


"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


lowercase_			     =  logging.get_logger(__name__)

lowercase_			     =  {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}
class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[int]										=				'xmod'

		def __init__(  self							, _a=30_522							, _a=768							, _a=12							, _a=12							, _a=3_072							, _a="gelu"							, _a=0.1							, _a=0.1							, _a=512							, _a=2							, _a=0.02							, _a=1E-12							, _a=1							, _a=0							, _a=2							, _a="absolute"							, _a=True							, _a=None							, _a=False							, _a=2							, _a=False							, _a=True							, _a=True							, _a=("en_XX",)							, _a=None							, **_a							, ):
				super().__init__(pad_token_id=_a							, bos_token_id=_a							, eos_token_id=_a							, **_a      )

				__a           =			vocab_size
				__a           =			hidden_size
				__a           =			num_hidden_layers
				__a           =			num_attention_heads
				__a           =			hidden_act
				__a           =			intermediate_size
				__a           =			hidden_dropout_prob
				__a           =			attention_probs_dropout_prob
				__a           =			max_position_embeddings
				__a           =			type_vocab_size
				__a           =			initializer_range
				__a           =			layer_norm_eps
				__a           =			position_embedding_type
				__a           =			use_cache
				__a           =			classifier_dropout
				__a           =			pre_norm
				__a           =			adapter_reduction_factor
				__a           =			adapter_layer_norm
				__a           =			adapter_reuse_layer_norm
				__a           =			ln_before_adapter
				__a           =			list(_a      )
				__a           =			default_language







class 					__lowerCAmelCase					(   __SCREAMING_SNAKE_CASE       ):


		'''simple docstring'''



		@property
		def 			__UpperCAmelCase       (  self      ):
				if self.task == "multiple-choice":
						__a           =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
						__a           =			{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				    ]      )










'''simple docstring'''



import os
import sys

__lowercase	:						List[Any]       =	os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__lowercase	:						int       =	[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[str]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoConfig.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoTokenizer.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   str						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoTokenizer.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModel.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int						,      **_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					return AutoModel.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForCausalLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[Any]						,      **_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					return AutoModelForCausalLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForMaskedLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoModelForMaskedLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForSequenceClassification.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoModelForSequenceClassification.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Any						,      **_SCREAMING_SNAKE_CASE      :   List[str]   ):
					return AutoModelForQuestionAnswering.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )



