
'''simple docstring'''



from math import ceil


def A				(__lowerCamelCase				:List[Any]			,     __lowerCamelCase				:str		):
						_lowerCAmelCase  						=			list(range(0			,     __lowerCamelCase		)		)

						_lowerCAmelCase  						=			[item for sublist in list(device_map.values()		) for item in sublist]

						# Duplicate check
						_lowerCAmelCase  						=			[]
						for i in device_map_blocks:
												if device_map_blocks.count(__lowerCamelCase		) > 1 and i not in duplicate_blocks:
																		duplicate_blocks.append(__lowerCamelCase		)
    # Missing blocks
						_lowerCAmelCase  						=			[i for i in blocks if i not in device_map_blocks]
						_lowerCAmelCase  						=			[i for i in device_map_blocks if i not in blocks]

						if len(__lowerCamelCase		) != 0:
												raise ValueError(
												    """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
												    """ These attention blocks were specified more than once: """ + str(__lowerCamelCase		)		)
						if len(__lowerCamelCase		) != 0:
												raise ValueError(
												    """There are attention blocks for this model that are not specified in the device_map. Add these attention """
												    """blocks to a device on the device_map: """ + str(__lowerCamelCase		)		)
						if len(__lowerCamelCase		) != 0:
												raise ValueError(
												    """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
												    + str(__lowerCamelCase		)		)





def A				(__lowerCamelCase				:int			,     __lowerCamelCase				:Optional[Any]		):
						_lowerCAmelCase  						=			list(range(__lowerCamelCase		)		)
						_lowerCAmelCase  						=			int(ceil(n_layers / len(__lowerCamelCase		)		)		)
						_lowerCAmelCase  						=			[layers[i : i + n_blocks] for i in range(0			,     __lowerCamelCase			,     __lowerCamelCase		)]

						return dict(zip(__lowerCamelCase			,     __lowerCamelCase		)		)


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


_lowercase    =		logging.get_logger(__name__)

_lowercase    =		{
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						_lowercase		:				List[Any]									=       '''convbert'''




						def __init__( self   ,       _lowercase=30_522   ,       _lowercase=768   ,       _lowercase=12   ,       _lowercase=12   ,       _lowercase=3_072   ,       _lowercase="gelu"   ,       _lowercase=0.1   ,       _lowercase=0.1   ,       _lowercase=512   ,       _lowercase=2   ,       _lowercase=0.02   ,       _lowercase=1e-12   ,       _lowercase=1   ,       _lowercase=0   ,       _lowercase=2   ,       _lowercase=768   ,       _lowercase=2   ,       _lowercase=9   ,       _lowercase=1   ,       _lowercase=None   ,       **_lowercase   ,       ):

												"""simple docstring"""



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

												_lowerCAmelCase  						=			vocab_size
												_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  						=			max_position_embeddings
												_lowerCAmelCase  						=			type_vocab_size
												_lowerCAmelCase  						=			initializer_range
												_lowerCAmelCase  						=			layer_norm_eps
												_lowerCAmelCase  						=			embedding_size
												_lowerCAmelCase  						=			head_ratio
												_lowerCAmelCase  						=			conv_kernel_size
												_lowerCAmelCase  						=			num_groups
												_lowerCAmelCase  						=			classifier_dropout






class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						@property
						def  _lowercase     ( self				):

												"""simple docstring"""



												if self.task == "multiple-choice":
																		_lowerCAmelCase  						=			{0: """batch""", 1: """choice""", 2: """sequence"""}
												else:
																		_lowerCAmelCase  						=			{0: """batch""", 1: """sequence"""}
												return OrderedDict(
												    [
												        ("""input_ids""", dynamic_axis),
												        ("""attention_mask""", dynamic_axis),
												        ("""token_type_ids""", dynamic_axis),
												    ]				)
