


'''simple docstring'''




import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


__snake_case    						={
    """sample_size""": 32,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [32, 64],
    """attention_head_dim""": 8,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__snake_case    						={
    """sample_size""": 64,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 3,
    """num_class_embeds""": 1_000,
    """block_out_channels""": [192, 192 * 2, 192 * 3, 192 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """scale_shift""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__snake_case    						={
    """sample_size""": 256,
    """in_channels""": 3,
    """out_channels""": 3,
    """layers_per_block""": 2,
    """num_class_embeds""": None,
    """block_out_channels""": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    """attention_head_dim""": 64,
    """down_block_types""": [
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """ResnetDownsampleBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
        """AttnDownBlock2D""",
    ],
    """up_block_types""": [
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """AttnUpBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
        """ResnetUpsampleBlock2D""",
    ],
    """resnet_time_scale_shift""": """default""",
    """upsample_type""": """resnet""",
    """downsample_type""": """resnet""",
}

__snake_case    						={
    """num_train_timesteps""": 40,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

__snake_case    						={
    """num_train_timesteps""": 201,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}

__snake_case    						={
    """num_train_timesteps""": 151,
    """sigma_min""": 0.0_0_2,
    """sigma_max""": 8_0.0,
}





def 	a_					( lowerCamelCase	:     Tuple				):
   if isinstance(lowerCamelCase	, lowerCamelCase				):
      return v
   if v.lower() in ("yes", "true", "t", "y", "1"):
      return True
   elif v.lower() in ("no", "false", "f", "n", "0"):
      return False
   else:
      raise argparse.ArgumentTypeError('boolean value expected'				)





def 	a_					( lowerCamelCase	:     Tuple	, lowerCamelCase	:     List[Any]	, lowerCamelCase	:     List[Any]	, lowerCamelCase	:     str	, lowerCamelCase	:     Union[str, Any]=False				):
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.in_layers.0.weight''']
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.in_layers.0.bias''']
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.in_layers.2.weight''']
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.in_layers.2.bias''']
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.emb_layers.1.weight''']
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.emb_layers.1.bias''']
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.out_layers.0.weight''']
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.out_layers.0.bias''']
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.out_layers.3.weight''']
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.out_layers.3.bias''']

   if has_skip:
      lowerCAmelCase     =			checkpoint[f'''{old_prefix}.skip_connection.weight''']
      lowerCAmelCase     =			checkpoint[f'''{old_prefix}.skip_connection.bias''']

   return new_checkpoint





def 	a_					( lowerCamelCase	:     Any	, lowerCamelCase	:     List[Any]	, lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Optional[int]=None				):
   lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			checkpoint[f'''{old_prefix}.qkv.weight'''].chunk(3	, dim=0				)
   lowerCAmelCase     ,  lowerCAmelCase     ,  lowerCAmelCase     =			checkpoint[f'''{old_prefix}.qkv.bias'''].chunk(3	, dim=0				)

   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.norm.weight''']
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.norm.bias''']

   lowerCAmelCase     =			weight_q.squeeze(-1				).squeeze(-1				)
   lowerCAmelCase     =			bias_q.squeeze(-1				).squeeze(-1				)
   lowerCAmelCase     =			weight_k.squeeze(-1				).squeeze(-1				)
   lowerCAmelCase     =			bias_k.squeeze(-1				).squeeze(-1				)
   lowerCAmelCase     =			weight_v.squeeze(-1				).squeeze(-1				)
   lowerCAmelCase     =			bias_v.squeeze(-1				).squeeze(-1				)

   lowerCAmelCase     =			(
       checkpoint[f'''{old_prefix}.proj_out.weight'''].squeeze(-1				).squeeze(-1				)
   )
   lowerCAmelCase     =			checkpoint[f'''{old_prefix}.proj_out.bias'''].squeeze(-1				).squeeze(-1				)

   return new_checkpoint





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     List[str]				):
   lowerCAmelCase     =			torch.load(lowerCamelCase	, map_location='cpu'				)
   lowerCAmelCase     =			{}

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

   if unet_config["num_class_embeds"] is not None:
      lowerCAmelCase     =			checkpoint['label_emb.weight']

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

   lowerCAmelCase     =			unet_config['down_block_types']
   lowerCAmelCase     =			unet_config['layers_per_block']
   lowerCAmelCase     =			unet_config['attention_head_dim']
   lowerCAmelCase     =			unet_config['block_out_channels']
   lowerCAmelCase     =			1
   lowerCAmelCase     =			channels_list[0]

   for i, layer_type in enumerate(lowerCamelCase				):
      lowerCAmelCase     =			channels_list[i]
      lowerCAmelCase     =			current_channels != prev_channels
      if layer_type == "ResnetDownsampleBlock2D":
         for j in range(lowerCamelCase				):
            lowerCAmelCase     =			f'''down_blocks.{i}.resnets.{j}'''
            lowerCAmelCase     =			f'''input_blocks.{current_layer}.0'''
            lowerCAmelCase     =			True if j == 0 and downsample_block_has_skip else False
            lowerCAmelCase     =			convert_resnet(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, has_skip=lowerCamelCase				)
            current_layer += 1

      elif layer_type == "AttnDownBlock2D":
         for j in range(lowerCamelCase				):
            lowerCAmelCase     =			f'''down_blocks.{i}.resnets.{j}'''
            lowerCAmelCase     =			f'''input_blocks.{current_layer}.0'''
            lowerCAmelCase     =			True if j == 0 and downsample_block_has_skip else False
            lowerCAmelCase     =			convert_resnet(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, has_skip=lowerCamelCase				)
            lowerCAmelCase     =			f'''down_blocks.{i}.attentions.{j}'''
            lowerCAmelCase     =			f'''input_blocks.{current_layer}.1'''
            lowerCAmelCase     =			convert_attention(
                lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
            current_layer += 1

      if i != len(lowerCamelCase				) - 1:
         lowerCAmelCase     =			f'''down_blocks.{i}.downsamplers.0'''
         lowerCAmelCase     =			f'''input_blocks.{current_layer}.0'''
         lowerCAmelCase     =			convert_resnet(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
         current_layer += 1

      lowerCAmelCase     =			current_channels

   # hardcoded the mid-block for now
   lowerCAmelCase     =			'mid_block.resnets.0'
   lowerCAmelCase     =			'middle_block.0'
   lowerCAmelCase     =			convert_resnet(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			'mid_block.attentions.0'
   lowerCAmelCase     =			'middle_block.1'
   lowerCAmelCase     =			convert_attention(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
   lowerCAmelCase     =			'mid_block.resnets.1'
   lowerCAmelCase     =			'middle_block.2'
   lowerCAmelCase     =			convert_resnet(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)

   lowerCAmelCase     =			0
   lowerCAmelCase     =			unet_config['up_block_types']

   for i, layer_type in enumerate(lowerCamelCase				):
      if layer_type == "ResnetUpsampleBlock2D":
         for j in range(layers_per_block + 1				):
            lowerCAmelCase     =			f'''up_blocks.{i}.resnets.{j}'''
            lowerCAmelCase     =			f'''output_blocks.{current_layer}.0'''
            lowerCAmelCase     =			convert_resnet(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, has_skip=lowerCamelCase				)
            current_layer += 1

         if i != len(lowerCamelCase				) - 1:
            lowerCAmelCase     =			f'''up_blocks.{i}.upsamplers.0'''
            lowerCAmelCase     =			f'''output_blocks.{current_layer-1}.1'''
            lowerCAmelCase     =			convert_resnet(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
      elif layer_type == "AttnUpBlock2D":
         for j in range(layers_per_block + 1				):
            lowerCAmelCase     =			f'''up_blocks.{i}.resnets.{j}'''
            lowerCAmelCase     =			f'''output_blocks.{current_layer}.0'''
            lowerCAmelCase     =			convert_resnet(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, has_skip=lowerCamelCase				)
            lowerCAmelCase     =			f'''up_blocks.{i}.attentions.{j}'''
            lowerCAmelCase     =			f'''output_blocks.{current_layer}.1'''
            lowerCAmelCase     =			convert_attention(
                lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)
            current_layer += 1

         if i != len(lowerCamelCase				) - 1:
            lowerCAmelCase     =			f'''up_blocks.{i}.upsamplers.0'''
            lowerCAmelCase     =			f'''output_blocks.{current_layer-1}.2'''
            lowerCAmelCase     =			convert_resnet(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)

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

   return new_checkpoint


if __name__ == "__main__":
       __snake_case    						=argparse.ArgumentParser()

       parser.add_argument("""--unet_path""", default=None, type=str, required=True, help="""Path to the unet.pt to convert.""")
       parser.add_argument(
           """--dump_path""", default=None, type=str, required=True, help="""Path to output the converted UNet model."""
       )
       parser.add_argument("""--class_cond""", default=True, type=str, help="""Whether the model is class-conditional.""")

       __snake_case    						=parser.parse_args()
       __snake_case    						=strabool(args.class_cond)

       __snake_case    						=os.path.basename(args.unet_path)
       print(F'''Checkpoint: {ckpt_name}''')

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              __snake_case    						=IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              __snake_case    						=LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              __snake_case    						=TEST_UNET_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       if not args.class_cond:
              __snake_case    						=None

       __snake_case    						=con_pt_to_diffuser(args.unet_path, unet_config)

       __snake_case    						=UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              __snake_case    						=CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              __snake_case    						=CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              __snake_case    						=CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       __snake_case    						=CMStochasticIterativeScheduler(**scheduler_config)

       __snake_case    						=ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)













import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_a      	=  get_tests_dir('''fixtures/spiece.model''')





@require_sentencepiece
@require_tokenizers
class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      AlbertTokenizer
	UpperCamelCase__								=      AlbertTokenizerFast
	UpperCamelCase__								=      True
	UpperCamelCase__								=      True
	UpperCamelCase__								=      True

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().setUp()

							# We have a SentencePiece fixture for testing
							_UpperCAmelCase									=			AlbertTokenizer(UpperCAmelCase    )
							tokenizer.save_pretrained(self.tmpdirname    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'this is a test'
							_UpperCAmelCase									=			'this is a test'
							return input_text, output_text

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'<pad>'
							_UpperCAmelCase									=			0

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

							self.assertEqual(vocab_keys[0]       ,    '<pad>'    )
							self.assertEqual(vocab_keys[1]       ,    '<unk>'    )
							self.assertEqual(vocab_keys[-1]       ,    '▁eloquent'    )
							self.assertEqual(len(UpperCAmelCase    )       ,    3_0000    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.assertEqual(self.get_tokenizer().vocab_size       ,    3_0000    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							if not self.test_rust_tokenizer:
													return

							_UpperCAmelCase									=			self.get_tokenizer()
							_UpperCAmelCase									=			self.get_rust_tokenizer()

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

							_UpperCAmelCase									=			tokenizer.tokenize(UpperCAmelCase    )
							_UpperCAmelCase									=			rust_tokenizer.tokenize(UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.encode(UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase    )
							_UpperCAmelCase									=			rust_tokenizer.encode(UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_rust_tokenizer()
							_UpperCAmelCase									=			tokenizer.encode(UpperCAmelCase    )
							_UpperCAmelCase									=			rust_tokenizer.encode(UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			AlbertTokenizer(UpperCAmelCase       ,    keep_accents=UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.tokenize('This is a test'    )
							self.assertListEqual(UpperCAmelCase       ,    ['▁this', '▁is', '▁a', '▁test']    )

							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )       ,    [48, 25, 21, 1289]    )

							_UpperCAmelCase									=			tokenizer.tokenize('I was born in 92000, and this is falsé.'    )
							self.assertListEqual(
							    UpperCAmelCase       ,    ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.']    )
							_UpperCAmelCase									=			tokenizer.convert_tokens_to_ids(UpperCAmelCase    )
							self.assertListEqual(UpperCAmelCase       ,    [31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]    )

							_UpperCAmelCase									=			tokenizer.convert_ids_to_tokens(UpperCAmelCase    )
							self.assertListEqual(
							    UpperCAmelCase       ,    ['▁i', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', '<unk>', '.']       ,    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			AlbertTokenizer(UpperCAmelCase    )

							_UpperCAmelCase									=			tokenizer.encode('sequence builders'    )
							_UpperCAmelCase									=			tokenizer.encode('multi-sequence build'    )

							_UpperCAmelCase									=			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase    )
							_UpperCAmelCase									=			tokenizer.build_inputs_with_special_tokens(UpperCAmelCase       ,    UpperCAmelCase    )

							assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
							assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
							    tokenizer.sep_token_id
							]

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{'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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'input_ids': [[2, 2_1970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 1_2051, 18, 17, 7103, 2153, 673, 8, 3515, 1_8684, 8, 4461, 6, 1927, 297, 8, 1_2060, 2607, 18, 13, 5, 4461, 15, 1_0538, 38, 8, 135, 15, 822, 58, 15, 993, 1_0363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 1_0641, 6, 29, 84, 2512, 2430, 782, 1_8684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 1_1712, 15, 7103, 2153, 673, 17, 2_4883, 9990, 9, 3], [2, 1_1502, 25, 1006, 20, 782, 8, 1_1809, 855, 1732, 1_9393, 1_8667, 37, 367, 2_1018, 69, 1854, 34, 1_1860, 1_9124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 1_7659, 84, 14, 1_6792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=UpperCAmelCase       ,    model_name='albert-base-v2'       ,    revision='6b6560eaf5ff2e250b00c50f380c5389a9c2d82e'       ,    )
