from transformers.models.tapas.modeling_tapas import TapasLayer

from parallelformers.policies.base import Layer, Policy
from parallelformers.utils import AllReduceLinear


class TapasPolicy(Policy):
    @staticmethod
    def replace_arguments(config, world_size):
        return {
            # 1. reduce hidden size
            "attention.self.all_head_size": config.hidden_size // world_size,
            "crossattention.self.all_head_size": config.hidden_size // world_size,
            # 2. reduce number of heads
            "attention.self.num_attention_heads": config.num_attention_heads
            // world_size,
            "crossattention.self.num_attention_heads": config.num_attention_heads
            // world_size,
        }

    @staticmethod
    def attn_qkv():
        return [
            Layer(
                weight="attention.self.query.weight",
                bias="attention.self.query.bias",
            ),
            Layer(
                weight="attention.self.key.weight",
                bias="attention.self.key.bias",
            ),
            Layer(
                weight="attention.self.value.weight",
                bias="attention.self.value.bias",
            ),
            Layer(
                weight="crossattention.self.query.weight",
                bias="crossattention.self.query.bias",
                ignore_checker=True,
            ),
            Layer(
                weight="crossattention.self.key.weight",
                bias="crossattention.self.key.bias",
                ignore_checker=True,
            ),
            Layer(
                weight="crossattention.self.value.weight",
                bias="crossattention.self.value.bias",
                ignore_checker=True,
            ),
        ]

    @staticmethod
    def attn_out():
        return [
            Layer(
                weight="attention.output.dense.weight",
                bias="attention.output.dense.bias",
                replace=AllReduceLinear,
            ),
            Layer(
                weight="crossattention.output.dense.weight",
                bias="crossattention.output.dense.bias",
                replace=AllReduceLinear,
                ignore_checker=True,
            ),
        ]

    @staticmethod
    def mlp_in():
        return [
            Layer(
                weight="intermediate.dense.weight",
                bias="intermediate.dense.bias",
            ),
        ]

    @staticmethod
    def mlp_out():
        return [
            Layer(
                weight="output.dense.weight",
                bias="output.dense.bias",
                replace=AllReduceLinear,
            ),
        ]

    @staticmethod
    def original_layer_class():
        return TapasLayer

2b3ee5985a5ff54bb070e5b457ace16df016c6c9
<mask token>


class TapasPolicy(Policy):
    <mask token>

    @staticmethod
    def attn_qkv():
        return [Layer(weight='attention.self.query.weight', bias=
            'attention.self.query.bias'), Layer(weight=
            'attention.self.key.weight', bias='attention.self.key.bias'),
            Layer(weight='attention.self.value.weight', bias=
            'attention.self.value.bias'), Layer(weight=
            'crossattention.self.query.weight', bias=
            'crossattention.self.query.bias', ignore_checker=True), Layer(
            weight='crossattention.self.key.weight', bias=
            'crossattention.self.key.bias', ignore_checker=True), Layer(
            weight='crossattention.self.value.weight', bias=
            'crossattention.self.value.bias', ignore_checker=True)]

    @staticmethod
    def attn_out():
        return [Layer(weight='attention.output.dense.weight', bias=
            'attention.output.dense.bias', replace=AllReduceLinear), Layer(
            weight='crossattention.output.dense.weight', bias=
            'crossattention.output.dense.bias', replace=AllReduceLinear,
            ignore_checker=True)]
    <mask token>

    @staticmethod
    def mlp_out():
        return [Layer(weight='output.dense.weight', bias=
            'output.dense.bias', replace=AllReduceLinear)]

    @staticmethod
    def original_layer_class():
        return TapasLayer

<mask token>


class TapasPolicy(Policy):

    @staticmethod
    def replace_arguments(config, world_size):
        return {'attention.self.all_head_size': config.hidden_size //
            world_size, 'crossattention.self.all_head_size': config.
            hidden_size // world_size, 'attention.self.num_attention_heads':
            config.num_attention_heads // world_size,
            'crossattention.self.num_attention_heads': config.
            num_attention_heads // world_size}

    @staticmethod
    def attn_qkv():
        return [Layer(weight='attention.self.query.weight', bias=
            'attention.self.query.bias'), Layer(weight=
            'attention.self.key.weight', bias='attention.self.key.bias'),
            Layer(weight='attention.self.value.weight', bias=
            'attention.self.value.bias'), Layer(weight=
            'crossattention.self.query.weight', bias=
            'crossattention.self.query.bias', ignore_checker=True), Layer(
            weight='crossattention.self.key.weight', bias=
            'crossattention.self.key.bias', ignore_checker=True), Layer(
            weight='crossattention.self.value.weight', bias=
            'crossattention.self.value.bias', ignore_checker=True)]

    @staticmethod
    def attn_out():
        return [Layer(weight='attention.output.dense.weight', bias=
            'attention.output.dense.bias', replace=AllReduceLinear), Layer(
            weight='crossattention.output.dense.weight', bias=
            'crossattention.output.dense.bias', replace=AllReduceLinear,
            ignore_checker=True)]
    <mask token>

    @staticmethod
    def mlp_out():
        return [Layer(weight='output.dense.weight', bias=
            'output.dense.bias', replace=AllReduceLinear)]

    @staticmethod
    def original_layer_class():
        return TapasLayer

<mask token>


class TapasPolicy(Policy):

    @staticmethod
    def replace_arguments(config, world_size):
        return {'attention.self.all_head_size': config.hidden_size //
            world_size, 'crossattention.self.all_head_size': config.
            hidden_size // world_size, 'attention.self.num_attention_heads':
            config.num_attention_heads // world_size,
            'crossattention.self.num_attention_heads': config.
            num_attention_heads // world_size}

    @staticmethod
    def attn_qkv():
        return [Layer(weight='attention.self.query.weight', bias=
            'attention.self.query.bias'), Layer(weight=
            'attention.self.key.weight', bias='attention.self.key.bias'),
            Layer(weight='attention.self.value.weight', bias=
            'attention.self.value.bias'), Layer(weight=
            'crossattention.self.query.weight', bias=
            'crossattention.self.query.bias', ignore_checker=True), Layer(
            weight='crossattention.self.key.weight', bias=
            'crossattention.self.key.bias', ignore_checker=True), Layer(
            weight='crossattention.self.value.weight', bias=
            'crossattention.self.value.bias', ignore_checker=True)]

    @staticmethod
    def attn_out():
        return [Layer(weight='attention.output.dense.weight', bias=
            'attention.output.dense.bias', replace=AllReduceLinear), Layer(
            weight='crossattention.output.dense.weight', bias=
            'crossattention.output.dense.bias', replace=AllReduceLinear,
            ignore_checker=True)]

    @staticmethod
    def mlp_in():
        return [Layer(weight='intermediate.dense.weight', bias=
            'intermediate.dense.bias')]

    @staticmethod
    def mlp_out():
        return [Layer(weight='output.dense.weight', bias=
            'output.dense.bias', replace=AllReduceLinear)]

    @staticmethod
    def original_layer_class():
        return TapasLayer

from transformers.models.tapas.modeling_tapas import TapasLayer
from parallelformers.policies.base import Layer, Policy
from parallelformers.utils import AllReduceLinear


class TapasPolicy(Policy):

    @staticmethod
    def replace_arguments(config, world_size):
        return {'attention.self.all_head_size': config.hidden_size //
            world_size, 'crossattention.self.all_head_size': config.
            hidden_size // world_size, 'attention.self.num_attention_heads':
            config.num_attention_heads // world_size,
            'crossattention.self.num_attention_heads': config.
            num_attention_heads // world_size}

    @staticmethod
    def attn_qkv():
        return [Layer(weight='attention.self.query.weight', bias=
            'attention.self.query.bias'), Layer(weight=
            'attention.self.key.weight', bias='attention.self.key.bias'),
            Layer(weight='attention.self.value.weight', bias=
            'attention.self.value.bias'), Layer(weight=
            'crossattention.self.query.weight', bias=
            'crossattention.self.query.bias', ignore_checker=True), Layer(
            weight='crossattention.self.key.weight', bias=
            'crossattention.self.key.bias', ignore_checker=True), Layer(
            weight='crossattention.self.value.weight', bias=
            'crossattention.self.value.bias', ignore_checker=True)]

    @staticmethod
    def attn_out():
        return [Layer(weight='attention.output.dense.weight', bias=
            'attention.output.dense.bias', replace=AllReduceLinear), Layer(
            weight='crossattention.output.dense.weight', bias=
            'crossattention.output.dense.bias', replace=AllReduceLinear,
            ignore_checker=True)]

    @staticmethod
    def mlp_in():
        return [Layer(weight='intermediate.dense.weight', bias=
            'intermediate.dense.bias')]

    @staticmethod
    def mlp_out():
        return [Layer(weight='output.dense.weight', bias=
            'output.dense.bias', replace=AllReduceLinear)]

    @staticmethod
    def original_layer_class():
        return TapasLayer

from transformers.models.tapas.modeling_tapas import TapasLayer

from parallelformers.policies.base import Layer, Policy
from parallelformers.utils import AllReduceLinear


class TapasPolicy(Policy):
    @staticmethod
    def replace_arguments(config, world_size):
        return {
            # 1. reduce hidden size
            "attention.self.all_head_size": config.hidden_size // world_size,
            "crossattention.self.all_head_size": config.hidden_size // world_size,
            # 2. reduce number of heads
            "attention.self.num_attention_heads": config.num_attention_heads
            // world_size,
            "crossattention.self.num_attention_heads": config.num_attention_heads
            // world_size,
        }

    @staticmethod
    def attn_qkv():
        return [
            Layer(
                weight="attention.self.query.weight",
                bias="attention.self.query.bias",
            ),
            Layer(
                weight="attention.self.key.weight",
                bias="attention.self.key.bias",
            ),
            Layer(
                weight="attention.self.value.weight",
                bias="attention.self.value.bias",
            ),
            Layer(
                weight="crossattention.self.query.weight",
                bias="crossattention.self.query.bias",
                ignore_checker=True,
            ),
            Layer(
                weight="crossattention.self.key.weight",
                bias="crossattention.self.key.bias",
                ignore_checker=True,
            ),
            Layer(
                weight="crossattention.self.value.weight",
                bias="crossattention.self.value.bias",
                ignore_checker=True,
            ),
        ]

    @staticmethod
    def attn_out():
        return [
            Layer(
                weight="attention.output.dense.weight",
                bias="attention.output.dense.bias",
                replace=AllReduceLinear,
            ),
            Layer(
                weight="crossattention.output.dense.weight",
                bias="crossattention.output.dense.bias",
                replace=AllReduceLinear,
                ignore_checker=True,
            ),
        ]

    @staticmethod
    def mlp_in():
        return [
            Layer(
                weight="intermediate.dense.weight",
                bias="intermediate.dense.bias",
            ),
        ]

    @staticmethod
    def mlp_out():
        return [
            Layer(
                weight="output.dense.weight",
                bias="output.dense.bias",
                replace=AllReduceLinear,
            ),
        ]

    @staticmethod
    def original_layer_class():
        return TapasLayer
