



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),
											    ])




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin




class 							lowerCAmelCase__  (	unittest.TestCase					,							UpperCamelCase						):







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[Any]						    = load_tool("text-classification")
											self.tool.setup()
											A__	:							Any						    = load_tool("text-classification"			,		remote=_A)







					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[Any]						    = self.tool("That's quite cool"			,		["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     List[str]):
											A__	:							Any						    = self.remote_tool("That's quite cool"			,		["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							List[str]						    = self.tool(text="That's quite cool"			,		labels=["positive", "negative"])
											self.assertEqual(_A			,		"positive")







					def  _lowercase			(       self      :     Tuple):
											A__	:							Union[str, Any]						    = self.remote_tool(text="That's quite cool"			,		labels=["positive", "negative"])
											self.assertEqual(_A			,		"positive")