




import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase=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     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase=0   ):
  UpperCAmelCase__         = []
  for old_item in old_list:
    UpperCAmelCase__         = old_item.replace("""in_layers.0""" ,  """norm1"""   )
    UpperCAmelCase__         = new_item.replace("""in_layers.2""" ,  """conv1"""   )

    UpperCAmelCase__         = new_item.replace("""out_layers.0""" ,  """norm2"""   )
    UpperCAmelCase__         = new_item.replace("""out_layers.3""" ,  """conv2"""   )

    UpperCAmelCase__         = new_item.replace("""emb_layers.1""" ,  """time_emb_proj"""   )
    UpperCAmelCase__         = new_item.replace("""skip_connection""" ,  """conv_shortcut"""   )

    UpperCAmelCase__         = shave_segments(__UpperCamelCase ,  n_shave_prefix_segments=__UpperCamelCase   )

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

  return mapping


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase=0   ):
  UpperCAmelCase__         = []
  for old_item in old_list:
    UpperCAmelCase__         = old_item

    UpperCAmelCase__         = new_item.replace("""norm.weight""" ,  """group_norm.weight"""   )
    UpperCAmelCase__         = new_item.replace("""norm.bias""" ,  """group_norm.bias"""   )

    UpperCAmelCase__         = new_item.replace("""proj_out.weight""" ,  """proj_attn.weight"""   )
    UpperCAmelCase__         = new_item.replace("""proj_out.bias""" ,  """proj_attn.bias"""   )

    UpperCAmelCase__         = shave_segments(__UpperCamelCase ,  n_shave_prefix_segments=__UpperCamelCase   )

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

  return mapping


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase ,  _lowerCAmelCase=None ,  _lowerCAmelCase=None ,  _lowerCAmelCase=None   ):
  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():
      UpperCAmelCase__         = old_checkpoint[path]
      UpperCAmelCase__         = old_tensor.shape[0] // 3

      UpperCAmelCase__         = (-1, channels) if len(old_tensor.shape   ) == 3 else (-1)

      UpperCAmelCase__         = old_tensor.shape[0] // config['num_head_channels'] // 3

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

      UpperCAmelCase__         = query.reshape(__UpperCamelCase   )
      UpperCAmelCase__         = key.reshape(__UpperCamelCase   )
      UpperCAmelCase__         = value.reshape(__UpperCamelCase   )

  for path in paths:
    UpperCAmelCase__         = 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
    UpperCAmelCase__         = new_path.replace("""middle_block.0""" ,  """mid_block.resnets.0"""   )
    UpperCAmelCase__         = new_path.replace("""middle_block.1""" ,  """mid_block.attentions.0"""   )
    UpperCAmelCase__         = new_path.replace("""middle_block.2""" ,  """mid_block.resnets.1"""   )

    if additional_replacements is not None:
      for replacement in additional_replacements:
        UpperCAmelCase__         = 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:
      UpperCAmelCase__         = old_checkpoint[path['old']][:, :, 0]
    else:
      UpperCAmelCase__         = old_checkpoint[path['old']]


def     lowercase		(  _lowerCAmelCase ,  _lowerCAmelCase   ):
  UpperCAmelCase__         = {}

  UpperCAmelCase__         = checkpoint['time_embed.0.weight']
  UpperCAmelCase__         = checkpoint['time_embed.0.bias']
  UpperCAmelCase__         = checkpoint['time_embed.2.weight']
  UpperCAmelCase__         = checkpoint['time_embed.2.bias']

  UpperCAmelCase__         = checkpoint['input_blocks.0.0.weight']
  UpperCAmelCase__         = checkpoint['input_blocks.0.0.bias']

  UpperCAmelCase__         = checkpoint['out.0.weight']
  UpperCAmelCase__         = checkpoint['out.0.bias']
  UpperCAmelCase__         = checkpoint['out.2.weight']
  UpperCAmelCase__         = checkpoint['out.2.bias']

  # Retrieves the keys for the input blocks only
  UpperCAmelCase__         = len({""".""".join(layer.split("""."""   )[:2]   ) for layer in checkpoint if """input_blocks""" in layer}   )
  UpperCAmelCase__         = {
      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
  UpperCAmelCase__         = len({""".""".join(layer.split("""."""   )[:2]   ) for layer in checkpoint if """middle_block""" in layer}   )
  UpperCAmelCase__         = {
      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
  UpperCAmelCase__         = len({""".""".join(layer.split("""."""   )[:2]   ) for layer in checkpoint if """output_blocks""" in layer}   )
  UpperCAmelCase__         = {
      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   ):
    UpperCAmelCase__         = (i - 1) // (config['num_res_blocks'] + 1)
    UpperCAmelCase__         = (i - 1) % (config['num_res_blocks'] + 1)

    UpperCAmelCase__         = [key for key in input_blocks[i] if F'''input_blocks.{i}.0''' in key]
    UpperCAmelCase__         = [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:
      UpperCAmelCase__         = checkpoint[
          F'''input_blocks.{i}.0.op.weight'''
      ]
      UpperCAmelCase__         = checkpoint[
          F'''input_blocks.{i}.0.op.bias'''
      ]
      continue

    UpperCAmelCase__         = renew_resnet_paths(__UpperCamelCase   )
    UpperCAmelCase__         = {'old': F'''input_blocks.{i}.0''', 'new': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
    UpperCAmelCase__         = {'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   ):
      UpperCAmelCase__         = renew_attention_paths(__UpperCamelCase   )
      UpperCAmelCase__         = {
          'old': F'''input_blocks.{i}.1''',
          'new': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
      }
      UpperCAmelCase__         = {
          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 ,  )

  UpperCAmelCase__         = middle_blocks[0]
  UpperCAmelCase__         = middle_blocks[1]
  UpperCAmelCase__         = middle_blocks[2]

  UpperCAmelCase__         = renew_resnet_paths(__UpperCamelCase   )
  assign_to_checkpoint(__UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase ,  config=__UpperCamelCase   )

  UpperCAmelCase__         = renew_resnet_paths(__UpperCamelCase   )
  assign_to_checkpoint(__UpperCamelCase ,  __UpperCamelCase ,  __UpperCamelCase ,  config=__UpperCamelCase   )

  UpperCAmelCase__         = renew_attention_paths(__UpperCamelCase   )
  UpperCAmelCase__         = {
      '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   ):
    UpperCAmelCase__         = i // (config['num_res_blocks'] + 1)
    UpperCAmelCase__         = i % (config['num_res_blocks'] + 1)
    UpperCAmelCase__         = [shave_segments(__UpperCamelCase ,  2   ) for name in output_blocks[i]]
    UpperCAmelCase__         = {}

    for layer in output_block_layers:
      UpperCAmelCase__         = layer.split("""."""   )[0], shave_segments(__UpperCamelCase ,  1   )
      if layer_id in output_block_list:
        output_block_list[layer_id].append(__UpperCamelCase   )
      else:
        UpperCAmelCase__         = [layer_name]

    if len(__UpperCamelCase   ) > 1:
      UpperCAmelCase__         = [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
      UpperCAmelCase__         = [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]

      UpperCAmelCase__         = renew_resnet_paths(__UpperCamelCase   )
      UpperCAmelCase__         = renew_resnet_paths(__UpperCamelCase   )

      UpperCAmelCase__         = {'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():
        UpperCAmelCase__         = list(output_block_list.values()   ).index(["""conv.weight""", """conv.bias"""]   )
        UpperCAmelCase__         = checkpoint[
            F'''output_blocks.{i}.{index}.conv.weight'''
        ]
        UpperCAmelCase__         = checkpoint[
            F'''output_blocks.{i}.{index}.conv.bias'''
        ]

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

      if len(__UpperCamelCase   ):
        UpperCAmelCase__         = renew_attention_paths(__UpperCamelCase   )
        UpperCAmelCase__         = {
            'old': F'''output_blocks.{i}.1''',
            'new': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
        }
        UpperCAmelCase__         = {
            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:
      UpperCAmelCase__         = renew_resnet_paths(__UpperCamelCase ,  n_shave_prefix_segments=1   )
      for path in resnet_0_paths:
        UpperCAmelCase__         = '.'.join(["""output_blocks""", str(__UpperCamelCase   ), path["""old"""]]   )
        UpperCAmelCase__         = '.'.join(["""up_blocks""", str(__UpperCamelCase   ), """resnets""", str(__UpperCamelCase   ), path["""new"""]]   )

        UpperCAmelCase__         = checkpoint[old_path]

  return new_checkpoint


if __name__ == "__main__":
   snake_case__			:    Tuple			     =      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.''')

   snake_case__			:    Dict			     =      parser.parse_args()

   snake_case__			:    Any			     =      torch.load(args.checkpoint_path)

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

   snake_case__			:    Dict			     =      convert_ldm_checkpoint(checkpoint, config)

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

   snake_case__			:    Tuple			     =      UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

   try:
      snake_case__			:    Union[str, Any]			     =      DDPMScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))
      snake_case__			:    Union[str, Any]			     =      VQModel.from_pretrained('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

      snake_case__			:    List[str]			     =      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  )),							)


