





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


a						: Union[str, Any]     	=			logging.get_logger(__name__)

a						: List[str]     	=			{
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Optional[int]      					="""roformer"""






							def __init__(   self      ,					lowerCAmelCase__=5_0000      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=768      ,					lowerCAmelCase__=12      ,					lowerCAmelCase__=12      ,					lowerCAmelCase__=3072      ,					lowerCAmelCase__="gelu"      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=1536      ,					lowerCAmelCase__=2      ,					lowerCAmelCase__=0.02      ,					lowerCAmelCase__=1E-12      ,					lowerCAmelCase__=0      ,					lowerCAmelCase__=False      ,					lowerCAmelCase__=True      ,					**lowerCAmelCase__      ,					)	-> List[Any]:
								super().__init__(pad_token_id=lowerCAmelCase__      ,					**lowerCAmelCase__      )

								a      :		Any	=				vocab_size
								a      :		Any	=				hidden_size if embedding_size is None else embedding_size
								a      :		Optional[Any]	=				hidden_size
								a      :		List[str]	=				num_hidden_layers
								a      :		List[str]	=				num_attention_heads
								a      :		Optional[int]	=				hidden_act
								a      :		Any	=				intermediate_size
								a      :		str	=				hidden_dropout_prob
								a      :		Tuple	=				attention_probs_dropout_prob
								a      :		str	=				max_position_embeddings
								a      :		List[Any]	=				type_vocab_size
								a      :		Optional[Any]	=				initializer_range
								a      :		Dict	=				layer_norm_eps
								a      :		str	=				rotary_value
								a      :		str	=				use_cache


class        __UpperCamelCase				(						a__						):






							@property
							def     __a   (   self      )	-> Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
									a      :		Tuple	=				{0: "batch", 1: "choice", 2: "sequence"}
								else:
									a      :		Any	=				{0: "batch", 1: "sequence"}
								a      :		Union[str, Any]	=				{0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]      )




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,
)


if is_sentencepiece_available():
   from ..ta.tokenization_ta import TaTokenizer
else:
   from ...utils.dummy_sentencepiece_objects import TaTokenizer

__lowerCamelCase							=       TaTokenizer

if is_tokenizers_available():
   from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
   from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__lowerCamelCase							=       TaTokenizerFast

__lowerCamelCase							=       {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """MT5EncoderModel""",
       """MT5ForConditionalGeneration""",
       """MT5ForQuestionAnswering""",
       """MT5Model""",
       """MT5PreTrainedModel""",
       """MT5Stack""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
   from .configuration_mta import MTaConfig, MTaOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mta import (
          MTaEncoderModel,
          MTaForConditionalGeneration,
          MTaForQuestionAnswering,
          MTaModel,
          MTaPreTrainedModel,
          MTaStack,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
   import sys

   __lowerCamelCase							=       _LazyModule(
       __name__,
       globals()["""__file__"""],
       _import_structure,
       extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
       module_spec=__spec__,
   )





