






"""simple docstring"""



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class        lowercase__      (							snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:List[Any]             =			DebertaTokenizer
			_UpperCAmelCase		:Union[str, Any]             =			True
			_UpperCAmelCase		:Tuple             =			DebertaTokenizerFast


			def      UpperCAmelCase__						(				self					:  List[str] ):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase_	:							int									=[
										    "l",
										    "o",
										    "w",
										    "e",
										    "r",
										    "s",
										    "t",
										    "i",
										    "d",
										    "n",
										    "\u0120",
										    "\u0120l",
										    "\u0120n",
										    "\u0120lo",
										    "\u0120low",
										    "er",
										    "\u0120lowest",
										    "\u0120newer",
										    "\u0120wider",
										    "[UNK]",
										]
										lowerCamelCase_	:							Tuple									=dict(zip(snake_case__       ,	range(len(snake_case__ ) ) ) )
										lowerCamelCase_	:							Dict									=["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
										lowerCamelCase_	:							Dict									={"unk_token": "[UNK]"}

										lowerCamelCase_	:							str									=os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["vocab_file"] )
										lowerCamelCase_	:							str									=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(snake_case__ ) + "\n" )
										with open(self.merges_file       ,	"w"       ,	encoding="utf-8" ) as fp:
																	fp.write("\n".join(snake_case__ ) )


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	**snake_case__					:  List[str] ):
										kwargs.update(self.special_tokens_map )
										return self.tokenizer_class.from_pretrained(self.tmpdirname       ,	**snake_case__ )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  List[str] ):
										lowerCamelCase_	:							Dict									="lower newer"
										lowerCamelCase_	:							List[Any]									="lower newer"
										return input_text, output_text


			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							Optional[int]									=self.get_tokenizer()
										lowerCamelCase_	:							Optional[int]									="lower newer"
										lowerCamelCase_	:							Optional[int]									=["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
										lowerCamelCase_	:							Optional[Any]									=tokenizer.tokenize(snake_case__ )
										self.assertListEqual(snake_case__       ,	snake_case__ )

										lowerCamelCase_	:							Any									=tokens + [tokenizer.unk_token]
										lowerCamelCase_	:							Tuple									=[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ )       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Union[str, Any]									=self.get_tokenizer()
										lowerCamelCase_	:							Union[str, Any]									=tokenizer("Hello"       ,	"World" )
										lowerCamelCase_	:							Any									=[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd["token_type_ids"]       ,	snake_case__ )


			@slow
			def      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							Tuple									=self.tokenizer_class.from_pretrained("microsoft/deberta-base" )

										lowerCamelCase_	:							Tuple									=tokenizer.encode("sequence builders"       ,	add_special_tokens=snake_case__ )
										lowerCamelCase_	:							Optional[Any]									=tokenizer.encode("multi-sequence build"       ,	add_special_tokens=snake_case__ )

										lowerCamelCase_	:							Optional[Any]									=tokenizer.encode(
										    "sequence builders"       ,	add_special_tokens=snake_case__       ,	add_prefix_space=snake_case__ )
										lowerCamelCase_	:							List[Any]									=tokenizer.encode(
										    "sequence builders"       ,	"multi-sequence build"       ,	add_special_tokens=snake_case__       ,	add_prefix_space=snake_case__ )

										lowerCamelCase_	:							Union[str, Any]									=tokenizer.build_inputs_with_special_tokens(snake_case__ )
										lowerCamelCase_	:							Union[str, Any]									=tokenizer.build_inputs_with_special_tokens(snake_case__       ,	snake_case__ )

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode


			@slow
			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										lowerCamelCase_	:							Union[str, Any]									=[self.tokenizer_class]
										if self.test_rust_tokenizer:
																	tokenizer_classes.append(self.rust_tokenizer_class )

										for tokenizer_class in tokenizer_classes:
																	lowerCamelCase_	:							Optional[int]									=tokenizer_class.from_pretrained("microsoft/deberta-base" )

																	lowerCamelCase_	:							Optional[int]									=[
																	    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
																	    "ALBERT incorporates two parameter reduction techniques",
																	    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
																	    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
																	    " vocabulary embedding.",
																	]

																	lowerCamelCase_	:							int									=tokenizer(snake_case__       ,	padding=snake_case__ )
																	lowerCamelCase_	:							List[str]									=[tokenizer.decode(snake_case__       ,	skip_special_tokens=snake_case__ ) for seq in encoding["input_ids"]]

																	# fmt: off
																	lowerCamelCase_	:							Optional[Any]									={
																	    "input_ids": [
																	        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																	        [1, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																	        [1, 133, 78, 65, 16, 10, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 4, 2]
																	    ],
																	    "token_type_ids": [
																	        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																	        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																	        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																	    ],
																	    "attention_mask": [
																	        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																	        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																	        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
																	    ]
																	}
																	# fmt: on

																	lowerCamelCase_	:							int									=[
																	    "ALBERT: A Lite BERT for Self-supervised Learning of Language Representations",
																	    "ALBERT incorporates two parameter reduction techniques",
																	    "The first one is a factorized embedding parameterization. By decomposing the large vocabulary"
																	    " embedding matrix into two small matrices, we separate the size of the hidden layers from the size of"
																	    " vocabulary embedding.",
																	]

																	self.assertDictEqual(encoding.data       ,	snake_case__ )

																	for expected, decoded in zip(snake_case__       ,	snake_case__ ):
																								self.assertEqual(snake_case__       ,	snake_case__ )











"""simple docstring"""



from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


A__	:    Dict			 = logging.get_logger(__name__)

A__	:    Dict			 = {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

A__	:    List[Any]			 = {
    'vocab_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
    },
    'merges_file': {
        'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
    },
    'tokenizer_config_file': {
        'facebook/blenderbot_small-90M': (
            'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
        )
    },
}

A__	:    Optional[int]			 = {
    'facebook/blenderbot_small-90M': 512,
}







class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:Optional[int]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Tuple             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:Any             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Tuple             =			BlenderbotSmallTokenizer


			def __init__(				self					:  Tuple       ,	snake_case__					:  Optional[Any]=None       ,	snake_case__					:  str=None       ,	snake_case__					:  Any="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  Tuple="<|endoftext|>"       ,	snake_case__					:  str=False       ,	snake_case__					:  int=True       ,	**snake_case__					:  Tuple       ,	):
										super().__init__(
										    ByteLevelBPETokenizer(
										        vocab=snake_case__       ,	merges=snake_case__       ,	add_prefix_space=snake_case__       ,	trim_offsets=snake_case__       ,	)       ,	bos_token=snake_case__       ,	eos_token=snake_case__       ,	unk_token=snake_case__       ,	**snake_case__       ,	)
										lowerCamelCase_	:							Optional[int]									=add_prefix_space


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]=None ):
										lowerCamelCase_	:							Optional[Any]									=[self.bos_token_id] + token_ids_a + [self.eos_token_id]
										if token_ids_a is None:
																	return output

										return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  List[int]       ,	snake_case__					:  Optional[List[int]] = None ):
										lowerCamelCase_	:							int									=[self.sep_token_id]
										lowerCamelCase_	:							List[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]



