"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
logger = logging.get_logger(__name__)
class TransfoXLConfig(PretrainedConfig):
    model_type = "transfo-xl"
    keys_to_ignore_at_inference = ["mems"]
    attribute_map = {'n_token': 'vocab_size', 'hidden_size': 'd_model', 'num_attention_heads': 'n_head', 'num_hidden_layers': 'n_layer'}
    def __init__(self, vocab_size=267735, cutoffs=[20000, 40000, 200000], d_model=1024, d_embed=1024, n_head=16, d_head=64, d_inner=4096, div_val=4, pre_lnorm=False,
    n_layer=18, mem_len=1600, clamp_len=1000, same_length=True, proj_share_all_but_first=True, attn_type=0, sample_softmax=-1, adaptive=True, dropout=0.1, dropatt=0.0,
    untie_r=True, init="normal", init_range=0.01, proj_init_std=0.01, init_std=0.02, layer_norm_epsilon=1e-5, eos_token_id=0, **kwargs):
        self.vocab_size = vocab_size
        self.cutoffs = []
        self.cutoffs.extend(cutoffs)
        if proj_share_all_but_first: self.tie_projs = [False] + [True] * len(self.cutoffs)
        else: self.tie_projs = [False] + [False] * len(self.cutoffs)
        self.d_model = d_model
        self.d_embed = d_embed
        self.d_head = d_head
        self.d_inner = d_inner
        self.div_val = div_val
        self.pre_lnorm = pre_lnorm
        self.n_layer = n_layer
        self.n_head = n_head
        self.mem_len = mem_len
        self.same_length = same_length
        self.attn_type = attn_type
        self.clamp_len = clamp_len
        self.sample_softmax = sample_softmax
        self.adaptive = adaptive
        self.dropout = dropout
        self.dropatt = dropatt
        self.untie_r = untie_r
        self.init = init
        self.init_range = init_range
        self.proj_init_std = proj_init_std
        self.init_std = init_std
        self.layer_norm_epsilon = layer_norm_epsilon
        super().__init__(eos_token_id=eos_token_id, **kwargs)
    @property
    def max_position_embeddings(self):
        logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit.")
        return -1
    @max_position_embeddings.setter
    def max_position_embeddings(self, value): raise NotImplementedError(f"The model {self.model_type} is one of the few models that has no sequence length limit.")
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
