'''simple docstring'''





import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:	Dict    =logging.get_logger(__name__)





def       lowerCAmelCase_							(   snake_case_			:					str     )       -> int:


     '''simple docstring'''




     UpperCAmelCase_			       =  SwinConfig.from_pretrained(
         "microsoft/swin-tiny-patch4-window7-224"   ,       out_features=["stage1", "stage2", "stage3", "stage4"]     )
     UpperCAmelCase_			       =  MaskFormerConfig(backbone_config=snake_case_     )

     UpperCAmelCase_			       =  "huggingface/label-files"
     if "ade20k-full" in model_name:
          # this should be ok
          UpperCAmelCase_			       =  8_47
          UpperCAmelCase_			       =  "maskformer-ade20k-full-id2label.json"
     elif "ade" in model_name:
          # this should be ok
          UpperCAmelCase_			       =  1_50
          UpperCAmelCase_			       =  "ade20k-id2label.json"
     elif "coco-stuff" in model_name:
          # this should be ok
          UpperCAmelCase_			       =  1_71
          UpperCAmelCase_			       =  "maskformer-coco-stuff-id2label.json"
     elif "coco" in model_name:
          # TODO
          UpperCAmelCase_			       =  1_33
          UpperCAmelCase_			       =  "coco-panoptic-id2label.json"
     elif "cityscapes" in model_name:
          # this should be ok
          UpperCAmelCase_			       =  19
          UpperCAmelCase_			       =  "cityscapes-id2label.json"
     elif "vistas" in model_name:
          # this should be ok
          UpperCAmelCase_			       =  65
          UpperCAmelCase_			       =  "mapillary-vistas-id2label.json"

     UpperCAmelCase_			       =  json.load(open(hf_hub_download(snake_case_   ,       snake_case_   ,       repo_type="dataset"     )   ,       "r"     )     )
     UpperCAmelCase_			       =  {int(snake_case_     ): v for k, v in idalabel.items()}

     return config





def       lowerCAmelCase_							(   snake_case_			:					List[str]     )       -> List[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  []
     # stem
     # fmt: off
     rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")     )
     rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")     )
     rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")     )
     rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias")     )
     # stages
     for i in range(len(config.backbone_config.depths     )     ):
          for j in range(config.backbone_config.depths[i]     ):
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.norm2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

          if i < 3:
               rename_keys.append((f"""backbone.layers.{i}.downsample.reduction.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.downsample.norm.weight""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")     )
               rename_keys.append((f"""backbone.layers.{i}.downsample.norm.bias""", f"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")     )
          rename_keys.append((f"""backbone.norm{i}.weight""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")     )
          rename_keys.append((f"""backbone.norm{i}.bias""", f"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")     )

     # FPN
     rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")     )
     rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")     )
     rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")     )
     for source_index, target_index in zip(range(3   ,       0   ,       -1     )   ,       range(0   ,       3     )     ):
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")     )
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")     )
          rename_keys.append((f"""sem_seg_head.adapter_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")     )
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")     )
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.weight""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")     )
          rename_keys.append((f"""sem_seg_head.layer_{source_index}.norm.bias""", f"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")     )
     rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")     )
     rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")     )

     # Transformer decoder
     for idx in range(config.decoder_config.decoder_layers     ):
          # self-attention out projection
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")     )
          # cross-attention out projection
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")     )
          # MLP 1
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")     )
          # MLP 2
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", f"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", f"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")     )
          # layernorm 1 (self-attention layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", f"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")     )
          # layernorm 2 (cross-attention layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", f"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")     )
          # layernorm 3 (final layernorm)
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", f"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")     )

     rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")     )
     rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")     )

     # heads on top
     rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")     )

     rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")     )
     rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")     )

     rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")     )
     rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")     )

     for i in range(3     ):
          rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", f"""mask_embedder.{i}.0.weight""")     )
          rename_keys.append((f"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", f"""mask_embedder.{i}.0.bias""")     )
     # fmt: on

     return rename_keys





def       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					Dict   ,       snake_case_			:					Any     )       -> Any:


     '''simple docstring'''




     UpperCAmelCase_			       =  dct.pop(snake_case_     )
     UpperCAmelCase_			       =  val





def       lowerCAmelCase_							(   snake_case_			:					List[str]   ,       snake_case_			:					List[str]     )       -> List[str]:


     '''simple docstring'''




     UpperCAmelCase_			       =  [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
     for i in range(len(backbone_config.depths     )     ):
          UpperCAmelCase_			       =  num_features[i]
          for j in range(backbone_config.depths[i]     ):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               UpperCAmelCase_			       =  state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""     )
               UpperCAmelCase_			       =  state_dict.pop(f"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""     )
               # next, add query, keys and values (in that order) to the state dict
               UpperCAmelCase_			       =  in_proj_weight[:dim, :]
               UpperCAmelCase_			       =  in_proj_bias[: dim]
               UpperCAmelCase_			       =  in_proj_weight[
                   dim : dim * 2, :
               ]
               UpperCAmelCase_			       =  in_proj_bias[
                   dim : dim * 2
               ]
               UpperCAmelCase_			       =  in_proj_weight[
                   -dim :, :
               ]
               UpperCAmelCase_			       =  in_proj_bias[-dim :]
               # fmt: on





def       lowerCAmelCase_							(   snake_case_			:					Dict   ,       snake_case_			:					str     )       -> str:


     '''simple docstring'''




     UpperCAmelCase_			       =  config.decoder_config.hidden_size
     for idx in range(config.decoder_config.decoder_layers     ):
          # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
          UpperCAmelCase_			       =  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""     )
          UpperCAmelCase_			       =  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""     )
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase_			       =  in_proj_weight[: hidden_size, :]
          UpperCAmelCase_			       =  in_proj_bias[:config.hidden_size]
          UpperCAmelCase_			       =  in_proj_weight[hidden_size : hidden_size * 2, :]
          UpperCAmelCase_			       =  in_proj_bias[hidden_size : hidden_size * 2]
          UpperCAmelCase_			       =  in_proj_weight[-hidden_size :, :]
          UpperCAmelCase_			       =  in_proj_bias[-hidden_size :]
          # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
          UpperCAmelCase_			       =  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""     )
          UpperCAmelCase_			       =  state_dict.pop(f"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""     )
          # next, add query, keys and values (in that order) to the state dict
          UpperCAmelCase_			       =  in_proj_weight[: hidden_size, :]
          UpperCAmelCase_			       =  in_proj_bias[:config.hidden_size]
          UpperCAmelCase_			       =  in_proj_weight[hidden_size : hidden_size * 2, :]
          UpperCAmelCase_			       =  in_proj_bias[hidden_size : hidden_size * 2]
          UpperCAmelCase_			       =  in_proj_weight[-hidden_size :, :]
          UpperCAmelCase_			       =  in_proj_bias[-hidden_size :]
     # fmt: on





def       lowerCAmelCase_							(   )       -> torch.Tensor:


     '''simple docstring'''




     UpperCAmelCase_			       =  "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_			       =  Image.open(requests.get(snake_case_   ,       stream=snake_case_     ).raw     )
     return im





@torch.no_grad()
def       lowerCAmelCase_							(   snake_case_			:					str   ,       snake_case_			:					str   ,       snake_case_			:					str   ,       snake_case_			:					bool = False     )       -> List[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  get_maskformer_config(snake_case_     )

     # load original state_dict
     with open(snake_case_   ,       "rb"     ) as f:
          UpperCAmelCase_			       =  pickle.load(snake_case_     )
     UpperCAmelCase_			       =  data["model"]

     # for name, param in state_dict.items():
     #     print(name, param.shape)

     # rename keys
     UpperCAmelCase_			       =  create_rename_keys(snake_case_     )
     for src, dest in rename_keys:
          rename_key(snake_case_   ,       snake_case_   ,       snake_case_     )
     read_in_swin_q_k_v(snake_case_   ,       config.backbone_config     )
     read_in_decoder_q_k_v(snake_case_   ,       snake_case_     )

     # update to torch tensors
     for key, value in state_dict.items():
          UpperCAmelCase_			       =  torch.from_numpy(snake_case_     )

     # load 🤗 model
     UpperCAmelCase_			       =  MaskFormerForInstanceSegmentation(snake_case_     )
     model.eval()

     for name, param in model.named_parameters():
          print(snake_case_   ,       param.shape     )

     UpperCAmelCase_       ,					UpperCAmelCase_			       =  model.load_state_dict(snake_case_   ,       strict=snake_case_     )
     assert missing_keys == [
         "model.pixel_level_module.encoder.model.layernorm.weight",
         "model.pixel_level_module.encoder.model.layernorm.bias",
     ]
     assert len(snake_case_     ) == 0, f"""Unexpected keys: {unexpected_keys}"""

     # verify results
     UpperCAmelCase_			       =  prepare_img()
     if "vistas" in model_name:
          UpperCAmelCase_			       =  65
     elif "cityscapes" in model_name:
          UpperCAmelCase_			       =  6_55_35
     else:
          UpperCAmelCase_			       =  2_55
     UpperCAmelCase_			       =  True if "ade" in model_name else False
     UpperCAmelCase_			       =  MaskFormerImageProcessor(ignore_index=snake_case_   ,       reduce_labels=snake_case_     )

     UpperCAmelCase_			       =  image_processor(snake_case_   ,       return_tensors="pt"     )

     UpperCAmelCase_			       =  model(**snake_case_     )

     print("Logits:"   ,       outputs.class_queries_logits[0, :3, :3]     )

     if model_name == "maskformer-swin-tiny-ade":
          UpperCAmelCase_			       =  torch.tensor(
              [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]     )
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,       snake_case_   ,       atol=1E-4     )
     print("Looks ok!"     )

     if pytorch_dump_folder_path is not None:
          print(f"""Saving model and image processor to {pytorch_dump_folder_path}"""     )
          Path(snake_case_     ).mkdir(exist_ok=snake_case_     )
          model.save_pretrained(snake_case_     )
          image_processor.save_pretrained(snake_case_     )

     if push_to_hub:
          print("Pushing model and image processor to the hub..."     )
          model.push_to_hub(f"""nielsr/{model_name}"""     )
          image_processor.push_to_hub(f"""nielsr/{model_name}"""     )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:	List[Any]    =argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default='maskformer-swin-tiny-ade',
        type=str,
        help=('Name of the MaskFormer model you\'d like to convert',),
    )
    parser.add_argument(
        '--checkpoint_path',
        default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
        type=str,
        help='Path to the original state dict (.pth file).',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )
    parser.add_argument(
        '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
    )

    SCREAMING_SNAKE_CASE_:	Tuple    =parser.parse_args()
    convert_maskformer_checkpoint(
        args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )











from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[torch.FloatTensor]      =							None
							snake_case  :   torch.FloatTensor      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None






class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def __init__(    self	,			__lowerCAmelCase=1	,			__lowerCAmelCase=0	,			__lowerCAmelCase=2	,			__lowerCAmelCase=512	,			__lowerCAmelCase="cls"	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			**__lowerCAmelCase	,			):
														super().__init__(pad_token_id=__lowerCAmelCase	,			bos_token_id=__lowerCAmelCase	,			eos_token_id=__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      project_dim
														UpperCamelCase__					  =      pooler_fn
														UpperCamelCase__					  =      learn_encoder
														UpperCamelCase__					  =      use_attention_mask






class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   int      =							[r"""pooler""", r"""logit_scale"""]
							snake_case  :   Tuple      =							[r"""position_ids""", r"""predictions.decoder.bias"""]
							snake_case  :   str      =							"""roberta"""
							snake_case  :   Dict      =							RobertaSeriesConfig






							def __init__(    self	,			__lowerCAmelCase	):
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      XLMRobertaModel(__lowerCAmelCase	)
														UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""has_pre_transformation"""	,			__lowerCAmelCase	)
														if self.has_pre_transformation:
																					UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
																					UpperCamelCase__					  =      nn.LayerNorm(config.hidden_size	,			eps=config.layer_norm_eps	)
														self.post_init()







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict

														UpperCamelCase__					  =      self.base_model(
														    input_ids=__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			token_type_ids=__lowerCAmelCase	,			position_ids=__lowerCAmelCase	,			head_mask=__lowerCAmelCase	,			inputs_embeds=__lowerCAmelCase	,			encoder_hidden_states=__lowerCAmelCase	,			encoder_attention_mask=__lowerCAmelCase	,			output_attentions=__lowerCAmelCase	,			output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,			return_dict=__lowerCAmelCase	,			)

														if self.has_pre_transformation:
																					UpperCamelCase__					  =      outputs["""hidden_states"""][-2]
																					UpperCamelCase__					  =      self.pre_LN(__lowerCAmelCase	)
																					UpperCamelCase__					  =      self.transformation_pre(__lowerCAmelCase	)

																					return TransformationModelOutput(
																					    projection_state=__lowerCAmelCase	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)
														else:
																					UpperCamelCase__					  =      self.transformation(outputs.last_hidden_state	)
																					return TransformationModelOutput(
																					    projection_state=__lowerCAmelCase	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)




