"""Places Dataset Loading Script for Hugging Face"""

import csv
import json
import os
import datasets
from typing import Dict, List, Any


_CITATION = """\
@dataset{places_dataset_2025,
  title={Places Dataset},
  author={patrick piemonte},
  year={2025},
  publisher={Hugging Face}
}
"""

_DESCRIPTION = """\
This dataset contains information about close to 70,000 places with associated metadata including 
locations, attribution tags, and contact details. The data includes geographic coordinates, 
place descriptions, categorization through attribution tags, and social media presence information.
"""

_HOMEPAGE = ""
_LICENSE = "cc-by-4.0"

_URLS = {
    "place": "place.csv",
    "location": "location.csv",
    "place_contact": "place_contact.csv",
    "tag": "tag.csv",
    "place_tag": "place_tag.csv",
}


class PlacesDataset(datasets.GeneratorBasedBuilder):
    """Places dataset with multiple related tables."""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="all", version=VERSION, description="Load all tables"),
        datasets.BuilderConfig(name="place", version=VERSION, description="Main places table"),
        datasets.BuilderConfig(name="location", version=VERSION, description="Geographic coordinates"),
        datasets.BuilderConfig(name="place_contact", version=VERSION, description="Contact information"),
        datasets.BuilderConfig(name="tag", version=VERSION, description="Categorization tags"),
        datasets.BuilderConfig(name="place_tag", version=VERSION, description="Place-tag relationships"),
        datasets.BuilderConfig(name="denormalized", version=VERSION, description="Denormalized view with places, locations, and primary tags"),
    ]

    DEFAULT_CONFIG_NAME = "all"

    def _info(self):
        if self.config.name == "place":
            features = datasets.Features({
                "id": datasets.Value("string"),
                "created_at": datasets.Value("string"),
                "name": datasets.Value("string"),
                "description": datasets.Value("string"),
                "address": datasets.Value("string"),
                "address_formatted": datasets.Value("string"),
                "cross_street": datasets.Value("string"),
                "locality": datasets.Value("string"),
                "administrative_area": datasets.Value("string"),
                "postal_code": datasets.Value("string"),
                "country_code": datasets.Value("string"),
                "verified": datasets.Value("bool"),
                "flagged": datasets.Value("bool"),
                "place_contact_id": datasets.Value("string"),
                "location_id": datasets.Value("string"),
                "author_id": datasets.Value("string"),
                "owner_id": datasets.Value("string"),
                "locale_id": datasets.Value("string"),
                "primary_tag_id": datasets.Value("string"),
                "country": datasets.Value("string"),
                "sublocality": datasets.Value("string"),
                "subadministrative_area": datasets.Value("string"),
                "updated_at": datasets.Value("string"),
                "radius_in_meters": datasets.Value("int32"),
                "stamp_id": datasets.Value("string"),
                "z_priority": datasets.Value("int32"),
                "clustering_category": datasets.Value("string"),
                "places_token_id": datasets.Value("string"),
                "nano_id": datasets.Value("string"),
                "slug": datasets.Value("string"),
            })
        elif self.config.name == "location":
            features = datasets.Features({
                "id": datasets.Value("string"),
                "latitude": datasets.Value("float64"),
                "longitude": datasets.Value("float64"),
                "horizontal_accuracy": datasets.Value("float32"),
                "altitude": datasets.Value("float32"),
                "vertical_accuracy": datasets.Value("float32"),
                "geom": datasets.Value("string"),
                "geog": datasets.Value("string"),
            })
        elif self.config.name == "place_contact":
            features = datasets.Features({
                "id": datasets.Value("string"),
                "instagram": datasets.Value("string"),
                "x": datasets.Value("string"),
                "website": datasets.Value("string"),
            })
        elif self.config.name == "tag":
            features = datasets.Features({
                "id": datasets.Value("string"),
                "created_at": datasets.Value("string"),
                "name": datasets.Value("string"),
                "slug": datasets.Value("string"),
                "private_tag": datasets.Value("bool"),
                "tag_type": datasets.Value("string"),
                "image_asset_id": datasets.Value("string"),
                "theme_asset_id": datasets.Value("string"),
                "search_tokens": datasets.Value("string"),
                "keywords": datasets.Value("string"),
                "radius_in_meters": datasets.Value("int32"),
                "content_rating": datasets.Value("string"),
                "stamp_id": datasets.Value("string"),
                "hidden_tag": datasets.Value("bool"),
            })
        elif self.config.name == "place_tag":
            features = datasets.Features({
                "id": datasets.Value("string"),
                "tag_id": datasets.Value("string"),
                "place_id": datasets.Value("string"),
                "created_at": datasets.Value("string"),
            })
        elif self.config.name == "denormalized":
            features = datasets.Features({
                # Place fields
                "place_id": datasets.Value("string"),
                "name": datasets.Value("string"),
                "description": datasets.Value("string"),
                "address": datasets.Value("string"),
                "address_formatted": datasets.Value("string"),
                "locality": datasets.Value("string"),
                "administrative_area": datasets.Value("string"),
                "postal_code": datasets.Value("string"),
                "country_code": datasets.Value("string"),
                "country": datasets.Value("string"),
                "verified": datasets.Value("bool"),
                # Location fields
                "latitude": datasets.Value("float64"),
                "longitude": datasets.Value("float64"),
                "horizontal_accuracy": datasets.Value("float32"),
                "altitude": datasets.Value("float32"),
                # Primary tag fields
                "primary_tag_name": datasets.Value("string"),
                "primary_tag_slug": datasets.Value("string"),
                "primary_tag_type": datasets.Value("string"),
                # Contact fields
                "website": datasets.Value("string"),
                "instagram": datasets.Value("string"),
                "twitter": datasets.Value("string"),
            })
        else:  # "all" config
            features = datasets.Features({
                "table_name": datasets.Value("string"),
                "data": datasets.Value("string"),  # JSON string
            })

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        if self.config.name == "denormalized":
            downloaded_files = dl_manager.download_and_extract(_URLS)
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepaths": downloaded_files,
                    },
                ),
            ]
        elif self.config.name != "all":
            downloaded_file = dl_manager.download_and_extract(_URLS[self.config.name])
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepath": downloaded_file,
                        "table_name": self.config.name,
                    },
                ),
            ]
        else:
            downloaded_files = dl_manager.download_and_extract(_URLS)
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TRAIN,
                    gen_kwargs={
                        "filepaths": downloaded_files,
                    },
                ),
            ]

    def _generate_examples(self, filepath=None, table_name=None, filepaths=None):
        if self.config.name == "denormalized":
            # Load all necessary tables for denormalized view
            places = {}
            locations = {}
            tags = {}
            contacts = {}
            
            # Load places
            with open(filepaths["place"], encoding="utf-8") as f:
                reader = csv.DictReader(f)
                for row in reader:
                    places[row["id"]] = row
            
            # Load locations
            with open(filepaths["location"], encoding="utf-8") as f:
                reader = csv.DictReader(f)
                for row in reader:
                    locations[row["id"]] = row
            
            # Load tags
            with open(filepaths["tag"], encoding="utf-8") as f:
                reader = csv.DictReader(f)
                for row in reader:
                    tags[row["id"]] = row
            
            # Load contacts
            with open(filepaths["place_contact"], encoding="utf-8") as f:
                reader = csv.DictReader(f)
                for row in reader:
                    contacts[row["id"]] = row
            
            # Generate denormalized rows
            idx = 0
            for place_id, place in places.items():
                result = {
                    "place_id": place_id,
                    "name": place.get("name"),
                    "description": place.get("description"),
                    "address": place.get("address"),
                    "address_formatted": place.get("address_formatted"),
                    "locality": place.get("locality"),
                    "administrative_area": place.get("administrative_area"),
                    "postal_code": place.get("postal_code"),
                    "country_code": place.get("country_code"),
                    "country": place.get("country"),
                    "verified": place.get("verified") == "true" if place.get("verified") else False,
                }
                
                # Add location data
                if place.get("location_id") and place["location_id"] in locations:
                    loc = locations[place["location_id"]]
                    # Parse and validate coordinates
                    lat = None
                    lon = None
                    if loc.get("latitude"):
                        try:
                            lat = float(loc["latitude"])
                            if not (-90 <= lat <= 90):
                                print(f"Warning: Invalid latitude {lat} for place {place_id}")
                                lat = None
                        except ValueError:
                            pass
                    if loc.get("longitude"):
                        try:
                            lon = float(loc["longitude"])
                            if not (-180 <= lon <= 180):
                                print(f"Warning: Invalid longitude {lon} for place {place_id}")
                                lon = None
                        except ValueError:
                            pass
                    
                    result.update({
                        "latitude": lat,
                        "longitude": lon,
                        "horizontal_accuracy": float(loc["horizontal_accuracy"]) if loc.get("horizontal_accuracy") else None,
                        "altitude": float(loc["altitude"]) if loc.get("altitude") else None,
                    })
                else:
                    result.update({
                        "latitude": None,
                        "longitude": None,
                        "horizontal_accuracy": None,
                        "altitude": None,
                    })
                
                # Add primary tag data
                if place.get("primary_tag_id") and place["primary_tag_id"] in tags:
                    tag = tags[place["primary_tag_id"]]
                    result.update({
                        "primary_tag_name": tag.get("name"),
                        "primary_tag_slug": tag.get("slug"),
                        "primary_tag_type": tag.get("tag_type"),
                    })
                else:
                    result.update({
                        "primary_tag_name": None,
                        "primary_tag_slug": None,
                        "primary_tag_type": None,
                    })
                
                # Add contact data
                if place.get("place_contact_id") and place["place_contact_id"] in contacts:
                    contact = contacts[place["place_contact_id"]]
                    result.update({
                        "website": contact.get("website"),
                        "instagram": contact.get("instagram"),
                        "twitter": contact.get("twitter"),
                    })
                else:
                    result.update({
                        "website": None,
                        "instagram": None,
                        "twitter": None,
                    })
                
                yield idx, result
                idx += 1
                
        elif self.config.name != "all":
            with open(filepath, encoding="utf-8") as f:
                reader = csv.DictReader(f)
                for idx, row in enumerate(reader):
                    # Convert boolean strings to actual booleans
                    for key, value in row.items():
                        if value in ["true", "false"]:
                            row[key] = value == "true"
                        elif value == "":
                            row[key] = None
                        # Convert numeric strings
                        elif key in ["radius_in_meters", "z_priority"]:
                            try:
                                row[key] = int(value) if value else None
                            except ValueError:
                                row[key] = None
                        elif key in ["latitude", "longitude", "horizontal_accuracy", "altitude", "vertical_accuracy"]:
                            try:
                                if value:
                                    val = float(value)
                                    # Validate coordinates
                                    if key == "latitude" and not (-90 <= val <= 90):
                                        print(f"Warning: Invalid latitude {val} in row {idx}")
                                        row[key] = None
                                    elif key == "longitude" and not (-180 <= val <= 180):
                                        print(f"Warning: Invalid longitude {val} in row {idx}")
                                        row[key] = None
                                    else:
                                        row[key] = val
                                else:
                                    row[key] = None
                            except ValueError:
                                row[key] = None
                    
                    yield idx, row
        else:
            # Load all tables
            idx = 0
            for table_name, filepath in filepaths.items():
                with open(filepath, encoding="utf-8") as f:
                    reader = csv.DictReader(f)
                    rows = list(reader)
                    yield idx, {
                        "table_name": table_name,
                        "data": json.dumps(rows)
                    }
                    idx += 1


# Alternative simple loading function for users who prefer pandas
def load_places_as_dict(data_dir: str) -> Dict[str, List[Dict[str, Any]]]:
    """
    Load all CSV files from the directory into a dictionary of tables.
    
    Args:
        data_dir: Directory containing the CSV files
        
    Returns:
        Dictionary where keys are table names and values are lists of row dictionaries
    """
    tables = {}
    
    for table_name, filename in _URLS.items():
        filepath = os.path.join(data_dir, filename)
        if os.path.exists(filepath):
            with open(filepath, encoding="utf-8") as f:
                reader = csv.DictReader(f)
                tables[table_name] = list(reader)
    
    return tables