"""
    #################################################################################################################################################################################################
    # 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 collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class YolosConfig(PretrainedConfig):
    model_type = "yolos"
    def __init__(self, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.0,
    attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-12, image_size=[512, 864], patch_size=16, num_channels=3, qkv_bias=True,
    num_detection_tokens=100, use_mid_position_embeddings=True, auxiliary_loss=False, class_cost=1, bbox_cost=5, giou_cost=2, bbox_loss_coefficient=5,
    giou_loss_coefficient=2, eos_coefficient=0.1, **kwargs):
        super().__init__(**kwargs)
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.qkv_bias = qkv_bias
        self.num_detection_tokens = num_detection_tokens
        self.use_mid_position_embeddings = use_mid_position_embeddings
        self.auxiliary_loss = auxiliary_loss
        self.class_cost = class_cost
        self.bbox_cost = bbox_cost
        self.giou_cost = giou_cost
        self.bbox_loss_coefficient = bbox_loss_coefficient
        self.giou_loss_coefficient = giou_loss_coefficient
        self.eos_coefficient = eos_coefficient
class YolosOnnxConfig(OnnxConfig):
    torch_onnx_minimum_version = version.parse("1.11")
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})])
    @property
    def atol_for_validation(self) -> float: return 1e-4
    @property
    def default_onnx_opset(self) -> int: return 12
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
