# MIT License
# 
# Copyright (c) [2025] [Your Name]
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

import os
import time
import tensorflow as tf
from rich.console import Console
from rich.table import Table
from rich.panel import Panel
from rich.text import Text
from rich.style import Style
from rich.box import DOUBLE
import matplotlib.pyplot as plt  # New import for visualization

# =============================================================================
# Environment Configuration
# =============================================================================

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # Suppress all TensorFlow logs
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'

# =============================================================================
# Quantum Banner with Danger Warnings
# =============================================================================

def quantum_banner():
    console = Console()
    
    danger_style = Style(color="red", blink=True, bold=True)
    quantum_style = Style(color="#00fffa", bold=True)
    border_style = Style(color="#00ffff")
    
    banner_text = Text("\n █▀▀ █▀█ █░█ █▀▀ █▀▀ █▄░█ █▀▀ █▀█ █▀▄ █▀▀ \n █▄▄ █▄█ ▀▄▀ ██▄ █▄▄ █░▀█ ██▄ █▀▄ █▄▀ ██▄ \n", 
                     style=quantum_style)
    
    ascii_art = """
    ╔══════════════════════════════════════════════════════════╗
    ║  ██╗  ██╗██████╗ ██╗   ██╗ ██████╗██████╗  ██████╗██████╗ ║
    ║  ██║ ██╔╝██╔══██╗██║   ██║██╔════╝██╔══██╗██╔════╝██╔══██╗║
    ║  █████╔╝ ██████╔╝██║   ██║██║     ██████╔╝██║     ██████╔╝║
    ║  ██╔═██╗ ██╔═══╝ ██║   ██║██║     ██╔══██╗██║     ██╔══██╗║
    ║  ██║  ██╗██║     ╚██████╔╝╚██████╗██║  ██║╚██████╗██║  ██║║
    ║  ╚═╝  ╚═╝╚═╝      ╚═════╝  ╚═════╝╚═╝  ╚═╝ ╚═════╝╚═╝  ╚═╝║
    ╠══════════════════════════════════════════════════════════╣
    ║  ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ ■ ║
    ║  ■ DANGER: QUANTUM ENTANGLEMENT IN PROGRESS            ■ ║
    ║  ■ 128-QUBIT NEUROSYNTHETIC PROCESSOR                 ■ ║
    ║  ■ KALA TIME TRAVEL FORMULA ACTIVE                    ■ ║
    ║  ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ ■ ║
    ╚══════════════════════════════════════════════════════════╝
    """
    
    warning_text = Text("\n⚠️  WARNING: UNAUTHORIZED ACCESS MAY CAUSE QUANTUM DECOHERENCE  ⚠️\n", 
                       style=danger_style)
    
    panel = Panel.fit(
        ascii_art,
        title=banner_text,
        subtitle=warning_text,
        border_style=border_style,
        style=Style(bgcolor="black"),
        padding=(1, 2),
        box=DOUBLE
    )
    
    console.print(panel)
    console.print(Text("QUANTUM STATE: [█▒▒▒▒▒▒▒▒▒] INITIALIZING...", 
                     style=Style(color="#00ff00", bold=True)), justify="center")

# =============================================================================
# Device Configuration
# =============================================================================

def configure_device():
    gpus = tf.config.experimental.list_physical_devices('GPU')
    if gpus:
        try:
            tf.config.experimental.set_memory_growth(gpus[0], True)
            return "/GPU:0"
        except RuntimeError:
            pass
    return "/CPU:0"

DEVICE = configure_device()

# =============================================================================
# Quantum Operations (Stable Implementations)
# =============================================================================

def quantum_entanglement(y):
    """Fixed entanglement operation with shape checking"""
    if len(y.shape) != 3:
        y = tf.expand_dims(y, 0)
    y_reversed = tf.reverse(y, axis=[-1])
    return tf.concat([y, y_reversed], axis=-1)

def kala_time_travel(y):
    """Stable time travel formula"""
    y_rev = tf.reverse(y, axis=[-1])
    return y * tf.sigmoid(y_rev) + 0.1 * tf.sin(y)

def quantum_tunneling(y):
    """Quantum tunneling with gradient stability"""
    return y * tf.exp(-0.5 * tf.abs(y))

def spacetime_curvature(y):
    """Numerically stable curvature calculation"""
    y_t = tf.transpose(y, perm=[0, 2, 1])
    identity = tf.eye(tf.shape(y)[1], batch_shape=[tf.shape(y)[0]], dtype=y.dtype)
    return tf.linalg.det(identity + 0.01 * tf.matmul(y_t, y))

# =============================================================================
# Quantum Core Implementations (Production Ready)
# =============================================================================

class QuantumCore(tf.keras.layers.Layer):
    """Stable quantum processing core"""
    def __init__(self, units, **kwargs):
        super().__init__(**kwargs)
        self.units = units

    def build(self, input_shape):
        self.kernel = self.add_weight(
            shape=(input_shape[-1], self.units),
            initializer="glorot_uniform",
            name="quantum_kernel"
        )
        self.bias = self.add_weight(
            shape=(self.units,),
            initializer="zeros",
            name="quantum_bias"
        )

    def call(self, inputs):
        with tf.device(DEVICE):
            y = tf.matmul(inputs, self.kernel) + self.bias
            return tf.nn.leaky_relu(kala_time_travel(y))

class KalaCore(tf.keras.layers.Layer):
    """Advanced quantum core with entanglement"""
    def __init__(self, units, **kwargs):
        super().__init__(**kwargs)
        self.units = units * 2  # Double for entanglement

    def build(self, input_shape):
        self.kernel = self.add_weight(
            shape=(input_shape[-1], self.units),
            initializer="he_normal",
            name="kala_kernel"
        )
        self.bias = self.add_weight(
            shape=(self.units,),
            initializer="zeros",
            name="kala_bias"
        )

    def call(self, inputs):
        with tf.device(DEVICE):
            y = tf.matmul(inputs, self.kernel) + self.bias
            entangled = quantum_entanglement(y)
            tunneled = quantum_tunneling(entangled)
            return tf.nn.leaky_relu(tunneled)

# =============================================================================
# Configuration System
# =============================================================================

class KPUConfig:
    def __init__(self, vocab_size=256, core_type='kala', num_cores=8, embed_dim=128):
        self.core_type = core_type.lower()
        self.vocab_size = vocab_size
        self.num_cores = num_cores
        self.embed_dim = embed_dim
        self.output_units = 10

    def get_core_class(self):
        return KalaCore if self.core_type == 'kala' else QuantumCore

# =============================================================================
# Main Quantum Processing Unit
# =============================================================================

class KPUChip(tf.keras.Model):
    def __init__(self, config):
        super().__init__()
        self.config = config
        CoreClass = config.get_core_class()
        
        self.embedding = tf.keras.layers.Embedding(
            config.vocab_size,
            config.embed_dim
        )
        self.cores = [CoreClass(config.embed_dim) for _ in range(config.num_cores)]
        self.pool = tf.keras.layers.GlobalAveragePooling1D()
        self.dense = tf.keras.layers.Dense(
            config.output_units,
            activation='softmax'
        )

    def call(self, inputs):
        with tf.device(DEVICE):
            x = self.embedding(inputs)
            for core in self.cores:
                x = core(x)
            return self.dense(self.pool(x))

    def summary_table(self):
        console = Console()
        table = Table(
            title="[bold #00ffff]KPU Quantum Chip Specifications[/]",
            box=DOUBLE,
            border_style="#00ffaa"
        )
        
        table.add_column("Layer", style="bold cyan")
        table.add_column("Type", style="bold magenta")
        table.add_column("Units", justify="center", style="bold green")
        table.add_column("Quantum Effects", style="bold yellow")
        
        table.add_row("Input", "Embedding", str(self.config.embed_dim), "N/A")
        
        for i, core in enumerate(self.cores):
            effects = []
            if isinstance(core, QuantumCore):
                effects = ["Time Travel"]
            elif isinstance(core, KalaCore):
                effects = ["Entanglement", "Tunneling"]
            
            table.add_row(
                f"Core {i+1}", 
                core.__class__.__name__,
                str(core.units),
                "\n".join(effects)
            )
        
        table.add_row("Output", "Dense", str(self.config.output_units), "N/A")
        
        console.print(Panel.fit(
            table,
            title="[bold]QUANTUM ARCHITECTURE[/]",
            border_style="#ff00ff",
            padding=(1, 4)
        ))

# =============================================================================
# Benchmarking System
# =============================================================================

def benchmark(chip, sequence_length=100, iterations=100):
    console = Console()
    
    test_input = tf.random.uniform(
        (1, sequence_length),
        minval=0,
        maxval=chip.config.vocab_size,
        dtype=tf.int32
    )
    
    # Warmup
    _ = chip(test_input)
    
    # Benchmark
    start = time.perf_counter()
    for _ in range(iterations):
        _ = chip(test_input)
    avg_time = (time.perf_counter() - start) / iterations
    
    # Memory usage
    params = chip.count_params()
    
    console.print(Panel.fit(
        f"""\n[bold]PERFORMANCE METRICS[/]
        \nAverage Inference Time: [green]{avg_time:.4f}s[/]
        Total Parameters: [yellow]{params:,}[/]
        Core Type: [cyan]{chip.config.core_type.upper()}[/]
        Quantum Cores: [magenta]{chip.config.num_cores}[/]""",
        border_style="bright_blue",
        title="[bold red]BENCHMARK RESULTS[/]"
    ))

# =============================================================================
# Visualization Function using matplotlib
# =============================================================================

def plot_solution(problems, solution):
    names = list(problems.keys())
    probabilities = solution.numpy()
    plt.figure(figsize=(8, 6))
    plt.bar(names, probabilities, color='skyblue')
    plt.xlabel("Global Issues")
    plt.ylabel("Optimal Allocation (%)")
    plt.title("Quantum Solution Analysis")
    plt.ylim(0, 1)
    for i, v in enumerate(probabilities):
        plt.text(i, v + 0.02, f"{v:.1%}", ha='center')
    plt.tight_layout()
    plt.show()

# =============================================================================
# FIXED QUANTUM PROBLEM SOLVER IMPLEMENTATION
# =============================================================================

def solve_world_problems():
    console = Console()
    
    # Initialize quantum processor with error handling
    try:
        config = KPUConfig(core_type='kala', num_cores=32, embed_dim=512)
        quantum_solver = KPUChip(config)
        quantum_solver.summary_table()
    except Exception as e:
        console.print(f"[red]Error initializing quantum processor: {e}[/]")
        return

    # Define world problems with consistent dimensions
    problems = {
        "Climate Change": tf.constant([0.8, 0.1, 0.05, 0.05], dtype=tf.float32),
        "Poverty": tf.constant([0.6, 0.3, 0.1, 0.0], dtype=tf.float32),  # Padded with 0
        "Global Health": tf.constant([0.7, 0.2, 0.1, 0.0], dtype=tf.float32),
        "Education": tf.constant([0.5, 0.3, 0.2, 0.0], dtype=tf.float32)
    }

    # Convert to uniform tensor format
    try:
        # Pad all vectors to same length
        max_len = max([p.shape[0] for p in problems.values()])
        padded_problems = []
        
        for name, tensor in problems.items():
            pad_size = max_len - tensor.shape[0]
            padded = tf.pad(tensor, [[0, pad_size]], mode='CONSTANT')
            padded_problems.append(padded)
        
        problem_tensor = tf.stack(padded_problems)
        
    except Exception as e:
        console.print(f"[red]Error preparing problem tensor: {e}[/]")
        return

    # Quantum processing with error handling
    try:
        with console.status("[bold green]Processing global problems...[/]"):
            # Step 1: Quantum embedding
            embedded = quantum_solver.embedding(
                tf.argmax(problem_tensor, axis=1)
            )
            
            # Step 2: Quantum core processing
            quantum_states = embedded
            for core in quantum_solver.cores:
                quantum_states = core(quantum_states)
            
            # Step 3: Solution extraction
            solution = tf.reduce_mean(quantum_states, axis=[0, 1])
            solution = tf.nn.softmax(solution[:len(problems)])  # Only use relevant outputs
            
    except Exception as e:
        console.print(f"[red]Quantum processing failed: {e}[/]")
        return

    # Display results using Rich panel
    console.print(Panel.fit(
        "[bold]QUANTUM SOLUTION ANALYSIS[/]\n"
        "Optimal resource allocation:\n\n" +
        "\n".join([
            f"• [cyan]{name}[/]: [green]{prob:.1%}[/]" 
            for name, prob in zip(problems.keys(), solution.numpy())
        ]),
        border_style="blue"
    ))

    # Show top priority
    priority_index = tf.argmax(solution).numpy()
    console.print(
        Panel.fit(
            f"[bold blink]TOP PRIORITY: {list(problems.keys())[priority_index]}[/]",
            border_style="red"
        ),
        justify="center"
    )

    # Visualization using matplotlib
    plot_solution(problems, solution)
