


"""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),
				    ]      )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     Optional[int]       =  ["""BloomTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     str       =  [
		    """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BloomForCausalLM""",
		    """BloomModel""",
		    """BloomPreTrainedModel""",
		    """BloomForSequenceClassification""",
		    """BloomForTokenClassification""",
		    """BloomForQuestionAnswering""",
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		__a  :     Dict       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)