



'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase		    =     logging.get_logger(__name__)

__lowerCAmelCase		    =     {
    '''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 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					List[Any]            =      SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase :					Tuple            =      'squeezebert'







							def __init__(							self      : Dict			,_UpperCAmelCase      : int=30522			,_UpperCAmelCase      : Optional[Any]=768			,_UpperCAmelCase      : Tuple=12			,_UpperCAmelCase      : Dict=12			,_UpperCAmelCase      : Tuple=3072			,_UpperCAmelCase      : str="gelu"			,_UpperCAmelCase      : Dict=0.1			,_UpperCAmelCase      : Dict=0.1			,_UpperCAmelCase      : List[Any]=512			,_UpperCAmelCase      : Dict=2			,_UpperCAmelCase      : Dict=0.02			,_UpperCAmelCase      : Dict=1E-12			,_UpperCAmelCase      : Any=0			,_UpperCAmelCase      : Dict=768			,_UpperCAmelCase      : List[Any]=4			,_UpperCAmelCase      : Dict=4			,_UpperCAmelCase      : str=4			,_UpperCAmelCase      : Dict=1			,_UpperCAmelCase      : Any=4			,_UpperCAmelCase      : Any=4			,**_UpperCAmelCase      : Dict			,):
									super().__init__(pad_token_id=_UpperCAmelCase			,**_UpperCAmelCase	)

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

class 			__magic_name__		(					_UpperCamelCase     ):







							@property
							def 							__lowercase    (							self      : Dict	):
									if self.task == "multiple-choice":
											_a  :   Optional[int]				 =							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_a  :   Optional[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_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase		    =     {'''configuration_t5''': ['''T5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''T5Config''', '''T5OnnxConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5Tokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     ['''T5TokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''T5EncoderModel''',
							    '''T5ForConditionalGeneration''',
							    '''T5Model''',
							    '''T5PreTrainedModel''',
							    '''load_tf_weights_in_t5''',
							    '''T5ForQuestionAnswering''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFT5EncoderModel''',
							    '''TFT5ForConditionalGeneration''',
							    '''TFT5Model''',
							    '''TFT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		    =     [
							    '''FlaxT5EncoderModel''',
							    '''FlaxT5ForConditionalGeneration''',
							    '''FlaxT5Model''',
							    '''FlaxT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta import TaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_ta_fast import TaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ta import (
														    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TaEncoderModel,
														    TaForConditionalGeneration,
														    TaForQuestionAnswering,
														    TaModel,
														    TaPreTrainedModel,
														    load_tf_weights_in_ta,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_ta import (
														    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFTaEncoderModel,
														    TFTaForConditionalGeneration,
														    TFTaModel,
														    TFTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_ta import (
														    FlaxTaEncoderModel,
														    FlaxTaForConditionalGeneration,
														    FlaxTaModel,
														    FlaxTaPreTrainedModel,
														)


else:
							import sys

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




