"""
---
title: GPT-NeoX Model Definition
summary: >
    This is the model definition of GPT-NeoX.
---

# GPT-NeoX Model

Here is the code for layers of GPT-NeoX model and the code to load
20B checkpoint.

The method `load_state` in the layers load the checkpoints of that layer.
The checkpoint loading helpers are on [`checkpoint.py`](checkpoint.html)
"""
import copy
import math
from typing import Dict, Optional, Set, Callable, Any, Generator, Tuple

import torch
from torch import nn
from torch.cuda.amp import autocast

from labml import monit, logger
from labml.logger import Text
from labml_nn.neox import checkpoint
from labml_nn.neox.utils.cache import get_cache


class NeoXModule(nn.Module):
    def load_state(self, p1: Dict[str, torch.Tensor], p2: Dict[str, torch.Tensor]):
        pass


class Embedding(NeoXModule):
    """
    ## Embedding layer

    This is a standard embeddings layer with code to load the checkpoint.
    """

    def __init__(self, n_vocab: int = 50_432, n_hidden: int = 6_144):
        """
        :param n_vocab: is the size of the vocabulary
        :param n_hidden: is the size of the embeddings
        """
        super().__init__()

        self.emb = nn.Embedding(n_vocab, n_hidden)

    def forward(self, x: torch.Tensor):
        """
        :param x: are the token ids of shape `[batch_size, seq_len]`
        """
        return self.emb(x)

    def load_state(self, p1: Dict[str, torch.Tensor], p2: Dict[str, torch.Tensor]):
        """
        Code to load the checkpoint
        """
        with monit.section('Load embedding layer'):
            checkpoint.merge_params_dim_0(self.emb.weight, 'word_embeddings.weight', p1, p2)


class RoPE(nn.Module):
    """
    ## Rotary Positional Embeddings

    GPT-NeoX uses [rotary positional embeddings (RoPE)](https://arxiv.org/abs/2104.09864).

    WE have annotated implementation of RoPE [here](https://nn.labml.ai/transformers/rope/index.html)
    with more notes the theory.
    """

    def __init__(self, d_rope: int, base: float = 10_000.):
        """
        :param d_rope: is the number of features for RoPE embeddings
        :param base: is the base for $\theta_i = 10000^{\frac{2(i-1)}{d}}$, which defaults to $10000$
        """
        super().__init__()

        # To store $\theta_i$ for the features
        self.theta = None
        # Cache $\cos m\theta_i$ and $\sin m\theta_i$
        self.cos_cached = None
        self.sin_cached = None

        # Base for $\theta_i = 10000^{\frac{2(i-1)}{d}}$
        self.base = base
        # Number of features for RoPE
        self.d_rope = d_rope

    @staticmethod
    def rotate_half(x: torch.Tensor):
        """
        ### Rotate the features

        $[-x^{(\frac{d}{2} + 1)}, -x^{(\frac{d}{2} + 2)}, ..., -x^{(d)}, x^{(1)}, x^{(2)}, ..., -x^{(\frac{d}{2})}]$
        """
        x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
        return torch.cat((-x2, x1), dim=-1)

    def forward(self, x: torch.Tensor, offset: int = 0):
        """
        :param x: has shape `[..., seq, n_heads, d_k]`
        :param offset: is the starting position of `x`. This is $\gt 0$ when we have
        cached the keys and queries of previous positions
        """

        # Get the actual sequence length
        seq_len = x.shape[-3] + offset

        # Initialize $\theta$
        if self.theta is None:
            #  $\theta_i = 10000^{\frac{2(i-1)}{d}}$
            theta = 1.0 / (self.base ** (torch.arange(0, self.d_rope, 2).float() / self.d_rope))
            self.theta = theta.to(x.device).to(x.dtype)

        # Initialize $\cos m\theta_i$ and $\sin m\theta_i$ cache
        if (
                self.cos_cached is None or
                seq_len > self.cos_cached.shape[1] or
                self.cos_cached.device != x.device or
                self.cos_cached.dtype != x.dtype
        ):
            # Get position indexes $m$
            seq_idx = torch.arange(seq_len, device=x.device).type_as(self.theta)
            # $m \theta_i$
            idx_theta = torch.einsum("s,d->sd", seq_idx, self.theta)
            # Concatenate so that for row $m$ we have
            #
            # $$[m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}, m \theta_0, m \theta_1, ..., m \theta_{\frac{d}{2}}]$$
            idx_theta2 = torch.cat((idx_theta, idx_theta), dim=-1).to(x.device)

            # Calculate $\cos m\theta_i$ and $\sin m\theta_i$ in fp32
            with autocast(enabled=False):
                idx_theta2 = idx_theta2.float()
                # Add head dimension
                self.cos_cached = idx_theta2.cos()[:, None, :]
                self.sin_cached = idx_theta2.sin()[:, None, :]

            # Cache them
            self.cos_cached = self.cos_cached.to(x.dtype)
            self.sin_cached = self.sin_cached.to(x.dtype)

        # Split the features. We apply RoPE to only `d_rope` features
        x_rope, x_pass = x[..., :self.d_rope], x[..., self.d_rope:]

        # Get the sin and cos values from the cache
        cos, sin = self.cos_cached[offset: seq_len], self.sin_cached[offset: seq_len]

        # RoPE embeddings
        #
        # \begin{align}
        # \begin{pmatrix}
        # x^{(i)}_m \cos m \theta_i - x^{(i + \frac{d}{2})}_m \sin m \theta_i \\
        # x^{(i + \frac{d}{2})}_m \cos m\theta_i + x^{(i)}_m \sin m \theta_i \\
        # \end{pmatrix} \\
        # \end{align}
        #
        # for $i \in {1, 2, ..., \frac{d}{2}}$
        x_rope = (x_rope * cos) + (self.rotate_half(x_rope) * sin)

        # Concatenate with features that didn't get RoPE embeddings
        return torch.cat((x_rope, x_pass), dim=-1)


class AttentionLayer(nn.Module):
    """
    ## Attention layer
    """

    def __init__(self, n_hidden: int = 6_144, n_heads: int = 64, rope_percentage: float = 0.25,
                 mask_fill: float = -10_000.0, *, is_flash_attention: bool = False):
        """
        :param n_hidden: the number of features in embeddings
        :param n_heads: the number of attention heads
        :param rope_percentage: percentage of features to add RoPE embeddings
        :param mask_fill: masking fill value for attention matrix
        :param is_flash_attention: specifies whether to use
            [FlashAttention](https://github.com/HazyResearch/flash-attention)
        """
        super().__init__()

        self.n_heads = n_heads
        self.mask_fill = mask_fill

        # Linear layer for query, key and value
        self.qkv_lin = nn.Linear(n_hidden, n_hidden * 3)
        # Final linear layer
        self.output = nn.Linear(n_hidden, n_hidden)

        # Number of features per head
        d_k = n_hidden // n_heads
        # RoPE embedding module
        self.rope = RoPE(int(d_k * rope_percentage))

        # Attention scaling factor
        self.scale = 1 / math.sqrt(d_k)

        # To cache causal mask
        self.causal_mask = None

        # Attention softmax module
        self.softmax = nn.Softmax(dim=-2)

        # [FlashAttention](https://github.com/HazyResearch/flash-attention)
        if is_flash_attention:
            try:
                from flash_attn.flash_attention import FlashAttention
                self.flash_attention = FlashAttention()
            except ImportError:
                logger.log('Install flash attention github.com/HazyResearch/flash-attention. '
                           'Falling back to normal attention', Text.warning)
                self.flash_attention = None
        else:
            self.flash_attention = None

    def _get_mask(self, attn: torch.Tensor):
        """
        #### Calculate the causal mask

        * `attn` has shape [batch_size, query_seq_len, key_seq_len, n_heads]
        """

        # Query and key lengths
        nq, nk = attn.shape[1:3]

        # Create mask
        if (
                self.causal_mask is None or
                self.causal_mask.shape[0] != nq or
                self.causal_mask.shape[1] != nk or
                self.causal_mask.device != attn.device
        ):
            self.causal_mask = torch.triu(attn.new_ones([nq, nk], dtype=torch.bool), 1 + nk - nq)

        # Return from cache
        return self.causal_mask[None, :, :, None]

    def forward(self, x: torch.Tensor):
        """
        :param x: has shape `[batch_size, seq_len, n_hidden]`
        """
        # Get query, key and value embeddings (all concatenated).
        # The last dimension size will change from n_hidden -> `3 x n_hidden`
        qkv = self.qkv_lin(x)

        # Split into heads by changing the shape to `[batch_size, seq_len, n_heads, 3 * d_k]`
        qkv = qkv.view(*qkv.shape[:-1], self.n_heads, -1)
        # Split into query, key and value each of shape `[batch_size, seq_len, n_heads, 3 * d_k]`
        q, k, v = torch.split(qkv, qkv.shape[-1] // 3, dim=-1)

        # If we are caching the states of previous tokens
        if get_cache().get('use_cache', False):
            # Get the state id's. We use to retrieve previous states and store the next states
            prev_state_id, next_state_id = get_cache().get('state_ids')
            # If there's cache
            if prev_state_id is not None:
                # Get the past keys and values. These will have shape `[batch_size, prev_seq_len, n_heads, d_k]`
                k_past, v_past = get_cache().pop(f'attn_kv_{prev_state_id}')
                # Offset of the current embeddings
                offset = k_past.shape[1]

                # Add RoPE embeddings
                q = self.rope(q, offset=offset)
                k = self.rope(k, offset=offset)

                # Concatenate the past
                k = torch.cat([k_past, k], dim=1)
                v = torch.cat([v_past, v], dim=1)
            else:
                # Add RoPE embeddings
                q = self.rope(q)
                k = self.rope(k)

            # Save the current state
            get_cache().push(f'attn_kv_{next_state_id}', (k, v))
        else:
            # No cache - simply add RoPE embeddings
            q = self.rope(q)
            k = self.rope(k)

        # Use flash attention
        if self.flash_attention is not None and q.shape[1] == k.shape[1] and q.shape[-1] <= 128:
            output = self.compute_flash_attention(q, k, v)
        # Otherwise, use normal attention
        else:
            output = self.compute_attention(q, k, v)

        # Reshape from `[batch_size, seq_len, n_heads, d_k] to `[batch_size, seq_len, n_hidden]`
        output = output.reshape(*x.shape)

        # Final linear layer
        return self.output(output)

    def compute_flash_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor):
        # Stack them into shape `[batch_size, seq_len, 3, n_heads, d_k]`
        qkv = torch.stack((q, k, v), dim=2)
        d_k = qkv.shape[-1]
        if d_k <= 32:
            pad = 32 - d_k
        elif d_k <= 64:
            pad = 64 - d_k
        elif d_k <= 128:
            pad = 128 - d_k
        else:
            raise ValueError(f'Head size {d_k} too large for flash attention')

        if pad > 0:
            qkv = torch.cat((qkv, qkv.new_zeros(*qkv.shape[:-1], pad)), dim=-1)

        output, _ = self.flash_attention(qkv, causal=True)
        # The output is of shape `[batch_size, seq_len, n_heads, d_k + padding]`
        output = output[:, :, :, :d_k]

        return output

    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor):
        # Disable auto-casting to fp16 for attention computation
        with autocast(enabled=False):
            if q.dtype == torch.float16:
                # Convert to fp32 if the current dtype is fp16
                attn = torch.einsum('bihk,bjhk->bijh', q.float(), k.float())
            else:
                # Do not cast for bfloat
                attn = torch.einsum('bihk,bjhk->bijh', q, k)

            # Scale attention
            attn = attn * self.scale

            # Get causal mask
            mask = self._get_mask(attn)
            # Apply mask
            attn.masked_fill_(mask, self.mask_fill)

            # Attention softmax
            attn = self.softmax(attn)

        # Get attention weighted values
        output = torch.einsum('bijh,bjhk->bihk', attn.to(v.dtype), v)

        return output


class FFNLayer(nn.Module):
    """
    ## Feedforward Network
    """

    def __init__(self, n_hidden: int = 6_144, d_ff: int = 0):
        """
        :param n_hidden: is the embedding size
        """
        super().__init__()

        if not d_ff:
            d_ff = n_hidden * 4

        # Expansion linear layer
        self.dense_h_h4 = nn.Linear(n_hidden, d_ff)
        # GELU activation
        self.activation = nn.GELU()
        # Contraction linear layer
        self.dense_h4_h = nn.Linear(d_ff, n_hidden)

    def forward(self, x: torch.Tensor):
        """
        :param x: has shape `[batch_size, seq_len, n_hidden]`
        """
        x = self.dense_h_h4(x)
        x = self.activation(x)
        x = self.dense_h4_h(x)

        return x


class TransformerLayer(NeoXModule):
    """
    ## Transformer Layer
    """

    def __init__(self, n_hidden: int = 6_144, n_heads: int = 64, *, is_flash_attention: bool = False):
        """
        :param n_hidden: is the embedding size
        :param n_heads: is the number of heads
        :param is_flash_attention: specifies whether to use
            [FlashAttention](https://github.com/HazyResearch/flash-attention)

        *Out implementation doesn't include dropout*.
        """
        super().__init__()

        # Layer normalization before attention
        self.pre_ln_attn = nn.LayerNorm(n_hidden)
        # Layer normalization before FFN
        self.pre_ln_ffn = nn.LayerNorm(n_hidden)

        # Attention layer
        self.attention = AttentionLayer(n_hidden, n_heads, is_flash_attention=is_flash_attention)
        # FFN layer
        self.ffn = FFNLayer(n_hidden)

    def forward(self, x: torch.Tensor):
        """
        :param x: are the embeddings of shape `[batch_size, seq_len, n_hidden]`
        """

        # Residual connection
        residual = x
        # NeoX runs attention and feedforward network in parallel
        attn = self.attention(self.pre_ln_attn(x))
        ffn = self.ffn(self.pre_ln_ffn(x))
        # Add them and the residual connection
        return attn + ffn + residual

    def load_state(self, p1: Dict[str, torch.Tensor], p2: Dict[str, torch.Tensor]):
        """
        Code to load the checkpoint
        """
        with monit.section('Load transformer layer'):
            # Attention output transform
            checkpoint.merge_params_sum(self.attention.output.bias, 'attention.dense.bias', p1, p2)
            checkpoint.merge_params_dim_1(self.attention.output.weight, 'attention.dense.weight', p1, p2)

            # Attention query, key and value transform
            checkpoint.merge_params_dim_0(self.attention.qkv_lin.bias, 'attention.query_key_value.bias', p1, p2)
            checkpoint.merge_params_dim_0(self.attention.qkv_lin.weight, 'attention.query_key_value.weight', p1, p2)

            # Layer norm before attention
            checkpoint.merge_params_duplicate(self.pre_ln_attn.bias, 'input_layernorm.bias', p1, p2)
            checkpoint.merge_params_duplicate(self.pre_ln_attn.weight, 'input_layernorm.weight', p1, p2)

            # FFN second transform
            checkpoint.merge_params_dim_0(self.ffn.dense_h_h4.bias, 'mlp.dense_h_to_4h.bias', p1, p2)
            checkpoint.merge_params_dim_0(self.ffn.dense_h_h4.weight, 'mlp.dense_h_to_4h.weight', p1, p2)

            # FFN first transform
            checkpoint.merge_params_sum(self.ffn.dense_h4_h.bias, 'mlp.dense_4h_to_h.bias', p1, p2)
            checkpoint.merge_params_dim_1(self.ffn.dense_h4_h.weight, 'mlp.dense_4h_to_h.weight', p1, p2)

            # Layer norm before FFN
            checkpoint.merge_params_duplicate(self.pre_ln_ffn.bias, 'post_attention_layernorm.bias', p1, p2)
            checkpoint.merge_params_duplicate(self.pre_ln_ffn.weight, 'post_attention_layernorm.weight', p1, p2)


class FinalNorm(NeoXModule):
    """
    ## Final normalization layer
    """

    def __init__(self, n_hidden: int = 6_144):
        """
        :param n_hidden: is the embedding size
        """
        super().__init__()

        self.ln = nn.LayerNorm(n_hidden)

    def forward(self, x: torch.Tensor):
        """
        :param x: are the embeddings of shape `[batch_size, seq_len, n_hidden]`
        """
        return self.ln(x)

    def load_state(self, p1: Dict[str, torch.Tensor], p2: Dict[str, torch.Tensor]):
        """
        Code to load the checkpoint
        """
        with monit.section('Load final normalization layer'):
            checkpoint.merge_params_duplicate(self.ln.bias, 'norm.bias', p1, p2)
            checkpoint.merge_params_duplicate(self.ln.weight, 'norm.weight', p1, p2)


class ReadoutLayer(NeoXModule):
    """
    Readout layer
    """

    def __init__(self, n_hidden: int = 6_144, n_vocab: int = 50_432):
        """
        :param n_hidden: is the embedding size
        :param n_vocab: is the size of the vocabulary
        """
        super().__init__()

        self.linear = nn.Linear(n_hidden, n_vocab, bias=False)

    def forward(self, x: torch.Tensor):
        """
        :param x: are the embeddings of shape `[batch_size, seq_len, n_hidden]`
        """
        return self.linear(x)

    def load_state(self, p1: Dict[str, torch.Tensor], p2: Dict[str, torch.Tensor]):
        """
        Code to load the checkpoint
        """
        with monit.section('Load final linear layer'):
            checkpoint.merge_params_dim_0(self.linear.weight, 'final_linear.weight', p1, p2)


class LayerGenerator:
    pre_created_layers: Dict[Any, Optional[NeoXModule]]

    def __init__(self, *, n_vocab: int = 50_432, n_hidden: int = 6_144,
                 n_layers: int = 44, n_heads: int = 64,
                 filter_layers: Optional[Set] = None,
                 is_clone_layers: bool = True,
                 dtype: torch.dtype = torch.float,
                 device: torch.device = torch.device('cpu'),
                 is_llm_int8: bool = False,
                 llm_int8_threshold: float = 6.0,
                 is_flash_attention: bool = False
                 ):
        """
        ### Generator to create layers

        The layers are generated in the same order as checkpoints.

        It gives `None` when a layer is not available; we use the layer indices as NeoX and there are two
        transformation layers we don't need in our implementation.

        :param n_vocab: is the number of tokens in the vocabulary
        :param n_hidden: is the number of features in the embeddings
        :param n_layers: is the number of transformer layers
        :param n_heads: is the number of attention heads
        :param filter_layers: are the set of layers to be used. All layers will be used if None.
            This is used to test smaller versions of the model with fewer layers
        :param is_clone_layers: specifies whether to clone the transformer layers (a bit faster)
        :param dtype: is the data type of the model
        :param device: is the device of the model
        :param is_llm_int8: specifies whether to use int8 quantization
        :param llm_int8_threshold: is the threshold $\alpha$ used to separate outlier features
        :param is_flash_attention: specifies whether to use
            [FlashAttention](https://github.com/HazyResearch/flash-attention)
        """
        if filter_layers is None:
            filter_layers = set(range(n_layers + 3))

        self.n_vocab = n_vocab
        self.n_hidden = n_hidden
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.filter_layers = filter_layers
        self.is_clone_layers = is_clone_layers
        self.dtype = dtype
        self.device = device
        self.is_llm_int8 = is_llm_int8
        self.llm_int8_threshold = llm_int8_threshold
        self.is_flash_attention = is_flash_attention

        self.pre_created_layers = dict(
            transformer_layer=None,
        )

    def _prepare_layer(self, layer: NeoXModule):
        """
        #### Prepares the layer for usage

        We move the layer to the device and convert it to the correct data type

        :param layer: is the layer to prepare
        :return: the prepared layer
        """
        return layer.to(self.device, self.dtype)

    @torch.no_grad()
    def post_load_prepare(self, layer: NeoXModule, *,
                          is_llm_int8: bool = None,
                          device: torch.device = None,
                          llm_int8_threshold: float = None,
                          ):
        """
        <a id="post_load_prepare"></a>

        ### Layer transformations after loading the checkpoint

        This function implements layer transformations after loading the checkpoint.

        Currently, it only applies the int8 quantization.

        :param layer: is the layer to prepare
        :param is_llm_int8: specifies whether to use int8 quantization
        :param device: is the device of the model
        :param llm_int8_threshold: is the threshold $\alpha$ used to separate outlier features
        :return: the prepared layer
        """

        # Get default values if not specified
        if is_llm_int8 is None:
            is_llm_int8 = self.is_llm_int8
        if device is None:
            device = self.device
        if llm_int8_threshold is None:
            llm_int8_threshold = self.llm_int8_threshold

        # Skip if not using int8 quantization
        if not is_llm_int8:
            return layer

        # Only convert the linear layers in the transformer layers
        if not isinstance(layer, TransformerLayer):
            return layer

        # Use `make_llm_int8_linear` defined in [utilities](./utils/llm_int8.html).
        from labml_nn.neox.utils.llm_int8 import make_llm_int8_linear

        # Convert the linear layers
        with monit.section('Convert to int8'):
            layer.attention.output = make_llm_int8_linear(layer.attention.output,
                                                          device=device,
                                                          threshold=llm_int8_threshold)
            layer.attention.qkv_lin = make_llm_int8_linear(layer.attention.qkv_lin,
                                                           device=device,
                                                           threshold=llm_int8_threshold)
            layer.ffn.dense_h_h4 = make_llm_int8_linear(layer.ffn.dense_h_h4,
                                                        device=device,
                                                        threshold=llm_int8_threshold)
            layer.ffn.dense_h4_h = make_llm_int8_linear(layer.ffn.dense_h4_h,
                                                        device=device,
                                                        threshold=llm_int8_threshold)
        #
        return layer

    def _create_and_cache_layer(self, name: str, creator: Callable[[], NeoXModule]):
        """
        #### Creates and caches a layer

        Copying cached layers is faster than initializing new layers because it takes time to
        initialize parameters.

        :param name: is the name of the layer
        :param creator: is the function to create the layer
        :return: the created layer or a copy of the cached layer
        """

        if not self.is_clone_layers:
            return self._prepare_layer(creator())

        if self.pre_created_layers[name] is None:
            self.pre_created_layers[name] = self._prepare_layer(creator())

        layer = copy.deepcopy(self.pre_created_layers[name])
        return layer

    def _create_transformer_layer(self):
        return self._create_and_cache_layer(
            'transformer_layer',
            lambda: TransformerLayer(self.n_hidden, self.n_heads, is_flash_attention=self.is_flash_attention)
        )

    def _create_embedding_layer(self):
        return Embedding(self.n_vocab, self.n_hidden)

    def _create_final_norm_layer(self):
        return FinalNorm(self.n_hidden)

    def _create_readout_layer(self):
        return ReadoutLayer(self.n_hidden, self.n_vocab)

    @torch.no_grad()
    def get_layers(self) -> Generator[Tuple[NeoXModule, Tuple[str, str]], None, None]:
        """
        ### Generator to get layers
        """
        # Embedding layer
        if 0 in self.filter_layers:
            with monit.section('Embedding layer'):
                layer = self._prepare_layer(self._create_embedding_layer())
            yield layer, ('layer_00-model_00-model_states.pt', 'layer_00-model_01-model_states.pt')

        # Transformer layers
        for i in range(self.n_layers):
            # Transformer layer
            if i + 1 in self.filter_layers:
                with monit.section(f'Transformer Layer {i}'):
                    yield self._create_transformer_layer(), \
                          (f'layer_{i + 2 :02d}-model_00-model_states.pt',
                           f'layer_{i + 2 :02d}-model_01-model_states.pt')

        # Final normalization layer
        if self.n_layers + 1 in self.filter_layers:
            with monit.section('Final norm layer'):
                layer = self._prepare_layer(self._create_final_norm_layer())
            yield layer, ('layer_47-model_00-model_states.pt', 'layer_47-model_01-model_states.pt')

        # Readout layer
        if self.n_layers + 2 in self.filter_layers:
            with monit.section('Readout layer'):
                layer = self._prepare_layer(self._create_readout_layer())
            yield layer, ('layer_48-model_00-model_states.pt', 'layer_48-model_01-model_states.pt')

        for k in self.pre_created_layers.keys():
            self.pre_created_layers[k] = None

    @property
    def total_layers(self):
        """
        ### Returns the total number of layers
        """
        return self.n_layers + 3

    @torch.no_grad()
    def load(self) -> Generator[NeoXModule, None, None]:
        """
        ### Generator to load layers
        """
        with monit.section("Layers"):
            for i, (layer, files) in enumerate(self.get_layers()):
                if files is not None:
                    layer.load_state(*checkpoint.load_checkpoint_files(files))

                layer = self.post_load_prepare(layer)

                monit.progress(min(0.99, (i + 1) / self.total_layers))
                yield layer