"""
    #################################################################################################################################################################################################
    # 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 Phi3Config(PretrainedConfig):
    model_type = "phi3"
    keys_to_ignore_at_inference = ["past_key_values"]
    def __init__(self, vocab_size=32064, hidden_size=3072, intermediate_size=8192, num_hidden_layers=32, num_attention_heads=32, num_key_value_heads=None, resid_pdrop=0.0,
    embd_pdrop=0.0, attention_dropout=0.0, hidden_act="silu", max_position_embeddings=4096, original_max_position_embeddings=4096, initializer_range=0.02, rms_norm_eps=1e-5,
    use_cache=True, tie_word_embeddings=False, rope_theta=10000.0, rope_scaling=None, bos_token_id=1, eos_token_id=32000, pad_token_id=32000, sliding_window=None, **kwargs):
        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        if num_key_value_heads is None: num_key_value_heads = num_attention_heads
        self.num_key_value_heads = num_key_value_heads
        self.resid_pdrop = resid_pdrop
        self.embd_pdrop = embd_pdrop
        self.attention_dropout = attention_dropout
        self.hidden_act = hidden_act
        self.max_position_embeddings = max_position_embeddings
        self.original_max_position_embeddings = original_max_position_embeddings
        self.initializer_range = initializer_range
        self.rms_norm_eps = rms_norm_eps
        self.use_cache = use_cache
        self.rope_theta = rope_theta
        self.rope_scaling = rope_scaling
        self._rope_scaling_adjustment()
        self._rope_scaling_validation()
        self.sliding_window = sliding_window
        super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs)
    def _rope_scaling_adjustment(self):
        if self.rope_scaling is None: return
        rope_scaling_type = self.rope_scaling.get("type", None)
        if rope_scaling_type is not None and rope_scaling_type in ["su", "yarn"]: self.rope_scaling["type"] = "longrope"
    def _rope_scaling_validation(self):
        if self.rope_scaling is None: return
        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 3: raise ValueError(f"`rope_scaling` must be a dictionary with three fields, `type`, `short_factor` and `long_factor`, got {self.rope_scaling}")
        rope_scaling_type = self.rope_scaling.get("type", None)
        rope_scaling_short_factor = self.rope_scaling.get("short_factor", None)
        rope_scaling_long_factor = self.rope_scaling.get("long_factor", None)
        if rope_scaling_type is None or rope_scaling_type not in ["longrope"]: raise ValueError(f"`rope_scaling`'s type field must be one of ['longrope'], got {rope_scaling_type}")
        if not (isinstance(rope_scaling_short_factor, list) and all(isinstance(x, (int, float)) for x in rope_scaling_short_factor)): raise ValueError(f"`rope_scaling`'s short_factor field must be a list of numbers, got {rope_scaling_short_factor}")
        if not len(rope_scaling_short_factor) == self.hidden_size // self.num_attention_heads // 2: raise ValueError(f"`rope_scaling`'s short_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_short_factor)}")
        if not (isinstance(rope_scaling_long_factor, list) and all(isinstance(x, (int, float)) for x in rope_scaling_long_factor)): raise ValueError(f"`rope_scaling`'s long_factor field must be a list of numbers, got {rope_scaling_long_factor}")
        if not len(rope_scaling_long_factor) == self.hidden_size // self.num_attention_heads // 2: raise ValueError(f"`rope_scaling`'s long_factor field must have length {self.hidden_size // self.num_attention_heads // 2}, got {len(rope_scaling_long_factor)}")
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
