from dataclasses import dataclass, make_dataclass, field
from enum import Enum

import pandas as pd

from src.about import Tasks
from src.about import SpeechTasks

def fields(raw_class):
    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]


# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass
class ColumnContent:
    name: str
    type: str
    displayed_by_default: bool
    hidden: bool = False
    never_hidden: bool = False

## Leaderboard columns
# Store column content instances separately
auto_eval_column_content = {}
auto_eval_column_dict = []
# Init
# auto_eval_column_content["model_type_symbol"] = ColumnContent("Model Type", "str", True, never_hidden=True)
# auto_eval_column_dict.append(["model_type_symbol", ColumnContent])
auto_eval_column_content["model"] = ColumnContent("Model", "markdown", True, never_hidden=True)
auto_eval_column_dict.append(["model", ColumnContent])
#Scores
auto_eval_column_content["average"] = ColumnContent("Average ⬆️", "number", True)
auto_eval_column_dict.append(["average", ColumnContent])
for task in Tasks:
    auto_eval_column_content[task.name] = ColumnContent(task.value.col_name, "number", True)
    auto_eval_column_dict.append([task.name, ColumnContent])

### Speech leaderboard columns
# Store column content instances separately
auto_eval_column_content_speech = {}
auto_eval_column_dict_speech = []
# Init
# auto_eval_column_content_speech["model_type_symbol"] = ColumnContent("Model Type", "str", True, never_hidden=True)
# auto_eval_column_dict_speech.append(["model_type_symbol", ColumnContent])
auto_eval_column_content_speech["model"] = ColumnContent("Model", "markdown", True, never_hidden=True)
auto_eval_column_dict_speech.append(["model", ColumnContent])
#Scores
auto_eval_column_content_speech["average"] = ColumnContent("Average ⬆️", "number", True)
auto_eval_column_dict_speech.append(["average", ColumnContent])
for task in SpeechTasks:
    auto_eval_column_content_speech[task.name] = ColumnContent(task.value.col_name, "number", True)
    auto_eval_column_dict_speech.append([task.name, ColumnContent])


# Model information
# auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
# auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
# auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
# auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
# auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
# auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
# auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
# auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
# auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])

# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
# Set class attributes with the ColumnContent instances
for col_name, col_content in auto_eval_column_content.items():
    setattr(AutoEvalColumn, col_name, col_content)

AutoEvalColumnSpeech = make_dataclass("AutoEvalColumnSpeech", auto_eval_column_dict_speech, frozen=True)
# Set class attributes with the ColumnContent instances
for col_name, col_content in auto_eval_column_content_speech.items():
    setattr(AutoEvalColumnSpeech, col_name, col_content)

## For the queue columns in the submission tab
class EvalQueueColumn:  # Queue column
    model = ColumnContent("model", "markdown", True)
    revision = ColumnContent("revision", "str", True)
    private = ColumnContent("private", "bool", True)
    precision = ColumnContent("precision", "str", True)
    weight_type = ColumnContent("weight_type", "str", "Original")
    status = ColumnContent("status", "str", True)

## All the model information that we might need
@dataclass
class ModelDetails:
    name: str
    display_name: str = ""
    symbol: str = "" # emoji


class ModelType(Enum):
    PT = ModelDetails(name="pretrained", symbol="🟢")
    FT = ModelDetails(name="fine-tuned", symbol="🔶")
    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
    RL = ModelDetails(name="RL-tuned", symbol="🟦")
    Unknown = ModelDetails(name="", symbol="?")

    def to_str(self, separator=" "):
        return f"{self.value.symbol}{separator}{self.value.name}"

    @staticmethod
    def from_str(type):
        if "fine-tuned" in type or "🔶" in type:
            return ModelType.FT
        if "pretrained" in type or "🟢" in type:
            return ModelType.PT
        if "RL-tuned" in type or "🟦" in type:
            return ModelType.RL
        if "instruction-tuned" in type or "⭕" in type:
            return ModelType.IFT
        return ModelType.Unknown

class WeightType(Enum):
    Adapter = ModelDetails("Adapter")
    Original = ModelDetails("Original")
    Delta = ModelDetails("Delta")

class Precision(Enum):
    float16 = ModelDetails("float16")
    bfloat16 = ModelDetails("bfloat16")
    Unknown = ModelDetails("?")

    def from_str(precision):
        if precision in ["torch.float16", "float16"]:
            return Precision.float16
        if precision in ["torch.bfloat16", "bfloat16"]:
            return Precision.bfloat16
        return Precision.Unknown

# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
COLS_SPEECH = [c.name for c in fields(AutoEvalColumnSpeech) if not c.hidden]

EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]

BENCHMARK_COLS = [t.value.col_name for t in Tasks]
SPEECH_BENCHMARK_COLS = [t.value.col_name for t in SpeechTasks]

REGION_MAP = {
    "All": "All",
    "Africa": "Africa",
    "Americas/Oceania": "Americas_Oceania",
    "Asia (S)": "Asia_S",
    "Asia (SE)": "Asia_SE",
    "Asia (W, C)": "Asia_W_C",
    "Asia (E)": "Asia_E",
    "Europe (W, N, S)": "Europe_W_N_S",
    "Europe (E)": "Europe_E",
}