
'''simple docstring'''






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


A			      =      logging.get_logger(__name__)


A			      =      '▁'

A			      =      {'vocab_file': 'spiece.model'}

A			      =      {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

A			      =      {
    'google/reformer-crime-and-punishment': 52_4288,
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	VOCAB_FILES_NAMES
					_lowerCAmelCase			     =	PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase			     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCAmelCase			     =	['''input_ids''', '''attention_mask''']
					def __init__(     self,    A,    A="</s>",    A="<unk>",    A=[],    A = None,    **A,    ):

									"""simple docstring"""


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

									super().__init__(
									    eos_token=A,    unk_token=A,    additional_special_tokens=A,    sp_model_kwargs=self.sp_model_kwargs,    **A,    )

									lowerCamelCase					:			Any									= vocab_file
									lowerCamelCase					:			List[Any]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(A      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.sp_model.get_piece_size()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= {self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
									vocab.update(self.added_tokens_encoder      )
									return vocab
					def __getstate__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.__dict__.copy()
									lowerCamelCase					:			List[Any]									= None
									return state
					def __setstate__(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= d

									# for backward compatibility
									if not hasattr(self,    'sp_model_kwargs'      ):
													lowerCamelCase					:			List[str]									= {}

									lowerCamelCase					:			Optional[int]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(self.vocab_file      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.encode(A,    out_type=A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.piece_to_id(A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if index < self.sp_model.get_piece_size():
													lowerCamelCase					:			Union[str, Any]									= self.sp_model.IdToPiece(A      )
									return token
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


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




					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									if not os.path.isdir(A      ):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
													return
									lowerCamelCase					:			str									= 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:
																	lowerCamelCase					:			Optional[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     __snake_case		(   unittest.TestCase):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			Optional[int]									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Union[str, Any]									= -1

									lowerCamelCase					:			Optional[Any]									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			List[Any]									= model.generate(A,    max_new_tokens=10,    do_sample=A      )
									lowerCamelCase					:			int									= tokenizer.decode(greedy_ids[0]      )

									with CaptureStdout() as cs:
													lowerCamelCase					:			Optional[int]									= 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
									lowerCamelCase					:			int									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			Dict									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			List[Any]									= -1

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

									lowerCamelCase					:			Union[str, Any]									= TextIteratorStreamer(A      )
									lowerCamelCase					:			Tuple									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			List[Any]									= Thread(target=model.generate,    kwargs=A      )
									thread.start()
									lowerCamelCase					:			List[Any]									= ''
									for new_text in streamer:
													streamer_text += new_text

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			int									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Optional[Any]									= -1

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

									with CaptureStdout() as cs:
													lowerCamelCase					:			Tuple									= 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
									lowerCamelCase					:			Optional[int]									= cs.out[:-1]

									self.assertEqual(A,    A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= AutoTokenizer.from_pretrained('distilgpt2'      )
									lowerCamelCase					:			Dict									= AutoModelForCausalLM.from_pretrained('distilgpt2'      ).to(A      )
									lowerCamelCase					:			Dict									= -1

									lowerCamelCase					:			Any									= torch.ones((1, 5),    device=A      ).long() * model.config.bos_token_id
									with CaptureStdout() as cs:
													lowerCamelCase					:			int									= 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
									lowerCamelCase					:			List[str]									= cs.out[:-1]  # Remove the final "\n"
									lowerCamelCase					:			Optional[Any]									= tokenizer(A,    return_tensors='pt'      )
									self.assertEqual(streamer_text_tokenized.input_ids.shape,    (1, 1)      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'      )
									lowerCamelCase					:			List[str]									= AutoModelForCausalLM.from_pretrained('hf-internal-testing/tiny-random-gpt2'      ).to(A      )
									lowerCamelCase					:			Any									= -1

									lowerCamelCase					:			int									= ids_tensor((1, 5),    vocab_size=model.config.vocab_size      ).to(A      )
									lowerCamelCase					:			int									= TextIteratorStreamer(A,    timeout=0.001      )
									lowerCamelCase					:			Any									= {'input_ids': input_ids, 'max_new_tokens': 10, 'do_sample': False, 'streamer': streamer}
									lowerCamelCase					:			Optional[Any]									= 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      ):
													lowerCamelCase					:			Dict									= ''
													for new_text in streamer:
																	streamer_text += new_text
