
"""simple docstring"""






from math import ceil






def 						_snake_case       (	lowercase__		:  str      ,     lowercase__		:  str		)  ->				str:


			'''simple docstring'''





			lowerCAmelCase_    :Union[str, Any]			     =  list(range(0      ,     _lowerCAmelCase		)		)

			lowerCAmelCase_    :Union[str, Any]			     =  [item for sublist in list(device_map.values()		) for item in sublist]

			# Duplicate check
			lowerCAmelCase_    :Any			     =  []
			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_    :Union[str, Any]			     =  [i for i in blocks if i not in device_map_blocks]
			lowerCAmelCase_    :List[str]			     =  [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 						_snake_case       (	lowercase__		:  Any      ,     lowercase__		:  Tuple		)  ->				Optional[Any]:


			'''simple docstring'''





			lowerCAmelCase_    :List[Any]			     =  list(range(_lowerCAmelCase		)		)
			lowerCAmelCase_    :Union[str, Any]			     =  int(ceil(n_layers / len(_lowerCAmelCase		)		)		)
			lowerCAmelCase_    :Union[str, Any]			     =  [layers[i : i + n_blocks] for i in range(0      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)]

			return dict(zip(_lowerCAmelCase      ,     _lowerCAmelCase		)		)











from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE_:Any             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Dict             =						{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      "xmod"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		lowerCamelCase__=False,		lowerCamelCase__=2,		lowerCamelCase__=False,		lowerCamelCase__=True,		lowerCamelCase__=True,		lowerCamelCase__=("en_XX",),		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       int          =   vocab_size
        A :       int          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[str]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Dict          =   intermediate_size
        A :       Optional[int]          =   hidden_dropout_prob
        A :       Union[str, Any]          =   attention_probs_dropout_prob
        A :       int          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Union[str, Any]          =   position_embedding_type
        A :       Any          =   use_cache
        A :       int          =   classifier_dropout
        A :       int          =   pre_norm
        A :       List[str]          =   adapter_reduction_factor
        A :       Any          =   adapter_layer_norm
        A :       Any          =   adapter_reuse_layer_norm
        A :       str          =   ln_before_adapter
        A :       Dict          =   list(lowerCamelCase__		)
        A :       Union[str, Any]          =   default_language






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






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


