






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_keras_nlp_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case			:			int			   =   {
    "configuration_gpt2": ["GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPT2Config", "GPT2OnnxConfig"],
    "tokenization_gpt2": ["GPT2Tokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case			:			List[Any]			   =   ["GPT2TokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case			:			str			   =   [
					    "GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "GPT2DoubleHeadsModel",
					    "GPT2ForQuestionAnswering",
					    "GPT2ForSequenceClassification",
					    "GPT2ForTokenClassification",
					    "GPT2LMHeadModel",
					    "GPT2Model",
					    "GPT2PreTrainedModel",
					    "load_tf_weights_in_gpt2",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case			:			Union[str, Any]			   =   [
					    "TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFGPT2DoubleHeadsModel",
					    "TFGPT2ForSequenceClassification",
					    "TFGPT2LMHeadModel",
					    "TFGPT2MainLayer",
					    "TFGPT2Model",
					    "TFGPT2PreTrainedModel",
					]

try:
					if not is_keras_nlp_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case			:			int			   =   ["TFGPT2Tokenizer"]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case			:			List[Any]			   =   ["FlaxGPT2LMHeadModel", "FlaxGPT2Model", "FlaxGPT2PreTrainedModel"]

if TYPE_CHECKING:
					from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
					from .tokenization_gpta import GPTaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpta_fast import GPTaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpta import (
										    GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTaDoubleHeadsModel,
										    GPTaForQuestionAnswering,
										    GPTaForSequenceClassification,
										    GPTaForTokenClassification,
										    GPTaLMHeadModel,
										    GPTaModel,
										    GPTaPreTrainedModel,
										    load_tf_weights_in_gpta,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_gpta import (
										    TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFGPTaDoubleHeadsModel,
										    TFGPTaForSequenceClassification,
										    TFGPTaLMHeadModel,
										    TFGPTaMainLayer,
										    TFGPTaModel,
										    TFGPTaPreTrainedModel,
										)

					try:
										if not is_keras_nlp_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpta_tf import TFGPTaTokenizer

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel

else:
					import sys

					__snake_case			:			Optional[Any]			   =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__      :			Optional[Any]           =					logging.get_logger(__name__)

lowercase__      :			Optional[int]           =					{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class 			a__       (					UpperCamelCase__      ):
     a		:			Dict							=	"""yoso"""







     def __init__(	self  ,		A=50265  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=4096  ,		A=1  ,		A=0.0_2  ,		A=1e-12  ,		A="absolute"  ,		A=True  ,		A=9  ,		A=64  ,		A=None  ,		A=True  ,		A=True  ,		A=1  ,		A=0  ,		A=2  ,		**A  ,		)  ->    Dict:



          '''simple docstring'''



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

          a      =      vocab_size
          a      =      max_position_embeddings
          a      =      hidden_size
          a      =      num_hidden_layers
          a      =      num_attention_heads
          a      =      intermediate_size
          a      =      hidden_act
          a      =      hidden_dropout_prob
          a      =      attention_probs_dropout_prob
          a      =      initializer_range
          a      =      type_vocab_size
          a      =      layer_norm_eps
          a      =      position_embedding_type
          a      =      use_expectation
          a      =      hash_code_len
          a      =      num_hash
          a      =      conv_window
          a      =      use_fast_hash
          a      =      lsh_backward

