


"""simple docstring"""


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


A     :   Tuple			    =			logging.get_logger(__name__)

A     :   int			    =			{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[Any]				      ="""roc_bert"""

							def __init__(     self    ,   __a=3_05_22    ,   __a=7_68    ,   __a=12    ,   __a=12    ,   __a=30_72    ,   __a="gelu"    ,   __a=0.1    ,   __a=0.1    ,   __a=5_12    ,   __a=2    ,   __a=0.0_2    ,   __a=1e-1_2    ,   __a=True    ,   __a=0    ,   __a="absolute"    ,   __a=None    ,   __a=True    ,   __a=True    ,   __a=7_68    ,   __a=9_10    ,   __a=5_12    ,   __a=2_48_58    ,   __a=True    ,   **__a    ,   ):
										__lowerCAmelCase				    =			vocab_size
										__lowerCAmelCase				    =			max_position_embeddings
										__lowerCAmelCase				    =			hidden_size
										__lowerCAmelCase				    =			num_hidden_layers
										__lowerCAmelCase				    =			num_attention_heads
										__lowerCAmelCase				    =			intermediate_size
										__lowerCAmelCase				    =			hidden_act
										__lowerCAmelCase				    =			hidden_dropout_prob
										__lowerCAmelCase				    =			attention_probs_dropout_prob
										__lowerCAmelCase				    =			initializer_range
										__lowerCAmelCase				    =			type_vocab_size
										__lowerCAmelCase				    =			layer_norm_eps
										__lowerCAmelCase				    =			use_cache
										__lowerCAmelCase				    =			enable_pronunciation
										__lowerCAmelCase				    =			enable_shape
										__lowerCAmelCase				    =			pronunciation_embed_dim
										__lowerCAmelCase				    =			pronunciation_vocab_size
										__lowerCAmelCase				    =			shape_embed_dim
										__lowerCAmelCase				    =			shape_vocab_size
										__lowerCAmelCase				    =			concat_input
										__lowerCAmelCase				    =			position_embedding_type
										__lowerCAmelCase				    =			classifier_dropout
										super().__init__(pad_token_id=__a    ,   **__a	)








"""simple docstring"""


from typing import TYPE_CHECKING

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


A     :   Tuple			    =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Dict			    =			["BlenderbotTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   str			    =			[
	    "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BlenderbotForCausalLM",
	    "BlenderbotForConditionalGeneration",
	    "BlenderbotModel",
	    "BlenderbotPreTrainedModel",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "TFBlenderbotForConditionalGeneration",
	    "TFBlenderbotModel",
	    "TFBlenderbotPreTrainedModel",
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "FlaxBlenderbotForConditionalGeneration",
	    "FlaxBlenderbotModel",
	    "FlaxBlenderbotPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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




