import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


_lowerCamelCase   :				int     			=		{
    'sample_size': 32,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': 10_00,
    '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',
}

_lowerCamelCase   :				List[str]     			=		{
    'sample_size': 64,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 3,
    'num_class_embeds': 10_00,
    'block_out_channels': [1_92, 1_92 * 2, 1_92 * 3, 1_92 * 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',
}

_lowerCamelCase   :				List[Any]     			=		{
    'sample_size': 2_56,
    'in_channels': 3,
    'out_channels': 3,
    'layers_per_block': 2,
    'num_class_embeds': None,
    'block_out_channels': [2_56, 2_56, 2_56 * 2, 2_56 * 2, 2_56 * 4, 2_56 * 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',
}

_lowerCamelCase   :				str     			=		{
    'num_train_timesteps': 40,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_lowerCamelCase   :				List[str]     			=		{
    'num_train_timesteps': 2_01,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}

_lowerCamelCase   :				int     			=		{
    'num_train_timesteps': 1_51,
    'sigma_min': 0.002,
    'sigma_max': 80.0,
}





def 		__lowerCamelCase							(UpperCAmelCase__       :							Any					):
					if isinstance(lowercase__ ,	lowercase__					):
										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 		__lowerCamelCase							(UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							Optional[Any] ,	UpperCAmelCase__       :							Dict ,	UpperCAmelCase__       :							Union[str, Any] ,	UpperCAmelCase__       :							str=False					):
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.in_layers.0.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.in_layers.0.bias"]
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.in_layers.2.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.in_layers.2.bias"]
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.emb_layers.1.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.emb_layers.1.bias"]
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.out_layers.0.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.out_layers.0.bias"]
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.out_layers.3.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.out_layers.3.bias"]

					if has_skip:
										SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.skip_connection.weight"]
										SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.skip_connection.bias"]

					return new_checkpoint





def 		__lowerCamelCase							(UpperCAmelCase__       :							int ,	UpperCAmelCase__       :							Tuple ,	UpperCAmelCase__       :							List[Any] ,	UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							List[Any]=None					):
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.qkv.weight"].chunk(3 ,	dim=0					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.qkv.bias"].chunk(3 ,	dim=0					)

					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.norm.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.norm.bias"]

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

					SCREAMING_SNAKE_CASE           =    (
					    checkpoint[F"{old_prefix}.proj_out.weight"].squeeze(-1					).squeeze(-1					)
					)
					SCREAMING_SNAKE_CASE           =    checkpoint[F"{old_prefix}.proj_out.bias"].squeeze(-1					).squeeze(-1					)

					return new_checkpoint





def 		__lowerCamelCase							(UpperCAmelCase__       :							List[str] ,	UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    torch.load(lowercase__ ,	map_location="cpu"					)
					SCREAMING_SNAKE_CASE           =    {}

					SCREAMING_SNAKE_CASE           =    checkpoint["time_embed.0.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint["time_embed.0.bias"]
					SCREAMING_SNAKE_CASE           =    checkpoint["time_embed.2.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint["time_embed.2.bias"]

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

					SCREAMING_SNAKE_CASE           =    checkpoint["input_blocks.0.0.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint["input_blocks.0.0.bias"]

					SCREAMING_SNAKE_CASE           =    unet_config["down_block_types"]
					SCREAMING_SNAKE_CASE           =    unet_config["layers_per_block"]
					SCREAMING_SNAKE_CASE           =    unet_config["attention_head_dim"]
					SCREAMING_SNAKE_CASE           =    unet_config["block_out_channels"]
					SCREAMING_SNAKE_CASE           =    1
					SCREAMING_SNAKE_CASE           =    channels_list[0]

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

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

										if i != len(lowercase__					) - 1:
															SCREAMING_SNAKE_CASE           =    F"down_blocks.{i}.downsamplers.0"
															SCREAMING_SNAKE_CASE           =    F"input_blocks.{current_layer}.0"
															SCREAMING_SNAKE_CASE           =    convert_resnet(lowercase__ ,	lowercase__ ,	lowercase__ ,	lowercase__					)
															current_layer += 1

										SCREAMING_SNAKE_CASE           =    current_channels

					# hardcoded the mid-block for now
					SCREAMING_SNAKE_CASE           =    "mid_block.resnets.0"
					SCREAMING_SNAKE_CASE           =    "middle_block.0"
					SCREAMING_SNAKE_CASE           =    convert_resnet(lowercase__ ,	lowercase__ ,	lowercase__ ,	lowercase__					)
					SCREAMING_SNAKE_CASE           =    "mid_block.attentions.0"
					SCREAMING_SNAKE_CASE           =    "middle_block.1"
					SCREAMING_SNAKE_CASE           =    convert_attention(lowercase__ ,	lowercase__ ,	lowercase__ ,	lowercase__ ,	lowercase__					)
					SCREAMING_SNAKE_CASE           =    "mid_block.resnets.1"
					SCREAMING_SNAKE_CASE           =    "middle_block.2"
					SCREAMING_SNAKE_CASE           =    convert_resnet(lowercase__ ,	lowercase__ ,	lowercase__ ,	lowercase__					)

					SCREAMING_SNAKE_CASE           =    0
					SCREAMING_SNAKE_CASE           =    unet_config["up_block_types"]

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

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

															if i != len(lowercase__					) - 1:
																				SCREAMING_SNAKE_CASE           =    F"up_blocks.{i}.upsamplers.0"
																				SCREAMING_SNAKE_CASE           =    F"output_blocks.{current_layer-1}.2"
																				SCREAMING_SNAKE_CASE           =    convert_resnet(lowercase__ ,	lowercase__ ,	lowercase__ ,	lowercase__					)

					SCREAMING_SNAKE_CASE           =    checkpoint["out.0.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint["out.0.bias"]
					SCREAMING_SNAKE_CASE           =    checkpoint["out.2.weight"]
					SCREAMING_SNAKE_CASE           =    checkpoint["out.2.bias"]

					return new_checkpoint


if __name__ == "__main__":
							_lowerCamelCase   :				List[str]     			=		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.''')

							_lowerCamelCase   :				List[Any]     			=		parser.parse_args()
							_lowerCamelCase   :				Tuple     			=		strabool(args.class_cond)

							_lowerCamelCase   :				Any     			=		os.path.basename(args.unet_path)
							print(f"""Checkpoint: {ckpt_name}""")

							# Get U-Net config
							if "imagenet64" in ckpt_name:
														_lowerCamelCase   :				Optional[Any]     			=		IMAGENET_64_UNET_CONFIG
							elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
														_lowerCamelCase   :				List[Any]     			=		LSUN_256_UNET_CONFIG
							elif "test" in ckpt_name:
														_lowerCamelCase   :				List[Any]     			=		TEST_UNET_CONFIG
							else:
														raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

							if not args.class_cond:
														_lowerCamelCase   :				Union[str, Any]     			=		None

							_lowerCamelCase   :				Dict     			=		con_pt_to_diffuser(args.unet_path, unet_config)

							_lowerCamelCase   :				List[Any]     			=		UNetaDModel(**unet_config)
							image_unet.load_state_dict(converted_unet_ckpt)

							# Get scheduler config
							if "cd" in ckpt_name or "test" in ckpt_name:
														_lowerCamelCase   :				Dict     			=		CD_SCHEDULER_CONFIG
							elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
														_lowerCamelCase   :				Optional[int]     			=		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)):
														_lowerCamelCase   :				List[Any]     			=		CT_LSUN_256_SCHEDULER_CONFIG
							else:
														raise ValueError(f"""Checkpoint type {ckpt_name} is not currently supported.""")

							_lowerCamelCase   :				Optional[int]     			=		CMStochasticIterativeScheduler(**scheduler_config)

							_lowerCamelCase   :				List[str]     			=		ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
							consistency_model.save_pretrained(args.dump_path)





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)

_lowerCamelCase   :				Dict     			=						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				List[Any]     			=						[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

_lowerCamelCase   :				Optional[Any]     			=						{
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
_lowerCamelCase   :				Any     			=						{f"""funnel-transformer/{name}""": 5_12 for name in _model_names}
_lowerCamelCase   :				Optional[Any]     			=						{f"""funnel-transformer/{name}""": {'''do_lower_case''': True} for name in _model_names}


class   lowercase      (       a       ):
							lowercase__  :    Optional[int]									=							VOCAB_FILES_NAMES
							lowercase__  :    Optional[Any]									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Optional[Any]									=							PRETRAINED_INIT_CONFIGURATION
							lowercase__  :    Union[str, Any]									=							FunnelTokenizer
							lowercase__  :    List[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    int									=							2




							def __init__(  self				:   str		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   Union[str, Any]=True		,							_UpperCamelCase				:   str="<unk>"		,							_UpperCamelCase				:   Optional[Any]="<sep>"		,							_UpperCamelCase				:   Optional[int]="<pad>"		,							_UpperCamelCase				:   int="<cls>"		,							_UpperCamelCase				:   Dict="<mask>"		,							_UpperCamelCase				:   Union[str, Any]="<s>"		,							_UpperCamelCase				:   Optional[int]="</s>"		,							_UpperCamelCase				:   Dict=True		,							_UpperCamelCase				:   List[Any]=True		,							_UpperCamelCase				:   Any=None		,							_UpperCamelCase				:   Dict="##"		,							**_UpperCamelCase				:   Dict		,							)   ->			Optional[int]:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							do_lower_case=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							bos_token=_UpperCamelCase		,							eos_token=_UpperCamelCase		,							clean_text=_UpperCamelCase		,							tokenize_chinese_chars=_UpperCamelCase		,							strip_accents=_UpperCamelCase		,							wordpieces_prefix=_UpperCamelCase		,							**_UpperCamelCase		,							)

												SCREAMING_SNAKE_CASE           =    json.loads(self.backend_tokenizer.normalizer.__getstate__() )
												if (
												    normalizer_state.get("lowercase"		,							_UpperCamelCase ) != do_lower_case
												    or normalizer_state.get("strip_accents"		,							_UpperCamelCase ) != strip_accents
												    or normalizer_state.get("handle_chinese_chars"		,							_UpperCamelCase ) != tokenize_chinese_chars
												):
																	SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							normalizer_state.pop("type" ) )
																	SCREAMING_SNAKE_CASE           =    do_lower_case
																	SCREAMING_SNAKE_CASE           =    strip_accents
																	SCREAMING_SNAKE_CASE           =    tokenize_chinese_chars
																	SCREAMING_SNAKE_CASE           =    normalizer_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_lower_case




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Union[str, Any]		,							_UpperCamelCase				:   Dict=None )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																	output += token_ids_a + [self.sep_token_id]

												return output




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]
												if token_ids_a is None:
																	return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0]
												return len(cls ) * [self.cls_token_type_id] + len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]





							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




