




'''simple docstring'''



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					Tuple=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   lowercase__(			__UpperCamelCase:					Any      ,__UpperCamelCase:					Optional[int]=0    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      List[str]    					=						[]
      for old_item in old_list:
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						old_item.replace('in_layers.0'      ,'norm1'    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						new_item.replace('in_layers.2'      ,'conv1'    )

            SCREAMING_SNAKE_CASE   :      Dict    					=						new_item.replace('out_layers.0'      ,'norm2'    )
            SCREAMING_SNAKE_CASE   :      int    					=						new_item.replace('out_layers.3'      ,'conv2'    )

            SCREAMING_SNAKE_CASE   :      Tuple    					=						new_item.replace('emb_layers.1'      ,'time_emb_proj'    )
            SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						new_item.replace('skip_connection'      ,'conv_shortcut'    )

            SCREAMING_SNAKE_CASE   :      List[Any]    					=						shave_segments(__UpperCamelCase      ,n_shave_prefix_segments=__UpperCamelCase    )

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

      return mapping




def   lowercase__(			__UpperCamelCase:					int      ,__UpperCamelCase:					List[str]=0    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      int    					=						[]
      for old_item in old_list:
            SCREAMING_SNAKE_CASE   :      str    					=						old_item

            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						new_item.replace('norm.weight'      ,'group_norm.weight'    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						new_item.replace('norm.bias'      ,'group_norm.bias'    )

            SCREAMING_SNAKE_CASE   :      Dict    					=						new_item.replace('proj_out.weight'      ,'proj_attn.weight'    )
            SCREAMING_SNAKE_CASE   :      List[str]    					=						new_item.replace('proj_out.bias'      ,'proj_attn.bias'    )

            SCREAMING_SNAKE_CASE   :      List[Any]    					=						shave_segments(__UpperCamelCase      ,n_shave_prefix_segments=__UpperCamelCase    )

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

      return mapping




def   lowercase__(			__UpperCamelCase:					Any      ,__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					List[Any]      ,__UpperCamelCase:					Dict=None      ,__UpperCamelCase:					Union[str, Any]=None      ,__UpperCamelCase:					Any=None    ):

      """simple docstring"""






      assert isinstance(__UpperCamelCase      ,__UpperCamelCase    ), "Paths should be a list of dicts containing 'old' and 'new' keys."

      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
            for path, path_map in attention_paths_to_split.items():
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						old_checkpoint[path]
                  SCREAMING_SNAKE_CASE   :      str    					=						old_tensor.shape[0] // 3

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

                  SCREAMING_SNAKE_CASE   :      Any    					=						old_tensor.shape[0] // config['num_head_channels'] // 3

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

                  SCREAMING_SNAKE_CASE   :      Tuple    					=						query.reshape(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      int    					=						key.reshape(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      Dict    					=						value.reshape(__UpperCamelCase    )

      for path in paths:
            SCREAMING_SNAKE_CASE   :      List[str]    					=						path['new']

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            # Global renaming happens here
            SCREAMING_SNAKE_CASE   :      str    					=						new_path.replace('middle_block.0'      ,'mid_block.resnets.0'    )
            SCREAMING_SNAKE_CASE   :      int    					=						new_path.replace('middle_block.1'      ,'mid_block.attentions.0'    )
            SCREAMING_SNAKE_CASE   :      Any    					=						new_path.replace('middle_block.2'      ,'mid_block.resnets.1'    )

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

        # proj_attn.weight has to be converted from conv 1D to linear
            if "proj_attn.weight" in new_path:
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						old_checkpoint[path['old']][:, :, 0]
            else:
                  SCREAMING_SNAKE_CASE   :      Tuple    					=						old_checkpoint[path['old']]




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					str    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						{}

      SCREAMING_SNAKE_CASE   :      int    					=						checkpoint['time_embed.0.weight']
      SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint['time_embed.0.bias']
      SCREAMING_SNAKE_CASE   :      List[str]    					=						checkpoint['time_embed.2.weight']
      SCREAMING_SNAKE_CASE   :      List[str]    					=						checkpoint['time_embed.2.bias']

      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						checkpoint['input_blocks.0.0.weight']
      SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint['input_blocks.0.0.bias']

      SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint['out.0.weight']
      SCREAMING_SNAKE_CASE   :      str    					=						checkpoint['out.0.bias']
      SCREAMING_SNAKE_CASE   :      str    					=						checkpoint['out.2.weight']
      SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint['out.2.bias']

      # Retrieves the keys for the input blocks only
      SCREAMING_SNAKE_CASE   :      List[str]    					=						len({'.'.join(layer.split('.'    )[:2]    ) for layer in checkpoint if 'input_blocks' in layer}    )
      SCREAMING_SNAKE_CASE   :      Any    					=						{
          layer_id: [key for key in checkpoint if f"input_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase    )
      }

      # Retrieves the keys for the middle blocks only
      SCREAMING_SNAKE_CASE   :      Tuple    					=						len({'.'.join(layer.split('.'    )[:2]    ) for layer in checkpoint if 'middle_block' in layer}    )
      SCREAMING_SNAKE_CASE   :      str    					=						{
          layer_id: [key for key in checkpoint if f"middle_block.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase    )
      }

      # Retrieves the keys for the output blocks only
      SCREAMING_SNAKE_CASE   :      Any    					=						len({'.'.join(layer.split('.'    )[:2]    ) for layer in checkpoint if 'output_blocks' in layer}    )
      SCREAMING_SNAKE_CASE   :      int    					=						{
          layer_id: [key for key in checkpoint if f"output_blocks.{layer_id}" in key]
          for layer_id in range(__UpperCamelCase    )
      }

      for i in range(1      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      Tuple    					=						(i - 1) // (config['num_res_blocks'] + 1)
            SCREAMING_SNAKE_CASE   :      List[str]    					=						(i - 1) % (config['num_res_blocks'] + 1)

            SCREAMING_SNAKE_CASE   :      Any    					=						[key for key in input_blocks[i] if f"input_blocks.{i}.0" in key]
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]

            if f"input_blocks.{i}.0.op.weight" in checkpoint:
                  SCREAMING_SNAKE_CASE   :      List[str]    					=						checkpoint[
                      f"input_blocks.{i}.0.op.weight"
                  ]
                  SCREAMING_SNAKE_CASE   :      Dict    					=						checkpoint[
                      f"input_blocks.{i}.0.op.bias"
                  ]
                  continue

            SCREAMING_SNAKE_CASE   :      int    					=						renew_resnet_paths(__UpperCamelCase    )
            SCREAMING_SNAKE_CASE   :      Dict    					=						{'old': f"input_blocks.{i}.0", 'new': f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
            SCREAMING_SNAKE_CASE   :      str    					=						{'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
            assign_to_checkpoint(
                __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,additional_replacements=[meta_path, resnet_op]      ,config=__UpperCamelCase    )

            if len(__UpperCamelCase    ):
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						renew_attention_paths(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{
                      'old': f"input_blocks.{i}.1",
                      'new': f"down_blocks.{block_id}.attentions.{layer_in_block_id}",
                  }
                  SCREAMING_SNAKE_CASE   :      str    					=						{
                      f"input_blocks.{i}.1.qkv.bias": {
                          'key': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                          'query': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                          'value': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                      },
                      f"input_blocks.{i}.1.qkv.weight": {
                          'key': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                          'query': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                          'value': f"down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                      },
                  }
                  assign_to_checkpoint(
                      __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,additional_replacements=[meta_path]      ,attention_paths_to_split=__UpperCamelCase      ,config=__UpperCamelCase      ,)

      SCREAMING_SNAKE_CASE   :      List[str]    					=						middle_blocks[0]
      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						middle_blocks[1]
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						middle_blocks[2]

      SCREAMING_SNAKE_CASE   :      Any    					=						renew_resnet_paths(__UpperCamelCase    )
      assign_to_checkpoint(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,config=__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						renew_resnet_paths(__UpperCamelCase    )
      assign_to_checkpoint(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,config=__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Tuple    					=						renew_attention_paths(__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      str    					=						{
          'middle_block.1.qkv.bias': {
              'key': 'mid_block.attentions.0.key.bias',
              'query': 'mid_block.attentions.0.query.bias',
              'value': 'mid_block.attentions.0.value.bias',
          },
          'middle_block.1.qkv.weight': {
              'key': 'mid_block.attentions.0.key.weight',
              'query': 'mid_block.attentions.0.query.weight',
              'value': 'mid_block.attentions.0.value.weight',
          },
      }
      assign_to_checkpoint(
          __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,attention_paths_to_split=__UpperCamelCase      ,config=__UpperCamelCase    )

      for i in range(__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						i // (config['num_res_blocks'] + 1)
            SCREAMING_SNAKE_CASE   :      str    					=						i % (config['num_res_blocks'] + 1)
            SCREAMING_SNAKE_CASE   :      Dict    					=						[shave_segments(__UpperCamelCase      ,2    ) for name in output_blocks[i]]
            SCREAMING_SNAKE_CASE   :      str    					=						{}

            for layer in output_block_layers:
                  SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Tuple    					=						layer.split('.'    )[0], shave_segments(__UpperCamelCase      ,1    )
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(__UpperCamelCase    )
                  else:
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						[layer_name]

            if len(__UpperCamelCase    ) > 1:
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]

                  SCREAMING_SNAKE_CASE   :      Dict    					=						renew_resnet_paths(__UpperCamelCase    )
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						renew_resnet_paths(__UpperCamelCase    )

                  SCREAMING_SNAKE_CASE   :      Any    					=						{'old': f"output_blocks.{i}.0", 'new': f"up_blocks.{block_id}.resnets.{layer_in_block_id}"}
                  assign_to_checkpoint(__UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,additional_replacements=[meta_path]      ,config=__UpperCamelCase    )

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						list(output_block_list.values()    ).index(['conv.weight', 'conv.bias']    )
                        SCREAMING_SNAKE_CASE   :      Optional[int]    					=						checkpoint[
                            f"output_blocks.{i}.{index}.conv.weight"
                        ]
                        SCREAMING_SNAKE_CASE   :      List[str]    					=						checkpoint[
                            f"output_blocks.{i}.{index}.conv.bias"
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(__UpperCamelCase    ) == 2:
                              SCREAMING_SNAKE_CASE   :      List[str]    					=						[]

                  if len(__UpperCamelCase    ):
                        SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						renew_attention_paths(__UpperCamelCase    )
                        SCREAMING_SNAKE_CASE   :      str    					=						{
                            'old': f"output_blocks.{i}.1",
                            'new': f"up_blocks.{block_id}.attentions.{layer_in_block_id}",
                        }
                        SCREAMING_SNAKE_CASE   :      int    					=						{
                            f"output_blocks.{i}.1.qkv.bias": {
                                'key': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias",
                                'query': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias",
                                'value': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias",
                            },
                            f"output_blocks.{i}.1.qkv.weight": {
                                'key': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight",
                                'query': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight",
                                'value': f"up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight",
                            },
                        }
                        assign_to_checkpoint(
                            __UpperCamelCase      ,__UpperCamelCase      ,__UpperCamelCase      ,additional_replacements=[meta_path]      ,attention_paths_to_split=to_split if any('qkv' in key for key in attentions    ) else None      ,config=__UpperCamelCase      ,)
            else:
                  SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						renew_resnet_paths(__UpperCamelCase      ,n_shave_prefix_segments=1    )
                  for path in resnet_0_paths:
                        SCREAMING_SNAKE_CASE   :      Dict    					=						'.'.join(['output_blocks', str(__UpperCamelCase    ), path['old']]    )
                        SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'.'.join(['up_blocks', str(__UpperCamelCase    ), 'resnets', str(__UpperCamelCase    ), path['new']]    )

                        SCREAMING_SNAKE_CASE   :      int    					=						checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
      UpperCamelCase_    	=				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_    	=				parser.parse_args()

      UpperCamelCase_    	=				torch.load(args.checkpoint_path)

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

      UpperCamelCase_    	=				convert_ldm_checkpoint(checkpoint, config)

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

      UpperCamelCase_    	=				UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)

      try:
            UpperCamelCase_    	=				DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
            UpperCamelCase_    	=				VQModel.from_pretrained("/".join(args.checkpoint_path.split("/")[:-1]))

            UpperCamelCase_    	=				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
            pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
            model.save_pretrained(args.dump_path)








'''simple docstring'''



import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 							_a				(			SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				str        =						LongformerTokenizer
       A						:				List[str]        =						True
       A						:				Optional[int]        =						LongformerTokenizerFast
       A						:				Tuple        =						True







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().setUp()

             # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
             SCREAMING_SNAKE_CASE   :      Any    					=						[
                 'l',
                 'o',
                 'w',
                 'e',
                 'r',
                 's',
                 't',
                 'i',
                 'd',
                 'n',
                 '\u0120',
                 '\u0120l',
                 '\u0120n',
                 '\u0120lo',
                 '\u0120low',
                 'er',
                 '\u0120lowest',
                 '\u0120newer',
                 '\u0120wider',
                 '<unk>',
             ]
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						dict(zip(A,							range(len(A  )  )  )  )
             SCREAMING_SNAKE_CASE   :      str    					=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
             SCREAMING_SNAKE_CASE   :      Tuple    					=						{'unk_token': '<unk>'}

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['vocab_file']  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['merges_file']  )
             with open(self.vocab_file,							'w',							encoding='utf-8'  ) as fp:
                   fp.write(json.dumps(A  ) + '\n'  )
             with open(self.merges_file,							'w',							encoding='utf-8'  ) as fp:
                   fp.write('\n'.join(A  )  )







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             kwargs.update(self.special_tokens_map  )
             return self.tokenizer_class.from_pretrained(self.tmpdirname,							**A  )







       def        UpperCamelCase_							(      self,							**A  ):







             '''simple docstring'''







             kwargs.update(self.special_tokens_map  )
             return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,							**A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						'lower newer'
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						'lower newer'
             return input_text, output_text







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.tokenizer_class(self.vocab_file,							self.merges_file,							**self.special_tokens_map  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'lower newer'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.tokenize(A  )  # , add_prefix_space=True)
             self.assertListEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokens + [tokenizer.unk_token]
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(A  ),							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.get_tokenizer()

             self.assertListEqual(tokenizer.encode('Hello world!',							add_special_tokens=A  ),							[0, 31_414, 232, 328, 2]  )
             self.assertListEqual(
                 tokenizer.encode('Hello world! cécé herlolip 418',							add_special_tokens=A  ),							[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2],							)







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.tokenizer_class.from_pretrained('allenai/longformer-base-4096'  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.encode('sequence builders',							add_special_tokens=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.encode('multi-sequence build',							add_special_tokens=A  )

             SCREAMING_SNAKE_CASE   :      int    					=						tokenizer.encode(
                 'sequence builders',							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.encode(
                 'sequence builders',							'multi-sequence build',							add_special_tokens=A,							add_prefix_space=A  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.build_inputs_with_special_tokens(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer.build_inputs_with_special_tokens(A,							A  )

             assert encoded_sentence == encoded_text_from_decode
             assert encoded_pair == encoded_pair_from_decode







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_tokenizer()

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'Encode this sequence.'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.byte_encoder[' '.encode('utf-8'  )[0]]

             # Testing encoder arguments
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.encode(A,							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
             self.assertNotEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.encode(A,							add_special_tokens=A,							add_prefix_space=A  )
             SCREAMING_SNAKE_CASE   :      str    					=						tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
             self.assertEqual(A,							A  )

             tokenizer.add_special_tokens({'bos_token': '<s>'}  )
             SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer.encode(A,							add_special_tokens=A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[1]  )[0]
             self.assertNotEqual(A,							A  )

             # Testing spaces after special tokens
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'<mask>'
             tokenizer.add_special_tokens(
                 {'mask_token': AddedToken(A,							lstrip=A,							rstrip=A  )}  )  # mask token has a left space
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.convert_tokens_to_ids(A  )

             SCREAMING_SNAKE_CASE   :      List[str]    					=						'Encode <mask> sequence'
             SCREAMING_SNAKE_CASE   :      List[str]    					=						'Encode <mask>sequence'

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer.encode(A  )
             SCREAMING_SNAKE_CASE   :      Tuple    					=						encoded.index(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
             self.assertEqual(A,							A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer.encode(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						encoded.index(A  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
             self.assertNotEqual(A,							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                         SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.rust_tokenizer_class.from_pretrained(A,							**A  )
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer_class.from_pretrained(A,							**A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'A, <mask> AllenNLP sentence.'
                         SCREAMING_SNAKE_CASE   :      Any    					=						tokenizer_r.encode_plus(A,							add_special_tokens=A,							return_token_type_ids=A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer_p.encode_plus(A,							add_special_tokens=A,							return_token_type_ids=A  )

                         # token_type_ids should put 0 everywhere
                         self.assertEqual(sum(tokens_r['token_type_ids']  ),							sum(tokens_p['token_type_ids']  )  )

                         # attention_mask should put 1 everywhere, so sum over length should be 1
                         self.assertEqual(
                             sum(tokens_r['attention_mask']  ) / len(tokens_r['attention_mask']  ),							sum(tokens_p['attention_mask']  ) / len(tokens_p['attention_mask']  ),							)

                         SCREAMING_SNAKE_CASE   :      Dict    					=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']  )

                         # Rust correctly handles the space before the mask while python doesnt
                         self.assertSequenceEqual(tokens_p['input_ids'],							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )
                         self.assertSequenceEqual(tokens_r['input_ids'],							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]  )

                         self.assertSequenceEqual(
                             A,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
                         self.assertSequenceEqual(
                             A,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for trim_offsets, add_prefix_space in itertools.product([True, False],							repeat=2  ):
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.rust_tokenizer_class.from_pretrained(
                       self.tmpdirname,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )

                   SCREAMING_SNAKE_CASE   :      Tuple    					=						json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()  )

                   self.assertEqual(pre_tokenizer_state['add_prefix_space'],							A  )

                   self.assertEqual(post_processor_state['add_prefix_space'],							A  )
                   self.assertEqual(post_processor_state['trim_offsets'],							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"  ):
                         SCREAMING_SNAKE_CASE   :      str    					=						'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						F"{text_of_1_token} {text_of_1_token}"

                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      Tuple    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ) + 1, len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ) + 1, len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      List[str]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ), len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Any    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(len(A  ), len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Any    					=						F" {text}"

                         # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                         #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                         # )
                         # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                         # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                         # self.assertEqual(
                         #     encoding.offset_mapping[1],
                         #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                         # )

                         SCREAMING_SNAKE_CASE   :      str    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(1, 1 + len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(1 + len(A  ) + 1, 1 + len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      str    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, 1 + len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(1 + len(A  ), 1 + len(A  ) + 1 + len(A  )),							)

                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.rust_tokenizer_class.from_pretrained(
                             A,							use_fast=A,							add_prefix_space=A,							trim_offsets=A  )
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						tokenizer_r(A,							return_offsets_mapping=A,							add_special_tokens=A  )
                         self.assertEqual(encoding.offset_mapping[0],							(0, 1 + len(A  ))  )
                         self.assertEqual(
                             encoding.offset_mapping[1],							(1 + len(A  ), 1 + len(A  ) + 1 + len(A  )),							)


