################################################################################
# Copyright(c)2020-2025 Shanghai Biren Technology Co., Ltd. All rights reserved.
# 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.
#
################################################################################

# SPDX-License-Identifier: Apache-2.0
"""Minimal implementation of CLIPVisionModel intended to be only used
within a vision language model."""

import torch
import torch_br

from vllm.model_executor.models.clip import CLIPVisionEmbeddings


def clip_vision_embeddings_forward(self,
                                   pixel_values: torch.Tensor) -> torch.Tensor:
    batch_size = pixel_values.shape[0]
    target_dtype = self.patch_embedding.weight.dtype
    if self.patch_size == 14:
        import torch_br.supa._debug as supa_debug

        supa_debug.set_disable_zero_ws(False)
        supa_debug.set_disable_zero_output_uma(False)
        supa_debug.set_disable_zero_output_numa(False)
        supa_debug.set_disable_reorder_zero(False)

        #TODO(shouqing): this op need to do internal clear_zeros operation
        patch_embeds = torch_br.supa_conv2d_knxn_snxn_p0x0_fwd(
            pixel_values.to(dtype=target_dtype), self.patch_embedding.weight,
            self.patch_size, self.patch_size, 0)

        supa_debug.set_disable_zero_ws(True)
        supa_debug.set_disable_zero_output_uma(True)
        supa_debug.set_disable_zero_output_numa(True)
        supa_debug.set_disable_reorder_zero(True)
    else:
        patch_embeds = self.patch_embedding(pixel_values.to(
            dtype=target_dtype))  # shape = [*, width, grid, grid]
    patch_embeds = patch_embeds.flatten(2).transpose(1, 2)

    class_embeds = self.class_embedding.expand(batch_size, 1, -1)
    embeddings = torch.cat([class_embeds, patch_embeds], dim=1)
    data_in_cpu = lambda t: t.device == torch.device('cpu')
    if data_in_cpu(self.position_ids):
        cur_device = torch.supa.current_device()
        self.position_ids = self.position_ids.to(cur_device)
    embeddings = embeddings + self.position_embedding(self.position_ids)

    return embeddings


#logger.debug('[Patch] patch CLIPVisionEmbeddings forward')
CLIPVisionEmbeddings.forward = clip_vision_embeddings_forward