






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 DetrImageProcessor

class 		_snake_case  (  unittest.TestCase					):






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






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







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a=False)      ->       Dict:
						if not batched:
									SCREAMING_SNAKE_CASE						= image_inputs[0]
									if isinstance(a		,					Image.Image):
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= image.size
									else:
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= image.shape[1], image.shape[2]
									if w < h:
												SCREAMING_SNAKE_CASE						= int(self.size['shortest_edge'] * h / w)
												SCREAMING_SNAKE_CASE						= self.size['shortest_edge']
									elif w > h:
												SCREAMING_SNAKE_CASE						= self.size['shortest_edge']
												SCREAMING_SNAKE_CASE						= int(self.size['shortest_edge'] * w / h)
									else:
												SCREAMING_SNAKE_CASE						= self.size['shortest_edge']
												SCREAMING_SNAKE_CASE						= self.size['shortest_edge']

						else:
									SCREAMING_SNAKE_CASE						= []
									for image in image_inputs:
												SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.get_expected_values([image])
												expected_values.append((expected_height, expected_width))
									SCREAMING_SNAKE_CASE						= max(a		,					key=lambda a: item[0])[0]
									SCREAMING_SNAKE_CASE						= max(a		,					key=lambda a: item[1])[1]

						return expected_height, expected_width



@require_torch
@require_vision
class 		_snake_case  (  A__   ,    unittest.TestCase					):
			_lowercase		:    int			     =							DetrImageProcessor if is_vision_available() else None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= DetrImageProcessingTester(self)






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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(a		,					'image_mean'))
						self.assertTrue(hasattr(a		,					'image_std'))
						self.assertTrue(hasattr(a		,					'do_normalize'))
						self.assertTrue(hasattr(a		,					'do_rescale'))
						self.assertTrue(hasattr(a		,					'rescale_factor'))
						self.assertTrue(hasattr(a		,					'do_resize'))
						self.assertTrue(hasattr(a		,					'size'))
						self.assertTrue(hasattr(a		,					'do_pad'))






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= 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		,					a)

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						pass






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

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a)

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

						# Test batched
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a		,					batched=a)

						SCREAMING_SNAKE_CASE						= image_processing(a		,					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    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					numpify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					np.ndarray)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a)

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

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a		,					batched=a)

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						# Initialize image_processing
						SCREAMING_SNAKE_CASE						= self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						SCREAMING_SNAKE_CASE						= prepare_image_inputs(self.image_processor_tester		,					equal_resolution=a		,					torchify=a)
						for image in image_inputs:
									self.assertIsInstance(a		,					torch.Tensor)

						# Test not batched input
						SCREAMING_SNAKE_CASE						= image_processing(image_inputs[0]		,					return_tensors='pt').pixel_values

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a)

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

						# Test batched
						SCREAMING_SNAKE_CASE						= image_processing(a		,					return_tensors='pt').pixel_values

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.image_processor_tester.get_expected_values(a		,					batched=a)

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






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

						SCREAMING_SNAKE_CASE						= {'image_id': 3_9769, 'annotations': target}

						# encode them
						SCREAMING_SNAKE_CASE						= DetrImageProcessor.from_pretrained('facebook/detr-resnet-50')
						SCREAMING_SNAKE_CASE						= image_processing(images=a		,					annotations=a		,					return_tensors='pt')

						# verify pixel values
						SCREAMING_SNAKE_CASE						= torch.Size([1, 3, 800, 1066])
						self.assertEqual(encoding['pixel_values'].shape		,					a)

						SCREAMING_SNAKE_CASE						= torch.tensor([0.27_96, 0.31_38, 0.34_81])
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,					a		,					atol=1E-4))

						# verify area
						SCREAMING_SNAKE_CASE						= torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38])
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,					a))
						# verify boxes
						SCREAMING_SNAKE_CASE						= torch.Size([6, 4])
						self.assertEqual(encoding['labels'][0]['boxes'].shape		,					a)
						SCREAMING_SNAKE_CASE						= torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15])
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,					a		,					atol=1E-3))
						# verify image_id
						SCREAMING_SNAKE_CASE						= torch.tensor([3_9769])
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,					a))
						# verify is_crowd
						SCREAMING_SNAKE_CASE						= torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,					a))
						# verify class_labels
						SCREAMING_SNAKE_CASE						= torch.tensor([75, 75, 63, 65, 17, 17])
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,					a))
						# verify orig_size
						SCREAMING_SNAKE_CASE						= torch.tensor([480, 640])
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,					a))
						# verify size
						SCREAMING_SNAKE_CASE						= torch.tensor([800, 1066])
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,					a))







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						# prepare image, target and masks_path
						SCREAMING_SNAKE_CASE						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
						with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		,					'r') as f:
									SCREAMING_SNAKE_CASE						= json.loads(f.read())

						SCREAMING_SNAKE_CASE						= {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

						SCREAMING_SNAKE_CASE						= pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic')

						# encode them
						SCREAMING_SNAKE_CASE						= DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic')
						SCREAMING_SNAKE_CASE						= image_processing(images=a		,					annotations=a		,					masks_path=a		,					return_tensors='pt')

						# verify pixel values
						SCREAMING_SNAKE_CASE						= torch.Size([1, 3, 800, 1066])
						self.assertEqual(encoding['pixel_values'].shape		,					a)

						SCREAMING_SNAKE_CASE						= torch.tensor([0.27_96, 0.31_38, 0.34_81])
						self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]		,					a		,					atol=1E-4))

						# verify area
						SCREAMING_SNAKE_CASE						= torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47])
						self.assertTrue(torch.allclose(encoding['labels'][0]['area']		,					a))
						# verify boxes
						SCREAMING_SNAKE_CASE						= torch.Size([6, 4])
						self.assertEqual(encoding['labels'][0]['boxes'].shape		,					a)
						SCREAMING_SNAKE_CASE						= torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25])
						self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]		,					a		,					atol=1E-3))
						# verify image_id
						SCREAMING_SNAKE_CASE						= torch.tensor([3_9769])
						self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		,					a))
						# verify is_crowd
						SCREAMING_SNAKE_CASE						= torch.tensor([0, 0, 0, 0, 0, 0])
						self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		,					a))
						# verify class_labels
						SCREAMING_SNAKE_CASE						= torch.tensor([17, 17, 63, 75, 75, 93])
						self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		,					a))
						# verify masks
						SCREAMING_SNAKE_CASE						= 82_2873
						self.assertEqual(encoding['labels'][0]['masks'].sum().item()		,					a)
						# verify orig_size
						SCREAMING_SNAKE_CASE						= torch.tensor([480, 640])
						self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		,					a))
						# verify size
						SCREAMING_SNAKE_CASE						= torch.tensor([800, 1066])
						self.assertTrue(torch.allclose(encoding['labels'][0]['size']		,					a))












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

class 		_snake_case  (  A__					):






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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= 0
						SCREAMING_SNAKE_CASE						= False
						while not completed:
									if counter == 1:
												self.reset()
									SCREAMING_SNAKE_CASE						= self.advance()
									if not self.does_advance(a):
												raise Exception(
												    'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.')

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.update(a)
									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    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						raise NotImplementedError(
						    f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''')






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






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






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






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







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



class 		_snake_case  (  A__					):






			def __init__(       self		,					a)      ->       Dict:
						super(a		,					self).__init__()

						if not isinstance(a		,					a) or len(a) == 0:
									raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''')
						if any((not isinstance(a		,					a) 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}.''')

						SCREAMING_SNAKE_CASE						= token_ids

						SCREAMING_SNAKE_CASE						= len(self.token_ids)
						SCREAMING_SNAKE_CASE						= -1  # the index of the currently fulfilled step
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						if self.completed:
									return None
						return self.token_ids[self.fulfilled_idx + 1]






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

						if self.completed:
									return False

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






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

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False

						if self.does_advance(a):
									self.fulfilled_idx += 1
									SCREAMING_SNAKE_CASE						= True
									if self.fulfilled_idx == (self.seqlen - 1):
												SCREAMING_SNAKE_CASE						= True
									SCREAMING_SNAKE_CASE						= completed
						else:
									# failed to make progress.
									SCREAMING_SNAKE_CASE						= True
									self.reset()
						return stepped, completed, reset






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= 0






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						return self.seqlen - (self.fulfilled_idx + 1)







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       int:
						SCREAMING_SNAKE_CASE						= PhrasalConstraint(self.token_ids)

						if stateful:
									SCREAMING_SNAKE_CASE						= self.seqlen
									SCREAMING_SNAKE_CASE						= self.fulfilled_idx
									SCREAMING_SNAKE_CASE						= self.completed

						return new_constraint



class 		_snake_case  :






			def __init__(       self		,					a		,					a=True)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= max([len(a) for one in nested_token_ids])

						SCREAMING_SNAKE_CASE						= {}
						for token_ids in nested_token_ids:
									SCREAMING_SNAKE_CASE						= root
									for tidx, token_id in enumerate(a):
												if token_id not in level:
															SCREAMING_SNAKE_CASE						= {}

												SCREAMING_SNAKE_CASE						= level[token_id]

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

						SCREAMING_SNAKE_CASE						= root






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= self.trie

						for current_token in current_seq:
									SCREAMING_SNAKE_CASE						= start[current_token]

						SCREAMING_SNAKE_CASE						= list(start.keys())

						return next_tokens






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= self.next_tokens(a)

						return len(a) == 0






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= list(root.values())
						if len(a) == 0:
									return 1
						else:
									return sum([self.count_leaves(a) for nn in next_nodes])







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.count_leaves(a)
						return len(a) != leaf_count



class 		_snake_case  (  A__					):






			def __init__(       self		,					a)      ->       Dict:
						super(a		,					self).__init__()

						if not isinstance(a		,					a) or len(a) == 0:
									raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''')
						if any(not isinstance(a		,					a) 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(a		,					a) 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}.''')

						SCREAMING_SNAKE_CASE						= DisjunctiveTrie(a)
						SCREAMING_SNAKE_CASE						= nested_token_ids

						SCREAMING_SNAKE_CASE						= self.trie.max_height
						SCREAMING_SNAKE_CASE						= []
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.trie.next_tokens(self.current_seq)

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[str]:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(a)}''')

						SCREAMING_SNAKE_CASE						= self.trie.next_tokens(self.current_seq)

						return token_id in next_tokens






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       List[Any]:
						if not isinstance(a		,					a):
									raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(a)}''')

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= False

						if self.does_advance(a):
									self.current_seq.append(a)
									SCREAMING_SNAKE_CASE						= True
						else:
									SCREAMING_SNAKE_CASE						= True
									self.reset()

						SCREAMING_SNAKE_CASE						= self.trie.reached_leaf(self.current_seq)
						SCREAMING_SNAKE_CASE						= completed

						return stepped, completed, reset






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= []






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







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=False)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= DisjunctiveConstraint(self.token_ids)

						if stateful:
									SCREAMING_SNAKE_CASE						= self.seqlen
									SCREAMING_SNAKE_CASE						= self.current_seq
									SCREAMING_SNAKE_CASE						= self.completed

						return new_constraint



class 		_snake_case  :






			def __init__(       self		,					a)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= constraints

						# max # of steps required to fulfill a given constraint
						SCREAMING_SNAKE_CASE						= max([c.seqlen for c in constraints])
						SCREAMING_SNAKE_CASE						= len(a)
						SCREAMING_SNAKE_CASE						= False

						self.init_state()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= []
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= [constraint.copy(stateful=a) for constraint in self.constraints]






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= 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    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= []
						if self.inprogress_constraint is None:
									for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
												SCREAMING_SNAKE_CASE						= constraint.advance()
												if isinstance(a		,					a):
															token_list.append(a)
												elif isinstance(a		,					a):
															token_list.extend(a)
						else:
									SCREAMING_SNAKE_CASE						= self.inprogress_constraint.advance()
									if isinstance(a		,					a):
												token_list.append(a)
									elif isinstance(a		,					a):
												token_list.extend(a)

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       int:
						self.init_state()

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

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






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

						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= False, False

						if self.completed:
									SCREAMING_SNAKE_CASE						= True
									SCREAMING_SNAKE_CASE						= 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

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.inprogress_constraint.update(a)
									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=a))
												SCREAMING_SNAKE_CASE						= 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)
												SCREAMING_SNAKE_CASE						= None

												if len(self.pending_constraints) == 0:
															# we're done!
															SCREAMING_SNAKE_CASE						= 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(a):
															SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= pending_constraint.update(a)

															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(a)
																		SCREAMING_SNAKE_CASE						= None

															if not complete and stepped:
																		SCREAMING_SNAKE_CASE						= pending_constraint

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

																		SCREAMING_SNAKE_CASE						= (
																		    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.

																					SCREAMING_SNAKE_CASE						= True

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

						return complete, stepped







			def    SCREAMING_SNAKE_CASE__    (       self		,					a=True)      ->       str:
						SCREAMING_SNAKE_CASE						= ConstraintListState(self.constraints)  # we actually never though self.constraints objects
						# throughout this process. So it's at initialization state.

						if stateful:
									SCREAMING_SNAKE_CASE						= [
									    constraint.copy(stateful=a) for constraint in self.complete_constraints
									]
									if self.inprogress_constraint is not None:
												SCREAMING_SNAKE_CASE						= self.inprogress_constraint.copy(stateful=a)
									SCREAMING_SNAKE_CASE						= [constraint.copy() for constraint in self.pending_constraints]

						return new_state




