

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		:Union[str, Any]						=     logging.get_logger(__name__)




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						List[str]:



    """simple docstring"""






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

    UpperCamelCase_			  =						"huggingface/label-files"
    if "ade20k-full" in model_name:
        # this should be ok
        UpperCamelCase_			  =						8_4_7
        UpperCamelCase_			  =						"maskformer-ade20k-full-id2label.json"
    elif "ade" in model_name:
        # this should be ok
        UpperCamelCase_			  =						1_5_0
        UpperCamelCase_			  =						"ade20k-id2label.json"
    elif "coco-stuff" in model_name:
        # this should be ok
        UpperCamelCase_			  =						1_7_1
        UpperCamelCase_			  =						"maskformer-coco-stuff-id2label.json"
    elif "coco" in model_name:
        # TODO
        UpperCamelCase_			  =						1_3_3
        UpperCamelCase_			  =						"coco-panoptic-id2label.json"
    elif "cityscapes" in model_name:
        # this should be ok
        UpperCamelCase_			  =						1_9
        UpperCamelCase_			  =						"cityscapes-id2label.json"
    elif "vistas" in model_name:
        # this should be ok
        UpperCamelCase_			  =						6_5
        UpperCamelCase_			  =						"mapillary-vistas-id2label.json"

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

    return config




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						List[str]:



    """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(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Any:



    """simple docstring"""






    UpperCamelCase_			  =						dct.pop(SCREAMING_SNAKE_CASE_			)
    UpperCamelCase_			  =						val




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						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(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						Dict:



    """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(SCREAMING_SNAKE_CASE_			,						stream=SCREAMING_SNAKE_CASE_			).raw			)
    return im




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_ = False			)->						List[Any]:



    """simple docstring"""






    UpperCamelCase_			  =						get_maskformer_config(SCREAMING_SNAKE_CASE_			)

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

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

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

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

    # load 🤗 model
    UpperCamelCase_			  =						MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE_			)
    model.eval()

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

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

    # verify results
    UpperCamelCase_			  =						prepare_img()
    if "vistas" in model_name:
        UpperCamelCase_			  =						6_5
    elif "cityscapes" in model_name:
        UpperCamelCase_			  =						6_5_5_3_5
    else:
        UpperCamelCase_			  =						2_5_5
    UpperCamelCase_			  =						True if "ade" in model_name else False
    UpperCamelCase_			  =						MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE_			,						reduce_labels=SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						image_processor(SCREAMING_SNAKE_CASE_			,						return_tensors="pt"			)

    UpperCamelCase_			  =						model(**SCREAMING_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]			,						SCREAMING_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(SCREAMING_SNAKE_CASE_			).mkdir(exist_ok=SCREAMING_SNAKE_CASE_			)
        model.save_pretrained(SCREAMING_SNAKE_CASE_			)
        image_processor.save_pretrained(SCREAMING_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[str]						=     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		:Dict						=     parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """configuration_rembert""": ["""REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RemBertConfig""", """RemBertOnnxConfig"""]
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str						=     ["""RemBertTokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[Any]						=     ["""RemBertTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict						=     [
     """REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """RemBertForCausalLM""",
     """RemBertForMaskedLM""",
     """RemBertForMultipleChoice""",
     """RemBertForQuestionAnswering""",
     """RemBertForSequenceClassification""",
     """RemBertForTokenClassification""",
     """RemBertLayer""",
     """RemBertModel""",
     """RemBertPreTrainedModel""",
     """load_tf_weights_in_rembert""",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Optional[int]						=     [
     """TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFRemBertForCausalLM""",
     """TFRemBertForMaskedLM""",
     """TFRemBertForMultipleChoice""",
     """TFRemBertForQuestionAnswering""",
     """TFRemBertForSequenceClassification""",
     """TFRemBertForTokenClassification""",
     """TFRemBertLayer""",
     """TFRemBertModel""",
     """TFRemBertPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert import RemBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert_fast import RemBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rembert import (
      REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      RemBertForCausalLM,
      RemBertForMaskedLM,
      RemBertForMultipleChoice,
      RemBertForQuestionAnswering,
      RemBertForSequenceClassification,
      RemBertForTokenClassification,
      RemBertLayer,
      RemBertModel,
      RemBertPreTrainedModel,
      load_tf_weights_in_rembert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rembert import (
      TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRemBertForCausalLM,
      TFRemBertForMaskedLM,
      TFRemBertForMultipleChoice,
      TFRemBertForQuestionAnswering,
      TFRemBertForSequenceClassification,
      TFRemBertForTokenClassification,
      TFRemBertLayer,
      TFRemBertModel,
      TFRemBertPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
