# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py

import math
import torch
from typing import Literal, overload, Optional, Tuple

from torch import Tensor, nn
from transformers.cache_utils import Cache

from transformers import LlamaModel, LlamaConfig, LlamaForCausalLM
from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb, repeat_kv

AVAILABLE_ATTENTIONS = []

try:
	from transformers.utils import is_flash_attn_2_available

	if is_flash_attn_2_available():
		AVAILABLE_ATTENTIONS.append("flash_attention_2")
except Exception as e:
	print("Error while querying for `flash_attention_2` support", e)

is_rocm = any("AMD" in torch.cuda.get_device_properties(i).name for i in range(torch.cuda.device_count()))
is_ampere_or_newer_gpu = any(torch.cuda.get_device_properties(i).major >= 8 for i in range(torch.cuda.device_count()))

try:
	if is_rocm and False:
		# try to use triton flash attention / fused attention
		# currently only forward works, backwards throws an assert
		# even then it's extremely slow on my 7900XTX so the provided code is probably botched since it's a benchmark sample
		from einops import rearrange
		from .triton_flash_attention import triton_attention, MetaData

		def flash_attn_func(q, k, v, softmax_scale=None, causal=False, *args, **kwargs):
			metadata = MetaData(sm_scale=softmax_scale)
			batch_size, seqlen_q, seqlen_k = q.shape[0], q.shape[1], k.shape[1]
			
			metadata.max_seqlens_q = seqlen_q
			metadata.max_seqlens_k = seqlen_k

			# varlen but doesn't seem necessary
			if False:
				q, k, v = [rearrange(x, 'b s ... -> (b s) ...').contiguous() for x in [q, k, v]]
		
				cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q.device)
				cu_seqlens_k = cu_seqlens_q

				metadata.set_varlen_params(cu_seqlens_q, cu_seqlens_k)

			if causal:
				metadata.need_causal()

			return triton_attention( q, k, v, None, metadata )[0]

		AVAILABLE_ATTENTIONS.append("flash_attn")
		AVAILABLE_ATTENTIONS.append("flash_attn_rocm")
	elif not is_ampere_or_newer_gpu:
		# Uses https://github.com/ZRayZzz/flash-attention-v100/
		# Currently doesn't work because it's hard-coded to use a head dim of 128, will throw NaNs otherwise...
		from flash_attn_v100 import flash_attn_func as flash_attn_v100_func

		AVAILABLE_ATTENTIONS.append("flash_attn")
		AVAILABLE_ATTENTIONS.append("flash_attn_v100") # needed to signal to use padding
		def flash_attn_func(q, k, v, softmax_scale=None, causal=False, *args, **kwargs):
			return flash_attn_v100_func(
				q,
				k,
				v,
				softmax_scale,
				causal
			)
	else:
		# Borrowed from https://github.com/turboderp/exllamav2/blob/master/exllamav2/attn.py#L32
		# Adapted to provide flash_attn_v1 support
		import flash_attn
		flash_attn_ver = [int(t) for t in flash_attn.__version__.split(".") if t.isdigit()]

		if flash_attn_ver <= [1, 0, 9]:
			AVAILABLE_ATTENTIONS.append("flash_attn")
			from flash_attn.flash_attn_interface import flash_attn_unpadded_func
			from einops import rearrange

			# converts the flash_attn_2 calling convention to flash_attn_1's
			def flash_attn_func(q, k, v, dropout_p=0.0, softmax_scale=None, causal=False, return_attn_probs=False, deterministic=False, *args, **kwargs):
				batch_size, seqlen_q = q.shape[0], q.shape[1]
				seqlen_k = k.shape[1]
				q, k, v = [rearrange(x, 'b s ... -> (b s) ...').contiguous() for x in [q, k, v]]

				cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q.device)
				cu_seqlens_k = cu_seqlens_q

				return flash_attn_unpadded_func(
					q, k, v,
					cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
					dropout_p, softmax_scale, causal, return_attn_probs, deterministic
				)
			
			has_flash_attn = True
		elif [2, 2, 1] <= flash_attn_ver < [2, 5, 7]:
			AVAILABLE_ATTENTIONS.append("flash_attn")
			from flash_attn import flash_attn_func
			has_flash_attn = True
		elif [2, 5, 7] <= flash_attn_ver:
			AVAILABLE_ATTENTIONS.append("flash_attn")
			from flash_attn import flash_attn_func, flash_attn_with_kvcache

			signature = list(inspect.signature(flash_attn_func).parameters)
			has_flash_attn_with_window = "window_size" in signature
			has_flash_attn_with_softcap = "softcap" in signature

			import flash_attn_2_cuda as flash_attn_cuda

			has_flash_attn = True
			has_flash_attn_with_paged = True
except Exception as e:
	print("Error while querying for `flash_attn` support", e)

try:
	from xformers.ops.fmha import memory_efficient_attention
	from xformers.ops.fmha.attn_bias import LowerTriangularFromBottomRightMask, LowerTriangularMask

	AVAILABLE_ATTENTIONS.append("xformers")
except Exception as e:
	print("Error while importing `xformers`", e)

# to-do: find a better way to query for if there's available kernels since these return true regardless
if torch.backends.cuda.flash_sdp_enabled():
	AVAILABLE_ATTENTIONS.append("flash")

if torch.backends.cuda.mem_efficient_sdp_enabled():
	AVAILABLE_ATTENTIONS.append("mem_efficient")	

if torch.backends.cuda.math_sdp_enabled():
	AVAILABLE_ATTENTIONS.append("math")	

if torch.backends.cuda.cudnn_sdp_enabled():
	AVAILABLE_ATTENTIONS.append("cudnn")	

if AVAILABLE_ATTENTIONS:
	AVAILABLE_ATTENTIONS.append("sdpa")	

class LlamaAttention_Adapted(LlamaAttention):
	def __init__(self, *args, **kwargs):
		if 'mode' in kwargs:
			self.mode = kwargs['mode']
			kwargs.pop("mode")
		else:
			self.mode = "math"

		if self.mode == "math":
			self.mode = torch.nn.attention.SDPBackend.MATH
		elif self.mode == "mem_efficient":
			self.mode = torch.nn.attention.SDPBackend.EFFICIENT_ATTENTION
		elif self.mode == "flash":
			self.mode = torch.nn.attention.SDPBackend.FLASH_ATTENTION
		elif self.mode == "cudnn":
			self.mode = torch.nn.attention.SDPBackend.CUDNN_ATTENTION

		super().__init__(*args, **kwargs)

	# Adapted from LlamaAttention.forward
	def forward(
		self,
		hidden_states: torch.Tensor,
		attention_mask: Optional[torch.Tensor] = None,
		position_ids: Optional[torch.LongTensor] = None,
		past_key_value: Optional[Cache] = None,
		output_attentions: bool = False,
		use_cache: bool = False,
		cache_position: Optional[torch.LongTensor] = None,
		position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.45
		**kwargs,
	) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
		if output_attentions:
			# TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
			return super().forward(
				hidden_states=hidden_states,
				attention_mask=attention_mask,
				position_ids=position_ids,
				past_key_value=past_key_value,
				output_attentions=output_attentions,
				use_cache=use_cache,
				cache_position=cache_position,
				position_embeddings=position_embeddings,
			)

		dropout_rate = self.attention_dropout if self.training else 0.0
		bsz, q_len, _ = hidden_states.size()

		query_states = self.q_proj(hidden_states)
		key_states = self.k_proj(hidden_states)
		value_states = self.v_proj(hidden_states)

		query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
		key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
		value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)

		if position_embeddings is None:
			cos, sin = self.rotary_emb(value_states, position_ids)
		else:
			cos, sin = position_embeddings
		query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

		if past_key_value is not None:
			# sin and cos are specific to RoPE models; cache_position needed for the static cache
			cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
			key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

		if self.mode in ["xformers", "flash_attn"]:
			# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
			# to be able to avoid many of these transpose/reshape/view.
			query_states = query_states.transpose(1, 2)
			key_states = key_states.transpose(1, 2)
			value_states = value_states.transpose(1, 2)

			"""
			# In PEFT, usually we cast the layer norms in float32 for training stability reasons
			# therefore the input hidden states gets silently casted in float32. Hence, we need
			# cast them back in the correct dtype just to be sure everything works as expected.
			# This might slowdown training & inference so it is recommended to not cast the LayerNorms
			# in fp32. (LlamaRMSNorm handles it correctly)

			input_dtype = query_states.dtype
			if input_dtype == torch.float32:
				if torch.is_autocast_enabled():
					target_dtype = torch.get_autocast_gpu_dtype()
				# Handle the case where the model is quantized
				elif hasattr(self.config, "_pre_quantization_dtype"):
					target_dtype = self.config._pre_quantization_dtype
				else:
					target_dtype = self.q_proj.weight.dtype

				query_states = query_states.to(target_dtype)
				key_states = key_states.to(target_dtype)
				value_states = value_states.to(target_dtype)
			"""

			if self.mode == "flash_attn":
				attn_output = flash_attn_func(
					query_states,
					key_states,
					value_states,
					causal=True,
					softmax_scale=1.0 / math.sqrt(self.head_dim),
					dropout_p=dropout_rate,
				)
				
				attn_output = attn_output.reshape(bsz, q_len, -1).contiguous()
			elif self.mode == "xformers":
				attn_output = memory_efficient_attention(
					query_states,
					key_states,
					value_states,
					attn_bias = LowerTriangularMask() if attention_mask is None or attention_mask[0, 0, 0, 1] == 0 else None,
					scale = 1.0 / math.sqrt(self.head_dim),
					p=dropout_rate
				)
				attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

			attn_output = self.o_proj(attn_output)
			return attn_output, None, past_key_value

		key_states = repeat_kv(key_states, self.num_key_value_groups)
		value_states = repeat_kv(value_states, self.num_key_value_groups)

		causal_mask = attention_mask
		if attention_mask is not None:
			causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]

		# SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
		# Reference: https://github.com/pytorch/pytorch/issues/112577.
		if query_states.device.type == "cuda" and causal_mask is not None:
			query_states = query_states.contiguous()
			key_states = key_states.contiguous()
			value_states = value_states.contiguous()

		# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
		# in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
		is_causal = True if causal_mask is None and q_len > 1 else False
		
		with torch.nn.attention.sdpa_kernel(self.mode):
			attn_output = torch.nn.functional.scaled_dot_product_attention(
				query_states,
				key_states,
				value_states,
				attn_mask=causal_mask,
				dropout_p=dropout_rate,
				is_causal=is_causal,
			)

		attn_output = attn_output.transpose(1, 2).contiguous()
		attn_output = attn_output.view(bsz, q_len, -1)

		attn_output = self.o_proj(attn_output)

		return attn_output, None, past_key_value