


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation

__snake_case            =   logging.get_logger(__name__)


__snake_case            =   {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__snake_case            =   {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__snake_case            =   {"facebook/blenderbot-3B": 128}




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= PRETRAINED_VOCAB_FILES_MAP
			lowercase						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= ["""input_ids""", """attention_mask"""]
			lowercase						= BlenderbotTokenizer




			def __init__(						self							:		List[Any]       ,       __magic_name__							:		int=None       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		List[str]=None       ,       __magic_name__							:		Any="replace"       ,       __magic_name__							:		Union[str, Any]="<s>"       ,       __magic_name__							:		Tuple="</s>"       ,       __magic_name__							:		Optional[Any]="</s>"       ,       __magic_name__							:		Optional[int]="<s>"       ,       __magic_name__							:		str="<unk>"       ,       __magic_name__							:		Tuple="<pad>"       ,       __magic_name__							:		Union[str, Any]="<mask>"       ,       __magic_name__							:		Union[str, Any]=False       ,       __magic_name__							:		Tuple=True       ,       **__magic_name__							:		str       ,       ):







						"""simple docstring"""







						super().__init__(
						    __magic_name__       ,       __magic_name__       ,       tokenizer_file=__magic_name__       ,       errors=__magic_name__       ,       bos_token=__magic_name__       ,       eos_token=__magic_name__       ,       sep_token=__magic_name__       ,       cls_token=__magic_name__       ,       unk_token=__magic_name__       ,       pad_token=__magic_name__       ,       mask_token=__magic_name__       ,       add_prefix_space=__magic_name__       ,       trim_offsets=__magic_name__       ,       **__magic_name__       ,       )

						UpperCamelCase         =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
						if pre_tok_state.get("""add_prefix_space"""       ,       __magic_name__						) != add_prefix_space:
									UpperCamelCase         =					getattr(__magic_name__       ,       pre_tok_state.pop("""type"""						)						)
									UpperCamelCase         =					add_prefix_space
									UpperCamelCase         =					pre_tok_class(**__magic_name__						)

						UpperCamelCase         =					add_prefix_space

						UpperCamelCase         =					"""post_processor"""
						UpperCamelCase         =					getattr(self.backend_tokenizer       ,       __magic_name__       ,       __magic_name__						)
						if tokenizer_component_instance:
									UpperCamelCase         =					json.loads(tokenizer_component_instance.__getstate__()						)

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
												UpperCamelCase         =					tuple(state["""sep"""]						)
									if "cls" in state:
												UpperCamelCase         =					tuple(state["""cls"""]						)

									UpperCamelCase         =					False

									if state.get("""add_prefix_space"""       ,       __magic_name__						) != add_prefix_space:
												UpperCamelCase         =					add_prefix_space
												UpperCamelCase         =					True

									if state.get("""trim_offsets"""       ,       __magic_name__						) != trim_offsets:
												UpperCamelCase         =					trim_offsets
												UpperCamelCase         =					True

									if changes_to_apply:
												UpperCamelCase         =					getattr(__magic_name__       ,       state.pop("""type"""						)						)
												UpperCamelCase         =					component_class(**__magic_name__						)
												setattr(self.backend_tokenizer       ,       __magic_name__       ,       __magic_name__						)




			@property
			# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
			def   lowerCamelCase_					(						self							:		Tuple						):







						"""simple docstring"""







						if self._mask_token is None:
									if self.verbose:
												logger.error("""Using mask_token, but it is not set yet."""						)
									return None
						return str(self._mask_token						)




			@mask_token.setter
			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					AddedToken(__magic_name__       ,       lstrip=__magic_name__       ,       rstrip=__magic_name__						) if isinstance(__magic_name__       ,       __magic_name__						) else value
						UpperCamelCase         =					value




			def   lowerCamelCase_					(						self							:		List[Any]       ,       *__magic_name__							:		int       ,       **__magic_name__							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					kwargs.get("""is_split_into_words"""       ,       __magic_name__						)
						assert self.add_prefix_space or not is_split_into_words, (
						    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
						    "to use it with pretokenized inputs."
						)

						return super()._batch_encode_plus(*__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Any       ,       *__magic_name__							:		int       ,       **__magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					kwargs.get("""is_split_into_words"""       ,       __magic_name__						)

						assert self.add_prefix_space or not is_split_into_words, (
						    F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
						    "to use it with pretokenized inputs."
						)

						return super()._encode_plus(*__magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str] = None						):







						"""simple docstring"""







						UpperCamelCase         =					self._tokenizer.model.save(__magic_name__       ,       name=__magic_name__						)
						return tuple(__magic_name__						)




			def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						UpperCamelCase         =					[self.sep_token_id]
						UpperCamelCase         =					[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]




			def   lowerCamelCase_					(						self							:		List[Any]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						return token_ids_a + [self.eos_token_id]





			def   lowerCamelCase_					(						self							:		str       ,       __magic_name__							:		"Conversation"						):







						"""simple docstring"""







						UpperCamelCase         =					[]
						for is_user, text in conversation.iter_texts():
									if is_user:
												# We need to space prefix as it's being done within blenderbot
												inputs.append(""" """ + text						)
									else:
												# Generated responses should contain them already.
												inputs.append(__magic_name__						)

						UpperCamelCase         =					"""  """.join(__magic_name__						)
						UpperCamelCase         =					self.encode(__magic_name__						)
						if len(__magic_name__						) > self.model_max_length:
									UpperCamelCase         =					input_ids[-self.model_max_length :]
									logger.warning(F'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'						)
						return input_ids








import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class    UpperCAmelCase					(       __snake_case      , unittest.TestCase    ):
			lowercase						= XLMTokenizer
			lowercase						= False




			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						UpperCamelCase         =					[
						    """l""",
						    """o""",
						    """w""",
						    """e""",
						    """r""",
						    """s""",
						    """t""",
						    """i""",
						    """d""",
						    """n""",
						    """w</w>""",
						    """r</w>""",
						    """t</w>""",
						    """lo""",
						    """low""",
						    """er</w>""",
						    """low</w>""",
						    """lowest</w>""",
						    """newer</w>""",
						    """wider</w>""",
						    """<unk>""",
						]
						UpperCamelCase         =					dict(zip(__magic_name__       ,       range(len(__magic_name__						)						)						)						)
						UpperCamelCase         =					["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""vocab_file"""]						)
						UpperCamelCase         =					os.path.join(self.tmpdirname       ,       VOCAB_FILES_NAMES["""merges_file"""]						)
						with open(self.vocab_file       ,       """w"""						) as fp:
									fp.write(json.dumps(__magic_name__						)						)
						with open(self.merges_file       ,       """w"""						) as fp:
									fp.write("""\n""".join(__magic_name__						)						)




			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					"""lower newer"""
						UpperCamelCase         =					"""lower newer"""
						return input_text, output_text




			def   lowerCamelCase_					(						self							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer(self.vocab_file       ,       self.merges_file						)

						UpperCamelCase         =					"""lower"""
						UpperCamelCase         =					["""low""", """er</w>"""]
						UpperCamelCase         =					tokenizer.tokenize(__magic_name__						)
						self.assertListEqual(__magic_name__       ,       __magic_name__						)

						UpperCamelCase         =					tokens + ["""<unk>"""]
						UpperCamelCase         =					[1_4, 1_5, 2_0]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__						)       ,       __magic_name__						)





			@slow
			def   lowerCamelCase_					(						self							:		Optional[int]						):







						"""simple docstring"""







						UpperCamelCase         =					XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""						)

						UpperCamelCase         =					tokenizer.encode("""sequence builders"""       ,       add_special_tokens=__magic_name__						)
						UpperCamelCase         =					tokenizer.encode("""multi-sequence build"""       ,       add_special_tokens=__magic_name__						)

						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__						)
						UpperCamelCase         =					tokenizer.build_inputs_with_special_tokens(__magic_name__       ,       __magic_name__						)

						assert encoded_sentence == [0] + text + [1]
						assert encoded_pair == [0] + text + [1] + text_a + [1]




