

import argparse
import os.path as osp
import re

import torch
from safetensors.torch import load_file, save_file


# =================#
# UNet Conversion #
# =================#

lowerCAmelCase__								=		[
    # (stable-diffusion, HF Diffusers)
    ("""time_embed.0.weight""", """time_embedding.linear_1.weight"""),
    ("""time_embed.0.bias""", """time_embedding.linear_1.bias"""),
    ("""time_embed.2.weight""", """time_embedding.linear_2.weight"""),
    ("""time_embed.2.bias""", """time_embedding.linear_2.bias"""),
    ("""input_blocks.0.0.weight""", """conv_in.weight"""),
    ("""input_blocks.0.0.bias""", """conv_in.bias"""),
    ("""out.0.weight""", """conv_norm_out.weight"""),
    ("""out.0.bias""", """conv_norm_out.bias"""),
    ("""out.2.weight""", """conv_out.weight"""),
    ("""out.2.bias""", """conv_out.bias"""),
]

lowerCAmelCase__								=		[
    # (stable-diffusion, HF Diffusers)
    ("""in_layers.0""", """norm1"""),
    ("""in_layers.2""", """conv1"""),
    ("""out_layers.0""", """norm2"""),
    ("""out_layers.3""", """conv2"""),
    ("""emb_layers.1""", """time_emb_proj"""),
    ("""skip_connection""", """conv_shortcut"""),
]

lowerCAmelCase__								=		[]
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
							# loop over downblocks/upblocks

							for j in range(2):
														# loop over resnets/attentions for downblocks
														lowerCAmelCase__								=		f'down_blocks.{i}.resnets.{j}.'
														lowerCAmelCase__								=		f'input_blocks.{3*i + j + 1}.0.'
														unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))

														if i < 3:
																					# no attention layers in down_blocks.3
																					lowerCAmelCase__								=		f'down_blocks.{i}.attentions.{j}.'
																					lowerCAmelCase__								=		f'input_blocks.{3*i + j + 1}.1.'
																					unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))

							for j in range(3):
														# loop over resnets/attentions for upblocks
														lowerCAmelCase__								=		f'up_blocks.{i}.resnets.{j}.'
														lowerCAmelCase__								=		f'output_blocks.{3*i + j}.0.'
														unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))

														if i > 0:
																					# no attention layers in up_blocks.0
																					lowerCAmelCase__								=		f'up_blocks.{i}.attentions.{j}.'
																					lowerCAmelCase__								=		f'output_blocks.{3*i + j}.1.'
																					unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))

							if i < 3:
														# no downsample in down_blocks.3
														lowerCAmelCase__								=		f'down_blocks.{i}.downsamplers.0.conv.'
														lowerCAmelCase__								=		f'input_blocks.{3*(i+1)}.0.op.'
														unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))

														# no upsample in up_blocks.3
														lowerCAmelCase__								=		f'up_blocks.{i}.upsamplers.0.'
														lowerCAmelCase__								=		f'output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'
														unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))

lowerCAmelCase__								=		"""mid_block.attentions.0."""
lowerCAmelCase__								=		"""middle_block.1."""
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))

for j in range(2):
							lowerCAmelCase__								=		f'mid_block.resnets.{j}.'
							lowerCAmelCase__								=		f'middle_block.{2*j}.'
							unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				int:




	'''simple docstring'''




	_UpperCamelCase   :		List[str]			=						{k: k for k in unet_state_dict.keys()}
	for sd_name, hf_name in unet_conversion_map:
		_UpperCamelCase   :		Union[str, Any]			=						sd_name
	for k, v in mapping.items():
		if "resnets" in k:
			for sd_part, hf_part in unet_conversion_map_resnet:
				_UpperCamelCase   :		Tuple			=						v.replace(UpperCAmelCase_							,       UpperCAmelCase_	)
			_UpperCamelCase   :		List[str]			=						v
	for k, v in mapping.items():
		for sd_part, hf_part in unet_conversion_map_layer:
			_UpperCamelCase   :		Tuple			=						v.replace(UpperCAmelCase_							,       UpperCAmelCase_	)
		_UpperCamelCase   :		Optional[Any]			=						v
	_UpperCamelCase   :		int			=						{v: unet_state_dict[k] for k, v in mapping.items()}
	return new_state_dict


# ================#
# VAE Conversion #
# ================#

lowerCAmelCase__								=		[
    # (stable-diffusion, HF Diffusers)
    ("""nin_shortcut""", """conv_shortcut"""),
    ("""norm_out""", """conv_norm_out"""),
    ("""mid.attn_1.""", """mid_block.attentions.0."""),
]

for i in range(4):
							# down_blocks have two resnets
							for j in range(2):
														lowerCAmelCase__								=		f'encoder.down_blocks.{i}.resnets.{j}.'
														lowerCAmelCase__								=		f'encoder.down.{i}.block.{j}.'
														vae_conversion_map.append((sd_down_prefix, hf_down_prefix))

							if i < 3:
														lowerCAmelCase__								=		f'down_blocks.{i}.downsamplers.0.'
														lowerCAmelCase__								=		f'down.{i}.downsample.'
														vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))

														lowerCAmelCase__								=		f'up_blocks.{i}.upsamplers.0.'
														lowerCAmelCase__								=		f'up.{3-i}.upsample.'
														vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))

							# up_blocks have three resnets
							# also, up blocks in hf are numbered in reverse from sd
							for j in range(3):
														lowerCAmelCase__								=		f'decoder.up_blocks.{i}.resnets.{j}.'
														lowerCAmelCase__								=		f'decoder.up.{3-i}.block.{j}.'
														vae_conversion_map.append((sd_up_prefix, hf_up_prefix))

# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
							lowerCAmelCase__								=		f'mid_block.resnets.{i}.'
							lowerCAmelCase__								=		f'mid.block_{i+1}.'
							vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))


lowerCAmelCase__								=		[
    # (stable-diffusion, HF Diffusers)
    ("""norm.""", """group_norm."""),
    ("""q.""", """query."""),
    ("""k.""", """key."""),
    ("""v.""", """value."""),
    ("""proj_out.""", """proj_attn."""),
]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Tuple	)						->				Any:




	'''simple docstring'''




	return w.reshape(*w.shape							,       1							,       1	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				Tuple:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, Any]			=						{k: k for k in vae_state_dict.keys()}
	for k, v in mapping.items():
		for sd_part, hf_part in vae_conversion_map:
			_UpperCamelCase   :		List[str]			=						v.replace(UpperCAmelCase_							,       UpperCAmelCase_	)
		_UpperCamelCase   :		Any			=						v
	for k, v in mapping.items():
		if "attentions" in k:
			for sd_part, hf_part in vae_conversion_map_attn:
				_UpperCamelCase   :		Dict			=						v.replace(UpperCAmelCase_							,       UpperCAmelCase_	)
			_UpperCamelCase   :		Optional[Any]			=						v
	_UpperCamelCase   :		List[str]			=						{v: vae_state_dict[k] for k, v in mapping.items()}
	_UpperCamelCase   :		int			=						['q', 'k', 'v', 'proj_out']
	for k, v in new_state_dict.items():
		for weight_name in weights_to_convert:
			if F'''mid.attn_1.{weight_name}.weight''' in k:
				print(F'''Reshaping {k} for SD format'''	)
				_UpperCamelCase   :		Any			=						reshape_weight_for_sd(UpperCAmelCase_	)
	return new_state_dict


# =========================#
# Text Encoder Conversion #
# =========================#


lowerCAmelCase__								=		[
    # (stable-diffusion, HF Diffusers)
    ("""resblocks.""", """text_model.encoder.layers."""),
    ("""ln_1""", """layer_norm1"""),
    ("""ln_2""", """layer_norm2"""),
    (""".c_fc.""", """.fc1."""),
    (""".c_proj.""", """.fc2."""),
    (""".attn""", """.self_attn"""),
    ("""ln_final.""", """transformer.text_model.final_layer_norm."""),
    ("""token_embedding.weight""", """transformer.text_model.embeddings.token_embedding.weight"""),
    ("""positional_embedding""", """transformer.text_model.embeddings.position_embedding.weight"""),
]
lowerCAmelCase__								=		{re.escape(x[1]): x[0] for x in textenc_conversion_lst}
lowerCAmelCase__								=		re.compile("""|""".join(protected.keys()))

# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
lowerCAmelCase__								=		{"""q""": 0, """k""": 1, """v""": 2}






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						{}
	_UpperCamelCase   :		Tuple			=						{}
	_UpperCamelCase   :		List[Any]			=						{}
	for k, v in text_enc_dict.items():
		if (
		    k.endswith('.self_attn.q_proj.weight'	)
		    or k.endswith('.self_attn.k_proj.weight'	)
		    or k.endswith('.self_attn.v_proj.weight'	)
		):
			_UpperCamelCase   :		Dict			=						k[: -len('.q_proj.weight'	)]
			_UpperCamelCase   :		List[str]			=						k[-len('q_proj.weight'	)]
			if k_pre not in capture_qkv_weight:
				_UpperCamelCase   :		Union[str, Any]			=						[None, None, None]
			_UpperCamelCase   :		str			=						v
			continue

		if (
		    k.endswith('.self_attn.q_proj.bias'	)
		    or k.endswith('.self_attn.k_proj.bias'	)
		    or k.endswith('.self_attn.v_proj.bias'	)
		):
			_UpperCamelCase   :		Optional[int]			=						k[: -len('.q_proj.bias'	)]
			_UpperCamelCase   :		Dict			=						k[-len('q_proj.bias'	)]
			if k_pre not in capture_qkv_bias:
				_UpperCamelCase   :		Optional[Any]			=						[None, None, None]
			_UpperCamelCase   :		List[str]			=						v
			continue

		_UpperCamelCase   :		Union[str, Any]			=						textenc_pattern.sub(lambda UpperCAmelCase_	: protected[re.escape(m.group(0	)	)]							,       UpperCAmelCase_	)
		_UpperCamelCase   :		int			=						v

	for k_pre, tensors in capture_qkv_weight.items():
		if None in tensors:
			raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'	)
		_UpperCamelCase   :		List[Any]			=						textenc_pattern.sub(lambda UpperCAmelCase_	: protected[re.escape(m.group(0	)	)]							,       UpperCAmelCase_	)
		_UpperCamelCase   :		List[str]			=						torch.cat(UpperCAmelCase_	)

	for k_pre, tensors in capture_qkv_bias.items():
		if None in tensors:
			raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing'	)
		_UpperCamelCase   :		List[str]			=						textenc_pattern.sub(lambda UpperCAmelCase_	: protected[re.escape(m.group(0	)	)]							,       UpperCAmelCase_	)
		_UpperCamelCase   :		Tuple			=						torch.cat(UpperCAmelCase_	)

	return new_state_dict






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Any	)						->				List[str]:




	'''simple docstring'''




	return text_enc_dict


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

							parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
							parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
							parser.add_argument(
							    """--use_safetensors""", action="""store_true""", help="""Save weights use safetensors, default is ckpt."""
							)

							lowerCAmelCase__								=		parser.parse_args()

							assert args.model_path is not None, "Must provide a model path!"

							assert args.checkpoint_path is not None, "Must provide a checkpoint path!"

							# Path for safetensors
							lowerCAmelCase__								=		osp.join(args.model_path, """unet""", """diffusion_pytorch_model.safetensors""")
							lowerCAmelCase__								=		osp.join(args.model_path, """vae""", """diffusion_pytorch_model.safetensors""")
							lowerCAmelCase__								=		osp.join(args.model_path, """text_encoder""", """model.safetensors""")

							# Load models from safetensors if it exists, if it doesn't pytorch
							if osp.exists(unet_path):
														lowerCAmelCase__								=		load_file(unet_path, device="""cpu""")
							else:
														lowerCAmelCase__								=		osp.join(args.model_path, """unet""", """diffusion_pytorch_model.bin""")
														lowerCAmelCase__								=		torch.load(unet_path, map_location="""cpu""")

							if osp.exists(vae_path):
														lowerCAmelCase__								=		load_file(vae_path, device="""cpu""")
							else:
														lowerCAmelCase__								=		osp.join(args.model_path, """vae""", """diffusion_pytorch_model.bin""")
														lowerCAmelCase__								=		torch.load(vae_path, map_location="""cpu""")

							if osp.exists(text_enc_path):
														lowerCAmelCase__								=		load_file(text_enc_path, device="""cpu""")
							else:
														lowerCAmelCase__								=		osp.join(args.model_path, """text_encoder""", """pytorch_model.bin""")
														lowerCAmelCase__								=		torch.load(text_enc_path, map_location="""cpu""")

							# Convert the UNet model
							lowerCAmelCase__								=		convert_unet_state_dict(unet_state_dict)
							lowerCAmelCase__								=		{"""model.diffusion_model.""" + k: v for k, v in unet_state_dict.items()}

							# Convert the VAE model
							lowerCAmelCase__								=		convert_vae_state_dict(vae_state_dict)
							lowerCAmelCase__								=		{"""first_stage_model.""" + k: v for k, v in vae_state_dict.items()}

							# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
							lowerCAmelCase__								=		"""text_model.encoder.layers.22.layer_norm2.bias""" in text_enc_dict

							if is_vaa_model:
														# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
														lowerCAmelCase__								=		{"""transformer.""" + k: v for k, v in text_enc_dict.items()}
														lowerCAmelCase__								=		convert_text_enc_state_dict_vaa(text_enc_dict)
														lowerCAmelCase__								=		{"""cond_stage_model.model.""" + k: v for k, v in text_enc_dict.items()}
							else:
														lowerCAmelCase__								=		convert_text_enc_state_dict(text_enc_dict)
														lowerCAmelCase__								=		{"""cond_stage_model.transformer.""" + k: v for k, v in text_enc_dict.items()}

							# Put together new checkpoint
							lowerCAmelCase__								=		{**unet_state_dict, **vae_state_dict, **text_enc_dict}
							if args.half:
														lowerCAmelCase__								=		{k: v.half() for k, v in state_dict.items()}

							if args.use_safetensors:
														save_file(state_dict, args.checkpoint_path)
							else:
														lowerCAmelCase__								=		{"""state_dict""": state_dict}
														torch.save(state_dict, args.checkpoint_path)







import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		"""▁"""

lowerCAmelCase__								=		{"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCAmelCase__								=		{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowerCAmelCase__								=		{
    """xlm-roberta-base""": 5_1_2,
    """xlm-roberta-large""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-english""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-german""": 5_1_2,
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  VOCAB_FILES_NAMES
		a__					       =  PRETRAINED_VOCAB_FILES_MAP
		a__					       =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a__					       =  ["input_ids", "attention_mask"]


		def __init__(				self	,       __snake_case	,       __snake_case="<s>"	,       __snake_case="</s>"	,       __snake_case="</s>"	,       __snake_case="<s>"	,       __snake_case="<unk>"	,       __snake_case="<pad>"	,       __snake_case="<mask>"	,       __snake_case = None	,       **__snake_case	,       ):
			# Mask token behave like a normal word, i.e. include the space before it
			_UpperCamelCase   :		Dict			=						AddedToken(__snake_case	,       lstrip=__snake_case	,       rstrip=__snake_case) if isinstance(__snake_case	,       __snake_case) else mask_token

			_UpperCamelCase   :		str			=						{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=__snake_case	,       eos_token=__snake_case	,       unk_token=__snake_case	,       sep_token=__snake_case	,       cls_token=__snake_case	,       pad_token=__snake_case	,       mask_token=__snake_case	,       sp_model_kwargs=self.sp_model_kwargs	,       **__snake_case	,       )

			_UpperCamelCase   :		Union[str, Any]			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.Load(str(__snake_case))
			_UpperCamelCase   :		Dict			=						vocab_file

			# Original fairseq vocab and spm vocab must be "aligned":
			# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
			# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
			# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
			# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

			# Mimic fairseq token-to-id alignment for the first 4 token
			_UpperCamelCase   :		int			=						{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

			# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
			_UpperCamelCase   :		List[Any]			=						1

			_UpperCamelCase   :		Any			=						len(self.sp_model) + self.fairseq_offset
			_UpperCamelCase   :		List[str]			=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}


		def __getstate__(				self):
			_UpperCamelCase   :		List[Any]			=						self.__dict__.copy()
			_UpperCamelCase   :		Optional[Any]			=						None
			_UpperCamelCase   :		Any			=						self.sp_model.serialized_model_proto()
			return state


		def __setstate__(				self	,       __snake_case):
			_UpperCamelCase   :		int			=						d

			# for backward compatibility
			if not hasattr(self	,       'sp_model_kwargs'):
				_UpperCamelCase   :		Tuple			=						{}

			_UpperCamelCase   :		Optional[int]			=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto)


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):

			if token_ids_a is None:
				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			_UpperCamelCase   :		Tuple			=						[self.cls_token_id]
			_UpperCamelCase   :		int			=						[self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep


		def 		A__					(				self	,       __snake_case	,       __snake_case = None	,       __snake_case = False):

			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=__snake_case	,       token_ids_a=__snake_case	,       already_has_special_tokens=__snake_case)

			if token_ids_a is None:
				return [1] + ([0] * len(__snake_case)) + [1]
			return [1] + ([0] * len(__snake_case)) + [1, 1] + ([0] * len(__snake_case)) + [1]


		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			_UpperCamelCase   :		Optional[Any]			=						[self.sep_token_id]
			_UpperCamelCase   :		Optional[Any]			=						[self.cls_token_id]

			if token_ids_a is None:
				return len(cls + token_ids_a + sep) * [0]
			return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]


		@property
		def 		A__					(				self):
			return len(self.sp_model) + self.fairseq_offset + 1  # Add the <mask> token


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						{self.convert_ids_to_tokens(__snake_case): i for i in range(self.vocab_size)}
			vocab.update(self.added_tokens_encoder)
			return vocab


		def 		A__					(				self	,       __snake_case):
			return self.sp_model.encode(__snake_case	,       out_type=__snake_case)


		def 		A__					(				self	,       __snake_case):
			if token in self.fairseq_tokens_to_ids:
				return self.fairseq_tokens_to_ids[token]
			_UpperCamelCase   :		str			=						self.sp_model.PieceToId(__snake_case)

			# Need to return unknown token if the SP model returned 0
			return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


		def 		A__					(				self	,       __snake_case):
			if index in self.fairseq_ids_to_tokens:
				return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(index - self.fairseq_offset)


		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		Optional[int]			=						''.join(__snake_case).replace(__snake_case	,       ' ').strip()
			return out_string






		def 		A__					(				self	,       __snake_case	,       __snake_case = None):
			if not os.path.isdir(__snake_case):
				logger.error(f'''Vocabulary path ({save_directory}) should be a directory''')
				return
			_UpperCamelCase   :		str			=						os.path.join(
			    __snake_case	,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

			if os.path.abspath(self.vocab_file) != os.path.abspath(__snake_case) and os.path.isfile(self.vocab_file):
				copyfile(self.vocab_file	,       __snake_case)
			elif not os.path.isfile(self.vocab_file):
				with open(__snake_case	,       'wb') as fi:
					_UpperCamelCase   :		Any			=						self.sp_model.serialized_model_proto()
					fi.write(__snake_case)

			return (out_vocab_file,)




