



import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel

def   _lowerCAmelCase   (						A__						,  A__=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   _lowerCAmelCase   (						A__						,  A__=0	):
      lowercase__     =  []
      for old_item in old_list:
            lowercase__     =  old_item.replace('in_layers.0'						,  'norm1'	)
            lowercase__     =  new_item.replace('in_layers.2'						,  'conv1'	)

            lowercase__     =  new_item.replace('out_layers.0'						,  'norm2'	)
            lowercase__     =  new_item.replace('out_layers.3'						,  'conv2'	)

            lowercase__     =  new_item.replace('emb_layers.1'						,  'time_emb_proj'	)
            lowercase__     =  new_item.replace('skip_connection'						,  'conv_shortcut'	)

            lowercase__     =  shave_segments(A__						,  n_shave_prefix_segments=A__	)

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

      return mapping

def   _lowerCAmelCase   (						A__						,  A__=0	):
      lowercase__     =  []
      for old_item in old_list:
            lowercase__     =  old_item

            lowercase__     =  new_item.replace('norm.weight'						,  'group_norm.weight'	)
            lowercase__     =  new_item.replace('norm.bias'						,  'group_norm.bias'	)

            lowercase__     =  new_item.replace('proj_out.weight'						,  'proj_attn.weight'	)
            lowercase__     =  new_item.replace('proj_out.bias'						,  'proj_attn.bias'	)

            lowercase__     =  shave_segments(A__						,  n_shave_prefix_segments=A__	)

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

      return mapping

def   _lowerCAmelCase   (						A__						,  A__						,  A__						,  A__=None						,  A__=None						,  A__=None	):
      assert isinstance(A__						,  A__	), "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():
                  lowercase__     =  old_checkpoint[path]
                  lowercase__     =  old_tensor.shape[0] // 3

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

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

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

                  lowercase__     =  query.reshape(A__	)
                  lowercase__     =  key.reshape(A__	)
                  lowercase__     =  value.reshape(A__	)

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

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

def   _lowerCAmelCase   (						A__						,  A__	):
      lowercase__     =  {}

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

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

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

      # Retrieves the keys for the input blocks only
      lowercase__     =  len({'.'.join(layer.split('.'	)[:2]	) for layer in checkpoint if 'input_blocks' in layer}	)
      lowercase__     =  {
          layer_id: [key for key in checkpoint if F'''input_blocks.{layer_id}''' in key]
          for layer_id in range(A__	)
      }

      # Retrieves the keys for the middle blocks only
      lowercase__     =  len({'.'.join(layer.split('.'	)[:2]	) for layer in checkpoint if 'middle_block' in layer}	)
      lowercase__     =  {
          layer_id: [key for key in checkpoint if F'''middle_block.{layer_id}''' in key]
          for layer_id in range(A__	)
      }

      # Retrieves the keys for the output blocks only
      lowercase__     =  len({'.'.join(layer.split('.'	)[:2]	) for layer in checkpoint if 'output_blocks' in layer}	)
      lowercase__     =  {
          layer_id: [key for key in checkpoint if F'''output_blocks.{layer_id}''' in key]
          for layer_id in range(A__	)
      }

      for i in range(1						,  A__	):
            lowercase__     =  (i - 1) // (config['num_res_blocks'] + 1)
            lowercase__     =  (i - 1) % (config['num_res_blocks'] + 1)

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

            lowercase__     =  renew_resnet_paths(A__	)
            lowercase__     =  {'old': F'''input_blocks.{i}.0''', 'new': F'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
            lowercase__     =  {'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
            assign_to_checkpoint(
                A__						,  A__						,  A__						,  additional_replacements=[meta_path, resnet_op]						,  config=A__	)

            if len(A__	):
                  lowercase__     =  renew_attention_paths(A__	)
                  lowercase__     =  {
                      'old': F'''input_blocks.{i}.1''',
                      'new': F'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
                  }
                  lowercase__     =  {
                      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(
                      A__						,  A__						,  A__						,  additional_replacements=[meta_path]						,  attention_paths_to_split=A__						,  config=A__						,  )

      lowercase__     =  middle_blocks[0]
      lowercase__     =  middle_blocks[1]
      lowercase__     =  middle_blocks[2]

      lowercase__     =  renew_resnet_paths(A__	)
      assign_to_checkpoint(A__						,  A__						,  A__						,  config=A__	)

      lowercase__     =  renew_resnet_paths(A__	)
      assign_to_checkpoint(A__						,  A__						,  A__						,  config=A__	)

      lowercase__     =  renew_attention_paths(A__	)
      lowercase__     =  {
          '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(
          A__						,  A__						,  A__						,  attention_paths_to_split=A__						,  config=A__	)

      for i in range(A__	):
            lowercase__     =  i // (config['num_res_blocks'] + 1)
            lowercase__     =  i % (config['num_res_blocks'] + 1)
            lowercase__     =  [shave_segments(A__						,  2	) for name in output_blocks[i]]
            lowercase__     =  {}

            for layer in output_block_layers:
                  lowercase__,	lowercase__     =  layer.split('.'	)[0], shave_segments(A__						,  1	)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(A__	)
                  else:
                        lowercase__     =  [layer_name]

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

                  lowercase__     =  renew_resnet_paths(A__	)
                  lowercase__     =  renew_resnet_paths(A__	)

                  lowercase__     =  {'old': F'''output_blocks.{i}.0''', 'new': F'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
                  assign_to_checkpoint(A__						,  A__						,  A__						,  additional_replacements=[meta_path]						,  config=A__	)

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

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

                  if len(A__	):
                        lowercase__     =  renew_attention_paths(A__	)
                        lowercase__     =  {
                            'old': F'''output_blocks.{i}.1''',
                            'new': F'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
                        }
                        lowercase__     =  {
                            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(
                            A__						,  A__						,  A__						,  additional_replacements=[meta_path]						,  attention_paths_to_split=to_split if any('qkv' in key for key in attentions	) else None						,  config=A__						,  )
            else:
                  lowercase__     =  renew_resnet_paths(A__						,  n_shave_prefix_segments=1	)
                  for path in resnet_0_paths:
                        lowercase__     =  '.'.join(['output_blocks', str(A__	), path['old']]	)
                        lowercase__     =  '.'.join(['up_blocks', str(A__	), 'resnets', str(A__	), path['new']]	)

                        lowercase__     =  checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
       a__      :				Any   	=			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.")

       a__      :				List[str]   	=			parser.parse_args()

       a__      :				Union[str, Any]   	=			torch.load(args.checkpoint_path)

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

       a__      :				List[str]   	=			convert_ldm_checkpoint(checkpoint, config)

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

       a__      :				Union[str, Any]   	=			UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)

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

              a__      :				Optional[Any]   	=			LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
              pipe.save_pretrained(args.dump_path)
       except:  # noqa: E722
              model.save_pretrained(args.dump_path)





import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a__      :				Any   	=			get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
@require_tokenizers
class       UpperCAmelCase__(							lowerCamelCase     ,							unittest.TestCase	):

   '''simple docstring'''




   A    :  str       				=    XGLMTokenizer
   A    :  List[Any]       				=    XGLMTokenizerFast
   A    :  int       				=    True
   A    :  Optional[Any]       				=    True


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




         """simple docstring"""

         super().setUp()

         # We have a SentencePiece fixture for testing
         lowercase__     =  XGLMTokenizer(lowerCAmelCase			,				keep_accents=lowerCAmelCase)
         tokenizer.save_pretrained(self.tmpdirname)


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




         """simple docstring"""

         lowercase__     =  '<pad>'
         lowercase__     =  1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase)			,				lowerCAmelCase)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase)			,				lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			str)	->				List[str]:




         """simple docstring"""

         lowercase__     =  list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0]			,				'<s>')
         self.assertEqual(vocab_keys[1]			,				'<pad>')
         self.assertEqual(len(lowerCAmelCase)			,				10_08)


   def 			UpperCAmelCase		(						self  :			List[str])	->				str:




         """simple docstring"""

         self.assertEqual(self.get_tokenizer().vocab_size			,				10_08)


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				List[str]:




         """simple docstring"""

         lowercase__     =  XGLMTokenizer(lowerCAmelCase			,				keep_accents=lowerCAmelCase)

         lowercase__     =  tokenizer.tokenize('This is a test')
         self.assertListEqual(lowerCAmelCase			,				['▁This', '▁is', '▁a', '▁t', 'est'])

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(lowerCAmelCase)			,				[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]			,				)

         lowercase__     =  tokenizer.tokenize('I was born in 92000, and this is falsé.')
         self.assertListEqual(
             lowerCAmelCase			,				[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ]			,				)
         lowercase__     =  tokenizer.convert_tokens_to_ids(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase			,				[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
             ]			,				)

         lowercase__     =  tokenizer.convert_ids_to_tokens(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase			,				[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '<unk>',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '<unk>',
                 '.',
             ]			,				)


   @cached_property
   def 			UpperCAmelCase		(						self  :			int)	->				Dict:




         """simple docstring"""

         return XGLMTokenizer.from_pretrained('facebook/xglm-564M')


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				Dict:




         """simple docstring"""

         with tempfile.NamedTemporaryFile() as f:
               shutil.copyfile(lowerCAmelCase			,				f.name)
               lowercase__     =  XGLMTokenizer(f.name			,				keep_accents=lowerCAmelCase)
               lowercase__     =  pickle.dumps(lowerCAmelCase)
         pickle.loads(lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[Any])	->				str:




         """simple docstring"""

         if not self.test_rust_tokenizer:
               return

         lowercase__     =  self.get_tokenizer()
         lowercase__     =  self.get_rust_tokenizer()

         lowercase__     =  'I was born in 92000, and this is falsé.'

         lowercase__     =  tokenizer.tokenize(lowerCAmelCase)
         lowercase__     =  rust_tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)

         lowercase__     =  tokenizer.encode(lowerCAmelCase			,				add_special_tokens=lowerCAmelCase)
         lowercase__     =  rust_tokenizer.encode(lowerCAmelCase			,				add_special_tokens=lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)

         lowercase__     =  self.get_rust_tokenizer()
         lowercase__     =  tokenizer.encode(lowerCAmelCase)
         lowercase__     =  rust_tokenizer.encode(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase			,				lowerCAmelCase)


   @slow
   def 			UpperCAmelCase		(						self  :			List[str])	->				List[str]:




         """simple docstring"""

         lowercase__     =  'Hello World!'
         lowercase__     =  [2, 3_12_27, 44_47, 35]

         self.assertListEqual(lowerCAmelCase			,				self.big_tokenizer.encode(lowerCAmelCase))


   @slow
   def 			UpperCAmelCase		(						self  :			List[str])	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  (
             'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
             ' add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth'
         )
         # fmt: off
         lowercase__     =  [2, 10_18, 67, 11, 19_88, 26_17, 56_31, 2_78, 11, 34_07, 48, 7_16_30, 2_80_85, 4, 32_34, 1_57, 13, 6, 5, 6, 4, 35_26, 7_68, 15, 6_59, 57, 2_98, 39_83, 8_64, 1_29, 21, 6, 5, 1_36_75, 3_77, 6_52, 75_80, 1_03_41, 1_55, 28_17, 4_22, 16_66, 7, 16_74, 53, 1_13, 20_22_77, 1_78_92, 33, 60, 87, 4, 32_34, 1_57, 61, 26_67, 5_23_76, 19, 88, 23, 7_35]
         # fmt: on

         self.assertListEqual(lowerCAmelCase			,				self.big_tokenizer.encode(lowerCAmelCase))



   @slow
   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  {
             'input_ids': [[2, 10_88_25, 11_63, 15, 8_80_10, 4_73, 1_58_98, 1_57, 1_36_72, 18_57, 3_12, 8, 23_80_21, 11_63, 53, 1_36_72, 18_57, 3_12, 8, 5_32_83, 18_23_96, 8, 1_85_66, 16, 3_67_33, 41_01, 8, 2_30, 24_40_17, 12_25_53, 7, 15, 13_25_97, 4, 2_93, 1_25_11, 76_10, 4, 34_14, 13_25_97, 9, 4, 3_23_61, 3_62, 4, 7_34, 2_85_12, 3_25_69, 18, 4, 3_23_61, 2_60_96, 1_49_82, 73, 1_87_15, 2_14_33, 23_52_61, 15, 4_92, 1_24_27, 16, 53, 1_87_15, 2_14_33, 6_54_54, 15, 2_36_59, 5_63, 16, 2_78, 5_97, 28_43, 5_95, 79_31, 18_23_96, 6_41_86, 22, 8_86, 5_95, 13_29_81, 53, 2_55_40, 34_49, 4_39_82, 3_99_01, 59_51, 8_78, 3_30, 4, 2_76_94, 8_02_69, 3_12, 53, 65_17, 1_17_80, 6_11, 2_04_08, 5], [2, 6, 13_25_97, 67, 4_28_97, 33, 5_92, 8, 16_37_29, 2_55_40, 3_61, 13_69_97, 10_95_14, 17_32_30, 7, 5_01, 60, 10_29_13, 1_96, 56_31, 2_35, 6_32_43, 4_73, 6, 23_17_57, 74, 52_77, 79_05, 53, 30_95, 3_73_17, 22, 4_54, 18_38_74, 5], [2, 2_68, 3_12_98, 4_65_30, 6, 13_29_35, 4_38_31, 7, 5_97, 32, 24, 36_88, 98_65, 5]],
             'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
         }  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=lowerCAmelCase			,				model_name='facebook/xglm-564M'			,				padding=lowerCAmelCase			,				)
