import requests
import json
import time
import random
from datetime import datetime
from urllib.parse import quote

# Thordata residential proxy configuration
# Country-specific endpoints for geographic diversity
THORDATA_ENDPOINTS = {
    'us': 'http://username:password@us.thordata.com:10000',
    'uk': 'http://username:password@uk.thordata.com:10000',
    'de': 'http://username:password@de.thordata.com:10000',
    'jp': 'http://username:password@jp.thordata.com:10000',
    'br': 'http://username:password@br.thordata.com:10000',
    'in': 'http://username:password@in.thordata.com:10000',
    'fr': 'http://username:password@fr.thordata.com:10000',
}

# Realistic browser headers matched to proxy location
def get_headers(location):
    headers_by_locale = {
        'us': {
            'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
            'Accept-Language': 'en-US,en;q=0.9',
            'Accept-Encoding': 'gzip, deflate, br',
            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
        },
        'jp': {
            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36',
            'Accept-Language': 'ja-JP,ja;q=0.9,en-US;q=0.8',
            'Accept-Encoding': 'gzip, deflate, br',
            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
        },
        # Add additional locales as needed
    }
    return headers_by_locale.get(location, headers_by_locale['us'])

def collect_ai_training_data(url, location='us', max_retries=5):
    """
    Collect training data with anti-bot resilience
    """
    proxy = {
        'http': THORDATA_ENDPOINTS[location],
        'https': THORDATA_ENDPOINTS[location]
    }
    headers = get_headers(location)

    for attempt in range(max_retries):
        try:
            # Exponential backoff with jitter
            if attempt > 0:
                sleep_time = (2 ** attempt) + random.uniform(0, 2)
                time.sleep(sleep_time)

            response = requests.get(
                url,
                proxies=proxy,
                headers=headers,
                timeout=30,
                allow_redirects=True
            )

            if response.status_code == 200:
                # Validate content quality before returning
                if len(response.text) > 1000 and 'captcha' not in response.text.lower():
                    return {
                        'content': response.text,
                        'status': 200,
                        'location': location,
                        'timestamp': datetime.now().isoformat(),
                        'content_length': len(response.text)
                    }
                else:
                    # Likely blocked or served simplified content
                    continue

            elif response.status_code == 429:
                # Rate limited - will retry with backoff
                continue

            elif response.status_code in [403, 503]:
                # Blocked - switch location on next attempt
                continue

        except Exception as e:
            if attempt == max_retries - 1:
                raise Exception(f"Failed after {max_retries} attempts: {e}")
            continue

    raise Exception("Max retries exceeded, content validation failed")

def validate_fingerprint_consistency(proxy_location, headers, timezone):
    """
    Ensure all identity signals align
    """
    locale_map = {
        'us': {'lang': 'en-US', 'tz': 'America/New_York'},
        'de': {'lang': 'de-DE', 'tz': 'Europe/Berlin'},
        'jp': {'lang': 'ja-JP', 'tz': 'Asia/Tokyo'},
    }

    expected = locale_map.get(proxy_location)
    if not expected:
        return True  # Unknown location, skip validation

    lang_match = expected['lang'] in headers.get('Accept-Language', '')
    tz_match = timezone == expected['tz']

    return lang_match and tz_match

def human_like_delay(action_type='navigation'):
    """
    Variable delays based on action type
    """
    delay_ranges = {
        'navigation': (2.0, 5.0),      # Between page loads
        'scroll': (0.4, 1.6),           # Between scroll actions
        'click': (0.5, 2.0),            # Before clicking
        'read': (3.0, 8.0),             # Simulated reading time
        'typing': (0.03, 0.18),         # Between keystrokes
    }
    min_delay, max_delay = delay_ranges.get(action_type, (1.0, 3.0))
    return random.uniform(min_delay, max_delay)

def simulate_human_scroll(driver, total_height, steps=6):
    """
    Piecewise scrolling with variable speed
    """
    for i in range(1, steps + 1):
        target_y = int((total_height / steps) * i)
        driver.execute_script(f"window.scrollTo(0, {target_y});")
        time.sleep(human_like_delay('scroll'))

from concurrent.futures import ThreadPoolExecutor, as_completed
import queue

class AI_DataPipeline:
    def __init__(self, target_urls, locations, max_workers=10):
        self.url_queue = queue.Queue()
        for url in target_urls:
            self.url_queue.put(url)
        self.locations = locations
        self.max_workers = max_workers
        self.results = []
        self.failed_urls = []

    def worker(self, location):
        while not self.url_queue.empty():
            try:
                url = self.url_queue.get(timeout=1)
                result = collect_ai_training_data(url, location)
                self.results.append(result)
            except Exception as e:
                self.failed_urls.append({'url': url, 'error': str(e)})
            finally:
                self.url_queue.task_done()

    def run(self):
        with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
            # Distribute workers across locations
            futures = []
            for i in range(self.max_workers):
                location = self.locations[i % len(self.locations)]
                futures.append(executor.submit(self.worker, location))

            for future in as_completed(futures):
                future.result()

        return {
            'successful': len(self.results),
            'failed': len(self.failed_urls),
            'data': self.results
        }

# Usage
pipeline = AI_DataPipeline(
    target_urls=training_urls,
    locations=['us', 'uk', 'de', 'jp', 'br'],
    max_workers=20
)
dataset = pipeline.run()

def validate_training_data(data_point):
    """
    Validate data quality for AI training
    """
    checks = {
        'min_length': len(data_point['content']) > 500,
        'no_captcha': 'captcha' not in data_point['content'].lower(),
        'no_error_pages': all(err not in data_point['content'] for err in
                             ['404 Not Found', '403 Forbidden', 'Access Denied']),
        'language_match': detect_language(data_point['content']) == data_point['location'],
        'timestamp_fresh': (datetime.now() - datetime.fromisoformat(data_point['timestamp'])).days < 7
    }

    return all(checks.values()), checks

# SERP API for search-aware training data
def collect_search_data(query, location="United States", pages=5):
    api_url = "https://api.thordata.com/serp"
    all_results = []

    for page in range(pages):
        params = {
            'q': query,
            'location': location,
            'page': page,
            'api_key': 'YOUR_API_KEY'
        }

        response = requests.get(api_url, params=params, timeout=60)
        if response.status_code == 200:
            data = response.json()
            all_results.extend(data.get('organic_results', []))
            # Respectful pacing even with API
            time.sleep(random.uniform(0.5, 1.5))

    return all_results

# Web Scraper API for structured platform data
def collect_structured_data(target_url, scraper_type='generic'):
    api_url = "https://api.thordata.com/scraper"

    payload = {
        'url': target_url,
        'scraper': scraper_type,
        'api_key': 'YOUR_API_KEY'
    }

    response = requests.post(api_url, json=payload, timeout=60)
    return response.json()

import hashlib

class FingerprintManager:
    def __init__(self):
        self.fingerprints = self._load_fingerprint_pool()
        self.current_index = 0

    def _load_fingerprint_pool(self):
        # Load diverse browser profiles: Chrome, Firefox, Safari, Edge
        # across Windows, macOS, Linux, iOS, Android
        return [...]  # Pre-built profile database

    def get_next_fingerprint(self, proxy_location):
        fingerprint = self.fingerprints[self.current_index]
        self.current_index = (self.current_index + 1) % len(self.fingerprints)

        # Ensure geographic alignment
        fingerprint['timezone'] = self._location_to_timezone(proxy_location)
        fingerprint['locale'] = self._location_to_locale(proxy_location)

        return fingerprint

    def _location_to_timezone(self, location):
        mapping = {
            'us': 'America/New_York',
            'uk': 'Europe/London',
            'de': 'Europe/Berlin',
            'jp': 'Asia/Tokyo',
        }
        return mapping.get(location, 'UTC')

def warm_up_session(proxy, headers, duration=90):
    """
    Simulate natural browsing before target data collection
    """
    warm_up_urls = [
        'https://www.google.com',
        'https://www.youtube.com',
        'https://www.reddit.com'
    ]

    session = requests.Session()
    session.proxies = proxy
    session.headers.update(headers)

    start_time = time.time()
    while time.time() - start_time < duration:
        url = random.choice(warm_up_urls)
        session.get(url, timeout=30)
        time.sleep(random.uniform(5, 15))

    return session  # Return aged session with cookies

class AdaptiveThrottler:
    def __init__(self):
        self.success_rate = 1.0
        self.recent_requests = []
        self.base_delay = 2.0

    def update_metrics(self, success, response_time):
        self.recent_requests.append({
            'success': success,
            'time': response_time,
            'timestamp': time.time()
        })

        # Keep only last 100 requests
        self.recent_requests = self.recent_requests[-100:]

        # Calculate rolling success rate
        successes = sum(1 for r in self.recent_requests if r['success'])
        self.success_rate = successes / len(self.recent_requests)

    def get_next_delay(self):
        """
        Increase delays when success rate drops
        """
        if self.success_rate > 0.95:
            return random.uniform(self.base_delay, self.base_delay * 1.5)
        elif self.success_rate > 0.85:
            return random.uniform(self.base_delay * 1.5, self.base_delay * 3)
        else:
            return random.uniform(self.base_delay * 3, self.base_delay * 6)

def detect_content_manipulation(html):
    """
    Check for signs of bot-specific content serving
    """
    manipulation_signals = [
        'captcha',
        'please verify you are human',
        'access denied',
        'bot detected',
        'unusual traffic',
        'simplified view',
        'basic html',
    ]

    html_lower = html.lower()
    for signal in manipulation_signals:
        if signal in html_lower:
            return True, signal

    # Check for excessive script stripping (simplified pages)
    script_count = html.count('<script')
    if script_count < 2 and len(html) > 10000:
        return True, 'suspected_simplified_content'

    return False, None