



import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin




class 							lowerCAmelCase__  (	UpperCamelCase					,							unittest.TestCase						):
					__A     :    Tuple           =						CTRLTokenizer
					__A     :    int           =						False
					__A     :    Optional[int]           =						False







					def  _lowercase			(       self      :     Optional[int]):
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											A__	:							int						    = ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
											A__	:							Dict						    = dict(zip(_A			,		range(len(_A))))
											A__	:							str						    = ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
											A__	:							int						    = {"unk_token": "<unk>"}

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







					def  _lowercase			(       self      :     Optional[Any]			,		**_A      :     Union[str, Any]):
											kwargs.update(self.special_tokens_map)
											return CTRLTokenizer.from_pretrained(self.tmpdirname			,		**_A)







					def  _lowercase			(       self      :     int			,		_A      :     Optional[int]):
											A__	:							int						    = "adapt react readapt apt"
											A__	:							Union[str, Any]						    = "adapt react readapt apt"
											return input_text, output_text







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							List[Any]						    = CTRLTokenizer(self.vocab_file			,		self.merges_file			,		**self.special_tokens_map)
											A__	:							Any						    = "adapt react readapt apt"
											A__	:							List[Any]						    = "adapt re@@ a@@ c@@ t re@@ adapt apt".split()
											A__	:							Optional[Any]						    = tokenizer.tokenize(_A)
											self.assertListEqual(_A			,		_A)

											A__	:							str						    = tokens + [tokenizer.unk_token]

											A__	:							Union[str, Any]						    = [0, 1, 2, 4, 5, 1, 0, 3, 6]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)			,		_A)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case    :						Optional[int]  =						logging.get_logger(__name__)

snake_case    :						Union[str, Any]  =						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Optional[Any]           =						'data2vec-text'







					def __init__(       self      :     Optional[Any]			,		_A      :     List[str]=3_0522			,		_A      :     Optional[int]=768			,		_A      :     Union[str, Any]=12			,		_A      :     str=12			,		_A      :     Optional[int]=3072			,		_A      :     Dict="gelu"			,		_A      :     List[str]=0.1			,		_A      :     Any=0.1			,		_A      :     str=512			,		_A      :     Any=2			,		_A      :     str=0.02			,		_A      :     List[str]=1e-12			,		_A      :     Optional[int]=1			,		_A      :     Any=0			,		_A      :     Any=2			,		_A      :     List[Any]="absolute"			,		_A      :     Tuple=True			,		_A      :     str=None			,		**_A      :     List[Any]			,		):
											super().__init__(pad_token_id=_A			,		bos_token_id=_A			,		eos_token_id=_A			,		**_A)

											A__	:							Optional[int]						    = vocab_size
											A__	:							Tuple						    = hidden_size
											A__	:							str						    = num_hidden_layers
											A__	:							Union[str, Any]						    = num_attention_heads
											A__	:							Dict						    = hidden_act
											A__	:							List[Any]						    = intermediate_size
											A__	:							int						    = hidden_dropout_prob
											A__	:							Dict						    = attention_probs_dropout_prob
											A__	:							Union[str, Any]						    = max_position_embeddings
											A__	:							Tuple						    = type_vocab_size
											A__	:							Union[str, Any]						    = initializer_range
											A__	:							str						    = layer_norm_eps
											A__	:							int						    = position_embedding_type
											A__	:							str						    = use_cache
											A__	:							int						    = classifier_dropout




class 							lowerCAmelCase__  (	UpperCamelCase						):







					@property
					def  _lowercase			(       self      :     Tuple):
											if self.task == "multiple-choice":
																	A__	:							List[str]						    = {0: "batch", 1: "choice", 2: "sequence"}
											else:
																	A__	:							List[str]						    = {0: "batch", 1: "sequence"}
											return OrderedDict(
											    [
											        ("input_ids", dynamic_axis),
											        ("attention_mask", dynamic_axis),
											    ])