
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


__magic_name__									=					logging.get_logger(__name__)

__magic_name__									=					{
    '''xlm-mlm-en-2048''': '''https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json''',
    '''xlm-mlm-ende-1024''': '''https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-enfr-1024''': '''https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json''',
    '''xlm-mlm-enro-1024''': '''https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json''',
    '''xlm-mlm-tlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-mlm-xnli15-1024''': '''https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json''',
    '''xlm-clm-enfr-1024''': '''https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json''',
    '''xlm-clm-ende-1024''': '''https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json''',
    '''xlm-mlm-17-1280''': '''https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json''',
    '''xlm-mlm-100-1280''': '''https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json''',
}


class  _lowerCAmelCase    (    _UpperCamelCase    ):
							lowercase_  :  Dict      =				'xlm'
							lowercase_  :  Optional[int]      =				{
							    'hidden_size': 'emb_dim',
							    'num_attention_heads': 'n_heads',
							    'num_hidden_layers': 'n_layers',
							    'n_words': 'vocab_size',  # For backward compatibility
							}



							def __init__(   self			,    a_=30145			,    a_=2048			,    a_=12			,    a_=16			,    a_=0.1			,    a_=0.1			,    a_=True			,    a_=False			,    a_=False			,    a_=False			,    a_=1			,    a_=True			,    a_=512			,    a_=2048**-0.5			,    a_=1e-12			,    a_=0.02			,    a_=0			,    a_=1			,    a_=2			,    a_=3			,    a_=5			,    a_=True			,    a_="first"			,    a_=True			,    a_=None			,    a_=True			,    a_=0.1			,    a_=5			,    a_=5			,    a_=0			,    a_=0			,    a_=2			,    a_=0			,    **a_			,    )				->				Any:
								_UpperCAmelCase			=    vocab_size
								_UpperCAmelCase			=    emb_dim
								_UpperCAmelCase			=    n_layers
								_UpperCAmelCase			=    n_heads
								_UpperCAmelCase			=    dropout
								_UpperCAmelCase			=    attention_dropout
								_UpperCAmelCase			=    gelu_activation
								_UpperCAmelCase			=    sinusoidal_embeddings
								_UpperCAmelCase			=    causal
								_UpperCAmelCase			=    asm
								_UpperCAmelCase			=    n_langs
								_UpperCAmelCase			=    use_lang_emb
								_UpperCAmelCase			=    layer_norm_eps
								_UpperCAmelCase			=    bos_index
								_UpperCAmelCase			=    eos_index
								_UpperCAmelCase			=    pad_index
								_UpperCAmelCase			=    unk_index
								_UpperCAmelCase			=    mask_index
								_UpperCAmelCase			=    is_encoder
								_UpperCAmelCase			=    max_position_embeddings
								_UpperCAmelCase			=    embed_init_std
								_UpperCAmelCase			=    init_std
								_UpperCAmelCase			=    summary_type
								_UpperCAmelCase			=    summary_use_proj
								_UpperCAmelCase			=    summary_activation
								_UpperCAmelCase			=    summary_proj_to_labels
								_UpperCAmelCase			=    summary_first_dropout
								_UpperCAmelCase			=    start_n_top
								_UpperCAmelCase			=    end_n_top
								_UpperCAmelCase			=    mask_token_id
								_UpperCAmelCase			=    lang_id

								if "n_words" in kwargs:
									_UpperCAmelCase			=    kwargs["""n_words"""]

								super().__init__(pad_token_id=a_			,    bos_token_id=a_			,    **a_ )





class  _lowerCAmelCase    (    _UpperCamelCase    ):



							@property
							def    _a (   self )				->				Mapping[str, Mapping[int, str]]:







								if self.task == "multiple-choice":
									_UpperCAmelCase			=    {0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
									_UpperCAmelCase			=    {0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ] )










import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate


@dataclass(frozen=_UpperCamelCase   )
class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase   ):



	"""simple docstring"""


	__magic_name__   :       str      	=		field(default='image-classification'	,  metadata={'include_in_asdict_even_if_is_default': True}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'image': Image()}   )
	__magic_name__   :       ClassVar[Features]      	=		Features({'labels': ClassLabel}   )
	__magic_name__   :       str      	=		"image"
	__magic_name__   :       str      	=		"labels"






	def 					lowerCamelCase__     (					self		:						Any					,     lowerCAmelCase		:						Optional[Any]	)	->       Union[str, Any]:







						"""simple docstring"""







						if self.label_column not in features:
											raise ValueError(F'''Column {self.label_column} is not present in features.'''	)
						if not isinstance(features[self.label_column]					,     lowerCAmelCase	):
											raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''	)
						__UpperCamelCase					:	List[str]             =      copy.deepcopy(self	)
						__UpperCamelCase					:	List[Any]             =      self.label_schema.copy()
						__UpperCamelCase					:	List[str]             =      features[self.label_column]
						__UpperCamelCase					:	List[str]             =      label_schema
						return task_template






	@property
	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       Dict[str, str]:







						"""simple docstring"""







						return {
						    self.image_column: "image",
						    self.label_column: "labels",
						}






