up."       ,      "up_blocks."					)

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

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


def     UpperCamelCase      (					__lowerCamelCase  :				List[str]       ,      __lowerCamelCase  :				str					):
       snake_case :     Optional[Any]           =   {}

       snake_case :     List[str]           =   checkpoint['temb.dense.0.weight']
       snake_case :     Optional[Any]           =   checkpoint['temb.dense.0.bias']
       snake_case :     List[Any]           =   checkpoint['temb.dense.1.weight']
       snake_case :     Any           =   checkpoint['temb.dense.1.bias']

       snake_case :     Optional[int]           =   checkpoint['norm_out.weight']
       snake_case :     List[str]           =   checkpoint['norm_out.bias']

       snake_case :     List[str]           =   checkpoint['conv_in.weight']
       snake_case :     Dict           =   checkpoint['conv_in.bias']
       snake_case :     Tuple           =   checkpoint['conv_out.weight']
       snake_case :     List[Any]           =   checkpoint['conv_out.bias']

       snake_case :     Dict           =   len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "down" in layer}					)
       snake_case :     Union[str, Any]           =   {
           layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(_SCREAMING_SNAKE_CASE					)
       }

       snake_case :     List[Any]           =   len({".".join(layer.split("."					)[:2]					) for layer in checkpoint if "up" in layer}					)
       snake_case :     Optional[Any]           =   {layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(_SCREAMING_SNAKE_CASE					)}

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

              if any("downsample" in layer for layer in down_blocks[i]					):
                     snake_case :     Any           =   checkpoint[
                         f"""down.{i}.downsample.op.weight"""
                     ]
                     snake_case :     Optional[int]           =   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]					):
                     snake_case :     List[str]           =   len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE       ,      2					).split("."					)[:2]					) for layer in down_blocks[i] if "block" in layer}					)
                     snake_case :     Dict           =   {
                         layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
                         for layer_id in range(_SCREAMING_SNAKE_CASE					)
                     }

                     if num_blocks > 0:
                            for j in range(config["layers_per_block"]					):
                                   snake_case :     Optional[int]           =   renew_resnet_paths(blocks[j]					)
                                   assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE					)

              if any("attn" in layer for layer in down_blocks[i]					):
                     snake_case :     Tuple           =   len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE       ,      2					).split("."					)[:2]					) for layer in down_blocks[i] if "attn" in layer}					)
                     snake_case :     List[Any]           =   {
                         layer_id: [key for key in down_blocks[i] if f"""attn.{layer_id}""" in key]
                         for layer_id in range(_SCREAMING_SNAKE_CASE					)
                     }

                     if num_attn > 0:
                            for j in range(config["layers_per_block"]					):
                                   snake_case :     Tuple           =   renew_attention_paths(attns[j]					)
                                   assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      config=_SCREAMING_SNAKE_CASE					)

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

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

       snake_case :     str           =   renew_resnet_paths(_SCREAMING_SNAKE_CASE					)
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,      )

       snake_case :     Optional[int]           =   renew_attention_paths(_SCREAMING_SNAKE_CASE       ,      in_mid=_SCREAMING_SNAKE_CASE					)
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]       ,      )

       for i in range(_SCREAMING_SNAKE_CASE					):
              snake_case :     Dict           =   num_up_blocks - 1 - i

              if any("upsample" in layer for layer in up_blocks[i]					):
                     snake_case :     str           =   checkpoint[
                         f"""up.{i}.upsample.conv.weight"""
                     ]
                     snake_case :     Optional[int]           =   checkpoint[f"""up.{i}.upsample.conv.bias"""]

              if any("block" in layer for layer in up_blocks[i]					):
                     snake_case :     Optional[Any]           =   len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE       ,      2					).split("."					)[:2]					) for layer in up_blocks[i] if "block" in layer}					)
                     snake_case :     str           =   {
                         layer_id: [key for key in up_blocks[i] if f"""block.{layer_id}""" in key] for layer_id in range(_SCREAMING_SNAKE_CASE					)
                     }

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

              if any("attn" in layer for layer in up_blocks[i]					):
                     snake_case :     Union[str, Any]           =   len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE       ,      2					).split("."					)[:2]					) for layer in up_blocks[i] if "attn" in layer}					)
                     snake_case :     int           =   {
                         layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(_SCREAMING_SNAKE_CASE					)
                     }

                     if num_attn > 0:
                            for j in range(config["layers_per_block"] + 1					):
                                   snake_case :     int           =   {'old': f"""up_blocks.{i}""", 'new': f"""up_blocks.{block_id}"""}
                                   snake_case :     Optional[int]           =   renew_attention_paths(attns[j]					)
                                   assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      additional_replacements=[replace_indices]					)

       snake_case :     str           =   {k.replace("mid_new_2"       ,      "mid_block"					): v for k, v in new_checkpoint.items()}
       return new_checkpoint


def     UpperCamelCase      (					__lowerCamelCase  :				Union[str, Any]       ,      __lowerCamelCase  :				List[Any]					):
       snake_case :     Tuple           =   {}

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

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

       snake_case :     List[str]           =   checkpoint['decoder.norm_out.weight']
       snake_case :     str           =   checkpoint['decoder.norm_out.bias']

       snake_case :     int           =   checkpoint['decoder.conv_in.weight']
       snake_case :     Optional[int]           =   checkpoint['decoder.conv_in.bias']
       snake_case :     Union[str, Any]           =   checkpoint['decoder.conv_out.weight']
       snake_case :     List[str]           =   checkpoint['decoder.conv_out.bias']

       snake_case :     Optional[Any]           =   len({".".join(layer.split("."					)[:3]					) for layer in checkpoint if "down" in layer}					)
       snake_case :     Any           =   {
           layer_id: [key for key in checkpoint if f"""down.{layer_id}""" in key] for layer_id in range(_SCREAMING_SNAKE_CASE					)
       }

       snake_case :     Dict           =   len({".".join(layer.split("."					)[:3]					) for layer in checkpoint if "up" in layer}					)
       snake_case :     int           =   {layer_id: [key for key in checkpoint if f"""up.{layer_id}""" in key] for layer_id in range(_SCREAMING_SNAKE_CASE					)}

       for i in range(_SCREAMING_SNAKE_CASE					):
              snake_case :     Any           =   (i - 1) // (config['layers_per_block'] + 1)

              if any("downsample" in layer for layer in down_blocks[i]					):
                     snake_case :     Optional[int]           =   checkpoint[
                         f"""encoder.down.{i}.downsample.conv.weight"""
                     ]
                     snake_case :     Union[str, Any]           =   checkpoint[
                         f"""encoder.down.{i}.downsample.conv.bias"""
                     ]

              if any("block" in layer for layer in down_blocks[i]					):
                     snake_case :     Optional[Any]           =   len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE       ,      3					).split("."					)[:3]					) for layer in down_blocks[i] if "block" in layer}					)
                     snake_case :     Union[str, Any]           =   {
                         layer_id: [key for key in down_blocks[i] if f"""block.{layer_id}""" in key]
                         for layer_id in range(_SCREAMING_SNAKE_CASE					)
                     }

                     if num_blocks > 0:
                            for j in range(config["layers_per_block"]					):
                                   snake_case :     Dict           =   renew_resnet_paths(blocks[j]					)
                                   assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE					)

              if any("attn" in layer for layer in down_blocks[i]					):
                     snake_case :     int           =   len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE       ,      3					).split("."					)[:3]					) for layer in down_blocks[i] if "attn" in layer}					)
                     snake_case :     Tuple           =   {
                         layer_id: [key for key in down_blocks[i] if f"""attn.{layer_id}""" in key]
                         for layer_id in range(_SCREAMING_SNAKE_CASE					)
                     }

                     if num_attn > 0:
                            for j in range(config["layers_per_block"]					):
                                   snake_case :     int           =   renew_attention_paths(attns[j]					)
                                   assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      config=_SCREAMING_SNAKE_CASE					)

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

       # Mid new 2
       snake_case :     Optional[int]           =   renew_resnet_paths(_SCREAMING_SNAKE_CASE					)
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_1", "new": "resnets.0"}]       ,      )

       snake_case :     Any           =   renew_resnet_paths(_SCREAMING_SNAKE_CASE					)
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "block_2", "new": "resnets.1"}]       ,      )

       snake_case :     Tuple           =   renew_attention_paths(_SCREAMING_SNAKE_CASE       ,      in_mid=_SCREAMING_SNAKE_CASE					)
       assign_to_checkpoint(
           _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      additional_replacements=[{"old": "mid.", "new": "mid_new_2."}, {"old": "attn_1", "new": "attentions.0"}]       ,      )

       for i in range(_SCREAMING_SNAKE_CASE					):
              snake_case :     Dict           =   num_up_blocks - 1 - i

              if any("upsample" in layer for layer in up_blocks[i]					):
                     snake_case :     Any           =   checkpoint[
                         f"""decoder.up.{i}.upsample.conv.weight"""
                     ]
                     snake_case :     Any           =   checkpoint[
                         f"""decoder.up.{i}.upsample.conv.bias"""
                     ]

              if any("block" in layer for layer in up_blocks[i]					):
                     snake_case :     Optional[int]           =   len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE       ,      3					).split("."					)[:3]					) for layer in up_blocks[i] if "block" in layer}					)
                     snake_case :     Tuple           =   {
                         layer_id: [key for key in up_blocks[i] if f"""block.{layer_id}""" in key] for layer_id in range(_SCREAMING_SNAKE_CASE					)
                     }

                     if num_blocks > 0:
                            for j in range(config["layers_per_block"] + 1					):
                                   snake_case :     List[Any]           =   {'old': f"""up_blocks.{i}""", 'new': f"""up_blocks.{block_id}"""}
                                   snake_case :     Union[str, Any]           =   renew_resnet_paths(blocks[j]					)
                                   assign_to_checkpoint(_SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      _SCREAMING_SNAKE_CASE       ,      additional_replacements=[replace_indices]					)

              if any("attn" in layer for layer in up_blocks[i]					):
                     snake_case :     Optional[int]           =   len(
                         {".".join(shave_segments(_SCREAMING_SNAKE_CASE       ,      3					).split("."					)[:3]					) for layer in up_blocks[i] if "attn" in layer}					)
                     snake_case :     str           =   {
                         layer_id: [key for key in up_blocks[i] if f"""attn.{layer_id}""" in key] for layer_id in range(_SCREAMING_SNAKE_CASE					)
                     }

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

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

       return new_checkpoint


if __name__ == "__main__":
   __lowerCamelCase							= 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.""")

   __lowerCamelCase							= parser.parse_args()
   __lowerCamelCase							= torch.load(args.checkpoint_path)

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

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

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

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

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

      __lowerCamelCase							= DDPMPipeline(unet=model, scheduler=scheduler)
      pipe.save_pretrained(args.dump_path)







import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						UpperCAmelCase	(		A_ ,unittest.TestCase				):
   A__      :	Dict		  =  PreTrainedTokenizerFast
   A__      :	str		  =  False
   A__      :	List[Any]		  =  True
   A__      :	List[str]		  =  "tokenizer_file"



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     Union[str, Any]           =   False  # because we don't have pretrained_vocab_files_map
          super().setUp()
          snake_case :     Optional[int]           =   True

          snake_case :     List[str]           =   ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
          snake_case :     Optional[Any]           =   "SaulLu/dummy-tokenizer-bytelevel-bpe"

          # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
          snake_case :     int           =   [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

          snake_case :     List[Any]           =   PreTrainedTokenizerFast.from_pretrained(model_paths[0]				)
          tokenizer.save_pretrained(self.tmpdirname				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Dict				)	->			int:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			int:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Any				)	->			Union[str, Any]:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			List[str]:


          '''simple docstring'''


          pass



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     Optional[int]           =   self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                        try:
                               snake_case :     Any           =   tempfile.mkdtemp()
                               snake_case :     int           =   self.rust_tokenizer_class.from_pretrained(snake_case__					,		**snake_case__				)

                               tokenizer.save_pretrained(self.tmpdirname				)
                               super().test_training_new_tokenizer()
                        finally:
                               # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                               # is restored
                               shutil.rmtree(self.tmpdirname				)
                               snake_case :     int           =   tmpdirname_orig



   def        _SCREAMING_SNAKE_CASE     (self	:      Tuple				)	->			Dict:


          '''simple docstring'''


          snake_case :     Optional[int]           =   self.tmpdirname
          # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
                        try:
                               snake_case :     Union[str, Any]           =   tempfile.mkdtemp()
                               snake_case :     str           =   self.rust_tokenizer_class.from_pretrained(snake_case__					,		**snake_case__				)

                               tokenizer.save_pretrained(self.tmpdirname				)
                               super().test_training_new_tokenizer_with_special_tokens_change()
                        finally:
                               # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                               # is restored
                               shutil.rmtree(self.tmpdirname				)
                               snake_case :     Dict           =   tmpdirname_orig



   def        _SCREAMING_SNAKE_CASE     (self	:      int				)	->			Union[str, Any]:


          '''simple docstring'''


          snake_case :     int           =   self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name				)

          snake_case :     int           =   ("a" for _ in range(10_00				))
          snake_case :     Optional[int]           =   tokenizer.train_new_from_iterator(text_iterator=snake_case__					,		length=10_00					,		vocab_size=50				)

          snake_case :     str           =   new_tokenizer.encode("a🤗"				)
          self.assertEqual(snake_case__					,		[64, 1_72, 2_53, 97, 2_45]				)






@require_tokenizers
class 						UpperCAmelCase	(		unittest.TestCase				):



   def        _SCREAMING_SNAKE_CASE     (self	:      Union[str, Any]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     List[str]           =   AutoTokenizer.from_pretrained("bert-base-cased"				)
          snake_case :     Union[str, Any]           =   json.loads(tokenizer._tokenizer.to_str()				)
          snake_case :     Tuple           =   len(snake_case__				)

          with tempfile.TemporaryDirectory() as tmp_dir:
                 # Hack to save this in the tokenizer_config.json
                 snake_case :     List[str]           =   ["tokenizer.4.0.0.json"]
                 tokenizer.save_pretrained(snake_case__				)
                 json.dump(snake_case__					,		open(os.path.join(snake_case__					,		"tokenizer.4.0.0.json"				)					,		"w"				)				)

                 # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
                 snake_case :     Optional[Any]           =   AutoTokenizer.from_pretrained(snake_case__				)
                 self.assertEqual(len(snake_case__				)					,		len(snake_case__				) + 1				)
                 snake_case :     Union[str, Any]           =   json.loads(new_tokenizer._tokenizer.to_str()				)
                 self.assertIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)

                 # Will need to be adjusted if we reach v42 and this test is still here.
                 # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
                 shutil.move(os.path.join(snake_case__					,		"tokenizer.4.0.0.json"				)					,		os.path.join(snake_case__					,		"tokenizer.42.0.0.json"				)				)
                 snake_case :     Optional[Any]           =   ["tokenizer.42.0.0.json"]
                 tokenizer.save_pretrained(snake_case__				)
                 snake_case :     Dict           =   AutoTokenizer.from_pretrained(snake_case__				)
                 self.assertEqual(len(snake_case__				)					,		len(snake_case__				)				)
                 snake_case :     Optional[int]           =   json.loads(new_tokenizer._tokenizer.to_str()				)
                 self.assertNotIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[str]:


          '''simple docstring'''


          snake_case :     int           =   "hf-internal-testing/test-two-tokenizers"

          # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
          snake_case :     int           =   AutoTokenizer.from_pretrained(snake_case__				)
          self.assertEqual(len(snake_case__				)					,		2_89_97				)
          snake_case :     Optional[int]           =   json.loads(tokenizer._tokenizer.to_str()				)
          self.assertIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)

          # Testing an older version by monkey-patching the version in the module it's used.
          import transformers as old_transformers

          snake_case :     Dict           =   "3.0.0"
          snake_case :     Union[str, Any]           =   old_transformers.models.auto.AutoTokenizer.from_pretrained(snake_case__				)
          self.assertEqual(len(snake_case__				)					,		2_89_96				)
          snake_case :     Optional[Any]           =   json.loads(old_tokenizer._tokenizer.to_str()				)
          self.assertNotIn("huggingface"					,		json_tokenizer["model"]["vocab"]				)






@require_tokenizers
class 						UpperCAmelCase	(		unittest.TestCase				):



   def        _SCREAMING_SNAKE_CASE     (self	:      Optional[int]				)	->			List[Any]:


          '''simple docstring'''


          snake_case :     Tuple           =   PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel"				)
          snake_case :     List[Any]           =   "The Matrix is a 1999 science fiction action film."

          with concurrent.futures.ThreadPoolExecutor() as executor:
                 snake_case :     Union[str, Any]           =   [executor.submit(self.fetch					,		snake_case__					,		snake_case__				) for i in range(10				)]
                 snake_case :     Optional[Any]           =   [future.result() for future in futures]
                 self.assertEqual(snake_case__					,		[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10				)]				)



   def        _SCREAMING_SNAKE_CASE     (self	:      str					,		snake_case__	:      List[str]					,		snake_case__	:      str				)	->			List[str]:


          '''simple docstring'''


          return tokenizer.encode(snake_case__					,		truncation="longest_first"					,		padding="longest"				)





