


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:
			_snake_case  :	Optional[int]			      =       False

if is_vision_available():
			from PIL import Image

			from transformers import PixaStructImageProcessor

class        a	(unittest.TestCase   ):




							"""simple docstring"""


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

							def 					__snake_case      (							self :						List[Any]       )			->					Tuple:
														return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}






							def 					__snake_case      (							self :						List[Any]       )			->					Dict:
														__snake_case	:    Dict											=     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
														__snake_case	:    int											=     Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase       ).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        a	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							str  						=						PixaStructImageProcessor if is_vision_available() else None

							def 					__snake_case      (							self :						Tuple       )			->					Optional[Any]:
														__snake_case	:    Optional[int]											=     PixaStructImageProcessingTester(self       )

							@property
							def 					__snake_case      (							self :						Any       )			->					List[Any]:
														return self.image_processor_tester.prepare_image_processor_dict()

							def 					__snake_case      (							self :						int       )			->					str:
														__snake_case	:    Union[str, Any]											=     self.image_processing_class(**self.image_processor_dict       )
														self.assertTrue(hasattr(lowerCamelCase					,			"do_normalize"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"do_convert_rgb"       )       )

							def 					__snake_case      (							self :						List[Any]       )			->					Dict:
														__snake_case	:    Union[str, Any]											=     self.image_processor_tester.prepare_dummy_image()

														__snake_case	:    Union[str, Any]											=     self.image_processing_class(**self.image_processor_dict       )
														__snake_case	:    Dict											=     2048

														__snake_case	:    Union[str, Any]											=     image_processor(lowerCamelCase					,			return_tensors="pt"					,			max_patches=lowerCamelCase       )
														self.assertTrue(torch.allclose(inputs.flattened_patches.mean()					,			torch.tensor(0.06_06       )					,			atol=1E-3					,			rtol=1E-3       )       )

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

														# Test not batched input
														__snake_case	:    Any											=     (
														    (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
																					__snake_case	:    str											=     image_processor(
																					    image_inputs[0]					,			return_tensors="pt"					,			max_patches=lowerCamelCase       ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape					,			(1, max_patch, expected_hidden_dim)					,			)

																					# Test batched
																					__snake_case	:    Tuple											=     image_processor(
																					    lowerCamelCase					,			return_tensors="pt"					,			max_patches=lowerCamelCase       ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape					,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,			)

							def 					__snake_case      (							self :						Tuple       )			->					Optional[int]:
														# Initialize image_processor
														__snake_case	:    Any											=     self.image_processing_class(**self.image_processor_dict       )
														# create random PIL images
														__snake_case	:    Optional[int]											=     prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase       )
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase					,			Image.Image       )

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

														__snake_case	:    Tuple											=     True

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

																					__snake_case	:    int											=     "Hello"

																					__snake_case	:    List[Any]											=     image_processor(
																					    image_inputs[0]					,			return_tensors="pt"					,			max_patches=lowerCamelCase					,			header_text=lowerCamelCase       ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape					,			(1, max_patch, expected_hidden_dim)					,			)

																					# Test batched
																					__snake_case	:    int											=     image_processor(
																					    lowerCamelCase					,			return_tensors="pt"					,			max_patches=lowerCamelCase					,			header_text=lowerCamelCase       ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape					,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,			)

							def 					__snake_case      (							self :						List[str]       )			->					Optional[Any]:
														# Initialize image_processor
														__snake_case	:    List[str]											=     self.image_processing_class(**self.image_processor_dict       )
														# create random numpy tensors
														__snake_case	:    Tuple											=     prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase					,			numpify=lowerCamelCase       )
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase					,			np.ndarray       )

														__snake_case	:    Dict											=     (
														    (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
																					__snake_case	:    Optional[int]											=     image_processor(
																					    image_inputs[0]					,			return_tensors="pt"					,			max_patches=lowerCamelCase       ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape					,			(1, max_patch, expected_hidden_dim)					,			)

																					# Test batched
																					__snake_case	:    Union[str, Any]											=     image_processor(
																					    lowerCamelCase					,			return_tensors="pt"					,			max_patches=lowerCamelCase       ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape					,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,			)






							def 					__snake_case      (							self :						Any       )			->					int:
														# Initialize image_processor
														__snake_case	:    Union[str, Any]											=     self.image_processing_class(**self.image_processor_dict       )
														# create random PyTorch tensors
														__snake_case	:    List[str]											=     prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase					,			torchify=lowerCamelCase       )
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase					,			torch.Tensor       )

														# Test not batched input
														__snake_case	:    Optional[int]											=     (
														    (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
																					__snake_case	:    Dict											=     image_processor(
																					    image_inputs[0]					,			return_tensors="pt"					,			max_patches=lowerCamelCase       ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape					,			(1, max_patch, expected_hidden_dim)					,			)

																					# Test batched
																					__snake_case	:    Optional[int]											=     image_processor(
																					    lowerCamelCase					,			return_tensors="pt"					,			max_patches=lowerCamelCase       ).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        a	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							List[Any]  						=						PixaStructImageProcessor if is_vision_available() else None

							def 					__snake_case      (							self :						List[Any]       )			->					Dict:
														__snake_case	:    Dict											=     PixaStructImageProcessingTester(self					,			num_channels=4       )
														__snake_case	:    Dict											=     3

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

							def 					__snake_case      (							self :						Any       )			->					Union[str, Any]:
														__snake_case	:    List[Any]											=     self.image_processing_class(**self.image_processor_dict       )
														self.assertTrue(hasattr(lowerCamelCase					,			"do_normalize"       )       )
														self.assertTrue(hasattr(lowerCamelCase					,			"do_convert_rgb"       )       )






							def 					__snake_case      (							self :						List[Any]       )			->					Dict:
														# Initialize image_processor
														__snake_case	:    Optional[Any]											=     self.image_processing_class(**self.image_processor_dict       )
														# create random PIL images
														__snake_case	:    Dict											=     prepare_image_inputs(self.image_processor_tester					,			equal_resolution=lowerCamelCase       )
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase					,			Image.Image       )

														# Test not batched input
														__snake_case	:    Any											=     (
														    (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
																					__snake_case	:    List[str]											=     image_processor(
																					    image_inputs[0]					,			return_tensors="pt"					,			max_patches=lowerCamelCase       ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape					,			(1, max_patch, expected_hidden_dim)					,			)

																					# Test batched
																					__snake_case	:    Optional[Any]											=     image_processor(
																					    lowerCamelCase					,			return_tensors="pt"					,			max_patches=lowerCamelCase       ).flattened_patches
																					self.assertEqual(
																					    encoded_images.shape					,			(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)					,			)





from __future__ import annotations

_snake_case  :	Union[str, Any]			      =       {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}

class        a	:




							"""simple docstring"""


							def __init__(							self :						Tuple					,			lowerCamelCase :						dict[str, list[str]]					,			lowerCamelCase :						str       )			->					None:
														__snake_case	:    Tuple											=     graph
														# mapping node to its parent in resulting breadth first tree
														__snake_case	:    dict[str, str | None]											=     {}
														__snake_case	:    Dict											=     source_vertex

							def 					__snake_case      (							self :						Optional[int]       )			->					None:
														__snake_case	:    Dict											=     {self.source_vertex}
														__snake_case	:    List[str]											=     None
														__snake_case	:    Optional[Any]											=     [self.source_vertex]  # first in first out queue

														while queue:
																					__snake_case	:    List[Any]											=     queue.pop(0       )
																					for adjacent_vertex in self.graph[vertex]:
																												if adjacent_vertex not in visited:
																																			visited.add(lowerCamelCase       )
																																			__snake_case	:    Any											=     vertex
																																			queue.append(lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						str       )			->					str:
														if target_vertex == self.source_vertex:
																					return self.source_vertex

														__snake_case	:    Optional[Any]											=     self.parent.get(lowerCamelCase       )
														if target_vertex_parent is None:
																					__snake_case	:    Optional[Any]											=     (
																					    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
																					)
																					raise ValueError(lowerCamelCase       )

														return self.shortest_path(lowerCamelCase       ) + F'->{target_vertex}'


if __name__ == "__main__":
			_snake_case  :	Optional[Any]			      =       Graph(graph, "G")
			g.breath_first_search()
			print(g.shortest_path("D"))
			print(g.shortest_path("G"))
			print(g.shortest_path("Foo"))

