
"""simple docstring"""





import unittest

import numpy as np
import requests

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

					from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
					lowercase  							=					False

if is_vision_available():
					from PIL import Image

					from transformers import PixaStructImageProcessor




class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case=7	,       snake_case=3	,       snake_case=18	,       snake_case=30	,       snake_case=400	,       snake_case=None	,       snake_case=True	,       snake_case=True	,       snake_case=None	,       )			->							Any:
									_UpperCAmelCase							 =		size if size is not None else {'height': 20, 'width': 20}
									_UpperCAmelCase							 =		parent
									_UpperCAmelCase							 =		batch_size
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		min_resolution
									_UpperCAmelCase							 =		max_resolution
									_UpperCAmelCase							 =		size
									_UpperCAmelCase							 =		do_normalize
									_UpperCAmelCase							 =		do_convert_rgb
									_UpperCAmelCase							 =		[512, 1024, 2048, 4096]
									_UpperCAmelCase							 =		patch_size if patch_size is not None else {'height': 16, 'width': 16}






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






							def        lowerCamelCase_   (  self						)			->							List[str]:
									_UpperCAmelCase							 =		'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
									_UpperCAmelCase							 =		Image.open(requests.get(snake_case	,       stream=snake_case						).raw						).convert('RGB'						)
									return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,					reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''',					)
@require_torch
@require_vision
class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					PixaStructImageProcessor if is_vision_available() else None






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		PixaStructImageProcessingTester(self						)






							@property
							def        lowerCamelCase_   (  self						)			->							Dict:
									return self.image_processor_tester.prepare_image_processor_dict()






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(snake_case	,       'do_normalize'						)						)
									self.assertTrue(hasattr(snake_case	,       'do_convert_rgb'						)						)






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		self.image_processor_tester.prepare_dummy_image()

									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									_UpperCAmelCase							 =		2048

									_UpperCAmelCase							 =		image_processor(snake_case	,       return_tensors='pt'	,       max_patches=snake_case						)
									self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,       torch.tensor(0.0606						)	,       atol=1E-3	,       rtol=1E-3						)						)






							def        lowerCamelCase_   (  self						)			->							List[str]:
									# Initialize image_processor
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case						)
									for image in image_inputs:
											self.assertIsInstance(snake_case	,       Image.Image						)

									# Test not batched input
									_UpperCAmelCase							 =		(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * self.image_processor_tester.num_channels
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											_UpperCAmelCase							 =		image_processor(
											    image_inputs[0]	,       return_tensors='pt'	,       max_patches=snake_case						).flattened_patches
											self.assertEqual(
											    encoded_images.shape	,       (1, max_patch, expected_hidden_dim)	,       )

											# Test batched
											_UpperCAmelCase							 =		image_processor(
											    snake_case	,       return_tensors='pt'	,       max_patches=snake_case						).flattened_patches
											self.assertEqual(
											    encoded_images.shape	,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,       )






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									# Initialize image_processor
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case						)
									for image in image_inputs:
											self.assertIsInstance(snake_case	,       Image.Image						)

									# Test not batched input
									_UpperCAmelCase							 =		(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * self.image_processor_tester.num_channels
									) + 2

									_UpperCAmelCase							 =		True

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											with self.assertRaises(snake_case						):
													_UpperCAmelCase							 =		image_processor(
													    image_inputs[0]	,       return_tensors='pt'	,       max_patches=snake_case						).flattened_patches

											_UpperCAmelCase							 =		'Hello'

											_UpperCAmelCase							 =		image_processor(
											    image_inputs[0]	,       return_tensors='pt'	,       max_patches=snake_case	,       header_text=snake_case						).flattened_patches
											self.assertEqual(
											    encoded_images.shape	,       (1, max_patch, expected_hidden_dim)	,       )

											# Test batched
											_UpperCAmelCase							 =		image_processor(
											    snake_case	,       return_tensors='pt'	,       max_patches=snake_case	,       header_text=snake_case						).flattened_patches
											self.assertEqual(
											    encoded_images.shape	,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,       )






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									# Initialize image_processor
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case	,       numpify=snake_case						)
									for image in image_inputs:
											self.assertIsInstance(snake_case	,       np.ndarray						)

									_UpperCAmelCase							 =		(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * self.image_processor_tester.num_channels
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											_UpperCAmelCase							 =		image_processor(
											    image_inputs[0]	,       return_tensors='pt'	,       max_patches=snake_case						).flattened_patches
											self.assertEqual(
											    encoded_images.shape	,       (1, max_patch, expected_hidden_dim)	,       )

											# Test batched
											_UpperCAmelCase							 =		image_processor(
											    snake_case	,       return_tensors='pt'	,       max_patches=snake_case						).flattened_patches
											self.assertEqual(
											    encoded_images.shape	,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,       )






							def        lowerCamelCase_   (  self						)			->							int:
									# Initialize image_processor
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case	,       torchify=snake_case						)
									for image in image_inputs:
											self.assertIsInstance(snake_case	,       torch.Tensor						)

									# Test not batched input
									_UpperCAmelCase							 =		(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * self.image_processor_tester.num_channels
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											_UpperCAmelCase							 =		image_processor(
											    image_inputs[0]	,       return_tensors='pt'	,       max_patches=snake_case						).flattened_patches
											self.assertEqual(
											    encoded_images.shape	,       (1, max_patch, expected_hidden_dim)	,       )

											# Test batched
											_UpperCAmelCase							 =		image_processor(
											    snake_case	,       return_tensors='pt'	,       max_patches=snake_case						).flattened_patches
											self.assertEqual(
											    encoded_images.shape	,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,       )







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11,					reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.''',					)
@require_torch
@require_vision
class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					PixaStructImageProcessor if is_vision_available() else None






							def        lowerCamelCase_   (  self						)			->							str:
									_UpperCAmelCase							 =		PixaStructImageProcessingTester(self	,       num_channels=4						)
									_UpperCAmelCase							 =		3






							@property
							def        lowerCamelCase_   (  self						)			->							int:
									return self.image_processor_tester.prepare_image_processor_dict()






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(snake_case	,       'do_normalize'						)						)
									self.assertTrue(hasattr(snake_case	,       'do_convert_rgb'						)						)






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									# Initialize image_processor
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case						)
									for image in image_inputs:
											self.assertIsInstance(snake_case	,       Image.Image						)

									# Test not batched input
									_UpperCAmelCase							 =		(
									    (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
									    * (self.image_processor_tester.num_channels - 1)
									) + 2

									for max_patch in self.image_processor_tester.max_patches:
											# Test not batched input
											_UpperCAmelCase							 =		image_processor(
											    image_inputs[0]	,       return_tensors='pt'	,       max_patches=snake_case						).flattened_patches
											self.assertEqual(
											    encoded_images.shape	,       (1, max_patch, expected_hidden_dim)	,       )

											# Test batched
											_UpperCAmelCase							 =		image_processor(
											    snake_case	,       return_tensors='pt'	,       max_patches=snake_case						).flattened_patches
											self.assertEqual(
											    encoded_images.shape	,       (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,       )







"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 			lowercase__			(      A,					A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					'''swin'''

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






							def __init__(  self	,       snake_case=224	,       snake_case=4	,       snake_case=3	,       snake_case=96	,       snake_case=[2, 2, 6, 2]	,       snake_case=[3, 6, 12, 24]	,       snake_case=7	,       snake_case=4.0	,       snake_case=True	,       snake_case=0.0	,       snake_case=0.0	,       snake_case=0.1	,       snake_case="gelu"	,       snake_case=False	,       snake_case=0.02	,       snake_case=1E-5	,       snake_case=32	,       snake_case=None	,       snake_case=None	,       **snake_case	,       )			->							List[Any]:
									super().__init__(**snake_case						)

									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_size
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		embed_dim
									_UpperCAmelCase							 =		depths
									_UpperCAmelCase							 =		len(snake_case						)
									_UpperCAmelCase							 =		num_heads
									_UpperCAmelCase							 =		window_size
									_UpperCAmelCase							 =		mlp_ratio
									_UpperCAmelCase							 =		qkv_bias
									_UpperCAmelCase							 =		hidden_dropout_prob
									_UpperCAmelCase							 =		attention_probs_dropout_prob
									_UpperCAmelCase							 =		drop_path_rate
									_UpperCAmelCase							 =		hidden_act
									_UpperCAmelCase							 =		use_absolute_embeddings
									_UpperCAmelCase							 =		layer_norm_eps
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		encoder_stride
									# 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
									_UpperCAmelCase							 =		int(embed_dim * 2 ** (len(snake_case						) - 1)						)
									_UpperCAmelCase							 =		['stem'] + [f'stage{idx}' for idx in range(1	,       len(snake_case						) + 1						)]
									_UpperCAmelCase ,					_UpperCAmelCase							 =		get_aligned_output_features_output_indices(
									    out_features=snake_case	,       out_indices=snake_case	,       stage_names=self.stage_names						)







class 			lowercase__			(      A    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					version.parse('''1.11'''    )






							@property
							def        lowerCamelCase_   (  self						)			->							Mapping[str, Mapping[int, str]]:
									return OrderedDict(
									    [
									        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
									    ]						)






							@property
							def        lowerCamelCase_   (  self						)			->							float:
									return 1E-4


