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


_a  :  Any=				logging.get_logger(__name__)

_a  :  Optional[Any]=				{"vocab_file": "sentencepiece.bpe.model"}

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

_a  :  Union[str, Any]=				{
    "camembert-base": 512,
}

_a  :  Optional[int]=				"▁"

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Any					=	VOCAB_FILES_NAMES
				UpperCAmelCase       :   Union[str, Any]					=	PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase       :   List[Any]					=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase       :   str					=	["""input_ids""", """attention_mask"""]



				def __init__(self       :   List[str]			,						_A       :   str			,						_A       :   Optional[Any]="<s>"			,						_A       :   List[str]="</s>"			,						_A       :   List[Any]="</s>"			,						_A       :   List[str]="<s>"			,						_A       :   str="<unk>"			,						_A       :   Any="<pad>"			,						_A       :   str="<mask>"			,						_A       :   Union[str, Any]=["<s>NOTUSED", "</s>NOTUSED"]			,						_A       :   Optional[Dict[str, Any]] = None			,						**_A       :   str			,						)					->			None:
					# Mask token behave like a normal word, i.e. include the space before it
					__snake_case    :							str   	=				AddedToken(_A			,						lstrip=_A			,						rstrip=_A) if isinstance(_A			,						_A) else mask_token

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

					super().__init__(
					    bos_token=_A			,						eos_token=_A			,						unk_token=_A			,						sep_token=_A			,						cls_token=_A			,						pad_token=_A			,						mask_token=_A			,						additional_special_tokens=_A			,						sp_model_kwargs=self.sp_model_kwargs			,						**_A			,						)
					__snake_case    :							Tuple   	=				spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(str(_A))
					__snake_case    :							Dict   	=				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>
					__snake_case    :							Optional[Any]   	=				{'<s>NOTUSED': 0, '<pad>': 1, '</s>NOTUSED': 2, '<unk>': 3}
					__snake_case    :							Any   	=				len(self.fairseq_tokens_to_ids)
					__snake_case    :							Union[str, Any]   	=				len(self.sp_model) + len(self.fairseq_tokens_to_ids)
					__snake_case    :							Optional[Any]   	=				{v: k for k, v in self.fairseq_tokens_to_ids.items()}



				def       _lowercase  (self       :   str			,						_A       :   List[int]			,						_A       :   Optional[List[int]] = None)					->			List[int]:

					if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__snake_case    :							Any   	=				[self.cls_token_id]
					__snake_case    :							Optional[int]   	=				[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   List[int]			,						_A       :   Optional[List[int]] = None			,						_A       :   bool = False)					->			List[int]:
					if already_has_special_tokens:
						return super().get_special_tokens_mask(
						    token_ids_a=_A			,						token_ids_a=_A			,						already_has_special_tokens=_A)

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



				def       _lowercase  (self       :   List[str]			,						_A       :   List[int]			,						_A       :   Optional[List[int]] = None)					->			List[int]:
					__snake_case    :							Any   	=				[self.sep_token_id]
					__snake_case    :							Union[str, 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       _lowercase  (self       :   Union[str, Any])					->			Union[str, Any]:
					return len(self.fairseq_tokens_to_ids) + len(self.sp_model)



				def       _lowercase  (self       :   Tuple)					->			List[Any]:
					__snake_case    :							Any   	=				{self.convert_ids_to_tokens(_A): i for i in range(self.vocab_size)}
					vocab.update(self.added_tokens_encoder)
					return vocab



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			List[str]:
					return self.sp_model.encode(_A			,						out_type=_A)



				def       _lowercase  (self       :   Tuple			,						_A       :   Optional[int])					->			int:
					if token in self.fairseq_tokens_to_ids:
						return self.fairseq_tokens_to_ids[token]
					elif self.sp_model.PieceToId(_A) == 0:
						# Convert sentence piece unk token to fairseq unk token index
						return self.unk_token_id
					return self.fairseq_offset + self.sp_model.PieceToId(_A)



				def       _lowercase  (self       :   Tuple			,						_A       :   Optional[int])					->			Dict:
					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       _lowercase  (self       :   Any			,						_A       :   Optional[int])					->			Union[str, Any]:
					__snake_case    :							List[Any]   	=				[]
					__snake_case    :							str   	=				''
					__snake_case    :							List[str]   	=				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(_A) + token
							__snake_case    :							int   	=				True
							__snake_case    :							List[Any]   	=				[]
						else:
							current_sub_tokens.append(_A)
							__snake_case    :							List[Any]   	=				False
					out_string += self.sp_model.decode(_A)
					return out_string.strip()



				def __getstate__(self       :   str)					->			List[Any]:
					__snake_case    :							Optional[Any]   	=				self.__dict__.copy()
					__snake_case    :							str   	=				None
					return state



				def __setstate__(self       :   List[Any]			,						_A       :   Tuple)					->			str:
					__snake_case    :							Union[str, Any]   	=				d

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

					__snake_case    :							int   	=				spm.SentencePieceProcessor(**self.sp_model_kwargs)
					self.sp_model.Load(self.vocab_file)






				def       _lowercase  (self       :   Tuple			,						_A       :   str			,						_A       :   Optional[str] = None)					->			Tuple[str]:
					if not os.path.isdir(_A):
						logger.error(f"Vocabulary path ({save_directory}) should be a directory")
						return
					__snake_case    :							List[Any]   	=				os.path.join(
					    _A			,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

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

					return (out_vocab_file,)






"""simple docstring"""







import unittest
from queue import Empty
from threading import Thread

from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
	import torch

	from transformers import AutoModelForCausalLM

@require_torch
class 	UpperCamelCase    (					unittest.TestCase		):



				def       _lowercase  (self       :   List[Any])					->			Dict:
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							Optional[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Any   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							str   	=				tokenizer.decode(greedy_ids[0])

					with CaptureStdout() as cs:
						__snake_case    :							List[str]   	=				TextStreamer(_A)
						model.generate(_A			,						max_new_tokens=10			,						do_sample=_A			,						streamer=_A)
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					__snake_case    :							Optional[Any]   	=				cs.out[:-1]

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   int)					->			Optional[Any]:
					__snake_case    :							Any   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							List[Any]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Optional[int]   	=				-1

					__snake_case    :							str   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							int   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							int   	=				tokenizer.decode(greedy_ids[0])

					__snake_case    :							int   	=				TextIteratorStreamer(_A)
					__snake_case    :							List[Any]   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							Any   	=				Thread(target=model.generate			,						kwargs=_A)
					thread.start()
					__snake_case    :							Dict   	=				''
					for new_text in streamer:
						streamer_text += new_text

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case    :							List[str]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Any   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							Optional[int]   	=				model.generate(_A			,						max_new_tokens=10			,						do_sample=_A)
					__snake_case    :							Union[str, Any]   	=				greedy_ids[:, input_ids.shape[1] :]
					__snake_case    :							Optional[Any]   	=				tokenizer.decode(new_greedy_ids[0])

					with CaptureStdout() as cs:
						__snake_case    :							str   	=				TextStreamer(_A			,						skip_prompt=_A)
						model.generate(_A			,						max_new_tokens=10			,						do_sample=_A			,						streamer=_A)
					# The greedy text should be printed to stdout, except for the final "\n" in the streamer
					__snake_case    :							List[str]   	=				cs.out[:-1]

					self.assertEqual(_A			,						_A)



				def       _lowercase  (self       :   List[str])					->			Dict:
					# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
					# with actual models -- the dummy models' tokenizers are not aligned with their models, and
					# `skip_special_tokens=True` has no effect on them
					__snake_case    :							int   	=				AutoTokenizer.from_pretrained('distilgpt2')
					__snake_case    :							Dict   	=				AutoModelForCausalLM.from_pretrained('distilgpt2').to(_A)
					__snake_case    :							Union[str, Any]   	=				-1

					__snake_case    :							List[str]   	=				torch.ones((1, 5)			,						device=_A).long() * model.config.bos_token_id
					with CaptureStdout() as cs:
						__snake_case    :							Tuple   	=				TextStreamer(_A			,						skip_special_tokens=_A)
						model.generate(_A			,						max_new_tokens=1			,						do_sample=_A			,						streamer=_A)

					# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
					# re-tokenized, must only contain one token
					__snake_case    :							Tuple   	=				cs.out[:-1]  # Remove the final "\n"
					__snake_case    :							int   	=				tokenizer(_A			,						return_tensors='pt')
					self.assertEqual(streamer_text_tokenized.input_ids.shape			,						(1, 1))






				def       _lowercase  (self       :   Optional[int])					->			List[str]:
					__snake_case    :							Optional[int]   	=				AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2')
					__snake_case    :							Optional[int]   	=				AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2').to(_A)
					__snake_case    :							int   	=				-1

					__snake_case    :							List[Any]   	=				ids_tensor((1, 5)			,						vocab_size=model.config.vocab_size).to(_A)
					__snake_case    :							List[Any]   	=				TextIteratorStreamer(_A			,						timeout=0.001)
					__snake_case    :							str   	=				{'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
					__snake_case    :							str   	=				Thread(target=model.generate			,						kwargs=_A)
					thread.start()

					# The streamer will timeout after 0.001 seconds, so an exception will be raised
					with self.assertRaises(_A):
						__snake_case    :							Any   	=				''
						for new_text in streamer:
							streamer_text += new_text





