

"""simple docstring"""






import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


snake_case_   :   Optional[Any]        =       """bart"""
snake_case_   :   List[Any]        =       True



@st.cache(allow_output_mutation=_lowercase	)
def   lowercase_	(    ):







					if LOAD_DENSE_INDEX:
										UpperCAmelCase			:   Any      =				AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased"	)
										UpperCAmelCase			:   Dict      =				AutoModel.from_pretrained("yjernite/retribert-base-uncased"	).to("cuda:0"	)
										UpperCAmelCase			:   List[str]      =				qar_model.eval()
					else:
										UpperCAmelCase      ,							UpperCAmelCase			:   Optional[Any]      =				(None, None)
					if MODEL_TYPE == "bart":
										UpperCAmelCase			:   Optional[int]      =				AutoTokenizer.from_pretrained("yjernite/bart_eli5"	)
										UpperCAmelCase			:   Optional[int]      =				AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5"	).to("cuda:0"	)
										UpperCAmelCase			:   str      =				torch.load("seq2seq_models/eli5_bart_model_blm_2.pth"	)
										sas_model.load_state_dict(save_dict["model"]	)
										UpperCAmelCase			:   Dict      =				sas_model.eval()
					else:
										UpperCAmelCase      ,							UpperCAmelCase			:   Any      =				make_qa_sas_model(
										    model_name="t5-small"							,      from_file="seq2seq_models/eli5_t5_model_1024_4.pth"							,      device="cuda:0"	)
					return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)



@st.cache(allow_output_mutation=_lowercase	)
def   lowercase_	(    ):







					if LOAD_DENSE_INDEX:
										UpperCAmelCase			:   Optional[Any]      =				faiss.StandardGpuResources()
										UpperCAmelCase			:   List[str]      =				datasets.load_dataset(path="wiki_snippets"							,      name="wiki40b_en_100_0"	)["train"]
										UpperCAmelCase			:   Any      =				np.memmap(
										    "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"							,      dtype="float32"							,      mode="r"							,      shape=(wikiaab_passages.num_rows, 1_28)							,      )
										UpperCAmelCase			:   Optional[int]      =				faiss.IndexFlatIP(1_28	)
										UpperCAmelCase			:   int      =				faiss.index_cpu_to_gpu(_lowercase							,      1							,      _lowercase	)
										wikiaab_gpu_index_flat.add(_lowercase	)  # TODO fix for larger GPU
					else:
										UpperCAmelCase      ,							UpperCAmelCase			:   str      =				(None, None)
					UpperCAmelCase			:   Optional[int]      =				Elasticsearch([{"host": "localhost", "port": "9200"}]	)
					return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=_lowercase	)
def   lowercase_	(    ):
					UpperCAmelCase			:   Optional[Any]      =				datasets.load_dataset("eli5"							,      name="LFQA_reddit"	)
					UpperCAmelCase			:   List[Any]      =				elia["train_eli5"]
					UpperCAmelCase			:   List[Any]      =				np.memmap(
					    "eli5_questions_reps.dat"							,      dtype="float32"							,      mode="r"							,      shape=(elia_train.num_rows, 1_28)	)
					UpperCAmelCase			:   List[str]      =				faiss.IndexFlatIP(1_28	)
					eli5_train_q_index.add(_lowercase	)
					return (elia_train, eli5_train_q_index)


snake_case_     ,      snake_case_     ,      snake_case_   :   Union[str, Any]        =       load_indexes()
snake_case_     ,      snake_case_     ,      snake_case_     ,      snake_case_   :   Union[str, Any]        =       load_models()
snake_case_     ,      snake_case_   :   str        =       load_train_data()



def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		Any=10	):
					UpperCAmelCase			:   Dict      =				embed_questions_for_retrieval([question]							,      _lowercase							,      _lowercase	)
					UpperCAmelCase      ,							UpperCAmelCase			:   int      =				eli5_train_q_index.search(_lowercase							,      _lowercase	)
					UpperCAmelCase			:   Dict      =				[elia_train[int(_lowercase	)] for i in I[0]]
					return nn_examples



def   lowercase_	(    _lowercase       :		List[Any]							,      _lowercase       :		List[Any]="wiki40b"							,      _lowercase       :		Tuple="dense"							,      _lowercase       :		Optional[Any]=10	):







					if source == "none":
										UpperCAmelCase      ,							UpperCAmelCase			:   Any      =				(" <P> ".join(["" for _ in range(11	)]	).strip(), [])
					else:
										if method == "dense":
															UpperCAmelCase      ,							UpperCAmelCase			:   Optional[Any]      =				query_qa_dense_index(
															    _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase							,      _lowercase	)
										else:
															UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				query_es_index(
															    _lowercase							,      _lowercase							,      index_name="english_wiki40b_snippets_100w"							,      n_results=_lowercase							,      )
					UpperCAmelCase			:   Any      =				[
					    (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
					]
					UpperCAmelCase			:   Dict      =				"question: {} context: {}".format(_lowercase							,      _lowercase	)
					return question_doc, support_list



@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _lowercase	: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _lowercase	: None),
    }	)
def   lowercase_	(    _lowercase       :		int							,      _lowercase       :		int							,      _lowercase       :		List[Any]							,      _lowercase       :		Any=64							,      _lowercase       :		Dict=2_56							,      _lowercase       :		str=False							,      _lowercase       :		Dict=2							,      _lowercase       :		str=0.9_5							,      _lowercase       :		List[str]=0.8	):







					with torch.no_grad():
										UpperCAmelCase			:   str      =				qa_sas_generate(
										    _lowercase							,      _lowercase							,      _lowercase							,      num_answers=1							,      num_beams=_lowercase							,      min_len=_lowercase							,      max_len=_lowercase							,      do_sample=_lowercase							,      temp=_lowercase							,      top_p=_lowercase							,      top_k=_lowercase							,      max_input_length=10_24							,      device="cuda:0"							,      )[0]
					return (answer, support_list)


st.title("""Long Form Question Answering with ELI5""")

# Start sidebar
snake_case_   :   str        =       """<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>"""
snake_case_   :   Dict        =       """\n<html>\n  <head>\n    <style>\n      .img-container {\n        padding-left: 90px;\n        padding-right: 90px;\n        padding-top: 50px;\n        padding-bottom: 50px;\n        background-color: #f0f3f9;\n      }\n    </style>\n  </head>\n  <body>\n    <span class=\"img-container\"> <!-- Inline parent element -->\n      %s\n    </span>\n  </body>\n</html>\n""" % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
snake_case_   :   Tuple        =       """\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"""
st.sidebar.markdown(description, unsafe_allow_html=True)

snake_case_   :   Optional[int]        =       [
    """Answer the question""",
    """View the retrieved document only""",
    """View the most similar ELI5 question and answer""",
    """Show me everything, please!""",
]
snake_case_   :   Union[str, Any]        =       st.sidebar.checkbox("""Demo options""")
if demo_options:
	snake_case_   :   Optional[int]        =       st.sidebar.selectbox(
	    """""",
	    action_list,
	    index=3,
	)
	snake_case_   :   List[Any]        =       action_list.index(action_st)
	snake_case_   :   Union[str, Any]        =       st.sidebar.selectbox(
	    """""",
	    ["""Show full text of passages""", """Show passage section titles"""],
	    index=0,
	)
	snake_case_   :   Optional[int]        =       show_type == """Show full text of passages"""
else:
	snake_case_   :   Union[str, Any]        =       3
	snake_case_   :   Dict        =       True

snake_case_   :   str        =       st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
	snake_case_   :   Optional[int]        =       """\n    ### Information retriever options\n\n    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n    """
	st.sidebar.markdown(retriever_info)
	snake_case_   :   Optional[int]        =       st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
	snake_case_   :   str        =       st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
	snake_case_   :   Any        =       """wiki40b"""
	snake_case_   :   Dict        =       """dense"""

snake_case_   :   Any        =       """beam"""
snake_case_   :   Union[str, Any]        =       2
snake_case_   :   List[Any]        =       6_4
snake_case_   :   str        =       2_5_6
snake_case_   :   Optional[Any]        =       None
snake_case_   :   Optional[int]        =       None
snake_case_   :   int        =       st.sidebar.checkbox("""Generation options""")
if generate_options:
	snake_case_   :   Any        =       """\n    ### Answer generation options\n\n    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n    **beam** search, or **sample** from the decoder\'s output probabilities.\n    """
	st.sidebar.markdown(generate_info)
	snake_case_   :   List[str]        =       st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
	snake_case_   :   Tuple        =       st.sidebar.slider(
	    """Minimum generation length""", min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
	)
	snake_case_   :   List[Any]        =       st.sidebar.slider(
	    """Maximum generation length""", min_value=6_4, max_value=5_1_2, value=2_5_6, step=1_6, format=None, key=None
	)
	if sampled == "beam":
		snake_case_   :   str        =       st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
	else:
		snake_case_   :   Any        =       st.sidebar.slider(
		    """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
		)
		snake_case_   :   List[str]        =       st.sidebar.slider(
		    """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
		)
		snake_case_   :   Optional[int]        =       None

# start main text
snake_case_   :   Tuple        =       [
    """<MY QUESTION>""",
    """How do people make chocolate?""",
    """Why do we get a fever when we are sick?""",
    """How can different animals perceive different colors?""",
    """What is natural language processing?""",
    """What\'s the best way to treat a sunburn?""",
    """What exactly are vitamins ?""",
    """How does nuclear energy provide electricity?""",
    """What\'s the difference between viruses and bacteria?""",
    """Why are flutes classified as woodwinds when most of them are made out of metal ?""",
    """Why do people like drinking coffee even though it tastes so bad?""",
    """What happens when wine ages? How does it make the wine taste better?""",
    """If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?""",
    """How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?""",
    """How does New Zealand have so many large bird predators?""",
]
snake_case_   :   Optional[Any]        =       st.selectbox(
    """What would you like to ask? ---- select <MY QUESTION> to enter a new query""",
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
	snake_case_   :   List[str]        =       st.text_input("""Enter your question here:""", """""")
else:
	snake_case_   :   Optional[int]        =       question_s

if st.button("""Show me!"""):
	if action in [0, 1, 3]:
		if index_type == "mixed":
			snake_case_     ,      snake_case_   :   Dict        =       make_support(question, source=wiki_source, method="""dense""", n_results=1_0)
			snake_case_     ,      snake_case_   :   Dict        =       make_support(question, source=wiki_source, method="""sparse""", n_results=1_0)
			snake_case_   :   List[str]        =       []
			for res_d, res_s in zip(support_list_dense, support_list_sparse):
				if tuple(res_d) not in support_list:
					support_list += [tuple(res_d)]
				if tuple(res_s) not in support_list:
					support_list += [tuple(res_s)]
			snake_case_   :   Any        =       support_list[:1_0]
			snake_case_   :   Optional[int]        =       """<P> """ + """ <P> """.join([res[-1] for res in support_list])
		else:
			snake_case_     ,      snake_case_   :   List[Any]        =       make_support(question, source=wiki_source, method=index_type, n_results=1_0)
	if action in [0, 3]:
		snake_case_     ,      snake_case_   :   Optional[int]        =       answer_question(
		    question_doc,
		    sas_model,
		    sas_tokenizer,
		    min_len=min_len,
		    max_len=int(max_len),
		    sampling=(sampled == """sampled"""),
		    n_beams=n_beams,
		    top_p=top_p,
		    temp=temp,
		)
		st.markdown("""### The model generated answer is:""")
		st.write(answer)
	if action in [0, 1, 3] and wiki_source != "none":
		st.markdown("""--- \n ### The model is drawing information from the following Wikipedia passages:""")
		for i, res in enumerate(support_list):
			snake_case_   :   Any        =       """https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
			snake_case_   :   Any        =       res[1].strip()
			if sec_titles == "":
				snake_case_   :   Tuple        =       """[{}]({})""".format(res[0], wiki_url)
			else:
				snake_case_   :   Dict        =       sec_titles.split(""" & """)
				snake_case_   :   Optional[Any]        =       """ & """.join(
				    ["""[{}]({}#{})""".format(sec.strip(), wiki_url, sec.strip().replace(""" """, """_""")) for sec in sec_list]
				)
			st.markdown(
			    """{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}""".format(i + 1, res[0], sections),
			    unsafe_allow_html=True,
			)
			if show_passages:
				st.write(
				    """> <span style=\"font-family:arial; font-size:10pt;\">""" + res[-1] + """</span>""", unsafe_allow_html=True
				)
	if action in [2, 3]:
		snake_case_   :   Any        =       find_nearest_training(question)
		snake_case_   :   Any        =       nn_train_list[0]
		st.markdown(
		    """--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
		)
		snake_case_   :   Dict        =       [
		    """{}. {}""".format(i + 1, """  \n""".join([line.strip() for line in ans.split("""\n""") if line.strip() != """"""]))
		    for i, (ans, sc) in enumerate(zip(train_exple["""answers"""]["""text"""], train_exple["""answers"""]["""score"""]))
		    if i == 0 or sc > 2
		]
		st.markdown("""##### Its answers were: \n\n {}""".format("""\n""".join(answers_st)))


snake_case_   :   Union[str, Any]        =       """\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"""




st.sidebar.markdown(disclaimer, unsafe_allow_html=True)





"""simple docstring"""






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


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

snake_case_   :   Tuple        =       {"""vocab_file""": """sentencepiece.bpe.model"""}

snake_case_   :   List[str]        =       {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

snake_case_   :   List[Any]        =       {
    """camembert-base""": 5_1_2,
}

snake_case_   :   Any        =       """▁"""







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE__     				=						PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE__     				=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE__     				=						['''input_ids''', '''attention_mask''']







			def __init__(			self			: int			,  lowercase			: Union[str, Any]			,  lowercase			: str="<s>"			,  lowercase			: str="</s>"			,  lowercase			: Optional[int]="</s>"			,  lowercase			: Dict="<s>"			,  lowercase			: Optional[Any]="<unk>"			,  lowercase			: List[Any]="<pad>"			,  lowercase			: Any="<mask>"			,  lowercase			: Optional[int]=["<s>NOTUSED", "</s>NOTUSED"]			,  lowercase			: Optional[Dict[str, Any]] = None			,  **lowercase			: Dict			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				AddedToken(lowercase			,  lstrip=lowercase			,  rstrip=lowercase  ) if isinstance(lowercase			,  lowercase  ) else mask_token

								UpperCAmelCase			:   int      =				{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=lowercase			,  eos_token=lowercase			,  unk_token=lowercase			,  sep_token=lowercase			,  cls_token=lowercase			,  pad_token=lowercase			,  mask_token=lowercase			,  additional_special_tokens=lowercase			,  sp_model_kwargs=self.sp_model_kwargs			,  **lowercase			,  )
								UpperCAmelCase			:   Tuple      =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(str(lowercase  )  )
								UpperCAmelCase			:   str      =				vocab_file
								# HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
								# sentencepiece vocabulary (this is the case for <s> and </s>
								UpperCAmelCase			:   Union[str, Any]      =				{"<s>NOTUSED": 0, "<pad>": 1, "</s>NOTUSED": 2, "<unk>": 3}
								UpperCAmelCase			:   Union[str, Any]      =				len(self.fairseq_tokens_to_ids  )
								UpperCAmelCase			:   Dict      =				len(self.sp_model  ) + len(self.fairseq_tokens_to_ids  )
								UpperCAmelCase			:   List[Any]      =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}







			def 			__lowerCAmelCase				(			self			: Union[str, Any]			,  lowercase			: List[int]			,  lowercase			: Optional[List[int]] = None  ):





								'''simple docstring'''



								if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase			:   Dict      =				[self.cls_token_id]
								UpperCAmelCase			:   List[str]      =				[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep







			def 			__lowerCAmelCase				(			self			: Dict			,  lowercase			: List[int]			,  lowercase			: Optional[List[int]] = None			,  lowercase			: bool = False  ):





								'''simple docstring'''



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

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







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: List[int]			,  lowercase			: Optional[List[int]] = None  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				[self.sep_token_id]
								UpperCAmelCase			:   List[str]      =				[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 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								return len(self.fairseq_tokens_to_ids  ) + len(self.sp_model  )







			def 			__lowerCAmelCase				(			self			: List[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				{self.convert_ids_to_tokens(lowercase  ): i for i in range(self.vocab_size  )}
								vocab.update(self.added_tokens_encoder  )
								return vocab







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: str  ):





								'''simple docstring'''



								return self.sp_model.encode(lowercase			,  out_type=lowercase  )







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
								elif self.sp_model.PieceToId(lowercase  ) == 0:
													# Convert sentence piece unk token to fairseq unk token index
													return self.unk_token_id
								return self.fairseq_offset + self.sp_model.PieceToId(lowercase  )







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: Dict  ):





								'''simple docstring'''



								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 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Tuple      =				[]
								UpperCAmelCase			:   Tuple      =				""
								UpperCAmelCase			:   Union[str, Any]      =				False
								for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																		if not prev_is_special:
																							out_string += " "
																		out_string += self.sp_model.decode(lowercase  ) + token
																		UpperCAmelCase			:   Optional[int]      =				True
																		UpperCAmelCase			:   str      =				[]
													else:
																		current_sub_tokens.append(lowercase  )
																		UpperCAmelCase			:   Any      =				False
								out_string += self.sp_model.decode(lowercase  )
								return out_string.strip()







			def __getstate__(			self			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				self.__dict__.copy()
								UpperCAmelCase			:   Dict      =				None
								return state







			def __setstate__(			self			: List[str]			,  lowercase			: str  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				d

								# for backward compatibility
								if not hasattr(self			,  "sp_model_kwargs"  ):
													UpperCAmelCase			:   Any      =				{}

								UpperCAmelCase			:   List[Any]      =				spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(self.vocab_file  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]			,  lowercase			: str			,  lowercase			: Optional[str] = None  ):





								'''simple docstring'''



								if not os.path.isdir(lowercase  ):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""  )
													return
								UpperCAmelCase			:   Optional[Any]      =				os.path.join(
								    lowercase			,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(lowercase  ) and os.path.isfile(self.vocab_file  ):
													copyfile(self.vocab_file			,  lowercase  )
								elif not os.path.isfile(self.vocab_file  ):
													with open(lowercase			,  "wb"  ) as fi:
																		UpperCAmelCase			:   List[Any]      =				self.sp_model.serialized_model_proto()
																		fi.write(lowercase  )

								return (out_vocab_file,)


