


'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import DeiTImageProcessor





class 					snake_case   (   unittest.TestCase      ):





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase      ,  UpperCamelCase=7      ,  UpperCamelCase=3      ,  UpperCamelCase=18      ,  UpperCamelCase=30      ,  UpperCamelCase=400      ,  UpperCamelCase=True      ,  UpperCamelCase=None      ,  UpperCamelCase=True      ,  UpperCamelCase=None      ,  UpperCamelCase=True      ,  UpperCamelCase=[0.5, 0.5, 0.5]      ,  UpperCamelCase=[0.5, 0.5, 0.5]      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  size if size is not None else {"height": 20, "width": 20}
   lowerCamelCase_								=  crop_size if crop_size is not None else {"height": 18, "width": 18}

   lowerCamelCase_								=  parent
   lowerCamelCase_								=  batch_size
   lowerCamelCase_								=  num_channels
   lowerCamelCase_								=  image_size
   lowerCamelCase_								=  min_resolution
   lowerCamelCase_								=  max_resolution
   lowerCamelCase_								=  do_resize
   lowerCamelCase_								=  size
   lowerCamelCase_								=  do_center_crop
   lowerCamelCase_								=  crop_size
   lowerCamelCase_								=  do_normalize
   lowerCamelCase_								=  image_mean
   lowerCamelCase_								=  image_std







  def   snake_case		(   self					):







   """simple docstring"""
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
   }





@require_torch
@require_vision
class 					snake_case   (   UpperCAmelCase__      ,						unittest.TestCase      ):





  """simple docstring"""




  _lowerCamelCase		   =  DeiTImageProcessor if is_vision_available() else None
  _lowerCamelCase		   =  True






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  DeiTImageProcessingTester(self					)






  @property
  def   snake_case		(   self					):







   """simple docstring"""
   return self.image_processor_tester.prepare_image_processor_dict()






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.image_processing_class(**self.image_processor_dict					)
   self.assertTrue(hasattr(__lowercase      ,  "do_resize"					)					)
   self.assertTrue(hasattr(__lowercase      ,  "size"					)					)
   self.assertTrue(hasattr(__lowercase      ,  "do_center_crop"					)					)
   self.assertTrue(hasattr(__lowercase      ,  "center_crop"					)					)
   self.assertTrue(hasattr(__lowercase      ,  "do_normalize"					)					)
   self.assertTrue(hasattr(__lowercase      ,  "image_mean"					)					)
   self.assertTrue(hasattr(__lowercase      ,  "image_std"					)					)






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.image_processing_class.from_dict(self.image_processor_dict					)
   self.assertEqual(image_processor.size      ,  {"height": 20, "width": 20}					)
   self.assertEqual(image_processor.crop_size      ,  {"height": 18, "width": 18}					)

   lowerCamelCase_								=  self.image_processing_class.from_dict(self.image_processor_dict      ,  size=42      ,  crop_size=84					)
   self.assertEqual(image_processor.size      ,  {"height": 42, "width": 42}					)
   self.assertEqual(image_processor.crop_size      ,  {"height": 84, "width": 84}					)






  def   snake_case		(   self					):







   """simple docstring"""
   pass






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.image_processing_class(**self.image_processor_dict					)
   # create random PIL images
   lowerCamelCase_								=  prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase					)
   for image in image_inputs:
    self.assertIsInstance(__lowercase      ,  Image.Image					)

   # Test not batched input
   lowerCamelCase_								=  image_processing(image_inputs[0]      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )

   # Test batched
   lowerCamelCase_								=  image_processing(__lowercase      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )






  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.image_processing_class(**self.image_processor_dict					)
   # create random numpy tensors
   lowerCamelCase_								=  prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  numpify=__lowercase					)
   for image in image_inputs:
    self.assertIsInstance(__lowercase      ,  np.ndarray					)

   # Test not batched input
   lowerCamelCase_								=  image_processing(image_inputs[0]      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )

   # Test batched
   lowerCamelCase_								=  image_processing(__lowercase      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )







  def   snake_case		(   self					):







   """simple docstring"""
   lowerCamelCase_								=  self.image_processing_class(**self.image_processor_dict					)
   # create random PyTorch tensors
   lowerCamelCase_								=  prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=__lowercase      ,  torchify=__lowercase					)
   for image in image_inputs:
    self.assertIsInstance(__lowercase      ,  torch.Tensor					)

   # Test not batched input
   lowerCamelCase_								=  image_processing(image_inputs[0]      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )

   # Test batched
   lowerCamelCase_								=  image_processing(__lowercase      ,  return_tensors="pt"					).pixel_values
   self.assertEqual(
       encoded_images.shape      ,  (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,  )







'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

a_  :    int      =			{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}





class 					snake_case   (   lowercase      ):





  """simple docstring"""




  _lowerCamelCase		   =  "nystromformer"






  def __init__(   self      ,  UpperCamelCase=3_0000      ,  UpperCamelCase=768      ,  UpperCamelCase=12      ,  UpperCamelCase=12      ,  UpperCamelCase=3072      ,  UpperCamelCase="gelu_new"      ,  UpperCamelCase=0.1      ,  UpperCamelCase=0.1      ,  UpperCamelCase=510      ,  UpperCamelCase=2      ,  UpperCamelCase=64      ,  UpperCamelCase=64      ,  UpperCamelCase=65      ,  UpperCamelCase=False      ,  UpperCamelCase=0.02      ,  UpperCamelCase=1e-5      ,  UpperCamelCase=1      ,  UpperCamelCase=0      ,  UpperCamelCase=2      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  vocab_size
   lowerCamelCase_								=  max_position_embeddings
   lowerCamelCase_								=  hidden_size
   lowerCamelCase_								=  num_hidden_layers
   lowerCamelCase_								=  num_attention_heads
   lowerCamelCase_								=  intermediate_size
   lowerCamelCase_								=  hidden_act
   lowerCamelCase_								=  hidden_dropout_prob
   lowerCamelCase_								=  attention_probs_dropout_prob
   lowerCamelCase_								=  initializer_range
   lowerCamelCase_								=  type_vocab_size
   lowerCamelCase_								=  segment_means_seq_len
   lowerCamelCase_								=  num_landmarks
   lowerCamelCase_								=  conv_kernel_size
   lowerCamelCase_								=  inv_coeff_init_option
   lowerCamelCase_								=  layer_norm_eps
   super().__init__(pad_token_id=UpperCamelCase      ,  bos_token_id=UpperCamelCase      ,  eos_token_id=UpperCamelCase      ,  **UpperCamelCase					)



