





"""simple docstring"""
from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowerCamelCase_       =		logging.get_logger(__name__)






@add_end_docstrings(A  )
class  _SCREAMING_SNAKE_CASE(       A  ):




       def __init__(						self		,**SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)
             requires_backends(self		,'''vision'''							)
             requires_backends(self		,'''torch'''							)

             if self.framework != "pt":
                   raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''							)

             self.check_model_type(SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,**SCREAMING_SNAKE_CASE__							)      ->				Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Tuple		    =  {}
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  {}
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  {}
             # preprocess args
             if "points_per_batch" in kwargs:
                   __SCREAMING_SNAKE_CASE       :str		    =  kwargs['''points_per_batch''']
             if "points_per_crop" in kwargs:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  kwargs['''points_per_crop''']
             if "crops_n_layers" in kwargs:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  kwargs['''crops_n_layers''']
             if "crop_overlap_ratio" in kwargs:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  kwargs['''crop_overlap_ratio''']
             if "crop_n_points_downscale_factor" in kwargs:
                   __SCREAMING_SNAKE_CASE       :Dict		    =  kwargs['''crop_n_points_downscale_factor''']
             # postprocess args
             if "pred_iou_thresh" in kwargs:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  kwargs['''pred_iou_thresh''']
             if "stability_score_offset" in kwargs:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  kwargs['''stability_score_offset''']
             if "mask_threshold" in kwargs:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  kwargs['''mask_threshold''']
             if "stability_score_thresh" in kwargs:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  kwargs['''stability_score_thresh''']
             if "crops_nms_thresh" in kwargs:
                   __SCREAMING_SNAKE_CASE       :str		    =  kwargs['''crops_nms_thresh''']
             if "output_rle_mask" in kwargs:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  kwargs['''output_rle_mask''']
             if "output_bboxes_mask" in kwargs:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  kwargs['''output_bboxes_mask''']
             return preprocess_kwargs, forward_params, postprocess_kwargs




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,*SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=None		,SCREAMING_SNAKE_CASE__=None		,**SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             return super().__call__(SCREAMING_SNAKE_CASE__		,*SCREAMING_SNAKE_CASE__		,num_workers=SCREAMING_SNAKE_CASE__		,batch_size=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=64		,SCREAMING_SNAKE_CASE__ = 0		,SCREAMING_SNAKE_CASE__ = 5_12 / 15_00		,SCREAMING_SNAKE_CASE__ = 32		,SCREAMING_SNAKE_CASE__ = 1		,)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  load_image(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.image_processor.size['''longest_edge''']
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  self.image_processor.generate_crop_boxes(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  self.image_processor(images=SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							)

             with self.device_placement():
                   if self.framework == "pt":
                         __SCREAMING_SNAKE_CASE       :List[str]		    =  self.get_inference_context()
                         with inference_context():
                               __SCREAMING_SNAKE_CASE       :List[Any]		    =  self._ensure_tensor_on_device(SCREAMING_SNAKE_CASE__		,device=self.device							)
                               __SCREAMING_SNAKE_CASE       :Tuple		    =  self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''							)							)
                               __SCREAMING_SNAKE_CASE       :List[str]		    =  image_embeddings

             __SCREAMING_SNAKE_CASE       :str		    =  grid_points.shape[1]
             __SCREAMING_SNAKE_CASE       :int		    =  points_per_batch if points_per_batch is not None else n_points

             if points_per_batch <= 0:
                   raise ValueError(
                       '''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
                       '''To return all points at once, set points_per_batch to None'''							)

             for i in range(0		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE       :str		    =  grid_points[:, i : i + points_per_batch, :, :]
                   __SCREAMING_SNAKE_CASE       :Any		    =  input_labels[:, i : i + points_per_batch]
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  i == n_points - points_per_batch
                   yield {
                       "input_points": batched_points,
                       "input_labels": labels,
                       "input_boxes": crop_boxes,
                       "is_last": is_last,
                       **model_inputs,
                   }




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=0.8_8		,SCREAMING_SNAKE_CASE__=0.9_5		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=1		,)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  model_inputs.pop('''input_boxes'''							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  model_inputs.pop('''is_last'''							)
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  model_inputs.pop('''original_sizes'''							).tolist()
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  model_inputs.pop('''reshaped_input_sizes'''							).tolist()

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  self.model(**SCREAMING_SNAKE_CASE__							)

             # post processing happens here in order to avoid CPU GPU copies of ALL the masks
             __SCREAMING_SNAKE_CASE       :Tuple		    =  model_outputs['''pred_masks''']
             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.image_processor.post_process_masks(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,binarize=SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :List[str]		    =  model_outputs['''iou_scores''']
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :List[Any]		    =  self.image_processor.filter_masks(
                 masks[0]		,iou_scores[0]		,original_sizes[0]		,input_boxes[0]		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,)
             return {
                 "masks": masks,
                 "is_last": is_last,
                 "boxes": boxes,
                 "iou_scores": iou_scores,
             }




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=0.7		,)      ->				Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  []
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  []
             __SCREAMING_SNAKE_CASE       :Dict		    =  []
             for model_output in model_outputs:
                   all_scores.append(model_output.pop('''iou_scores'''							)							)
                   all_masks.extend(model_output.pop('''masks'''							)							)
                   all_boxes.append(model_output.pop('''boxes'''							)							)

             __SCREAMING_SNAKE_CASE       :Dict		    =  torch.cat(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :int		    =  torch.cat(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Dict		    =  self.image_processor.post_process_for_mask_generation(
                 SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :str		    =  defaultdict(SCREAMING_SNAKE_CASE__							)
             for output in model_outputs:
                   for k, v in output.items():
                         extra[k].append(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  {}
             if output_rle_mask:
                   __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  rle_mask

             if output_bboxes_mask:
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  bounding_boxes

             return {"masks": output_masks, "scores": iou_scores, **optional, **extra}






"""simple docstring"""
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  _SCREAMING_SNAKE_CASE:




       def __init__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__=13		,SCREAMING_SNAKE_CASE__=7		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=99		,SCREAMING_SNAKE_CASE__=32		,SCREAMING_SNAKE_CASE__=5		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=37		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=2		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=4		,SCREAMING_SNAKE_CASE__=None		,)      ->				Dict:





             """simple docstring"""






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




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





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  None
             if self.use_input_mask:
                   __SCREAMING_SNAKE_CASE       :List[str]		    =  random_attention_mask([self.batch_size, self.seq_length]							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  None
             if self.use_token_type_ids:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  ids_tensor([self.batch_size, self.seq_length]		,self.type_vocab_size							)

             __SCREAMING_SNAKE_CASE       :Dict		    =  None
             __SCREAMING_SNAKE_CASE       :Dict		    =  None
             __SCREAMING_SNAKE_CASE       :Dict		    =  None
             if self.use_labels:
                   __SCREAMING_SNAKE_CASE       :List[Any]		    =  ids_tensor([self.batch_size]		,self.type_sequence_label_size							)
                   __SCREAMING_SNAKE_CASE       :Optional[int]		    =  ids_tensor([self.batch_size, self.seq_length]		,self.num_labels							)
                   __SCREAMING_SNAKE_CASE       :Dict		    =  ids_tensor([self.batch_size]		,self.num_choices							)

             __SCREAMING_SNAKE_CASE       :Tuple		    =  self.get_config()

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




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





             """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=SCREAMING_SNAKE_CASE__		,initializer_range=self.initializer_range		,)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  NystromformerForMaskedLM(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Tuple		    =  model(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__		,labels=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.vocab_size)							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  NystromformerForQuestionAnswering(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  model(
                 SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__		,start_positions=SCREAMING_SNAKE_CASE__		,end_positions=SCREAMING_SNAKE_CASE__		,)
             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    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  self.num_labels
             __SCREAMING_SNAKE_CASE       :Any		    =  NystromformerForSequenceClassification(SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Dict		    =  model(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__		,labels=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels)							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.num_labels
             __SCREAMING_SNAKE_CASE       :Tuple		    =  NystromformerForTokenClassification(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE       :Any		    =  model(SCREAMING_SNAKE_CASE__		,attention_mask=SCREAMING_SNAKE_CASE__		,token_type_ids=SCREAMING_SNAKE_CASE__		,labels=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.seq_length, self.num_labels)							)




       def    _UpperCamelCase					(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)      ->				Dict:





             """simple docstring"""






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




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






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






@require_torch
class  _SCREAMING_SNAKE_CASE(       A   ,     A   ,     unittest.TestCase  ):
       SCREAMING_SNAKE_CASE_       :				int     						=  (
           (
               NystromformerModel,
               NystromformerForMaskedLM,
               NystromformerForMultipleChoice,
               NystromformerForQuestionAnswering,
               NystromformerForSequenceClassification,
               NystromformerForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE_       :				Union[str, Any]     						=  (
           {
               '''feature-extraction''': NystromformerModel,
               '''fill-mask''': NystromformerForMaskedLM,
               '''question-answering''': NystromformerForQuestionAnswering,
               '''text-classification''': NystromformerForSequenceClassification,
               '''token-classification''': NystromformerForTokenClassification,
               '''zero-shot''': NystromformerForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE_       :				str     						=  False
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  False




       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  NystromformerModelTester(self							)
             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  ConfigTester(self		,config_class=SCREAMING_SNAKE_CASE__		,hidden_size=37							)




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             self.config_tester.run_common_tests()




       def    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                   __SCREAMING_SNAKE_CASE       :Any		    =  type
                   self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				Any:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[str]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__							)




       def    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Any		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__							)




       @slow
       def    _UpperCamelCase					(						self							)      ->				Union[str, Any]:





             """simple docstring"""






             for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE       :Tuple		    =  NystromformerModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)






@require_torch
class  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       @slow
       def    _UpperCamelCase					(						self							)      ->				List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :int		    =  NystromformerModel.from_pretrained('''uw-madison/nystromformer-512'''							)
             __SCREAMING_SNAKE_CASE       :Dict		    =  torch.tensor([[0, 1, 2, 3, 4, 5]]							)

             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE       :str		    =  model(SCREAMING_SNAKE_CASE__							)[0]

             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  torch.Size((1, 6, 7_68)							)
             self.assertEqual(output.shape		,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE       :int		    =  torch.tensor(
                 [[[-0.4_5_3_2, -0.0_9_3_6, 0.5_1_3_7], [-0.2_6_7_6, 0.0_6_2_8, 0.6_1_8_6], [-0.3_6_2_9, -0.1_7_2_6, 0.4_7_1_6]]]							)

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




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





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE       :Optional[Any]		    =  AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512'''							)
             __SCREAMING_SNAKE_CASE       :str		    =  NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512'''							)

             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  tokenizer(SCREAMING_SNAKE_CASE__		,return_tensors='''pt'''							)

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

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

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