





import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin







class 		_lowerCAmelCase(		__A						,       unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__     	=       SpeechaTextaTokenizer
  lowerCamelCase__     	=       False

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Union[str, Any]:
     super().setUp()

     __A    =  '<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab'.split(''' '''      )
     __A    =  ['he re</w> 123', 'here a 1456']
     __A    =  dict(zip(UpperCamelCase__    ,							range(len(UpperCamelCase__      )      )      )      )

     __A    =  {'pad_token': '<pad>', 'unk_token': '<unk>', 'bos_token': '<s>', 'eos_token': '</s>'}

     __A    =  tempfile.mkdtemp()
     __A    =  os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES['''vocab_file''']      )
     __A    =  os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES['''merges_file''']      )

     with open(self.vocab_file    ,							'''w'''    ,							encoding='''utf-8'''      ) as fp:
        fp.write(json.dumps(UpperCamelCase__      ) + '''\n'''      )

     with open(self.merges_file    ,							'''w'''      ) as fp:
        fp.write('''\n'''.join(UpperCamelCase__      )      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   List[str]:
     __A    =  list(self.get_tokenizer().get_vocab().keys()      )

     self.assertEqual(vocab_keys[0]    ,							'''<s>'''      )
     self.assertEqual(vocab_keys[1]    ,							'''<pad>'''      )
     self.assertEqual(vocab_keys[-1]    ,							'''vocab'''      )
     self.assertEqual(len(UpperCamelCase__      )    ,							14      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   int:
     self.assertEqual(self.get_tokenizer().vocab_size    ,							14      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   str:
     __A    =  SpeechaTextaTokenizer.from_pretrained(self.tmpdirname      )

     # make sure @@ is correctly concatenated
     __A    =  [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
     __A    =  tokenizer.decode(UpperCamelCase__      )

     self.assertTrue(output_string == '''herecouple words ofthe'''      )

  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Union[str, Any]:
     __A    =  SpeechaTextaTokenizer.from_pretrained(self.tmpdirname      )

     with tempfile.TemporaryDirectory() as tmp_dirname:
        tokenizer.save_pretrained(UpperCamelCase__      )
        os.remove(os.path.join(UpperCamelCase__    ,							'''merges.txt'''      )      )

        # load tokenizer without merges file should not throw an error
        __A    =  SpeechaTextaTokenizer.from_pretrained(UpperCamelCase__      )

     with tempfile.TemporaryDirectory() as tmp_dirname:
        # save tokenizer and load again
        tokenizer.save_pretrained(UpperCamelCase__      )
        __A    =  SpeechaTextaTokenizer.from_pretrained(UpperCamelCase__      )

     self.assertIsNotNone(UpperCamelCase__      )





  def 						SCREAMING_SNAKE_CASE__			(   self      )->   Dict:
     if not self.test_slow_tokenizer:
        return

     __A    =  inspect.signature(self.tokenizer_class.__init__      )
     __A    =  self.get_tokenizer()

     for parameter_name, parameter in signature.parameters.items():
        if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
           self.assertIn(UpperCamelCase__    ,							tokenizer.init_kwargs      )









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

import sentencepiece as spm

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


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation

A										=      logging.get_logger(__name__)

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

A										=      {
    'vocab_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model',
    },
    'tokenizer_file': {
        'hf-internal-testing/llama-tokenizer': 'https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json',
    },
}
A										=      {
    'hf-internal-testing/llama-tokenizer': 2048,
}
A										=      '▁'

A			,	A										=      '[INST]', '[/INST]'
A			,	A										=      '<<SYS>>\n', '\n<</SYS>>\n\n'

# fmt: off
A										=      'You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don\'t know the answer to a question, please don\'t share false information.'


class 				__a				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					Optional[int]					     =    VOCAB_FILES_NAMES
		UpperCAmelCase__   :					Dict					     =    PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__   :					int					     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__   :					Union[str, Any]					     =    ["""input_ids""", """attention_mask"""]


		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__="<unk>"			,     UpperCamelCase__="<s>"			,     UpperCamelCase__="</s>"			,     UpperCamelCase__=None			,     UpperCamelCase__ = None			,     UpperCamelCase__=True			,     UpperCamelCase__=False			,     UpperCamelCase__=False			,     UpperCamelCase__=True			,     **UpperCamelCase__			,     ):
						SCREAMING_SNAKE_CASE_ :						str    				= {} if sp_model_kwargs is None else sp_model_kwargs
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= AddedToken(UpperCamelCase__			,     lstrip=UpperCamelCase__			,     rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__			,     UpperCamelCase__							) else bos_token
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= AddedToken(UpperCamelCase__			,     lstrip=UpperCamelCase__			,     rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__			,     UpperCamelCase__							) else eos_token
						SCREAMING_SNAKE_CASE_ :						str    				= AddedToken(UpperCamelCase__			,     lstrip=UpperCamelCase__			,     rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__			,     UpperCamelCase__							) else unk_token
						SCREAMING_SNAKE_CASE_ :						int    				= AddedToken(UpperCamelCase__			,     lstrip=UpperCamelCase__			,     rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__			,     UpperCamelCase__							) else pad_token
						super().__init__(
						    bos_token=UpperCamelCase__			,     eos_token=UpperCamelCase__			,     unk_token=UpperCamelCase__			,     pad_token=UpperCamelCase__			,     add_bos_token=UpperCamelCase__			,     add_eos_token=UpperCamelCase__			,     sp_model_kwargs=self.sp_model_kwargs			,     clean_up_tokenization_spaces=UpperCamelCase__			,     legacy=UpperCamelCase__			,     **UpperCamelCase__			,     )
						if legacy:
										logger.warning_once(
										    F'''You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'''
										    ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'							)
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= legacy
						SCREAMING_SNAKE_CASE_ :						Dict    				= vocab_file
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= add_bos_token
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= add_eos_token
						SCREAMING_SNAKE_CASE_ :						Any    				= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.Load(UpperCamelCase__							)


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


		def __setstate__(    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						str    				= d
						SCREAMING_SNAKE_CASE_ :						str    				= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
						self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)


		@property
		def 	__snake_case      (    self							):
						return self.sp_model.get_piece_size()


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Tuple    				= {self.convert_ids_to_tokens(UpperCamelCase__							): i for i in range(self.vocab_size							)}
						vocab.update(self.added_tokens_encoder							)
						return vocab


		def 	__snake_case      (    self			,     UpperCamelCase__			,     **UpperCamelCase__							):
						# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
						# the beginning of the text
						if not self.legacy:
										SCREAMING_SNAKE_CASE_ :						List[str]    				= SPIECE_UNDERLINE + text.replace(UpperCamelCase__			,     ' '							)
						return super().tokenize(UpperCamelCase__			,     **UpperCamelCase__							)


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						if not self.legacy:
										SCREAMING_SNAKE_CASE_ :						Any    				= text.startswith(UpperCamelCase__							)
										if is_first:
														SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= text[1:]

						SCREAMING_SNAKE_CASE_ :						Tuple    				= self.sp_model.encode(UpperCamelCase__			,     out_type=UpperCamelCase__							)

						if not self.legacy and not is_first and not text.startswith(' '							) and tokens[0].startswith(UpperCamelCase__							):
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ([tokens[0][1:]] if len(tokens[0]							) > 1 else []) + tokens[1:]
						return tokens


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						return self.sp_model.piece_to_id(UpperCamelCase__							)


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.sp_model.IdToPiece(UpperCamelCase__							)
						return token


		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						List[str]    				= []
						SCREAMING_SNAKE_CASE_ :						int    				= ''
						SCREAMING_SNAKE_CASE_ :						List[str]    				= False
						for i, token in enumerate(UpperCamelCase__							):
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
														if not prev_is_special and i != 0:
																		out_string += " "
														out_string += self.sp_model.decode(UpperCamelCase__							) + token
														SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= True
														SCREAMING_SNAKE_CASE_ :						str    				= []
										else:
														current_sub_tokens.append(UpperCamelCase__							)
														SCREAMING_SNAKE_CASE_ :						int    				= False
						out_string += self.sp_model.decode(UpperCamelCase__							)
						return out_string


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

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

						return (out_vocab_file,)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__=None							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= [self.bos_token_id] if self.add_bos_token else []
						SCREAMING_SNAKE_CASE_ :						List[str]    				= [self.eos_token_id] if self.add_eos_token else []

						SCREAMING_SNAKE_CASE_ :						int    				= bos_token_id + token_ids_a + eos_token_id

						if token_ids_a is not None:
										SCREAMING_SNAKE_CASE_ :						int    				= output + bos_token_id + token_ids_a + eos_token_id

						return output


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__ = None			,     UpperCamelCase__ = False							):
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=UpperCamelCase__			,     token_ids_a=UpperCamelCase__			,     already_has_special_tokens=UpperCamelCase__							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= [1] if self.add_bos_token else []
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= [1] if self.add_eos_token else []

						if token_ids_a is None:
										return bos_token_id + ([0] * len(UpperCamelCase__							)) + eos_token_id
						return (
						    bos_token_id
						    + ([0] * len(UpperCamelCase__							))
						    + eos_token_id
						    + bos_token_id
						    + ([0] * len(UpperCamelCase__							))
						    + eos_token_id
						)


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__ = None							):
						SCREAMING_SNAKE_CASE_ :						Any    				= [self.bos_token_id] if self.add_bos_token else []
						SCREAMING_SNAKE_CASE_ :						Tuple    				= [self.eos_token_id] if self.add_eos_token else []

						SCREAMING_SNAKE_CASE_ :						Any    				= [0] * len(bos_token_id + token_ids_a + eos_token_id							)

						if token_ids_a is not None:
										output += [1] * len(bos_token_id + token_ids_a + eos_token_id							)

						return output






		def 	__snake_case      (    self			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						int    				= list(conversation.iter_texts()							)
						if not all([is_user for is_user, msg in dialogue[::2]]							) or not all(
						    [not is_user for is_user, msg in dialogue[1::2]]							):
										raise ValueError(
										    'The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'							)

						SCREAMING_SNAKE_CASE_ :						List[int]    				= []
						if len(conversation.past_user_inputs							) > 0:
										if not conversation.past_user_inputs[0].startswith(UpperCamelCase__							) or E_SYS not in conversation.past_user_inputs[0]:
														SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= (
														    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
														)
						elif not dialogue[0][1].startswith(UpperCamelCase__							) or E_SYS not in dialogue[0][1]:
										SCREAMING_SNAKE_CASE_ :						Tuple    				= (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

						dialog_tokens += sum(
						    [
						        [self.bos_token_id]
						        + self.encode(
						            F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''			,     add_special_tokens=UpperCamelCase__							)
						        + [self.eos_token_id]
						        for prompt, answer in zip(dialogue[::2]			,     dialogue[1::2]							)
						    ]			,     []			,     )
						if not (dialogue[-1][0]):
										raise ValueError(F'''Last message must be from user, got {dialogue[-1]['role']}'''							)
						dialog_tokens += [self.bos_token_id] + self.encode(
						    F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''			,     add_special_tokens=UpperCamelCase__							)
						return dialog_tokens