






"""simple docstring"""



import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor




class        lowerCAmelCase__       (  unittest.TestCase					):
   @property
   def 	__a				(						self	:     Optional[int]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				1
        UpperCAmelCase__		:  List[str]				=				3
        UpperCAmelCase__		:  Optional[int]				=				8
        UpperCAmelCase__		:  List[Any]				=				8

        UpperCAmelCase__		:  List[Any]				=				torch.rand((batch_size, num_channels, height, width)				)

        return sample
   def 	__a				(						self	:     Dict				,       snake_case__	:     List[str]				):


        '''simple docstring'''
        if isinstance(image[0]				,       PIL.Image.Image				):
             return np.stack([np.array(snake_case__				) for i in image]				,       axis=0				)
        elif isinstance(snake_case__				,       torch.Tensor				):
             return image.cpu().numpy().transpose(0				,       2				,       3				,       1				)
        return image
   def 	__a				(						self	:     List[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[Any]				=				VaeImageProcessor(do_resize=snake_case__				,       do_normalize=snake_case__				)

        UpperCAmelCase__		:  int				=				self.dummy_sample
        UpperCAmelCase__		:  str				=				self.to_np(snake_case__				)

        for output_type in ["pt", "np", "pil"]:
             UpperCAmelCase__		:  Any				=				image_processor.postprocess(
                 image_processor.preprocess(snake_case__				)				,       output_type=snake_case__				,       )
             UpperCAmelCase__		:  Optional[Any]				=				self.to_np(snake_case__				)
             UpperCAmelCase__		:  List[str]				=				(input_np * 2_5_5).round() if output_type == "pil" else input_np
             assert (
                 np.abs(in_np - out_np				).max() < 1e-6
             ), f'decoded output does not match input for output_type {output_type}'
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        UpperCAmelCase__		:  Union[str, Any]				=				VaeImageProcessor(do_resize=snake_case__				,       do_normalize=snake_case__				)
        UpperCAmelCase__		:  Optional[int]				=				self.dummy_sample.cpu().numpy().transpose(0				,       2				,       3				,       1				)

        for output_type in ["pt", "np", "pil"]:
             UpperCAmelCase__		:  Any				=				image_processor.postprocess(image_processor.preprocess(snake_case__				)				,       output_type=snake_case__				)

             UpperCAmelCase__		:  str				=				self.to_np(snake_case__				)
             UpperCAmelCase__		:  Dict				=				(input_np * 2_5_5).round() if output_type == "pil" else input_np
             assert (
                 np.abs(in_np - out_np				).max() < 1e-6
             ), f'decoded output does not match input for output_type {output_type}'
   def 	__a				(						self	:     Dict				):


        '''simple docstring'''
        UpperCAmelCase__		:  List[Any]				=				VaeImageProcessor(do_resize=snake_case__				,       do_normalize=snake_case__				)

        UpperCAmelCase__		:  int				=				self.dummy_sample.cpu().numpy().transpose(0				,       2				,       3				,       1				)
        UpperCAmelCase__		:  Union[str, Any]				=				image_processor.numpy_to_pil(snake_case__				)

        for output_type in ["pt", "np", "pil"]:
             UpperCAmelCase__		:  Any				=				image_processor.postprocess(image_processor.preprocess(snake_case__				)				,       output_type=snake_case__				)
             for i, o in zip(snake_case__				,       snake_case__				):
                  UpperCAmelCase__		:  List[str]				=				np.array(snake_case__				)
                  UpperCAmelCase__		:  Optional[int]				=				self.to_np(snake_case__				) if output_type == "pil" else (self.to_np(snake_case__				) * 2_5_5).round()
                  assert (
                      np.abs(in_np - out_np				).max() < 1e-6
                  ), f'decoded output does not match input for output_type {output_type}'
   def 	__a				(						self	:     List[str]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Any				=				VaeImageProcessor(do_resize=snake_case__				,       do_normalize=snake_case__				)

        UpperCAmelCase__		:  List[str]				=				self.dummy_sample
        UpperCAmelCase__		:  int				=				input_pt_ad.squeeze(0				)

        UpperCAmelCase__		:  Any				=				image_processor.postprocess(
            image_processor.preprocess(snake_case__				)				,       output_type="np"				,       )
        UpperCAmelCase__		:  Union[str, Any]				=				image_processor.postprocess(
            image_processor.preprocess(snake_case__				)				,       output_type="np"				,       )

        UpperCAmelCase__		:  Dict				=				self.to_np(self.dummy_sample				)
        UpperCAmelCase__		:  Any				=				input_np_ad.squeeze(0				)

        UpperCAmelCase__		:  int				=				image_processor.postprocess(
            image_processor.preprocess(snake_case__				)				,       output_type="np"				,       )
        UpperCAmelCase__		:  Tuple				=				image_processor.postprocess(
            image_processor.preprocess(snake_case__				)				,       output_type="np"				,       )

        assert np.abs(out_pt_ad - out_pt_ad				).max() < 1e-6
        assert np.abs(out_np_ad - out_np_ad				).max() < 1e-6




   def 	__a				(						self	:     Optional[Any]				):


        '''simple docstring'''
        UpperCAmelCase__		:  Any				=				VaeImageProcessor(do_resize=snake_case__				,       do_normalize=snake_case__				)

        UpperCAmelCase__		:  Dict				=				self.dummy_sample
        UpperCAmelCase__		:  List[Any]				=				list(snake_case__				)

        UpperCAmelCase__		:  int				=				image_processor.postprocess(
            image_processor.preprocess(snake_case__				)				,       output_type="np"				,       )

        UpperCAmelCase__		:  Dict				=				image_processor.postprocess(
            image_processor.preprocess(snake_case__				)				,       output_type="np"				,       )

        UpperCAmelCase__		:  List[Any]				=				self.to_np(self.dummy_sample				)
        list(snake_case__				)

        UpperCAmelCase__		:  Optional[int]				=				image_processor.postprocess(
            image_processor.preprocess(snake_case__				)				,       output_type="np"				,       )

        UpperCAmelCase__		:  Optional[int]				=				image_processor.postprocess(
            image_processor.preprocess(snake_case__				)				,       output_type="np"				,       )

        assert np.abs(out_pt_ad - out_pt_list				).max() < 1e-6
        assert np.abs(out_np_ad - out_np_list				).max() < 1e-6










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


__A          = logging.get_logger(__name__)

__A          = {
    '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   SCREAMING_SNAKE_CASE (       snake_case    ):





							"""simple docstring"""

							A_       =    "nystromformer"

							def __init__(     self:      List[str]						,   __A:      Dict=3_00_00						,   __A:      int=7_68						,   __A:      int=12						,   __A:      Union[str, Any]=12						,   __A:      List[str]=30_72						,   __A:      Tuple="gelu_new"						,   __A:      Optional[Any]=0.1						,   __A:      Union[str, Any]=0.1						,   __A:      int=5_10						,   __A:      List[Any]=2						,   __A:      List[Any]=64						,   __A:      Optional[Any]=64						,   __A:      str=65						,   __A:      List[str]=False						,   __A:      Tuple=0.02						,   __A:      Dict=1e-5						,   __A:      int=1						,   __A:      str=0						,   __A:      int=2						,   **__A:      Union[str, Any]						,   )			-> int:
												_A									=       vocab_size
												_A									=       max_position_embeddings
												_A									=       hidden_size
												_A									=       num_hidden_layers
												_A									=       num_attention_heads
												_A									=       intermediate_size
												_A									=       hidden_act
												_A									=       hidden_dropout_prob
												_A									=       attention_probs_dropout_prob
												_A									=       initializer_range
												_A									=       type_vocab_size
												_A									=       segment_means_seq_len
												_A									=       num_landmarks
												_A									=       conv_kernel_size
												_A									=       inv_coeff_init_option
												_A									=       layer_norm_eps
												super().__init__(pad_token_id=__A						,   bos_token_id=__A						,   eos_token_id=__A						,   **__A      )



