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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       '▁'

__lowerCAmelCase					   =       {'vocab_file': 'sentencepiece.bpe.model'}

__lowerCAmelCase					   =       {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

__lowerCAmelCase					   =       {
    'facebook/xglm-564M': 2_0_4_8,
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	VOCAB_FILES_NAMES
	UpperCamelCase_  :					List[str]    		=	PRETRAINED_VOCAB_FILES_MAP
	UpperCamelCase_  :					Union[str, Any]    		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	UpperCamelCase_  :					Dict    		=	['input_ids', 'attention_mask']







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {} if sp_model_kwargs is None else sp_model_kwargs

				# Compatibility with the original tokenizer
				SCREAMING_SNAKE_CASE     						= 7
				SCREAMING_SNAKE_CASE     						= [f"""<madeupword{i}>""" for i in range(self.num_madeup_words			)]

				SCREAMING_SNAKE_CASE     						= kwargs.get('''additional_special_tokens'''   ,     []			)
				kwargs["additional_special_tokens"] += [
				    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
				]

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

				SCREAMING_SNAKE_CASE     						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
				self.sp_model.Load(str(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= 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'

				# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
				SCREAMING_SNAKE_CASE     						= 1

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

				SCREAMING_SNAKE_CASE     						= len(self.sp_model			)
				SCREAMING_SNAKE_CASE     						= {f"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words			)}
				self.fairseq_tokens_to_ids.update(lowercase			)

				SCREAMING_SNAKE_CASE     						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}







	def __getstate__(     self      :Any			)		->      int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.__dict__.copy()
				SCREAMING_SNAKE_CASE     						= None
				SCREAMING_SNAKE_CASE     						= self.sp_model.serialized_model_proto()
				return state







	def __setstate__(     self      :int   ,     lowercase      :int			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= d

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

				SCREAMING_SNAKE_CASE     						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
				self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)







	def  snake_case__     (     self      :int   ,     lowercase      :List[int]   ,     lowercase      :Optional[List[int]] = None			)		->      List[int]:







				"""simple docstring"""






				if token_ids_a is None:
							return [self.sep_token_id] + token_ids_a
				SCREAMING_SNAKE_CASE     						= [self.sep_token_id]
				return sep + token_ids_a + sep + sep + token_ids_a







	def  snake_case__     (     self      :List[Any]   ,     lowercase      :List[int]   ,     lowercase      :Optional[List[int]] = None   ,     lowercase      :bool = False			)		->      List[int]:







				"""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			))
				return [1] + ([0] * len(lowercase			)) + [1, 1] + ([0] * len(lowercase			))







	def  snake_case__     (     self      :Dict   ,     lowercase      :List[int]   ,     lowercase      :Optional[List[int]] = None			)		->      List[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= [self.sep_token_id]

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







	@property
	def  snake_case__     (     self      :Any			)		->      str:







				"""simple docstring"""






				return len(self.sp_model			) + self.fairseq_offset + self.num_madeup_words







	def  snake_case__     (     self      :Dict			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= {self.convert_ids_to_tokens(lowercase			): i for i in range(self.vocab_size			)}
				vocab.update(self.added_tokens_encoder			)
				return vocab







	def  snake_case__     (     self      :Union[str, Any]   ,     lowercase      :str			)		->      List[str]:







				"""simple docstring"""






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







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :Any			)		->      List[Any]:







				"""simple docstring"""






				if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
				SCREAMING_SNAKE_CASE     						= self.sp_model.PieceToId(lowercase			)

				# 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  snake_case__     (     self      :Tuple   ,     lowercase      :Tuple			)		->      Tuple:







				"""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  snake_case__     (     self      :List[Any]   ,     lowercase      :List[str]			)		->      List[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ''''''.join(lowercase			).replace(lowercase   ,     ''' '''			).strip()
				return out_string







	def  snake_case__     (     self      :Tuple   ,     lowercase      :str   ,     lowercase      :Optional[str] = None			)		->      Tuple[str]:







				"""simple docstring"""






				if not os.path.isdir(lowercase			):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""			)
							return
				SCREAMING_SNAKE_CASE     						= 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:
										SCREAMING_SNAKE_CASE     						= self.sp_model.serialized_model_proto()
										fi.write(lowercase			)

				return (out_vocab_file,)

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch





@require_torch
@require_optimum
@slow
class     lowerCamelCase      (       unittest.TestCase					):







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained(lowercase			)
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer('''This is me'''   ,     return_tensors='''pt'''			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

							self.assertFalse(
							    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

							SCREAMING_SNAKE_CASE     						= model_reloaded.generate(**lowercase			)
							self.assertTrue(torch.allclose(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase			):
										model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()
							model.save_pretrained(lowercase			)