





'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_SCREAMING_SNAKE_CASE   =      {
    '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
    '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/config.json''',
    '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/config.json''',
    '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json''',
    '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/config.json''',
    '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/config.json''',
    '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/config.json''',
    '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json''',
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			List[Any]     =    "albert"


	def __init__(self       ,_lowerCamelCase=30000       ,_lowerCamelCase=128       ,_lowerCamelCase=4096       ,_lowerCamelCase=12       ,_lowerCamelCase=1       ,_lowerCamelCase=64       ,_lowerCamelCase=16384       ,_lowerCamelCase=1       ,_lowerCamelCase="gelu_new"       ,_lowerCamelCase=0       ,_lowerCamelCase=0       ,_lowerCamelCase=512       ,_lowerCamelCase=2       ,_lowerCamelCase=0.0_2       ,_lowerCamelCase=1E-1_2       ,_lowerCamelCase=0.1       ,_lowerCamelCase="absolute"       ,_lowerCamelCase=0       ,_lowerCamelCase=2       ,_lowerCamelCase=3       ,**_lowerCamelCase       ,)							->							Any:







				'''simple docstring'''





				super().__init__(pad_token_id=_lowerCamelCase       ,bos_token_id=_lowerCamelCase       ,eos_token_id=_lowerCamelCase       ,**_lowerCamelCase	)

				__lowercase			  =							vocab_size
				__lowercase			  =							embedding_size
				__lowercase			  =							hidden_size
				__lowercase			  =							num_hidden_layers
				__lowercase			  =							num_hidden_groups
				__lowercase			  =							num_attention_heads
				__lowercase			  =							inner_group_num
				__lowercase			  =							hidden_act
				__lowercase			  =							intermediate_size
				__lowercase			  =							hidden_dropout_prob
				__lowercase			  =							attention_probs_dropout_prob
				__lowercase			  =							max_position_embeddings
				__lowercase			  =							type_vocab_size
				__lowercase			  =							initializer_range
				__lowercase			  =							layer_norm_eps
				__lowercase			  =							classifier_dropout_prob
				__lowercase			  =							position_embedding_type






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

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







				'''simple docstring'''





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












'''simple docstring'''



from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''MvpTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''MvpForCausalLM''',
	    '''MvpForConditionalGeneration''',
	    '''MvpForQuestionAnswering''',
	    '''MvpForSequenceClassification''',
	    '''MvpModel''',
	    '''MvpPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
	from .tokenization_mvp import MvpTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mvp_fast import MvpTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mvp import (
		    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MvpForCausalLM,
		    MvpForConditionalGeneration,
		    MvpForQuestionAnswering,
		    MvpForSequenceClassification,
		    MvpModel,
		    MvpPreTrainedModel,
		)

else:
	import sys

	_SCREAMING_SNAKE_CASE   =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





