

"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_SCREAMING_SNAKE_CASE       :  Any        = logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE       :  List[Any]        = {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}

class     __a   (      snake_case__    ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_						     =						SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE_						     =						'squeezebert'






			def __init__(							self     :					Optional[Any]						,  lowercase_     :					List[Any]=3_0522						,  lowercase_     :					List[Any]=768						,  lowercase_     :					List[str]=12						,  lowercase_     :					str=12						,  lowercase_     :					Union[str, Any]=3072						,  lowercase_     :					int="gelu"						,  lowercase_     :					Union[str, Any]=0.1						,  lowercase_     :					Dict=0.1						,  lowercase_     :					Any=512						,  lowercase_     :					Union[str, Any]=2						,  lowercase_     :					Optional[Any]=0.0_2						,  lowercase_     :					Tuple=1e-12						,  lowercase_     :					Optional[int]=0						,  lowercase_     :					List[Any]=768						,  lowercase_     :					int=4						,  lowercase_     :					List[str]=4						,  lowercase_     :					Dict=4						,  lowercase_     :					Union[str, Any]=1						,  lowercase_     :					Optional[Any]=4						,  lowercase_     :					List[Any]=4						,  **lowercase_     :					Dict						,  ):
								super().__init__(pad_token_id=lowercase_						,  **lowercase_						)

								UpperCamelCase__		:    Any         =vocab_size
								UpperCamelCase__		:    Optional[int]         =hidden_size
								UpperCamelCase__		:    str         =num_hidden_layers
								UpperCamelCase__		:    Union[str, Any]         =num_attention_heads
								UpperCamelCase__		:    Any         =hidden_act
								UpperCamelCase__		:    Any         =intermediate_size
								UpperCamelCase__		:    Any         =hidden_dropout_prob
								UpperCamelCase__		:    Union[str, Any]         =attention_probs_dropout_prob
								UpperCamelCase__		:    Optional[int]         =max_position_embeddings
								UpperCamelCase__		:    Optional[Any]         =type_vocab_size
								UpperCamelCase__		:    List[Any]         =initializer_range
								UpperCamelCase__		:    Optional[int]         =layer_norm_eps
								UpperCamelCase__		:    Optional[int]         =embedding_size
								UpperCamelCase__		:    List[Any]         =q_groups
								UpperCamelCase__		:    List[Any]         =k_groups
								UpperCamelCase__		:    List[str]         =v_groups
								UpperCamelCase__		:    Any         =post_attention_groups
								UpperCamelCase__		:    List[str]         =intermediate_groups
								UpperCamelCase__		:    Union[str, Any]         =output_groups



class     __a   (      snake_case__    ):

			"""simple docstring"""

			@property
			def    _lowerCAmelCase			(							self     :					Dict						):
								if self.task == "multiple-choice":
													UpperCamelCase__		:    Dict         ={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
								else:
													UpperCamelCase__		:    List[Any]         ={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_sentencepiece_available, is_torch_available


_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = {
    """configuration_xlm_prophetnet""": ["""XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMProphetNetConfig"""],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = ["""XLMProphetNetTokenizer"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = [
				    """XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """XLMProphetNetDecoder""",
				    """XLMProphetNetEncoder""",
				    """XLMProphetNetForCausalLM""",
				    """XLMProphetNetForConditionalGeneration""",
				    """XLMProphetNetModel""",
				    """XLMProphetNetPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xlm_prophetnet import (
								    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    XLMProphetNetDecoder,
								    XLMProphetNetEncoder,
								    XLMProphetNetForCausalLM,
								    XLMProphetNetForConditionalGeneration,
								    XLMProphetNetModel,
								    XLMProphetNetPreTrainedModel,
								)

else:
				import sys

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


