# Path: flaxformer/components/dense.py
# def dense(features, name, inputs, kernel_axis_names):
#   if self.weight_params is not None or self.act_params is not None:
#     # TODO: Push the "quantized vs not" decision down into the
#     # AQT library. Currently we make that decision here, because the AQT
#     # library doesn't support DenseGeneral, so there's extra reshapes here
#     # whose performance impact I don't know.
#     aqt_context = aqt_config.QuantContext(
#         update_bounds=False, collect_acts_stats=False)
#     aqt_hparams = aqt_flax_layers.DenseAqt.HParams(
#         weight_prec=self.weight_params.prec,
#         weight_half_shift=self.weight_params.half_shift,
#         quant_act=self.act_params,  # currently supports fixed bounds only.
#         quant_type=aqt.QuantType.aqt,
#         weight_quant_granularity=aqt_config.QuantGranularity.per_channel,
#     )
#     batch, seq_len, channels = inputs.shape
#     inputs = inputs.reshape((batch * seq_len, channels))
#     result = aqt_flax_layers.DenseAqt(
#         features=features,
#         hparams=aqt_hparams,
#         train=enable_dropout,
#         quant_context=aqt_context,
#         paxis_name=None,
#         # No "cross-replica" reduction expressed in the XLA graph at this
#         # stage. Will be imposed later, automatically, by XLA SPMD.
#         use_bias=self.use_bias,
#         kernel_init=self.kernel_init,
#         bias_init=self.bias_init,
#         dtype=self.dtype,
#         kernel_axis_names=kernel_axis_names,
#         name=name,
#     )(inputs, padding_mask=None)
#     return result.reshape((batch, seq_len, features))
#   else:
#     return DenseGeneral(
#         features,
#         use_bias=self.use_bias,
#         dtype=self.dtype,
#         kernel_init=self.kernel_init,
#         bias_init=self.bias_init,
#         kernel_axis_names=kernel_axis_names,
#         name=name)(
#             inputs)
# 
# Path: flaxformer/types.py



import abc
import jax.numpy as jnp
import numpy as np
from typing import Any, Callable
from flax import linen as nn
from flax.linen import initializers
from flax.linen import partitioning
from jax import lax
from flaxformer.components import dense
from flaxformer.types import Array
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Defines an API for "rich attention" mechanisms.

These require the entire input vector.

"""




class RichAttentionApi(metaclass=abc.ABCMeta):
  """Interface for relative attention APIs that need the entire input vector."""

  @abc.abstractmethod
  def __call__(self,
               q_inputs: Array,
               k_inputs: Array,
               bidirectional: bool = True,

# Path: flaxformer/components/dense.py
# def dense(features, name, inputs, kernel_axis_names):
#   if self.weight_params is not None or self.act_params is not None:
#     # TODO: Push the "quantized vs not" decision down into the
#     # AQT library. Currently we make that decision here, because the AQT
#     # library doesn't support DenseGeneral, so there's extra reshapes here
#     # whose performance impact I don't know.
#     aqt_context = aqt_config.QuantContext(
#         update_bounds=False, collect_acts_stats=False)
#     aqt_hparams = aqt_flax_layers.DenseAqt.HParams(
#         weight_prec=self.weight_params.prec,
#         weight_half_shift=self.weight_params.half_shift,
#         quant_act=self.act_params,  # currently supports fixed bounds only.
#         quant_type=aqt.QuantType.aqt,
#         weight_quant_granularity=aqt_config.QuantGranularity.per_channel,
#     )
#     batch, seq_len, channels = inputs.shape
#     inputs = inputs.reshape((batch * seq_len, channels))
#     result = aqt_flax_layers.DenseAqt(
#         features=features,
#         hparams=aqt_hparams,
#         train=enable_dropout,
#         quant_context=aqt_context,
#         paxis_name=None,
#         # No "cross-replica" reduction expressed in the XLA graph at this
#         # stage. Will be imposed later, automatically, by XLA SPMD.
#         use_bias=self.use_bias,
#         kernel_init=self.kernel_init,
#         bias_init=self.bias_init,
#         dtype=self.dtype,
#         kernel_axis_names=kernel_axis_names,
#         name=name,
#     )(inputs, padding_mask=None)
#     return result.reshape((batch, seq_len, features))
#   else:
#     return DenseGeneral(
#         features,
#         use_bias=self.use_bias,
#         dtype=self.dtype,
#         kernel_init=self.kernel_init,
#         bias_init=self.bias_init,
#         kernel_axis_names=kernel_axis_names,
#         name=name)(
#             inputs)
# 
# Path: flaxformer/types.py



# Path: flaxformer/components/rich_attention_position_scores.py
import abc
import jax.numpy as jnp
import numpy as np
from typing import Any, Callable
from flax import linen as nn
from flax.linen import initializers
from flax.linen import partitioning
from jax import lax
from flaxformer.components import dense
from flaxformer.types import Array

#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Defines an API for "rich attention" mechanisms.

These require the entire input vector.

"""




class RichAttentionApi(metaclass=abc.ABCMeta):
  """Interface for relative attention APIs that need the entire input vector."""

  @abc.abstractmethod
  def __call__(self,
               q_inputs: Array,
               k_inputs: Array,
               bidirectional: bool = True,

               is_cross_attention: bool = False) -> Array: