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# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
# https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/07_gpt_to_llama/standalone-llama32.ipynb


import torch
import torch.nn as nn


LLAMA32_CONFIG_1B = {
    "vocab_size": 128_256,           # Vocabulary size
    "context_length": 131_072,       # Context length that was used to train the model
    "emb_dim": 2048,                 # Embedding dimension
    "n_heads": 32,                   # Number of attention heads
    "n_layers": 16,                  # Number of layers
    "hidden_dim": 8192,              # Size of the intermediate dimension in FeedForward
    "n_kv_groups": 8,                # Key-Value groups for grouped-query attention
    "rope_base": 500_000.0,          # The base in RoPE's "theta"
    "dtype": torch.bfloat16,         # Lower-precision dtype to reduce memory usage
    "rope_freq": {                   # RoPE frequency scaling
        "factor": 32.0,
        "low_freq_factor": 1.0,
        "high_freq_factor": 4.0,
        "original_context_length": 8192,
    }
}

LLAMA32_CONFIG_3B = {
    "vocab_size": 128_256,           # Vocabulary size
    "context_length": 131_072,       # Context length that was used to train the model
    "emb_dim": 3072,                 # Embedding dimension
    "n_heads": 24,                   # Number of attention heads
    "n_layers": 28,                  # Number of layers
    "hidden_dim": 8192,              # Size of the intermediate dimension in FeedForward
    "n_kv_groups": 8,                # Key-Value groups for grouped-query attention
    "rope_base": 500_000.0,          # The base in RoPE's "theta"
    "dtype": torch.bfloat16,         # Lower-precision dtype to reduce memory usage
    "rope_freq": {                   # RoPE frequency scaling
        "factor": 32.0,
        "low_freq_factor": 1.0,
        "high_freq_factor": 4.0,
        "original_context_length": 8192,
    }
}


class Llama3Model(nn.Module):
    def __init__(self, cfg):
        super().__init__()

        # Main model parameters
        self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"], dtype=cfg["dtype"])

        self.trf_blocks = nn.ModuleList(  # ModuleList since Sequential can only accept one input, and we need `x, mask, cos, sin`
            [TransformerBlock(cfg) for _ in range(cfg["n_layers"])]
        )

        self.final_norm = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
        self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False, dtype=cfg["dtype"])

        # Reusuable utilities
        cos, sin = compute_rope_params(
            head_dim=cfg["emb_dim"] // cfg["n_heads"],
            theta_base=cfg["rope_base"],
            context_length=cfg["context_length"],
            freq_config=cfg["rope_freq"]
        )
        self.register_buffer("cos", cos, persistent=False)
        self.register_buffer("sin", sin, persistent=False)
        self.cfg = cfg

    def forward(self, in_idx):
        tok_embeds = self.tok_emb(in_idx)
        x = tok_embeds

        num_tokens = x.shape[1]
        mask = torch.triu(torch.ones(num_tokens, num_tokens, device=x.device, dtype=torch.bool), diagonal=1)

        for block in self.trf_blocks:
            x = block(x, mask, self.cos, self.sin)
        x = self.final_norm(x)
        logits = self.out_head(x.to(self.cfg["dtype"]))
        return logits


class TransformerBlock(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.att = GroupedQueryAttention(
            d_in=cfg["emb_dim"],
            d_out=cfg["emb_dim"],
            num_heads=cfg["n_heads"],
            num_kv_groups=cfg["n_kv_groups"],
            dtype=cfg["dtype"]
        )
        self.ff = FeedForward(cfg)
        self.norm1 = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
        self.norm2 = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])

    def forward(self, x, mask, cos, sin):
        # Shortcut connection for attention block
        shortcut = x
        x = self.norm1(x)
        x = self.att(x, mask, cos, sin)  # Shape [batch_size, num_tokens, emb_size]
        x = x + shortcut  # Add the original input back

        # Shortcut connection for feed-forward block
        shortcut = x
        x = self.norm2(x)
        x = self.ff(x)
        x = x + shortcut  # Add the original input back

        return x


class FeedForward(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.fc1 = nn.Linear(cfg["emb_dim"], cfg["hidden_dim"], dtype=cfg["dtype"], bias=False)
        self.fc2 = nn.Linear(cfg["emb_dim"], cfg["hidden_dim"], dtype=cfg["dtype"], bias=False)
        self.fc3 = nn.Linear(cfg["hidden_dim"], cfg["emb_dim"], dtype=cfg["dtype"], bias=False)

    def forward(self, x):
        x_fc1 = self.fc1(x)
        x_fc2 = self.fc2(x)
        x = nn.functional.silu(x_fc1) * x_fc2
        return self.fc3(x)


class GroupedQueryAttention(nn.Module):
    def __init__(
            self, d_in, d_out, num_heads, num_kv_groups, dtype=None
    ):
        super().__init__()
        assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
        assert num_heads % num_kv_groups == 0, "num_heads must be divisible by num_kv_groups"

        self.d_out = d_out
        self.num_heads = num_heads
        self.head_dim = d_out // num_heads

        self.W_key = nn.Linear(d_in, num_kv_groups * self.head_dim, bias=False, dtype=dtype)
        self.W_value = nn.Linear(d_in, num_kv_groups * self.head_dim, bias=False, dtype=dtype)
        self.num_kv_groups = num_kv_groups
        self.group_size = num_heads // num_kv_groups

        self.W_query = nn.Linear(d_in, d_out, bias=False, dtype=dtype)
        self.out_proj = nn.Linear(d_out, d_out, bias=False, dtype=dtype)

    def forward(self, x, mask, cos, sin):
        b, num_tokens, d_in = x.shape

        queries = self.W_query(x)  # Shape: (b, num_tokens, d_out)
        keys = self.W_key(x)  # Shape: (b, num_tokens, num_kv_groups * head_dim)
        values = self.W_value(x)  # Shape: (b, num_tokens, num_kv_groups * head_dim)

        # Reshape queries, keys, and values
        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
        keys = keys.view(b, num_tokens, self.num_kv_groups, self.head_dim)
        values = values.view(b, num_tokens, self.num_kv_groups, self.head_dim)

        # Transpose keys, values, and queries
        keys = keys.transpose(1, 2)  # Shape: (b, num_heads, num_tokens, head_dim)
        values = values.transpose(1, 2)  # Shape: (b, num_heads, num_tokens, head_dim)
        queries = queries.transpose(1, 2)  # Shape: (b, num_query_groups, num_tokens, head_dim)

        # Apply RoPE
        keys = apply_rope(keys, cos, sin)
        queries = apply_rope(queries, cos, sin)

        # Expand keys and values to match the number of heads
        # Shape: (b, num_heads, num_tokens, head_dim)
        keys = keys.repeat_interleave(self.group_size, dim=1)  # Shape: (b, num_heads, num_tokens, head_dim)
        values = values.repeat_interleave(self.group_size, dim=1)  # Shape: (b, num_heads, num_tokens, head_dim)
        # For example, before repeat_interleave along dim=1 (query groups):
        #   [K1, K2]
        # After repeat_interleave (each query group is repeated group_size times):
        #   [K1, K1, K2, K2]
        # If we used regular repeat instead of repeat_interleave, we'd get:
        #   [K1, K2, K1, K2]

        # Compute scaled dot-product attention (aka self-attention) with a causal mask
        # Shape: (b, num_heads, num_tokens, num_tokens)
        attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head

        # Use the mask to fill attention scores
        attn_scores = attn_scores.masked_fill(mask[:num_tokens, :num_tokens], -torch.inf)

        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
        assert keys.shape[-1] == self.head_dim

        # Shape: (b, num_tokens, num_heads, head_dim)
        context_vec = (attn_weights @ values).transpose(1, 2)

        # Combine heads, where self.d_out = self.num_heads * self.head_dim
        context_vec = context_vec.reshape(b, num_tokens, self.d_out)
        context_vec = self.out_proj(context_vec)  # optional projection

        return context_vec


def compute_rope_params(head_dim, theta_base=10_000, context_length=4096, freq_config=None, dtype=torch.float32):
    assert head_dim % 2 == 0, "Embedding dimension must be even"

    # Compute the inverse frequencies
    inv_freq = 1.0 / (theta_base ** (torch.arange(0, head_dim, 2, dtype=dtype)[: (head_dim // 2)].float() / head_dim))

    # Frequency adjustments
    if freq_config is not None:
        low_freq_wavelen = freq_config["original_context_length"] / freq_config["low_freq_factor"]
        high_freq_wavelen = freq_config["original_context_length"] / freq_config["high_freq_factor"]

        wavelen = 2 * torch.pi / inv_freq

        inv_freq_llama = torch.where(
            wavelen > low_freq_wavelen, inv_freq / freq_config["factor"], inv_freq
        )

        smooth_factor = (freq_config["original_context_length"] / wavelen - freq_config["low_freq_factor"]) / (
            freq_config["high_freq_factor"] - freq_config["low_freq_factor"]
        )

        smoothed_inv_freq = (
            (1 - smooth_factor) * (inv_freq / freq_config["factor"]) + smooth_factor * inv_freq
        )

        is_medium_freq = (wavelen <= low_freq_wavelen) & (wavelen >= high_freq_wavelen)
        inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
        inv_freq = inv_freq_llama

    # Generate position indices
    positions = torch.arange(context_length, dtype=dtype)

    # Compute the angles
    angles = positions[:, None] * inv_freq[None, :]  # Shape: (context_length, head_dim // 2)

    # Expand angles to match the head_dim
    angles = torch.cat([angles, angles], dim=1)  # Shape: (context_length, head_dim)

    # Precompute sine and cosine
    cos = torch.cos(angles)
    sin = torch.sin(angles)

    return cos, sin


def apply_rope(x, cos, sin):
    # x: (batch_size, num_heads, seq_len, head_dim)
    batch_size, num_heads, seq_len, head_dim = x.shape
    assert head_dim % 2 == 0, "Head dimension must be even"

    # Split x into first half and second half
    x1 = x[..., : head_dim // 2]  # First half
    x2 = x[..., head_dim // 2:]  # Second half

    # Adjust sin and cos shapes
    cos = cos[:seq_len, :].unsqueeze(0).unsqueeze(0)  # Shape: (1, 1, seq_len, head_dim)
    sin = sin[:seq_len, :].unsqueeze(0).unsqueeze(0)

    # Apply the rotary transformation
    rotated = torch.cat((-x2, x1), dim=-1)
    x_rotated = (x * cos) + (rotated * sin)

    # It's ok to use lower-precision after applying cos and sin rotation
    return x_rotated.to(dtype=x.dtype)


def text_to_token_ids(text, tokenizer):
    encoded = tokenizer.encode(text)
    encoded_tensor = torch.tensor(encoded).unsqueeze(0)  # add batch dimension
    return encoded_tensor


def token_ids_to_text(token_ids, tokenizer):
    flat = token_ids.squeeze(0)  # remove batch dimension
    return tokenizer.decode(flat.tolist())


def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):

    # For-loop is the same as before: Get logits, and only focus on last time step
    for _ in range(max_new_tokens):
        idx_cond = idx[:, -context_size:]
        with torch.no_grad():
            logits = model(idx_cond)
        logits = logits[:, -1, :]

        # Filter logits with top_k sampling
        if top_k is not None:
            # Keep only top_k values
            top_logits, _ = torch.topk(logits, top_k)
            min_val = top_logits[:, -1]
            logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)

        # Apply temperature scaling
        if temperature > 0.0:
            logits = logits / temperature

            # Apply softmax to get probabilities
            probs = torch.softmax(logits, dim=-1)  # (batch_size, context_len)

            # Sample from the distribution
            idx_next = torch.multinomial(probs, num_samples=1)  # (batch_size, 1)

        # Otherwise same as before: get idx of the vocab entry with the highest logits value
        else:
            idx_next = torch.argmax(logits, dim=-1, keepdim=True)  # (batch_size, 1)

        if idx_next == eos_id:  # Stop generating early if end-of-sequence token is encountered and eos_id is specified
            break

        # Same as before: append sampled index to the running sequence
        idx = torch.cat((idx, idx_next), dim=1)  # (batch_size, num_tokens+1)

    return idx

	# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
	# Source for "Build a Large Language Model From Scratch"
	# https://github.com/rasbt/LLMs-from-scratch/blob/main/ch05/07_gpt_to_llama/standalone-llama32.ipynb


	import torch
	import torch.nn as nn


	LLAMA32_CONFIG_1B = {
	"vocab_size": 128_256, # Vocabulary size
	"context_length": 131_072, # Context length that was used to train the model
	"emb_dim": 2048, # Embedding dimension
	"n_heads": 32, # Number of attention heads
	"n_layers": 16, # Number of layers
	"hidden_dim": 8192, # Size of the intermediate dimension in FeedForward
	"n_kv_groups": 8, # Key-Value groups for grouped-query attention
	"rope_base": 500_000.0, # The base in RoPE's "theta"
	"dtype": torch.bfloat16, # Lower-precision dtype to reduce memory usage
	"rope_freq": { # RoPE frequency scaling
	"factor": 32.0,
	"low_freq_factor": 1.0,
	"high_freq_factor": 4.0,
	"original_context_length": 8192,
	}
	}

	LLAMA32_CONFIG_3B = {
	"vocab_size": 128_256, # Vocabulary size
	"context_length": 131_072, # Context length that was used to train the model
	"emb_dim": 3072, # Embedding dimension
	"n_heads": 24, # Number of attention heads
	"n_layers": 28, # Number of layers
	"hidden_dim": 8192, # Size of the intermediate dimension in FeedForward
	"n_kv_groups": 8, # Key-Value groups for grouped-query attention
	"rope_base": 500_000.0, # The base in RoPE's "theta"
	"dtype": torch.bfloat16, # Lower-precision dtype to reduce memory usage
	"rope_freq": { # RoPE frequency scaling
	"factor": 32.0,
	"low_freq_factor": 1.0,
	"high_freq_factor": 4.0,
	"original_context_length": 8192,
	}
	}


	class Llama3Model(nn.Module):
	def __init__(self, cfg):
	super().__init__()

	# Main model parameters
	self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"], dtype=cfg["dtype"])

	self.trf_blocks = nn.ModuleList( # ModuleList since Sequential can only accept one input, and we need `x, mask, cos, sin`
	[TransformerBlock(cfg) for _ in range(cfg["n_layers"])]
	)

	self.final_norm = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
	self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False, dtype=cfg["dtype"])

	# Reusuable utilities
	cos, sin = compute_rope_params(
	head_dim=cfg["emb_dim"] // cfg["n_heads"],
	theta_base=cfg["rope_base"],
	context_length=cfg["context_length"],
	freq_config=cfg["rope_freq"]
	)
	self.register_buffer("cos", cos, persistent=False)
	self.register_buffer("sin", sin, persistent=False)
	self.cfg = cfg

	def forward(self, in_idx):
	tok_embeds = self.tok_emb(in_idx)
	x = tok_embeds

	num_tokens = x.shape[1]
	mask = torch.triu(torch.ones(num_tokens, num_tokens, device=x.device, dtype=torch.bool), diagonal=1)

	for block in self.trf_blocks:
	x = block(x, mask, self.cos, self.sin)
	x = self.final_norm(x)
	logits = self.out_head(x.to(self.cfg["dtype"]))
	return logits


	class TransformerBlock(nn.Module):
	def __init__(self, cfg):
	super().__init__()
	self.att = GroupedQueryAttention(
	d_in=cfg["emb_dim"],
	d_out=cfg["emb_dim"],
	num_heads=cfg["n_heads"],
	num_kv_groups=cfg["n_kv_groups"],
	dtype=cfg["dtype"]
	)
	self.ff = FeedForward(cfg)
	self.norm1 = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
	self.norm2 = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])

	def forward(self, x, mask, cos, sin):
	# Shortcut connection for attention block
	shortcut = x
	x = self.norm1(x)
	x = self.att(x, mask, cos, sin) # Shape [batch_size, num_tokens, emb_size]
	x = x + shortcut # Add the original input back

	# Shortcut connection for feed-forward block
	shortcut = x
	x = self.norm2(x)
	x = self.ff(x)
	x = x + shortcut # Add the original input back

	return x


	class FeedForward(nn.Module):
	def __init__(self, cfg):
	super().__init__()
	self.fc1 = nn.Linear(cfg["emb_dim"], cfg["hidden_dim"], dtype=cfg["dtype"], bias=False)
	self.fc2 = nn.Linear(cfg["emb_dim"], cfg["hidden_dim"], dtype=cfg["dtype"], bias=False)
	self.fc3 = nn.Linear(cfg["hidden_dim"], cfg["emb_dim"], dtype=cfg["dtype"], bias=False)

	def forward(self, x):
	x_fc1 = self.fc1(x)
	x_fc2 = self.fc2(x)
	x = nn.functional.silu(x_fc1) * x_fc2
	return self.fc3(x)


	class GroupedQueryAttention(nn.Module):
	def __init__(
	self, d_in, d_out, num_heads, num_kv_groups, dtype=None
	):
	super().__init__()
	assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
	assert num_heads % num_kv_groups == 0, "num_heads must be divisible by num_kv_groups"

	self.d_out = d_out
	self.num_heads = num_heads
	self.head_dim = d_out // num_heads

	self.W_key = nn.Linear(d_in, num_kv_groups * self.head_dim, bias=False, dtype=dtype)
	self.W_value = nn.Linear(d_in, num_kv_groups * self.head_dim, bias=False, dtype=dtype)
	self.num_kv_groups = num_kv_groups
	self.group_size = num_heads // num_kv_groups

	self.W_query = nn.Linear(d_in, d_out, bias=False, dtype=dtype)
	self.out_proj = nn.Linear(d_out, d_out, bias=False, dtype=dtype)

	def forward(self, x, mask, cos, sin):
	b, num_tokens, d_in = x.shape

	queries = self.W_query(x) # Shape: (b, num_tokens, d_out)
	keys = self.W_key(x) # Shape: (b, num_tokens, num_kv_groups * head_dim)
	values = self.W_value(x) # Shape: (b, num_tokens, num_kv_groups * head_dim)

	# Reshape queries, keys, and values
	queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
	keys = keys.view(b, num_tokens, self.num_kv_groups, self.head_dim)
	values = values.view(b, num_tokens, self.num_kv_groups, self.head_dim)

	# Transpose keys, values, and queries
	keys = keys.transpose(1, 2) # Shape: (b, num_heads, num_tokens, head_dim)
	values = values.transpose(1, 2) # Shape: (b, num_heads, num_tokens, head_dim)
	queries = queries.transpose(1, 2) # Shape: (b, num_query_groups, num_tokens, head_dim)

	# Apply RoPE
	keys = apply_rope(keys, cos, sin)
	queries = apply_rope(queries, cos, sin)

	# Expand keys and values to match the number of heads
	# Shape: (b, num_heads, num_tokens, head_dim)
	keys = keys.repeat_interleave(self.group_size, dim=1) # Shape: (b, num_heads, num_tokens, head_dim)
	values = values.repeat_interleave(self.group_size, dim=1) # Shape: (b, num_heads, num_tokens, head_dim)
	# For example, before repeat_interleave along dim=1 (query groups):
	# [K1, K2]
	# After repeat_interleave (each query group is repeated group_size times):
	# [K1, K1, K2, K2]
	# If we used regular repeat instead of repeat_interleave, we'd get:
	# [K1, K2, K1, K2]

	# Compute scaled dot-product attention (aka self-attention) with a causal mask
	# Shape: (b, num_heads, num_tokens, num_tokens)
	attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head

	# Use the mask to fill attention scores
	attn_scores = attn_scores.masked_fill(mask[:num_tokens, :num_tokens], -torch.inf)

	attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
	assert keys.shape[-1] == self.head_dim

	# Shape: (b, num_tokens, num_heads, head_dim)
	context_vec = (attn_weights @ values).transpose(1, 2)

	# Combine heads, where self.d_out = self.num_heads * self.head_dim
	context_vec = context_vec.reshape(b, num_tokens, self.d_out)
	context_vec = self.out_proj(context_vec) # optional projection

	return context_vec


	def compute_rope_params(head_dim, theta_base=10_000, context_length=4096, freq_config=None, dtype=torch.float32):
	assert head_dim % 2 == 0, "Embedding dimension must be even"

	# Compute the inverse frequencies
	inv_freq = 1.0 / (theta_base ** (torch.arange(0, head_dim, 2, dtype=dtype)[: (head_dim // 2)].float() / head_dim))

	# Frequency adjustments
	if freq_config is not None:
	low_freq_wavelen = freq_config["original_context_length"] / freq_config["low_freq_factor"]
	high_freq_wavelen = freq_config["original_context_length"] / freq_config["high_freq_factor"]

	wavelen = 2 * torch.pi / inv_freq

	inv_freq_llama = torch.where(
	wavelen > low_freq_wavelen, inv_freq / freq_config["factor"], inv_freq
	)

	smooth_factor = (freq_config["original_context_length"] / wavelen - freq_config["low_freq_factor"]) / (
	freq_config["high_freq_factor"] - freq_config["low_freq_factor"]
	)

	smoothed_inv_freq = (
	(1 - smooth_factor) * (inv_freq / freq_config["factor"]) + smooth_factor * inv_freq
	)

	is_medium_freq = (wavelen <= low_freq_wavelen) & (wavelen >= high_freq_wavelen)
	inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
	inv_freq = inv_freq_llama

	# Generate position indices
	positions = torch.arange(context_length, dtype=dtype)

	# Compute the angles
	angles = positions[:, None] * inv_freq[None, :] # Shape: (context_length, head_dim // 2)

	# Expand angles to match the head_dim
	angles = torch.cat([angles, angles], dim=1) # Shape: (context_length, head_dim)

	# Precompute sine and cosine
	cos = torch.cos(angles)
	sin = torch.sin(angles)

	return cos, sin


	def apply_rope(x, cos, sin):
	# x: (batch_size, num_heads, seq_len, head_dim)
	batch_size, num_heads, seq_len, head_dim = x.shape
	assert head_dim % 2 == 0, "Head dimension must be even"

	# Split x into first half and second half
	x1 = x[..., : head_dim // 2] # First half
	x2 = x[..., head_dim // 2:] # Second half

	# Adjust sin and cos shapes
	cos = cos[:seq_len, :].unsqueeze(0).unsqueeze(0) # Shape: (1, 1, seq_len, head_dim)
	sin = sin[:seq_len, :].unsqueeze(0).unsqueeze(0)

	# Apply the rotary transformation
	rotated = torch.cat((-x2, x1), dim=-1)
	x_rotated = (x * cos) + (rotated * sin)

	# It's ok to use lower-precision after applying cos and sin rotation
	return x_rotated.to(dtype=x.dtype)


	def text_to_token_ids(text, tokenizer):
	encoded = tokenizer.encode(text)
	encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
	return encoded_tensor


	def token_ids_to_text(token_ids, tokenizer):
	flat = token_ids.squeeze(0) # remove batch dimension
	return tokenizer.decode(flat.tolist())


	def generate(model, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):

	# For-loop is the same as before: Get logits, and only focus on last time step
	for _ in range(max_new_tokens):
	idx_cond = idx[:, -context_size:]
	with torch.no_grad():
	logits = model(idx_cond)
	logits = logits[:, -1, :]

	# Filter logits with top_k sampling
	if top_k is not None:
	# Keep only top_k values
	top_logits, _ = torch.topk(logits, top_k)
	min_val = top_logits[:, -1]
	logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits)

	# Apply temperature scaling
	if temperature > 0.0:
	logits = logits / temperature

	# Apply softmax to get probabilities
	probs = torch.softmax(logits, dim=-1) # (batch_size, context_len)

	# Sample from the distribution
	idx_next = torch.multinomial(probs, num_samples=1) # (batch_size, 1)

	# Otherwise same as before: get idx of the vocab entry with the highest logits value
	else:
	idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch_size, 1)

	if idx_next == eos_id: # Stop generating early if end-of-sequence token is encountered and eos_id is specified
	break

	# Same as before: append sampled index to the running sequence
	idx = torch.cat((idx, idx_next), dim=1) # (batch_size, num_tokens+1)

	return idx