






"""simple docstring"""
import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image


def        A	(							__snake_case:   Tuple   ,    __snake_case:   Tuple=2_5_6     )						->		Any:

       """simple docstring"""





       __magic_name__										=						min(img.size     )

       if s < target_image_size:
              raise ValueError(F"""min dim for image {s} < {target_image_size}"""     )

       __magic_name__										=						target_image_size / s
       __magic_name__										=						(round(r * img.size[1]     ), round(r * img.size[0]     ))
       __magic_name__										=						TF.resize(__snake_case   ,    __snake_case   ,    interpolation=PIL.Image.LANCZOS     )
       __magic_name__										=						TF.center_crop(__snake_case   ,    output_size=2 * [target_image_size]     )
       __magic_name__										=						torch.unsqueeze(T.ToTensor()(__snake_case     )   ,    0     )
       return img


def        A	(							__snake_case:   int     )						->		Any:

       """simple docstring"""





       __magic_name__										=						2.0 * x - 1.0
       return x


def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]=True   ,    __snake_case:   Union[str, Any]="RGB"     )						->		int:

       """simple docstring"""





       __magic_name__										=						x.detach().cpu()
       if process:
              __magic_name__										=						post_process_tensor(__snake_case     )
       __magic_name__										=						x.numpy()
       if process:
              __magic_name__										=						(2_5_5 * x).astype(np.uinta     )
       __magic_name__										=						Image.fromarray(__snake_case     )
       if not x.mode == mode:
              __magic_name__										=						x.convert(__snake_case     )
       return x


def        A	(							__snake_case:   Optional[Any]     )						->		Optional[int]:

       """simple docstring"""





       __magic_name__										=						torch.clamp(__snake_case   ,    -1.0   ,    1.0     )
       __magic_name__										=						(x + 1.0) / 2.0
       __magic_name__										=						x.permute(1   ,    2   ,    0     )
       return x






def        A	(							__snake_case:   Union[str, Any]     )						->		Union[str, Any]:

       """simple docstring"""





       __magic_name__										=						post_process_tensor(x.squeeze()     )
       return x.permute(2   ,    0   ,    1     ).unsqueeze(0     )







"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case   :   int    		=		logging.get_logger(__name__)

snake_case   :   List[str]    		=		{
    """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 					UpperCamelCase__   (							a_):







    """simple docstring"""
    __UpperCAmelCase											=   """nystromformer"""







    def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[Any]=3_0_0_0_0							,						UpperCamelCase_   :  Dict=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  Any=1_2							,						UpperCamelCase_   :  List[str]=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu_new"							,						UpperCamelCase_   :  Optional[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Tuple=5_1_0							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Optional[int]=6_4							,						UpperCamelCase_   :  Tuple=6_4							,						UpperCamelCase_   :  Optional[int]=6_5							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  List[str]=0.02							,						UpperCamelCase_   :  List[Any]=1e-5							,						UpperCamelCase_   :  Union[str, Any]=1							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=2							,						**UpperCamelCase_   :  str							,						):





           '''simple docstring'''

           __magic_name__										=						vocab_size
           __magic_name__										=						max_position_embeddings
           __magic_name__										=						hidden_size
           __magic_name__										=						num_hidden_layers
           __magic_name__										=						num_attention_heads
           __magic_name__										=						intermediate_size
           __magic_name__										=						hidden_act
           __magic_name__										=						hidden_dropout_prob
           __magic_name__										=						attention_probs_dropout_prob
           __magic_name__										=						initializer_range
           __magic_name__										=						type_vocab_size
           __magic_name__										=						segment_means_seq_len
           __magic_name__										=						num_landmarks
           __magic_name__										=						conv_kernel_size
           __magic_name__										=						inv_coeff_init_option
           __magic_name__										=						layer_norm_eps
           super().__init__(pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						**UpperCamelCase_   )