



from __future__ import annotations

from collections import deque
class 				_snake_case			:




				def __init__(						self					:						List[Any],					__lowercase					:						list[str]						):
							lowercase__					  =				[]
							self.adlist.append(
							    {"value": "", "next_states": [], "fail_state": 0, "output": []}						)

							for keyword in keywords:
										self.add_keyword(__lowercase						)
							self.set_fail_transitions()




				def 							A__      (						self					:						int,					__lowercase					:						int,					__lowercase					:						str						):
							for state in self.adlist[current_state]["next_states"]:
										if char == self.adlist[state]["value"]:
													return state
							return None




				def 							A__      (						self					:						Optional[int],					__lowercase					:						str						):
							lowercase__					  =				0
							for character in keyword:
										lowercase__					  =				self.find_next_state(__lowercase,					__lowercase						)
										if next_state is None:
													self.adlist.append(
													    {
													        "value": character,
													        "next_states": [],
													        "fail_state": 0,
													        "output": [],
													    }						)
													self.adlist[current_state]["next_states"].append(len(self.adlist						) - 1						)
													lowercase__					  =				len(self.adlist						) - 1
										else:
													lowercase__					  =				next_state
							self.adlist[current_state]["output"].append(__lowercase						)




				def 							A__      (						self					:						Dict						):
							lowercase__					  =				deque()
							for node in self.adlist[0]["next_states"]:
										q.append(__lowercase						)
										lowercase__					  =				0
							while q:
										lowercase__					  =				q.popleft()
										for child in self.adlist[r]["next_states"]:
													q.append(__lowercase						)
													lowercase__					  =				self.adlist[r]["fail_state"]
													while (
													    self.find_next_state(__lowercase,					self.adlist[child]["value"]						) is None
													    and state != 0
													):
																lowercase__					  =				self.adlist[state]["fail_state"]
													lowercase__					  =				self.find_next_state(
													    __lowercase,					self.adlist[child]["value"]						)
													if self.adlist[child]["fail_state"] is None:
																lowercase__					  =				0
													lowercase__					  =				(
													    self.adlist[child]["output"]
													    + self.adlist[self.adlist[child]["fail_state"]]["output"]
													)




				def 							A__      (						self					:						int,					__lowercase					:						str						):
							lowercase__					  =				{}  # returns a dict with keywords and list of its occurrences
							lowercase__					  =				0
							for i in range(len(__lowercase						)						):
										while (
										    self.find_next_state(__lowercase,					string[i]						) is None
										    and current_state != 0
										):
													lowercase__					  =				self.adlist[current_state]["fail_state"]
										lowercase__					  =				self.find_next_state(__lowercase,					string[i]						)
										if next_state is None:
													lowercase__					  =				0
										else:
													lowercase__					  =				next_state
													for key in self.adlist[current_state]["output"]:
																if key not in result:
																			lowercase__					  =				[]
																result[key].append(i - len(__lowercase						) + 1						)
							return result


if __name__ == "__main__":
							import doctest

							doctest.testmod()









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 				_snake_case			(					unittest.TestCase):




				def __init__(						self					:						List[Any],					__lowercase					:						int,					__lowercase					:						Optional[int]=7,					__lowercase					:						List[str]=3,					__lowercase					:						Tuple=18,					__lowercase					:						List[Any]=30,					__lowercase					:						Tuple=400,					__lowercase					:						Any=None,					__lowercase					:						Optional[int]=True,					__lowercase					:						List[str]=True,					__lowercase					:						Union[str, Any]=None,					):
							lowercase__					  =				size if size is not None else {"height": 20, "width": 20}
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				num_channels
							lowercase__					  =				image_size
							lowercase__					  =				min_resolution
							lowercase__					  =				max_resolution
							lowercase__					  =				size
							lowercase__					  =				do_normalize
							lowercase__					  =				do_convert_rgb
							lowercase__					  =				[512, 1024, 2048, 4096]
							lowercase__					  =				patch_size if patch_size is not None else {"height": 16, "width": 16}




				def 							A__      (						self					:						List[str]						):
							return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}




				def 							A__      (						self					:						Any						):
							lowercase__					  =				"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
							lowercase__					  =				Image.open(requests.get(__lowercase,					stream=__lowercase						).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 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Any     		=PixaStructImageProcessor if is_vision_available() else None




				def 							A__      (						self					:						Any						):
							lowercase__					  =				PixaStructImageProcessingTester(self						)




				@property
				def 							A__      (						self					:						Union[str, Any]						):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowercase,					"do_normalize"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_convert_rgb"						)						)




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				self.image_processor_tester.prepare_dummy_image()

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

							lowercase__					  =				image_processor(__lowercase,					return_tensors="pt",					max_patches=__lowercase						)
							self.assertTrue(torch.allclose(inputs.flattened_patches.mean(),					torch.tensor(0.0606						),					atol=1e-3,					rtol=1e-3						)						)




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

							# Test not batched input
							lowercase__					  =				(
							    (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
										lowercase__					  =				image_processor(
										    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

										# Test batched
										lowercase__					  =				image_processor(
										    __lowercase,					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)




				def 							A__      (						self					:						int						):
							# Initialize image_processor
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					Image.Image						)

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

							lowercase__					  =				True

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

										lowercase__					  =				"Hello"

										lowercase__					  =				image_processor(
										    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase,					header_text=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

										# Test batched
										lowercase__					  =				image_processor(
										    __lowercase,					return_tensors="pt",					max_patches=__lowercase,					header_text=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)




				def 							A__      (						self					:						Tuple						):
							# Initialize image_processor
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							lowercase__					  =				prepare_image_inputs(self.image_processor_tester,					equal_resolution=__lowercase,					numpify=__lowercase						)
							for image in image_inputs:
										self.assertIsInstance(__lowercase,					np.ndarray						)

							lowercase__					  =				(
							    (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
										lowercase__					  =				image_processor(
										    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

										# Test batched
										lowercase__					  =				image_processor(
										    __lowercase,					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)




				def 							A__      (						self					:						Any						):
							# Initialize image_processor
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							lowercase__					  =				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
							lowercase__					  =				(
							    (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
										lowercase__					  =				image_processor(
										    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

										# Test batched
										lowercase__					  =				image_processor(
										    __lowercase,					return_tensors="pt",					max_patches=__lowercase						).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 				_snake_case			(					lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[int]     		=PixaStructImageProcessor if is_vision_available() else None




				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				PixaStructImageProcessingTester(self,					num_channels=4						)
							lowercase__					  =				3




				@property
				def 							A__      (						self					:						Union[str, Any]						):
							return self.image_processor_tester.prepare_image_processor_dict()




				def 							A__      (						self					:						Dict						):
							lowercase__					  =				self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(__lowercase,					"do_normalize"						)						)
							self.assertTrue(hasattr(__lowercase,					"do_convert_rgb"						)						)




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

							# Test not batched input
							lowercase__					  =				(
							    (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
										lowercase__					  =				image_processor(
										    image_inputs[0],					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(1, max_patch, expected_hidden_dim),					)

										# Test batched
										lowercase__					  =				image_processor(
										    __lowercase,					return_tensors="pt",					max_patches=__lowercase						).flattened_patches
										self.assertEqual(
										    encoded_images.shape,					(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim),					)




