


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_    :					str             =logging.get_logger(__name__)

A_    :					Union[str, Any]             ={"""vocab_file""": """tokenizer.model"""}

A_    :					Any             ={
    """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_    :					Optional[int]             ={
    """hf-internal-testing/llama-tokenizer""": 2048,
}
A_    :					Optional[Any]             ="""▁"""

A_    :					Optional[Any]             ="""[INST]""", """[/INST]"""
A_    :					Optional[int]             ="""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
A_    :					Optional[int]             ="""You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""





class        lowercase_				( UpperCamelCase__):






       """simple docstring"""







       snake_case_        =		VOCAB_FILES_NAMES
       snake_case_        =		PRETRAINED_VOCAB_FILES_MAP
       snake_case_        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       snake_case_        =		['''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			,    ):







             """simple docstring"""


             a_					=		{} if sp_model_kwargs is None else sp_model_kwargs
             a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else bos_token
             a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else eos_token
             a_					=		AddedToken(_UpperCAmelCase			,    lstrip=_UpperCAmelCase			,    rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) else unk_token
             a_					=		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"""		)
             a_					=		legacy
             a_					=		vocab_file
             a_					=		add_bos_token
             a_					=		add_eos_token
             a_					=		spm.SentencePieceProcessor(**self.sp_model_kwargs		)
             self.sp_model.Load(_UpperCAmelCase		)


       def __getstate__(   self		):







             """simple docstring"""


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


       def __setstate__(   self			,    _UpperCAmelCase		):







             """simple docstring"""


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


       @property
       def 						lowercase__     (   self		):







             """simple docstring"""


             return self.sp_model.get_piece_size()


       def 						lowercase__     (   self		):







             """simple docstring"""


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


       def 						lowercase__     (   self			,    _UpperCAmelCase			,    **_UpperCAmelCase		):







             """simple docstring"""


             if not self.legacy:
                   a_					=		SPIECE_UNDERLINE + text.replace(_UpperCAmelCase			,    """ """		)
             return super().tokenize(_UpperCAmelCase			,    **_UpperCAmelCase		)


       def 						lowercase__     (   self			,    _UpperCAmelCase		):







             """simple docstring"""


             if not self.legacy:
                   a_					=		text.startswith(_UpperCAmelCase		)
                   if is_first:
                         a_					=		text[1:]

             a_					=		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		):
                   a_					=		([tokens[0][1:]] if len(tokens[0]		) > 1 else []) + tokens[1:]
             return tokens


       def 						lowercase__     (   self			,    _UpperCAmelCase		):







             """simple docstring"""


             return self.sp_model.piece_to_id(_UpperCAmelCase		)


       def 						lowercase__     (   self			,    _UpperCAmelCase		):







             """simple docstring"""


             a_					=		self.sp_model.IdToPiece(_UpperCAmelCase		)
             return token


       def 						lowercase__     (   self			,    _UpperCAmelCase		):







             """simple docstring"""


             a_					=		[]
             a_					=		""""""
             a_					=		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
                         a_					=		True
                         a_					=		[]
                   else:
                         current_sub_tokens.append(_UpperCAmelCase		)
                         a_					=		False
             out_string += self.sp_model.decode(_UpperCAmelCase		)
             return out_string


       def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):







             """simple docstring"""


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

             return (out_vocab_file,)


       def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase=None		):







             """simple docstring"""


             a_					=		[self.bos_token_id] if self.add_bos_token else []
             a_					=		[self.eos_token_id] if self.add_eos_token else []

             a_					=		bos_token_id + token_ids_a + eos_token_id

             if token_ids_a is not None:
                   a_					=		output + bos_token_id + token_ids_a + eos_token_id

             return output


       def 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None			,    _UpperCAmelCase = False		):







             """simple docstring"""


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

             a_					=		[1] if self.add_bos_token else []
             a_					=		[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 						lowercase__     (   self			,    _UpperCAmelCase			,    _UpperCAmelCase = None		):







             """simple docstring"""


             a_					=		[self.bos_token_id] if self.add_bos_token else []
             a_					=		[self.eos_token_id] if self.add_eos_token else []

             a_					=		[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 						lowercase__     (   self			,    _UpperCAmelCase		):







             """simple docstring"""


             a_					=		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...)"""		)

             a_					=		[]
             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]:
                         a_					=		(
                             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]:
                   a_					=		(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



import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class        lowercase_				( UpperCamelCase__			,unittest.TestCase):






							"""simple docstring"""







							snake_case_        =		BartTokenizer
							snake_case_        =		BartTokenizerFast
							snake_case_        =		True
							snake_case_        =		filter_roberta_detectors


							def 						lowercase__     (   self		):







													"""simple docstring"""


													super().setUp()
													a_					=		[
													    """l""",
													    """o""",
													    """w""",
													    """e""",
													    """r""",
													    """s""",
													    """t""",
													    """i""",
													    """d""",
													    """n""",
													    """\u0120""",
													    """\u0120l""",
													    """\u0120n""",
													    """\u0120lo""",
													    """\u0120low""",
													    """er""",
													    """\u0120lowest""",
													    """\u0120newer""",
													    """\u0120wider""",
													    """<unk>""",
													]
													a_					=		dict(zip(_UpperCAmelCase			,    range(len(_UpperCAmelCase		)		)		)		)
													a_					=		["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
													a_					=		{"""unk_token""": """<unk>"""}

													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"""			,    encoding="""utf-8"""		) as fp:
																			fp.write("""\n""".join(_UpperCAmelCase		)		)


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													kwargs.update(self.special_tokens_map		)
													return self.tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)


							def 						lowercase__     (   self			,    **_UpperCAmelCase		):







													"""simple docstring"""


													kwargs.update(self.special_tokens_map		)
													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,    **_UpperCAmelCase		)


							def 						lowercase__     (   self			,    _UpperCAmelCase		):







													"""simple docstring"""


													return "lower newer", "lower newer"


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													return BartTokenizer.from_pretrained("""facebook/bart-large"""		)


							@cached_property
							def 						lowercase__     (   self		):







													"""simple docstring"""


													return BartTokenizerFast.from_pretrained("""facebook/bart-large"""		)


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
													a_					=		[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a_					=		tokenizer(_UpperCAmelCase			,    max_length=len(_UpperCAmelCase		)			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)

																			self.assertEqual((2, 9)			,    batch.input_ids.shape		)
																			self.assertEqual((2, 9)			,    batch.attention_mask.shape		)
																			a_					=		batch.input_ids.tolist()[0]
																			self.assertListEqual(_UpperCAmelCase			,    _UpperCAmelCase		)
																			# Test that special tokens are reset


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a_					=		tokenizer(_UpperCAmelCase			,    padding=_UpperCAmelCase			,    return_tensors="""pt"""		)
																			# check if input_ids are returned and no labels
																			self.assertIn("""input_ids"""			,    _UpperCAmelCase		)
																			self.assertIn("""attention_mask"""			,    _UpperCAmelCase		)
																			self.assertNotIn("""labels"""			,    _UpperCAmelCase		)
																			self.assertNotIn("""decoder_attention_mask"""			,    _UpperCAmelCase		)


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		[
													    """Summary of the text.""",
													    """Another summary.""",
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    max_length=32			,    padding="""max_length"""			,    return_tensors="""pt"""		)
																			self.assertEqual(32			,    targets["""input_ids"""].shape[1]		)


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a_					=		tokenizer(
																			    ["""I am a small frog""" * 1_024, """I am a small frog"""]			,    padding=_UpperCAmelCase			,    truncation=_UpperCAmelCase			,    return_tensors="""pt"""		)
																			self.assertIsInstance(_UpperCAmelCase			,    _UpperCAmelCase		)
																			self.assertEqual(batch.input_ids.shape			,    (2, 1_024)		)


							@require_torch
							def 						lowercase__     (   self		):







													"""simple docstring"""


													a_					=		["""A long paragraph for summarization."""]
													a_					=		[
													    """Summary of the text.""",
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			a_					=		tokenizer(_UpperCAmelCase			,    return_tensors="""pt"""		)
																			a_					=		tokenizer(text_target=_UpperCAmelCase			,    return_tensors="""pt"""		)
																			a_					=		inputs["""input_ids"""]
																			a_					=		targets["""input_ids"""]
																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)


							def 						lowercase__     (   self		):







													"""simple docstring"""


													pass






							def 						lowercase__     (   self		):







													"""simple docstring"""


													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
																									a_					=		self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
																									a_					=		self.tokenizer_class.from_pretrained(_UpperCAmelCase			,    **_UpperCAmelCase		)
																									a_					=		"""A, <mask> AllenNLP sentence."""
																									a_					=		tokenizer_r.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)
																									a_					=		tokenizer_p.encode_plus(_UpperCAmelCase			,    add_special_tokens=_UpperCAmelCase			,    return_token_type_ids=_UpperCAmelCase		)

																									# token_type_ids should put 0 everywhere
																									self.assertEqual(sum(tokens_r["""token_type_ids"""]		)			,    sum(tokens_p["""token_type_ids"""]		)		)

																									# attention_mask should put 1 everywhere, so sum over length should be 1
																									self.assertEqual(
																									    sum(tokens_r["""attention_mask"""]		) / len(tokens_r["""attention_mask"""]		)			,    sum(tokens_p["""attention_mask"""]		) / len(tokens_p["""attention_mask"""]		)			,    )

																									a_					=		tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]		)
																									a_					=		tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]		)

																									# Rust correctly handles the space before the mask while python doesnt
																									self.assertSequenceEqual(tokens_p["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
																									self.assertSequenceEqual(tokens_r["""input_ids"""]			,    [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)

																									self.assertSequenceEqual(
																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)
																									self.assertSequenceEqual(
																									    _UpperCAmelCase			,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]		)