


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:
    A      :    List[Any]		=					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor







class 			_lowercase (							unittest.TestCase):


       """simple docstring"""




       def __init__(   self							:			List[Any]					,      __lowerCamelCase							:			Union[str, Any]					,      __lowerCamelCase							:			Tuple=7					,      __lowerCamelCase							:			Tuple=3					,      __lowerCamelCase							:			Dict=18					,      __lowerCamelCase							:			Any=30					,      __lowerCamelCase							:			Dict=400					,      __lowerCamelCase							:			Optional[Any]=None					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			Tuple=True					,      __lowerCamelCase							:			Union[str, Any]=None					,      ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  size if size is not None else {"height": 20, "width": 20}
             lowerCamelCase__		:				Union[str, Any]    =  parent
             lowerCamelCase__		:				Optional[Any]    =  batch_size
             lowerCamelCase__		:				str    =  num_channels
             lowerCamelCase__		:				Any    =  image_size
             lowerCamelCase__		:				Union[str, Any]    =  min_resolution
             lowerCamelCase__		:				str    =  max_resolution
             lowerCamelCase__		:				Optional[int]    =  size
             lowerCamelCase__		:				Optional[Any]    =  do_normalize
             lowerCamelCase__		:				List[Any]    =  do_convert_rgb
             lowerCamelCase__		:				List[Any]    =  [512, 1024, 2048, 4096]
             lowerCamelCase__		:				List[str]    =  patch_size if patch_size is not None else {"height": 16, "width": 16}





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				int    =  "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
             lowerCamelCase__		:				Any    =  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 			_lowercase (							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			PixaStructImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  PixaStructImageProcessingTester(self       )





       @property
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_normalize"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_convert_rgb"       )       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  self.image_processor_tester.prepare_dummy_image()

             lowerCamelCase__		:				Optional[Any]    =  self.image_processing_class(**self.image_processor_dict       )
             lowerCamelCase__		:				str    =  2048

             lowerCamelCase__		:				List[str]    =  image_processor(__lowerCamelCase					,      return_tensors="pt"					,      max_patches=__lowerCamelCase       )
             self.assertTrue(torch.allclose(inputs.flattened_patches.mean()					,      torch.tensor(0.0_6_0_6       )					,      atol=1E-3					,      rtol=1E-3       )       )





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             lowerCamelCase__		:				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
             lowerCamelCase__		:				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
                   lowerCamelCase__		:				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
                   lowerCamelCase__		:				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 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Dict    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             lowerCamelCase__		:				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
             lowerCamelCase__		:				Tuple    =  (
                 (self.image_processor_tester.patch_size["height"] * self.image_processor_tester.patch_size["width"])
                 * self.image_processor_tester.num_channels
             ) + 2

             lowerCamelCase__		:				int    =  True

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

                   lowerCamelCase__		:				Optional[int]    =  "Hello"

                   lowerCamelCase__		:				List[str]    =  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
                   lowerCamelCase__		:				Tuple    =  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 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             lowerCamelCase__		:				str    =  prepare_image_inputs(self.image_processor_tester					,      equal_resolution=__lowerCamelCase					,      numpify=__lowerCamelCase       )
             for image in image_inputs:
                   self.assertIsInstance(__lowerCamelCase					,      np.ndarray       )

             lowerCamelCase__		:				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
                   lowerCamelCase__		:				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
                   lowerCamelCase__		:				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)					,      )





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Tuple    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             lowerCamelCase__		:				int    =  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
             lowerCamelCase__		:				List[str]    =  (
                 (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
                   lowerCamelCase__		:				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
                   lowerCamelCase__		:				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)					,      )







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,  reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."							,  )
@require_torch
@require_vision
class 			_lowercase (							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			PixaStructImageProcessor if is_vision_available() else None





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  PixaStructImageProcessingTester(self					,      num_channels=4       )
             lowerCamelCase__		:				Any    =  3





       @property
       def 						lowerCAmelCase     (   self							:			List[str]       ):


             '''simple docstring'''



             return self.image_processor_tester.prepare_image_processor_dict()





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_normalize"       )       )
             self.assertTrue(hasattr(__lowerCamelCase					,      "do_convert_rgb"       )       )





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             lowerCamelCase__		:				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
             lowerCamelCase__		:				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
                   lowerCamelCase__		:				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
                   lowerCamelCase__		:				List[str]    =  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

from typing import Any

def       lowercase_		(		_A			:    list[Any]						):

      """simple docstring"""




      create_state_space_tree(_A    ,					[]    ,					0						)

def       lowercase_		(		_A			:    list[Any]    ,					_A			:    list[Any]    ,					_A			:    int						):

      """simple docstring"""




      if index == len(_A						):
            print(_A						)
            return

      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.append(sequence[index]						)
      create_state_space_tree(_A    ,					_A    ,					index + 1						)
      current_subsequence.pop()


if __name__ == "__main__":
    A      :    list[Any]		=					[3, 1, 2, 4]
    generate_all_subsequences(seq)

    seq.clear()
    seq.extend(["A", "B", "C"])
    generate_all_subsequences(seq)


