
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							=       {
    """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_ ):
   A__					:		Tuple						     =     "roformer"



   def __init__(self		:					int    ,						snake_case__		:					Optional[Any]=5_00_00    ,						snake_case__		:					Optional[int]=None    ,						snake_case__		:					Any=7_68    ,						snake_case__		:					Optional[int]=12    ,						snake_case__		:					Optional[int]=12    ,						snake_case__		:					Union[str, Any]=30_72    ,						snake_case__		:					Optional[int]="gelu"    ,						snake_case__		:					int=0.1    ,						snake_case__		:					Dict=0.1    ,						snake_case__		:					Tuple=15_36    ,						snake_case__		:					List[str]=2    ,						snake_case__		:					str=0.02    ,						snake_case__		:					Dict=1e-12    ,						snake_case__		:					Any=0    ,						snake_case__		:					str=False    ,						snake_case__		:					Dict=True    ,						**snake_case__		:					Optional[Any]    ,						)		->	Dict:


          '''simple docstring'''


          super().__init__(pad_token_id=snake_case__    ,						**snake_case__ )

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






class   UpperCAmelCase		(						A_ ):



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


          '''simple docstring'''


          if self.task == "multiple-choice":
                 snake_case       :   Any							=      {0: "batch", 1: "choice", 2: "sequence"}
          else:
                 snake_case       :   Tuple							=      {0: "batch", 1: "sequence"}
          snake_case       :   str							=      {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,
)


__lowerCamelCase							=       {"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""XGLMTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       ["""XGLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XGLMForCausalLM""",
       """XGLMModel""",
       """XGLMPreTrainedModel""",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """FlaxXGLMForCausalLM""",
       """FlaxXGLMModel""",
       """FlaxXGLMPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXGLMForCausalLM""",
       """TFXGLMModel""",
       """TFXGLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm import XGLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm_fast import XGLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
          TFXGLMPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure)





