from transformers import PretrainedConfig
from typing import List, Dict


class ESGifyConfig(PretrainedConfig):
    model_type = "mpnet"

    def __init__(
        self,
        attention_probs_dropout_prob: float = 0.1,
        bos_token_id: int = 0,
        eos_token_id: int = 2,
        hidden_act: str = "gelu",
        hidden_dropout_prob: float = 0.1,
        hidden_size: int = 768,
        initializer_range: float = 0.02,
        intermediate_size: int = 3072,
        layer_norm_eps: float = 1e-05,
        max_position_embeddings: int = 514,
        num_attention_heads: int = 12,
        num_hidden_layers: int = 12,
        output_attentions: bool = True,
        pad_token_id: int = 1,
        relative_attention_num_buckets: int = 32,
        vocab_size: int = 30531,
        id2label: Dict = {"0": "Legal Proceedings & Law Violations",
                         "1": "Biodiversity",
                         "2": "Communities Health and Safety",
                         "3": "Land Acquisition and Resettlement (S)",
                         "4": "Emergencies (Social)",
                         "5": "Corporate Governance",
                         "6": "Responsible Investment & Greenwashing",
                         "7": "Not Relevant to ESG",
                         "8": "Economic Crime",
                         "9": "Emergencies (Environmental)",
                         "10": "Hazardous Materials Management",
                         "11": "Environmental Management",
                         "12": "Landscape Transformation",
                         "13": "Human Rights",
                         "14": "Climate Risks",
                         "15": "Labor Relations Management",
                         "16": "Freedom of Association and Right to Organise",
                         "17": "Employee Health and Safety",
                         "18": "Surface Water Pollution",
                         "19": "Animal Welfare",
                         "20": "Water Consumption",
                         "21": "Disclosure",
                         "22": "Product Safety and Quality",
                         "23": "Greenhouse Gas Emissions",
                         "24": "Indigenous People",
                         "25": "Cultural Heritage",
                         "26": "Air Pollution",
                         "27": "Waste Management",
                         "28": "Soil and Groundwater Impact",
                         "29": "Forced Labour",
                         "30": "Wastewater Management",
                         "31": "Natural Resources",
                         "32": "Physical Impacts",
                         "33": "Values and Ethics",
                         "34": "Risk Management and Internal Control",
                         "35": "Supply Chain (Environmental)",
                         "36": "Supply Chain (Social)",
                         "37": "Discrimination",
                         "38": "Minimum Age and Child Labour",
                         "39": "Planning Limitations",
                         "40": "Data Safety",
                         "41": "Strategy Implementation",
                         "42": "Energy Efficiency and Renewables",
                         "43": "Land Acquisition and Resettlement (E)",
                         "44": "Supply Chain (Economic / Governance)",
                         "45": "Land Rehabilitation",
                         "46": "Retrenchment"
                    },
        label2id: Dict = {"Legal Proceedings & Law Violations": "0",
                          "Biodiversity": "1",
                          "Communities Health and Safety": "2",
                          "Land Acquisition and Resettlement (S)": "3",
                          "Emergencies (Social)": "4",
                          "Corporate Governance": "5",
                          "Responsible Investment & Greenwashing": "6",
                          "Not Relevant to ESG": "7",
                          "Economic Crime": "8",
                         "Emergencies (Environmental)": "9",
                          "Hazardous Materials Management": "10",
                          "Environmental Management": "11",
                          "Landscape Transformation": "12",
                          "Human Rights": "13",
                          "Climate Risks": "14",
                          "Labor Relations Management": "15",
                          "Freedom of Association and Right to Organise": "16",
                          "Employee Health and Safety": "17",
                          "Surface Water Pollution": "18",
                          "Animal Welfare": "19",
                          "Water Consumption": "20",
                          "Disclosure": "21",
                          "Product Safety and Quality": "22",
                          "Greenhouse Gas Emissions": "23",
                          "Indigenous People": "24",
                          "Cultural Heritage": "25",
                          "Air Pollution": "26",
                          "Waste Management": "27",
                          "Soil and Groundwater Impact": "28",
                          "Forced Labour": "29",
                          "Wastewater Management": "30",
                          "Natural Resources": "31",
                          "Physical Impacts": "32",
                          "Values and Ethics": "33",
                          "Risk Management and Internal Control": "34",
                          "Supply Chain (Environmental)": "35",
                          "Supply Chain (Social)": "36",
                          "Discrimination": "37",
                          "Minimum Age and Child Labour": "38",
                          "Planning Limitations": "39",
                          "Data Safety": "40",
                          "Strategy Implementation": "41",
                          "Energy Efficiency and Renewables": "42",
                          "Land Acquisition and Resettlement (E)": "43",
                          "Supply Chain (Economic / Governance)": "44",
                          "Land Rehabilitation": "45",
                          "Retrenchment": "46"},
        **kwargs, 
    ):
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.bos_token_id = bos_token_id, 
        self.eos_token_id = eos_token_id, 
        self.hidden_act = hidden_act, 
        self.hidden_dropout_prob = hidden_dropout_prob, 
        self.hidden_size = hidden_size, 
        self.initializer_range = initializer_range, 
        self.intermediate_size = intermediate_size,  
        self.layer_norm_eps = layer_norm_eps
        self.max_position_embeddings = max_position_embeddings, 
        self.num_attention_heads = num_attention_heads, 
        self.num_hidden_layers = num_hidden_layers, 
        self.output_attentions = output_attentions, 
        self.pad_token_id = pad_token_id, 
        self.relative_attention_num_buckets = relative_attention_num_buckets, 
        self.vocab_size = vocab_size, 
        self.id2label = id2label, 
        self.label2id = label2id
        super().__init__(**kwargs)