


from math import ceil
def        UpperCamelCase_(				_A   :Tuple				,     _A   :List[Any]  )->		Any:
			UpperCamelCase__					=       list(range(0				,     _A  )  )

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

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

			if len(_A  ) != 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(_A  )  )
			if len(_A  ) != 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(_A  )  )
			if len(_A  ) != 0:
						raise ValueError(
						    "The device_map contains more attention blocks than this model has. Remove these from the device_map:"
						    + str(_A  )  )







def        UpperCamelCase_(				_A   :List[str]				,     _A   :List[str]  )->		List[Any]:
			UpperCamelCase__					=       list(range(_A  )  )
			UpperCamelCase__					=       int(ceil(n_layers / len(_A  )  )  )
			UpperCamelCase__					=       [layers[i : i + n_blocks] for i in range(0				,     _A				,     _A  )]

			return dict(zip(_A				,     _A  )  )







from collections import OrderedDict
from typing import Mapping

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


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					int    =		'data2vec-text'




			def __init__(							self      ,						snake_case=30522      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=True      ,						snake_case=None      ,						**snake_case      ,						):

						'''simple docstring'''


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

						UpperCamelCase__					=       vocab_size
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       max_position_embeddings
						UpperCamelCase__					=       type_vocab_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       layer_norm_eps
						UpperCamelCase__					=       position_embedding_type
						UpperCamelCase__					=       use_cache
						UpperCamelCase__					=       classifier_dropout







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


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


