


'''simple docstring'''



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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

if is_vision_available():
      from PIL import Image

      from transformers import DeformableDetrImageProcessor



class      a    (		unittest.TestCase	):


     """simple docstring"""






     def __init__(    self					:		Dict							,			snake_case					:		List[str]							,			snake_case					:		Optional[int]=7							,			snake_case					:		Tuple=3							,			snake_case					:		str=30							,			snake_case					:		Union[str, Any]=400							,			snake_case					:		List[Any]=True							,			snake_case					:		Any=None							,			snake_case					:		int=True							,			snake_case					:		Optional[int]=[0.5, 0.5, 0.5]							,			snake_case					:		List[str]=[0.5, 0.5, 0.5]							,			snake_case					:		Optional[Any]=True							,			snake_case					:		int=1 / 255							,			snake_case					:		str=True							,			)						->							Optional[Any]:
          # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
          __UpperCAmelCase     :			int  							=      size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
          __UpperCAmelCase     :			str  							=      parent
          __UpperCAmelCase     :			Union[str, Any]  							=      batch_size
          __UpperCAmelCase     :			str  							=      num_channels
          __UpperCAmelCase     :			Tuple  							=      min_resolution
          __UpperCAmelCase     :			Any  							=      max_resolution
          __UpperCAmelCase     :			Tuple  							=      do_resize
          __UpperCAmelCase     :			List[Any]  							=      size
          __UpperCAmelCase     :			Union[str, Any]  							=      do_normalize
          __UpperCAmelCase     :			Union[str, Any]  							=      image_mean
          __UpperCAmelCase     :			Union[str, Any]  							=      image_std
          __UpperCAmelCase     :			Any  							=      do_rescale
          __UpperCAmelCase     :			Optional[Any]  							=      rescale_factor
          __UpperCAmelCase     :			Optional[int]  							=      do_pad

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Any:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_rescale": self.do_rescale,
              "rescale_factor": self.rescale_factor,
              "do_pad": self.do_pad,
          }






     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		str							,			snake_case					:		Tuple=False      )						->							Dict:
          if not batched:
               __UpperCAmelCase     :			Union[str, Any]  							=      image_inputs[0]
               if isinstance(snake_case							,			Image.Image      ):
                    __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      image.size
               else:
                    __UpperCAmelCase							,       __UpperCAmelCase     :			Any  							=      image.shape[1], image.shape[2]
               if w < h:
                    __UpperCAmelCase     :			str  							=      int(self.size['''shortest_edge'''] * h / w      )
                    __UpperCAmelCase     :			Union[str, Any]  							=      self.size['''shortest_edge''']
               elif w > h:
                    __UpperCAmelCase     :			Tuple  							=      self.size['''shortest_edge''']
                    __UpperCAmelCase     :			str  							=      int(self.size['''shortest_edge'''] * w / h      )
               else:
                    __UpperCAmelCase     :			Tuple  							=      self.size['''shortest_edge''']
                    __UpperCAmelCase     :			Tuple  							=      self.size['''shortest_edge''']

          else:
               __UpperCAmelCase     :			Tuple  							=      []
               for image in image_inputs:
                    __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      self.get_expected_values([image]      )
                    expected_values.append((expected_height, expected_width)      )
               __UpperCAmelCase     :			List[str]  							=      max(snake_case							,			key=lambda snake_case      : item[0]      )[0]
               __UpperCAmelCase     :			List[Any]  							=      max(snake_case							,			key=lambda snake_case      : item[1]      )[1]

          return expected_height, expected_width



@require_torch
@require_vision
class      a    (		_a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Any							       =       DeformableDetrImageProcessor if is_vision_available() else None

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Any:
          __UpperCAmelCase     :			Union[str, Any]  							=      DeformableDetrImageProcessingTester(self      )

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

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			List[Any]  							=      self.image_processing_class(**self.image_processor_dict      )
          self.assertTrue(hasattr(snake_case							,			'''image_mean'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''image_std'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''do_normalize'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''do_resize'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''do_rescale'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''do_pad'''      )      )
          self.assertTrue(hasattr(snake_case							,			'''size'''      )      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Union[str, Any]:
          __UpperCAmelCase     :			List[str]  							=      self.image_processing_class.from_dict(self.image_processor_dict      )
          self.assertEqual(image_processor.size							,			{'''shortest_edge''': 18, '''longest_edge''': 1333}      )
          self.assertEqual(image_processor.do_pad							,			snake_case      )

          __UpperCAmelCase     :			str  							=      self.image_processing_class.from_dict(
              self.image_processor_dict							,			size=42							,			max_size=84							,			pad_and_return_pixel_mask=snake_case      )
          self.assertEqual(image_processor.size							,			{'''shortest_edge''': 42, '''longest_edge''': 84}      )
          self.assertEqual(image_processor.do_pad							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Any:
          pass

     def 			lowerCamelCase__			(    self					:		str      )						->							Tuple:
          # Initialize image_processing
          __UpperCAmelCase     :			Tuple  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random PIL images
          __UpperCAmelCase     :			Dict  							=      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     :			List[str]  							=      image_processing(image_inputs[0]							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      self.image_processor_tester.get_expected_values(snake_case      )

          self.assertEqual(
              encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

          # Test batched
          __UpperCAmelCase							,       __UpperCAmelCase     :			Union[str, Any]  							=      self.image_processor_tester.get_expected_values(snake_case							,			batched=snake_case      )

          __UpperCAmelCase     :			str  							=      image_processing(snake_case							,			return_tensors='''pt'''      ).pixel_values
          self.assertEqual(
              encoded_images.shape							,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )							,			)

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							str:
          # Initialize image_processing
          __UpperCAmelCase     :			Dict  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random numpy tensors
          __UpperCAmelCase     :			Optional[int]  							=      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      )

          # Test not batched input
          __UpperCAmelCase     :			List[Any]  							=      image_processing(image_inputs[0]							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			str  							=      self.image_processor_tester.get_expected_values(snake_case      )

          self.assertEqual(
              encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

          # Test batched
          __UpperCAmelCase     :			Optional[int]  							=      image_processing(snake_case							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      self.image_processor_tester.get_expected_values(snake_case							,			batched=snake_case      )

          self.assertEqual(
              encoded_images.shape							,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )							,			)

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Tuple:
          # Initialize image_processing
          __UpperCAmelCase     :			Tuple  							=      self.image_processing_class(**self.image_processor_dict      )
          # create random PyTorch tensors
          __UpperCAmelCase     :			Dict  							=      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     :			Dict  							=      image_processing(image_inputs[0]							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			Any  							=      self.image_processor_tester.get_expected_values(snake_case      )

          self.assertEqual(
              encoded_images.shape							,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)							,			)

          # Test batched
          __UpperCAmelCase     :			int  							=      image_processing(snake_case							,			return_tensors='''pt'''      ).pixel_values

          __UpperCAmelCase							,       __UpperCAmelCase     :			Optional[Any]  							=      self.image_processor_tester.get_expected_values(snake_case							,			batched=snake_case      )

          self.assertEqual(
              encoded_images.shape							,			(
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )							,			)

     @slow
     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							List[Any]:
          # prepare image and target
          __UpperCAmelCase     :			Tuple  							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''							,			'''r'''      ) as f:
               __UpperCAmelCase     :			Dict  							=      json.loads(f.read()      )

          __UpperCAmelCase     :			Tuple  							=      {'''image_id''': 3_9769, '''annotations''': target}

          # encode them
          __UpperCAmelCase     :			Dict  							=      DeformableDetrImageProcessor()
          __UpperCAmelCase     :			List[str]  							=      image_processing(images=snake_case							,			annotations=snake_case							,			return_tensors='''pt'''      )

          # verify pixel values
          __UpperCAmelCase     :			Union[str, Any]  							=      torch.Size([1, 3, 800, 1066]      )
          self.assertEqual(encoding['''pixel_values'''].shape							,			snake_case      )

          __UpperCAmelCase     :			Union[str, Any]  							=      torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,			snake_case							,			atol=1E-4      )      )

          # verify area
          __UpperCAmelCase     :			int  							=      torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,			snake_case      )      )
          # verify boxes
          __UpperCAmelCase     :			List[Any]  							=      torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,			snake_case      )
          __UpperCAmelCase     :			Dict  							=      torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,			snake_case							,			atol=1E-3      )      )
          # verify image_id
          __UpperCAmelCase     :			Any  							=      torch.tensor([3_9769]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,			snake_case      )      )
          # verify is_crowd
          __UpperCAmelCase     :			Tuple  							=      torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,			snake_case      )      )
          # verify class_labels
          __UpperCAmelCase     :			Tuple  							=      torch.tensor([75, 75, 63, 65, 17, 17]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,			snake_case      )      )
          # verify orig_size
          __UpperCAmelCase     :			List[str]  							=      torch.tensor([480, 640]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,			snake_case      )      )
          # verify size
          __UpperCAmelCase     :			Any  							=      torch.tensor([800, 1066]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,			snake_case      )      )






     @slow
     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Any:
          # prepare image, target and masks_path
          __UpperCAmelCase     :			Optional[int]  							=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
          with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,			'''r'''      ) as f:
               __UpperCAmelCase     :			Union[str, Any]  							=      json.loads(f.read()      )

          __UpperCAmelCase     :			List[str]  							=      {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

          __UpperCAmelCase     :			Any  							=      pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

          # encode them
          __UpperCAmelCase     :			Union[str, Any]  							=      DeformableDetrImageProcessor(format='''coco_panoptic'''      )
          __UpperCAmelCase     :			int  							=      image_processing(images=snake_case							,			annotations=snake_case							,			masks_path=snake_case							,			return_tensors='''pt'''      )

          # verify pixel values
          __UpperCAmelCase     :			Union[str, Any]  							=      torch.Size([1, 3, 800, 1066]      )
          self.assertEqual(encoding['''pixel_values'''].shape							,			snake_case      )

          __UpperCAmelCase     :			Union[str, Any]  							=      torch.tensor([0.2_796, 0.3_138, 0.3_481]      )
          self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]							,			snake_case							,			atol=1E-4      )      )

          # verify area
          __UpperCAmelCase     :			Union[str, Any]  							=      torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']							,			snake_case      )      )
          # verify boxes
          __UpperCAmelCase     :			List[str]  							=      torch.Size([6, 4]      )
          self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,			snake_case      )
          __UpperCAmelCase     :			List[Any]  							=      torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]							,			snake_case							,			atol=1E-3      )      )
          # verify image_id
          __UpperCAmelCase     :			int  							=      torch.tensor([3_9769]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,			snake_case      )      )
          # verify is_crowd
          __UpperCAmelCase     :			int  							=      torch.tensor([0, 0, 0, 0, 0, 0]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,			snake_case      )      )
          # verify class_labels
          __UpperCAmelCase     :			Optional[Any]  							=      torch.tensor([17, 17, 63, 75, 75, 93]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,			snake_case      )      )
          # verify masks
          __UpperCAmelCase     :			str  							=      82_2873
          self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,			snake_case      )
          # verify orig_size
          __UpperCAmelCase     :			Optional[int]  							=      torch.tensor([480, 640]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,			snake_case      )      )
          # verify size
          __UpperCAmelCase     :			Any  							=      torch.tensor([800, 1066]      )
          self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,			snake_case      )      )



'''simple docstring'''



from abc import ABC, abstractmethod
from typing import List, Optional



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Optional[Any]      )						->							int:
          # test for the above condition
          self.test()

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase     :			Optional[int]  							=      0
          __UpperCAmelCase     :			Any  							=      False
          while not completed:
               if counter == 1:
                    self.reset()
               __UpperCAmelCase     :			Optional[int]  							=      self.advance()
               if not self.does_advance(snake_case      ):
                    raise Exception(
                        '''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'''      )

               __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      self.update(snake_case      )
               counter += 1

               if counter > 1_0000:
                    raise Exception('''update() does not fulfill the constraint.'''      )

          if self.remaining() != 0:
               raise Exception('''Custom Constraint is not defined correctly.'''      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							List[str]:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int      )						->							Optional[int]:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		int      )						->							int:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		int      )						->							Optional[int]:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		int      )						->							Tuple:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )






     @abstractmethod
     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		List[Any]=False      )						->							Any:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		int							,			snake_case					:		List[int]      )						->							Tuple:
          super(snake_case							,			self      ).__init__()

          if not isinstance(snake_case							,			snake_case      ) or len(snake_case      ) == 0:
               raise ValueError(f'`token_ids` has to be a non-empty list, but is {token_ids}.'      )
          if any((not isinstance(snake_case							,			snake_case      ) or token_id < 0) for token_id in token_ids      ):
               raise ValueError(f'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'      )

          __UpperCAmelCase     :			Dict  							=      token_ids

          __UpperCAmelCase     :			Tuple  							=      len(self.token_ids      )
          __UpperCAmelCase     :			List[str]  							=      -1  # the index of the currently fulfilled step
          __UpperCAmelCase     :			int  							=      False

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							str:
          if self.completed:
               return None
          return self.token_ids[self.fulfilled_idx + 1]

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int      )						->							Optional[int]:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case      )}'      )

          if self.completed:
               return False

          return token_id == self.token_ids[self.fulfilled_idx + 1]

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		int      )						->							Optional[int]:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case      )}'      )

          __UpperCAmelCase     :			Any  							=      False
          __UpperCAmelCase     :			Tuple  							=      False
          __UpperCAmelCase     :			Tuple  							=      False

          if self.does_advance(snake_case      ):
               self.fulfilled_idx += 1
               __UpperCAmelCase     :			Union[str, Any]  							=      True
               if self.fulfilled_idx == (self.seqlen - 1):
                    __UpperCAmelCase     :			List[Any]  							=      True
               __UpperCAmelCase     :			Union[str, Any]  							=      completed
          else:
               # failed to make progress.
               __UpperCAmelCase     :			List[str]  							=      True
               self.reset()
          return stepped, completed, reset

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			Union[str, Any]  							=      False
          __UpperCAmelCase     :			Union[str, Any]  							=      0

     def 			lowerCamelCase__			(    self					:		str      )						->							Optional[int]:
          return self.seqlen - (self.fulfilled_idx + 1)






     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Dict=False      )						->							List[str]:
          __UpperCAmelCase     :			List[str]  							=      PhrasalConstraint(self.token_ids      )

          if stateful:
               __UpperCAmelCase     :			int  							=      self.seqlen
               __UpperCAmelCase     :			Optional[Any]  							=      self.fulfilled_idx
               __UpperCAmelCase     :			List[Any]  							=      self.completed

          return new_constraint



class      a    :


     """simple docstring"""






     def __init__(    self					:		List[str]							,			snake_case					:		List[List[int]]							,			snake_case					:		Dict=True      )						->							Any:
          __UpperCAmelCase     :			List[Any]  							=      max([len(snake_case      ) for one in nested_token_ids]      )

          __UpperCAmelCase     :			Union[str, Any]  							=      {}
          for token_ids in nested_token_ids:
               __UpperCAmelCase     :			List[str]  							=      root
               for tidx, token_id in enumerate(snake_case      ):
                    if token_id not in level:
                         __UpperCAmelCase     :			List[Any]  							=      {}

                    __UpperCAmelCase     :			Tuple  							=      level[token_id]

          if no_subsets and self.has_subsets(snake_case							,			snake_case      ):
               raise ValueError(
                   '''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
                   f' {nested_token_ids}.'      )

          __UpperCAmelCase     :			Tuple  							=      root

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		List[str]      )						->							List[Any]:
          __UpperCAmelCase     :			Dict  							=      self.trie

          for current_token in current_seq:
               __UpperCAmelCase     :			List[str]  							=      start[current_token]

          __UpperCAmelCase     :			str  							=      list(start.keys()      )

          return next_tokens

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		List[str]      )						->							Any:
          __UpperCAmelCase     :			Optional[Any]  							=      self.next_tokens(snake_case      )

          return len(snake_case      ) == 0

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		int      )						->							Optional[int]:
          __UpperCAmelCase     :			str  							=      list(root.values()      )
          if len(snake_case      ) == 0:
               return 1
          else:
               return sum([self.count_leaves(snake_case      ) for nn in next_nodes]      )






     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int							,			snake_case					:		Dict      )						->							str:
          __UpperCAmelCase     :			Dict  							=      self.count_leaves(snake_case      )
          return len(snake_case      ) != leaf_count



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Union[str, Any]							,			snake_case					:		List[List[int]]      )						->							str:
          super(snake_case							,			self      ).__init__()

          if not isinstance(snake_case							,			snake_case      ) or len(snake_case      ) == 0:
               raise ValueError(f'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'      )
          if any(not isinstance(snake_case							,			snake_case      ) for token_ids in nested_token_ids      ):
               raise ValueError(f'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'      )
          if any(
              any((not isinstance(snake_case							,			snake_case      ) or token_id < 0) for token_id in token_ids      )
              for token_ids in nested_token_ids      ):
               raise ValueError(
                   f'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'      )

          __UpperCAmelCase     :			Optional[int]  							=      DisjunctiveTrie(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      nested_token_ids

          __UpperCAmelCase     :			List[Any]  							=      self.trie.max_height
          __UpperCAmelCase     :			List[Any]  							=      []
          __UpperCAmelCase     :			Union[str, Any]  							=      False

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			Optional[int]  							=      self.trie.next_tokens(self.current_seq      )

          if len(snake_case      ) == 0:
               return None
          else:
               return token_list

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		int      )						->							Dict:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case      )}'      )

          __UpperCAmelCase     :			List[str]  							=      self.trie.next_tokens(self.current_seq      )

          return token_id in next_tokens

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int      )						->							Tuple:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case      )}'      )

          __UpperCAmelCase     :			Dict  							=      False
          __UpperCAmelCase     :			Optional[Any]  							=      False
          __UpperCAmelCase     :			str  							=      False

          if self.does_advance(snake_case      ):
               self.current_seq.append(snake_case      )
               __UpperCAmelCase     :			int  							=      True
          else:
               __UpperCAmelCase     :			Optional[Any]  							=      True
               self.reset()

          __UpperCAmelCase     :			Optional[Any]  							=      self.trie.reached_leaf(self.current_seq      )
          __UpperCAmelCase     :			Tuple  							=      completed

          return stepped, completed, reset

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[Any]:
          __UpperCAmelCase     :			str  							=      False
          __UpperCAmelCase     :			Tuple  							=      []

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Any:
          if self.completed:
               # since this can be completed without reaching max height
               return 0
          else:
               return self.seqlen - len(self.current_seq      )






     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		Optional[int]=False      )						->							Tuple:
          __UpperCAmelCase     :			str  							=      DisjunctiveConstraint(self.token_ids      )

          if stateful:
               __UpperCAmelCase     :			Tuple  							=      self.seqlen
               __UpperCAmelCase     :			Dict  							=      self.current_seq
               __UpperCAmelCase     :			str  							=      self.completed

          return new_constraint



class      a    :


     """simple docstring"""






     def __init__(    self					:		Union[str, Any]							,			snake_case					:		List[Constraint]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      constraints

          # max # of steps required to fulfill a given constraint
          __UpperCAmelCase     :			int  							=      max([c.seqlen for c in constraints]      )
          __UpperCAmelCase     :			int  							=      len(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      False

          self.init_state()

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							List[str]:
          __UpperCAmelCase     :			List[Any]  							=      []
          __UpperCAmelCase     :			List[str]  							=      None
          __UpperCAmelCase     :			str  							=      [constraint.copy(stateful=snake_case      ) for constraint in self.constraints]

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Optional[Any]:
          __UpperCAmelCase     :			Dict  							=      0
          if self.inprogress_constraint:
               # extra points for having a constraint mid-fulfilled
               add += self.max_seqlen - self.inprogress_constraint.remaining()

          return (len(self.complete_constraints      ) * self.max_seqlen) + add

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							int:
          __UpperCAmelCase     :			int  							=      []
          if self.inprogress_constraint is None:
               for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
                    __UpperCAmelCase     :			Optional[int]  							=      constraint.advance()
                    if isinstance(snake_case							,			snake_case      ):
                         token_list.append(snake_case      )
                    elif isinstance(snake_case							,			snake_case      ):
                         token_list.extend(snake_case      )
          else:
               __UpperCAmelCase     :			Optional[Any]  							=      self.inprogress_constraint.advance()
               if isinstance(snake_case							,			snake_case      ):
                    token_list.append(snake_case      )
               elif isinstance(snake_case							,			snake_case      ):
                    token_list.extend(snake_case      )

          if len(snake_case      ) == 0:
               return None
          else:
               return token_list

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		Optional[List[int]]      )						->							Optional[int]:
          self.init_state()

          if token_ids is not None:
               for token in token_ids:
                    # completes or steps **one** constraint
                    __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      self.add(snake_case      )

                    # the entire list of constraints are fulfilled
                    if self.completed:
                         break

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		int      )						->							List[str]:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.'      )

          __UpperCAmelCase							,       __UpperCAmelCase     :			str  							=      False, False

          if self.completed:
               __UpperCAmelCase     :			Union[str, Any]  							=      True
               __UpperCAmelCase     :			str  							=      False
               return complete, stepped

          if self.inprogress_constraint is not None:
               # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
               # job, simply update the state

               __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Any  							=      self.inprogress_constraint.update(snake_case      )
               if reset:
                    # 1. If the next token breaks the progress, then we must restart.
                    #     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

                    #     But that doesn't mean we self.init_state(), since we only reset the state for this particular
                    #     constraint, not the full list of constraints.

                    self.pending_constraints.append(self.inprogress_constraint.copy(stateful=snake_case      )      )
                    __UpperCAmelCase     :			Optional[Any]  							=      None

               if complete:
                    # 2. If the next token completes the constraint, move it to completed list, set
                    #     inprogress to None. If there are no pending constraints either, then this full list of constraints
                    #     is complete.

                    self.complete_constraints.append(self.inprogress_constraint      )
                    __UpperCAmelCase     :			str  							=      None

                    if len(self.pending_constraints      ) == 0:
                         # we're done!
                         __UpperCAmelCase     :			Optional[int]  							=      True

          else:
               # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
               # of constraints?

               for cidx, pending_constraint in enumerate(self.pending_constraints      ):
                    if pending_constraint.does_advance(snake_case      ):
                         __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Union[str, Any]  							=      pending_constraint.update(snake_case      )

                         if not stepped:
                              raise Exception(
                                  '''`constraint.update(token_id)` is not yielding incremental progress, '''
                                  '''even though `constraint.does_advance(token_id)` is true.'''      )

                         if complete:
                              self.complete_constraints.append(snake_case      )
                              __UpperCAmelCase     :			Tuple  							=      None

                         if not complete and stepped:
                              __UpperCAmelCase     :			List[Any]  							=      pending_constraint

                         if complete or stepped:
                              # If we made any progress at all, then it's at least not a "pending constraint".

                              __UpperCAmelCase     :			Optional[int]  							=      (
                                  self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
                              )

                              if len(self.pending_constraints      ) == 0 and self.inprogress_constraint is None:
                                   # If there's no longer any pending after this and no inprogress either, then we must be
                                   # complete.

                                   __UpperCAmelCase     :			Any  							=      True

                              break  # prevent accidentally stepping through multiple constraints with just one token.

          return complete, stepped






     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Optional[int]=True      )						->							Optional[int]:
          __UpperCAmelCase     :			Union[str, Any]  							=      ConstraintListState(self.constraints      )  # we actually never though self.constraints objects
          # throughout this process. So it's at initialization state.

          if stateful:
               __UpperCAmelCase     :			str  							=      [
                   constraint.copy(stateful=snake_case      ) for constraint in self.complete_constraints
               ]
               if self.inprogress_constraint is not None:
                    __UpperCAmelCase     :			Union[str, Any]  							=      self.inprogress_constraint.copy(stateful=snake_case      )
               __UpperCAmelCase     :			Tuple  							=      [constraint.copy() for constraint in self.pending_constraints]

          return new_state