




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


__A        =    logging.get_logger(__name__)

__A        =    {
    "naver-clova-ix/donut-base": "https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json",
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       '''donut-swin'''

			snake_case_   		=       {
			    '''num_attention_heads''': '''num_heads''',
			    '''num_hidden_layers''': '''num_layers''',
			}


			def __init__(      self			,						lowerCamelCase__=224			,						lowerCamelCase__=4			,						lowerCamelCase__=3			,						lowerCamelCase__=96			,						lowerCamelCase__=[2, 2, 6, 2]			,						lowerCamelCase__=[3, 6, 12, 24]			,						lowerCamelCase__=7			,						lowerCamelCase__=4.0			,						lowerCamelCase__=True			,						lowerCamelCase__=0.0			,						lowerCamelCase__=0.0			,						lowerCamelCase__=0.1			,						lowerCamelCase__="gelu"			,						lowerCamelCase__=False			,						lowerCamelCase__=0.02			,						lowerCamelCase__=1e-5			,						**lowerCamelCase__			,						)    ->      str:



							'''simple docstring'''
							super().__init__(**lowerCamelCase__							)

							__lowerCamelCase		       =    image_size
							__lowerCamelCase		       =    patch_size
							__lowerCamelCase		       =    num_channels
							__lowerCamelCase		       =    embed_dim
							__lowerCamelCase		       =    depths
							__lowerCamelCase		       =    len(lowerCamelCase__							)
							__lowerCamelCase		       =    num_heads
							__lowerCamelCase		       =    window_size
							__lowerCamelCase		       =    mlp_ratio
							__lowerCamelCase		       =    qkv_bias
							__lowerCamelCase		       =    hidden_dropout_prob
							__lowerCamelCase		       =    attention_probs_dropout_prob
							__lowerCamelCase		       =    drop_path_rate
							__lowerCamelCase		       =    hidden_act
							__lowerCamelCase		       =    use_absolute_embeddings
							__lowerCamelCase		       =    layer_norm_eps
							__lowerCamelCase		       =    initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							__lowerCamelCase		       =    int(embed_dim * 2 ** (len(lowerCamelCase__							) - 1)							)



"""simple docstring"""
import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
 import torch

if is_tf_available():
 import tensorflow as tf




@require_vision
@require_torchvision
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Optional[Any]										=	tempfile.mkdtemp()
        a__:     Optional[int]										=	SamImageProcessor()
        a__:     Tuple										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			List[Any]:





        '''simple docstring'''





        return AutoProcessor.from_pretrained(self.tmpdirname    ,			**lowercase).image_processor



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





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Any										=	[np.random.randint(2_55    ,			size=(3, 30, 4_00)    ,			dtype=np.uinta)]

        a__:     Optional[Any]										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     List[str]										=	SamProcessor(image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        a__:     Optional[int]										=	self.get_image_processor(do_normalize=lowercase    ,			padding_value=1.0)

        a__:     List[Any]										=	SamProcessor.from_pretrained(self.tmpdirname    ,			do_normalize=lowercase    ,			padding_value=1.0)

        self.assertEqual(processor.image_processor.to_json_string()    ,			image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





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

        a__:     List[Any]										=	SamProcessor(image_processor=lowercase)

        a__:     Optional[int]										=	self.prepare_image_inputs()

        a__:     Optional[Any]										=	image_processor(lowercase    ,			return_tensors='np')
        a__:     Tuple										=	processor(images=lowercase    ,			return_tensors='np')

        input_feat_extract.pop('original_sizes')  # pop original_sizes as it is popped in the processor
        input_feat_extract.pop('reshaped_input_sizes')  # pop original_sizes as it is popped in the processor

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()    ,			input_processor[key].sum()    ,			delta=1e-2)



      @require_torch
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     int										=	self.get_image_processor()

        a__:     List[str]										=	SamProcessor(image_processor=lowercase)
        a__:     Optional[Any]										=	[torch.ones((1, 3, 5, 5))]

        a__:     Union[str, Any]										=	[[17_64, 26_46]]

        a__:     Optional[Any]										=	[[6_83, 10_24]]
        a__:     int										=	processor.post_process_masks(lowercase    ,			lowercase    ,			lowercase)
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     Optional[int]										=	processor.post_process_masks(
            lowercase    ,			torch.tensor(lowercase)    ,			torch.tensor(lowercase))
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        # should also work with np
        a__:     Dict										=	[np.ones((1, 3, 5, 5))]
        a__:     Tuple										=	processor.post_process_masks(lowercase    ,			np.array(lowercase)    ,			np.array(lowercase))

        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     Tuple										=	[[1, 0], [0, 1]]
        with self.assertRaises(lowercase):
          a__:     List[Any]										=	processor.post_process_masks(lowercase    ,			np.array(lowercase)    ,			np.array(lowercase))







@require_vision
@require_tf
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	tempfile.mkdtemp()
        a__:     List[Any]										=	SamImageProcessor()
        a__:     Optional[int]										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			int:





        '''simple docstring'''





        return AutoProcessor.from_pretrained(self.tmpdirname    ,			**lowercase).image_processor



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[Any]										=	[np.random.randint(2_55    ,			size=(3, 30, 4_00)    ,			dtype=np.uinta)]

        a__:     Optional[int]										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     List[str]										=	SamProcessor(image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        a__:     Dict										=	self.get_image_processor(do_normalize=lowercase    ,			padding_value=1.0)

        a__:     Union[str, Any]										=	SamProcessor.from_pretrained(self.tmpdirname    ,			do_normalize=lowercase    ,			padding_value=1.0)

        self.assertEqual(processor.image_processor.to_json_string()    ,			image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.get_image_processor()

        a__:     str										=	SamProcessor(image_processor=lowercase)

        a__:     int										=	self.prepare_image_inputs()

        a__:     int										=	image_processor(lowercase    ,			return_tensors='np')
        a__:     Dict										=	processor(images=lowercase    ,			return_tensors='np')

        input_feat_extract.pop('original_sizes')  # pop original_sizes as it is popped in the processor
        input_feat_extract.pop('reshaped_input_sizes')  # pop reshaped_input_sizes as it is popped in the processor

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()    ,			input_processor[key].sum()    ,			delta=1e-2)



      @require_tf
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Tuple										=	self.get_image_processor()

        a__:     Any										=	SamProcessor(image_processor=lowercase)
        a__:     str										=	[tf.ones((1, 3, 5, 5))]

        a__:     List[Any]										=	[[17_64, 26_46]]

        a__:     List[Any]										=	[[6_83, 10_24]]
        a__:     List[Any]										=	processor.post_process_masks(lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='tf')
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     Tuple										=	processor.post_process_masks(
            lowercase    ,			tf.convert_to_tensor(lowercase)    ,			tf.convert_to_tensor(lowercase)    ,			return_tensors='tf'    ,			)
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        # should also work with np
        a__:     Optional[Any]										=	[np.ones((1, 3, 5, 5))]
        a__:     int										=	processor.post_process_masks(
            lowercase    ,			np.array(lowercase)    ,			np.array(lowercase)    ,			return_tensors='tf')

        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     List[str]										=	[[1, 0], [0, 1]]
        with self.assertRaises(tf.errors.InvalidArgumentError):
          a__:     Any										=	processor.post_process_masks(
              lowercase    ,			np.array(lowercase)    ,			np.array(lowercase)    ,			return_tensors='tf')







@require_vision
@require_torchvision
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     str										=	tempfile.mkdtemp()
        a__:     int										=	SamImageProcessor()
        a__:     Union[str, Any]										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



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





        '''simple docstring'''





        return AutoProcessor.from_pretrained(self.tmpdirname    ,			**lowercase).image_processor



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Any										=	[np.random.randint(2_55    ,			size=(3, 30, 4_00)    ,			dtype=np.uinta)]

        a__:     Any										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      @is_pt_tf_cross_test
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Optional[int]										=	self.get_image_processor()

        a__:     int										=	SamProcessor(image_processor=lowercase)
        a__:     int										=	np.random.randint(0    ,			2    ,			size=(1, 3, 5, 5)).astype(np.floataa)
        a__:     Dict										=	[tf.convert_to_tensor(lowercase)]
        a__:     Union[str, Any]										=	[torch.tensor(lowercase)]

        a__:     List[Any]										=	[[17_64, 26_46]]

        a__:     Optional[Any]										=	[[6_83, 10_24]]
        a__:     Tuple										=	processor.post_process_masks(
            lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='tf')
        a__:     str										=	processor.post_process_masks(
            lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='pt')

        self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))



      @is_pt_tf_cross_test
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Tuple										=	self.get_image_processor()

        a__:     Dict										=	SamProcessor(image_processor=lowercase)

        a__:     Any										=	self.prepare_image_inputs()

        a__:     List[Any]										=	image_processor(lowercase    ,			return_tensors='pt')['pixel_values'].numpy()
        a__:     Tuple										=	processor(images=lowercase    ,			return_tensors='pt')['pixel_values'].numpy()

        a__:     Any										=	image_processor(lowercase    ,			return_tensors='tf')['pixel_values'].numpy()
        a__:     Any										=	processor(images=lowercase    ,			return_tensors='tf')['pixel_values'].numpy()

        self.assertTrue(np.allclose(lowercase    ,			lowercase))
        self.assertTrue(np.allclose(lowercase    ,			lowercase))
        self.assertTrue(np.allclose(lowercase    ,			lowercase))


