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


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					{
    '''google/reformer-crime-and-punishment''': (
        '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json'''
    ),
    '''google/reformer-enwik8''': '''https://huggingface.co/google/reformer-enwik8/resolve/main/config.json''',
}




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Dict					     =			'reformer'
 _lowerCamelCase    :						List[Any]					     =			['past_buckets_states']
 _lowerCamelCase    :						Any					     =			{}

 def __init__(      self							:		List[str]							,      lowerCamelCase							:		List[str]=6_4							,      lowerCamelCase							:		Any=["local", "lsh", "local", "lsh", "local", "lsh"]							,      lowerCamelCase							:		Optional[Any]=1.0							,      lowerCamelCase							:		Union[str, Any]=True							,      lowerCamelCase							:		Union[str, Any]=[6_4, 6_4]							,      lowerCamelCase							:		List[Any]=[6_4, 1_9_2]							,      lowerCamelCase							:		Any=0							,      lowerCamelCase							:		Union[str, Any]=2							,      lowerCamelCase							:		List[Any]=5_1_2							,      lowerCamelCase							:		str=None							,      lowerCamelCase							:		Dict="relu"							,      lowerCamelCase							:		Optional[Any]=0.0_5							,      lowerCamelCase							:		Dict=2_5_6							,      lowerCamelCase							:		str=0.0_2							,      lowerCamelCase							:		Optional[Any]=False							,      lowerCamelCase							:		Optional[int]=1e-12							,      lowerCamelCase							:		Tuple=1							,      lowerCamelCase							:		Union[str, Any]=0							,      lowerCamelCase							:		str=0.0_5							,      lowerCamelCase							:		Tuple=6_4							,      lowerCamelCase							:		Union[str, Any]=6_4							,      lowerCamelCase							:		Optional[int]=0.0							,      lowerCamelCase							:		List[Any]=1							,      lowerCamelCase							:		Optional[Any]=0							,      lowerCamelCase							:		Dict=4_0_9_6							,      lowerCamelCase							:		Optional[int]=1_2							,      lowerCamelCase							:		List[Any]=None							,      lowerCamelCase							:		int=1							,      lowerCamelCase							:		Union[str, Any]=0							,      lowerCamelCase							:		int=3_2_0							,      lowerCamelCase							:		Optional[Any]=False							,      lowerCamelCase							:		Tuple=True							,      lowerCamelCase							:		int=None							,      **lowerCamelCase							:		Any							,      )			->				str:
        _UpperCamelCase  :							List[str]	    =	hash_seed
        _UpperCamelCase  :							List[str]	    =	vocab_size
        _UpperCamelCase  :							List[str]	    =	attention_head_size
        _UpperCamelCase  :							int	    =	hidden_size
        _UpperCamelCase  :							Optional[int]	    =	num_attention_heads
        _UpperCamelCase  :							Optional[int]	    =	num_hashes
        _UpperCamelCase  :							Optional[int]	    =	len(lowerCamelCase	)
        _UpperCamelCase  :							Union[str, Any]	    =	tuple(lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else num_buckets
        _UpperCamelCase  :							Optional[int]	    =	lsh_attn_chunk_length
        _UpperCamelCase  :							str	    =	local_attn_chunk_length
        _UpperCamelCase  :							Union[str, Any]	    =	lsh_num_chunks_after
        _UpperCamelCase  :							Dict	    =	lsh_num_chunks_before
        _UpperCamelCase  :							Optional[int]	    =	local_num_chunks_after
        _UpperCamelCase  :							Any	    =	local_num_chunks_before
        _UpperCamelCase  :							Optional[int]	    =	hidden_act
        _UpperCamelCase  :							str	    =	feed_forward_size
        _UpperCamelCase  :							Optional[Any]	    =	hidden_dropout_prob
        _UpperCamelCase  :							List[str]	    =	lsh_attention_probs_dropout_prob
        _UpperCamelCase  :							str	    =	local_attention_probs_dropout_prob
        _UpperCamelCase  :							Union[str, Any]	    =	max_position_embeddings
        _UpperCamelCase  :							List[Any]	    =	initializer_range
        _UpperCamelCase  :							int	    =	layer_norm_eps
        _UpperCamelCase  :							List[str]	    =	axial_pos_embds
        _UpperCamelCase  :							List[str]	    =	tuple(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	tuple(lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	axial_norm_std
        _UpperCamelCase  :							Optional[int]	    =	chunk_size_lm_head
        _UpperCamelCase  :							Dict	    =	attn_layers
        _UpperCamelCase  :							Union[str, Any]	    =	use_cache
        _UpperCamelCase  :							Optional[int]	    =	classifier_dropout
        super().__init__(
            pad_token_id=lowerCamelCase							,      eos_token_id=lowerCamelCase							,      is_decoder=lowerCamelCase							,      tie_word_embeddings=lowerCamelCase							,      **lowerCamelCase							,      )





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__lowercase	=    {
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''XLMProphetNetTokenizer''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XLMProphetNetDecoder''',
							    '''XLMProphetNetEncoder''',
							    '''XLMProphetNetForCausalLM''',
							    '''XLMProphetNetForConditionalGeneration''',
							    '''XLMProphetNetModel''',
							    '''XLMProphetNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_prophetnet import (
														    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMProphetNetDecoder,
														    XLMProphetNetEncoder,
														    XLMProphetNetForCausalLM,
														    XLMProphetNetForConditionalGeneration,
														    XLMProphetNetModel,
														    XLMProphetNetPreTrainedModel,
														)

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






