





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()
lowercase				:	str      						=  logging.get_logger(__name__)



def A_     (		A__    )  ->		Tuple:
       a__						:     Union[str, Any]			=    SwinConfig.from_pretrained(
           'microsoft/swin-tiny-patch4-window7-224'		,				out_features=['stage1', 'stage2', 'stage3', 'stage4']    )
       a__						:     Tuple			=    MaskFormerConfig(backbone_config=A__    )

       a__						:     List[str]			=    'huggingface/label-files'
       if "ade20k-full" in model_name:
              # this should be ok
              a__						:     str			=    847
              a__						:     Optional[Any]			=    'maskformer-ade20k-full-id2label.json'
       elif "ade" in model_name:
              # this should be ok
              a__						:     Optional[Any]			=    150
              a__						:     Optional[int]			=    'ade20k-id2label.json'
       elif "coco-stuff" in model_name:
              # this should be ok
              a__						:     Optional[Any]			=    171
              a__						:     Union[str, Any]			=    'maskformer-coco-stuff-id2label.json'
       elif "coco" in model_name:
              # TODO
              a__						:     List[str]			=    133
              a__						:     Union[str, Any]			=    'coco-panoptic-id2label.json'
       elif "cityscapes" in model_name:
              # this should be ok
              a__						:     Dict			=    19
              a__						:     int			=    'cityscapes-id2label.json'
       elif "vistas" in model_name:
              # this should be ok
              a__						:     Optional[int]			=    65
              a__						:     Dict			=    'mapillary-vistas-id2label.json'

       a__						:     Dict			=    json.load(open(hf_hub_download(A__		,				A__		,				repo_type='dataset'    )		,				'r'    )    )
       a__						:     str			=    {int(A__    ): v for k, v in idalabel.items()}

       return config



def A_     (		A__    )  ->		Union[str, Any]:
       a__						:     Dict			=    []
       # 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 A_     (		A__		,				A__		,				A__    )  ->		Optional[int]:
       a__						:     List[Any]			=    dct.pop(A__    )
       a__						:     str			=    val



def A_     (		A__		,				A__    )  ->		Tuple:
       a__						:     Optional[Any]			=    [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
       for i in range(len(backbone_config.depths    )    ):
              a__						:     Tuple			=    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)
                     a__						:     Optional[Any]			=    state_dict.pop(F'backbone.layers.{i}.blocks.{j}.attn.qkv.weight'    )
                     a__						:     List[Any]			=    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
                     a__						:     Union[str, Any]			=    in_proj_weight[:dim, :]
                     a__						:     Union[str, Any]			=    in_proj_bias[: dim]
                     a__						:     Tuple			=    in_proj_weight[
                         dim : dim * 2, :
                     ]
                     a__						:     Optional[Any]			=    in_proj_bias[
                         dim : dim * 2
                     ]
                     a__						:     Optional[int]			=    in_proj_weight[
                         -dim :, :
                     ]
                     a__						:     int			=    in_proj_bias[-dim :]
                     # fmt: on



def A_     (		A__		,				A__    )  ->		Dict:
       # fmt: off
       a__						:     Any			=    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)
              a__						:     List[Any]			=    state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'    )
              a__						:     Any			=    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
              a__						:     Tuple			=    in_proj_weight[: hidden_size, :]
              a__						:     Union[str, Any]			=    in_proj_bias[:config.hidden_size]
              a__						:     str			=    in_proj_weight[hidden_size : hidden_size * 2, :]
              a__						:     int			=    in_proj_bias[hidden_size : hidden_size * 2]
              a__						:     Tuple			=    in_proj_weight[-hidden_size :, :]
              a__						:     Union[str, Any]			=    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)
              a__						:     Any			=    state_dict.pop(F'sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'    )
              a__						:     Optional[int]			=    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
              a__						:     Dict			=    in_proj_weight[: hidden_size, :]
              a__						:     List[str]			=    in_proj_bias[:config.hidden_size]
              a__						:     List[Any]			=    in_proj_weight[hidden_size : hidden_size * 2, :]
              a__						:     int			=    in_proj_bias[hidden_size : hidden_size * 2]
              a__						:     List[str]			=    in_proj_weight[-hidden_size :, :]
              a__						:     Optional[int]			=    in_proj_bias[-hidden_size :]
       # fmt: on



def A_     (		)  ->		torch.Tensor:
       a__						:     Union[str, Any]			=    'http://images.cocodataset.org/val2017/000000039769.jpg'
       a__						:     List[str]			=    Image.open(requests.get(A__		,				stream=A__    ).raw    )
       return im



@torch.no_grad()
def A_     (		A__		,				A__		,				A__		,				A__ = False    )  ->		List[str]:
       a__						:     Optional[Any]			=    get_maskformer_config(A__    )

       # load original state_dict
       with open(A__		,				'rb'    ) as f:
              a__						:     Optional[Any]			=    pickle.load(A__    )
       a__						:     int			=    data['model']

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

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

       # update to torch tensors
       for key, value in state_dict.items():
              a__						:     List[str]			=    torch.from_numpy(A__    )

       # load 🤗 model
       a__						:     List[str]			=    MaskFormerForInstanceSegmentation(A__    )
       model.eval()

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

       a__			,							a__						:     str			=    model.load_state_dict(A__		,				strict=A__    )
       assert missing_keys == [
           "model.pixel_level_module.encoder.model.layernorm.weight",
           "model.pixel_level_module.encoder.model.layernorm.bias",
       ]
       assert len(A__    ) == 0, F'Unexpected keys: {unexpected_keys}'

       # verify results
       a__						:     Optional[int]			=    prepare_img()
       if "vistas" in model_name:
              a__						:     Optional[int]			=    65
       elif "cityscapes" in model_name:
              a__						:     str			=    6_5535
       else:
              a__						:     List[str]			=    255
       a__						:     List[Any]			=    True if 'ade' in model_name else False
       a__						:     int			=    MaskFormerImageProcessor(ignore_index=A__		,				reduce_labels=A__    )

       a__						:     Optional[int]			=    image_processor(A__		,				return_tensors='pt'    )

       a__						:     Dict			=    model(**A__    )

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

       if model_name == "maskformer-swin-tiny-ade":
              a__						:     Optional[Any]			=    torch.tensor(
                  [[3.63_53, -4.47_70, -2.60_65], [0.50_81, -4.23_94, -3.53_43], [2.19_09, -5.03_53, -1.93_23]]    )
       assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,				A__		,				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(A__    ).mkdir(exist_ok=A__    )
              model.save_pretrained(A__    )
              image_processor.save_pretrained(A__    )

       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__":
    lowercase				:	Tuple      						=  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."""
    )

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









import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        NystromformerForMaskedLM,
        NystromformerForMultipleChoice,
        NystromformerForQuestionAnswering,
        NystromformerForSequenceClassification,
        NystromformerForTokenClassification,
        NystromformerModel,
    )
    from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST





class 			A__   :




   """simple docstring"""



   def __init__( self ,  lowercase ,  lowercase=13 ,  lowercase=7 ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=True ,  lowercase=99 ,  lowercase=32 ,  lowercase=5 ,  lowercase=4 ,  lowercase=37 ,  lowercase="gelu" ,  lowercase=0.1 ,  lowercase=0.1 ,  lowercase=512 ,  lowercase=16 ,  lowercase=2 ,  lowercase=0.02 ,  lowercase=3 ,  lowercase=4 ,  lowercase=None ,  )      ->						List[Any]:







          '''simple docstring'''

          a__						:     Any			=    parent
          a__						:     int			=    batch_size
          a__						:     Dict			=    seq_length
          a__						:     Tuple			=    is_training
          a__						:     Any			=    use_input_mask
          a__						:     Optional[Any]			=    use_token_type_ids
          a__						:     Dict			=    use_labels
          a__						:     Optional[int]			=    vocab_size
          a__						:     List[Any]			=    hidden_size
          a__						:     int			=    num_hidden_layers
          a__						:     Optional[Any]			=    num_attention_heads
          a__						:     str			=    intermediate_size
          a__						:     Optional[int]			=    hidden_act
          a__						:     Dict			=    hidden_dropout_prob
          a__						:     Optional[int]			=    attention_probs_dropout_prob
          a__						:     Tuple			=    max_position_embeddings
          a__						:     Dict			=    type_vocab_size
          a__						:     Any			=    type_sequence_label_size
          a__						:     List[str]			=    initializer_range
          a__						:     List[str]			=    num_labels
          a__						:     Optional[Any]			=    num_choices
          a__						:     str			=    scope

   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size)

          a__						:     Tuple			=    None
          if self.use_input_mask:
                 a__						:     Optional[int]			=    random_attention_mask([self.batch_size, self.seq_length])

          a__						:     Any			=    None
          if self.use_token_type_ids:
                 a__						:     List[Any]			=    ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size)

          a__						:     str			=    None
          a__						:     List[Any]			=    None
          a__						:     List[str]			=    None
          if self.use_labels:
                 a__						:     Any			=    ids_tensor([self.batch_size] ,  self.type_sequence_label_size)
                 a__						:     str			=    ids_tensor([self.batch_size, self.seq_length] ,  self.num_labels)
                 a__						:     str			=    ids_tensor([self.batch_size] ,  self.num_choices)

          a__						:     Union[str, Any]			=    self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          return NystromformerConfig(
              vocab_size=self.vocab_size ,  hidden_size=self.hidden_size ,  num_hidden_layers=self.num_hidden_layers ,  num_attention_heads=self.num_attention_heads ,  intermediate_size=self.intermediate_size ,  hidden_act=self.hidden_act ,  hidden_dropout_prob=self.hidden_dropout_prob ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  type_vocab_size=self.type_vocab_size ,  is_decoder=lowercase ,  initializer_range=self.initializer_range ,  )

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    NystromformerModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     List[Any]			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase)
          a__						:     int			=    model(lowercase ,  token_type_ids=lowercase)
          a__						:     Optional[Any]			=    model(lowercase)
          self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						str:







          '''simple docstring'''

          a__						:     List[str]			=    NystromformerForMaskedLM(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     int			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     Any			=    NystromformerForQuestionAnswering(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     str			=    model(
              lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  start_positions=lowercase ,  end_positions=lowercase ,  )
          self.parent.assertEqual(result.start_logits.shape ,  (self.batch_size, self.seq_length))
          self.parent.assertEqual(result.end_logits.shape ,  (self.batch_size, self.seq_length))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     int			=    self.num_labels
          a__						:     Optional[Any]			=    NystromformerForSequenceClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     Tuple			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						List[str]:







          '''simple docstring'''

          a__						:     Tuple			=    self.num_labels
          a__						:     int			=    NystromformerForTokenClassification(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     str			=    model(lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.num_labels))

   def 							__lowercase		( self ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase ,  lowercase)      ->						Any:







          '''simple docstring'''

          a__						:     Optional[int]			=    self.num_choices
          a__						:     Tuple			=    NystromformerForMultipleChoice(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:     Optional[Any]			=    input_ids.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
          a__						:     Tuple			=    token_type_ids.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
          a__						:     str			=    input_mask.unsqueeze(1).expand(-1 ,  self.num_choices ,  -1).contiguous()
          a__						:     Optional[int]			=    model(
              lowercase ,  attention_mask=lowercase ,  token_type_ids=lowercase ,  labels=lowercase ,  )
          self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_choices))

   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     List[Any]			=    self.prepare_config_and_inputs()
          (
              (
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							(
              a__
          )			,							
          )						:     str			=    config_and_inputs
          a__						:     Any			=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
          return config, inputs_dict





@require_torch
class 			A__   (							__UpperCAmelCase			,  __UpperCAmelCase			,  unittest.TestCase     ):




   """simple docstring"""



   __A       :    Any	  =  (
       (
           NystromformerModel,
           NystromformerForMaskedLM,
           NystromformerForMultipleChoice,
           NystromformerForQuestionAnswering,
           NystromformerForSequenceClassification,
           NystromformerForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __A       :    str	  =  (
       {
           '''feature-extraction''': NystromformerModel,
           '''fill-mask''': NystromformerForMaskedLM,
           '''question-answering''': NystromformerForQuestionAnswering,
           '''text-classification''': NystromformerForSequenceClassification,
           '''token-classification''': NystromformerForTokenClassification,
           '''zero-shot''': NystromformerForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __A       :    Optional[Any]	  =  False
   __A       :    Tuple	  =  False

   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     int			=    NystromformerModelTester(self)
          a__						:     Any			=    ConfigTester(self ,  config_class=lowercase ,  hidden_size=37)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     str			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     Tuple			=    self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                 a__						:     Optional[Any]			=    type
                 self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		( self)      ->						Tuple:







          '''simple docstring'''

          a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*lowercase)

   def 							__lowercase		( self)      ->						Any:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowercase)

   def 							__lowercase		( self)      ->						Union[str, Any]:







          '''simple docstring'''

          a__						:     Union[str, Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*lowercase)

   def 							__lowercase		( self)      ->						int:







          '''simple docstring'''

          a__						:     List[Any]			=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowercase)

   @slow
   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:     int			=    NystromformerModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)





@require_torch
class 			A__   (							unittest.TestCase     ):




   """simple docstring"""



   @slow
   def 							__lowercase		( self)      ->						Optional[Any]:







          '''simple docstring'''

          a__						:     List[str]			=    NystromformerModel.from_pretrained('uw-madison/nystromformer-512')
          a__						:     Tuple			=    torch.tensor([[0, 1, 2, 3, 4, 5]])

          with torch.no_grad():
                 a__						:     List[Any]			=    model(lowercase)[0]

          a__						:     str			=    torch.Size((1, 6, 768))
          self.assertEqual(output.shape ,  lowercase)

          a__						:     str			=    torch.tensor(
              [[[-0.45_32, -0.09_36, 0.51_37], [-0.26_76, 0.06_28, 0.61_86], [-0.36_29, -0.17_26, 0.47_16]]])

          self.assertTrue(torch.allclose(output[:, :3, :3] ,  lowercase ,  atol=1e-4))

   @slow
   def 							__lowercase		( self)      ->						Optional[int]:







          '''simple docstring'''

          a__						:     Any			=    'the [MASK] of Belgium is Brussels'

          a__						:     List[str]			=    AutoTokenizer.from_pretrained('uw-madison/nystromformer-512')
          a__						:     Optional[int]			=    NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512')

          a__						:     List[Any]			=    tokenizer(lowercase ,  return_tensors='pt')

          with torch.no_grad():
                 a__						:     Union[str, Any]			=    model(encoding.input_ids).logits

          a__						:     str			=    token_logits[:, 2, :].argmax(-1)[0]

          self.assertEqual(tokenizer.decode(lowercase) ,  'capital')


