






from __future__ import annotations

import unittest

from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import (
				    TFFunnelBaseModel,
				    TFFunnelForMaskedLM,
				    TFFunnelForMultipleChoice,
				    TFFunnelForPreTraining,
				    TFFunnelForQuestionAnswering,
				    TFFunnelForSequenceClassification,
				    TFFunnelForTokenClassification,
				    TFFunnelModel,
				)



class 		_snake_case  :






			def __init__(       self		,					a		,					a=13		,					a=7		,					a=True		,					a=True		,					a=True		,					a=True		,					a=99		,					a=[1, 1, 2]		,					a=1		,					a=32		,					a=4		,					a=8		,					a=37		,					a="gelu_new"		,					a=0.1		,					a=0.1		,					a=0.0		,					a=512		,					a=3		,					a=0.02		,					a=3		,					a=4		,					a=None		,					a=False		,					)      ->       str:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= seq_length
						SCREAMING_SNAKE_CASE						= is_training
						SCREAMING_SNAKE_CASE						= use_input_mask
						SCREAMING_SNAKE_CASE						= use_token_type_ids
						SCREAMING_SNAKE_CASE						= use_labels
						SCREAMING_SNAKE_CASE						= vocab_size
						SCREAMING_SNAKE_CASE						= block_sizes
						SCREAMING_SNAKE_CASE						= num_decoder_layers
						SCREAMING_SNAKE_CASE						= d_model
						SCREAMING_SNAKE_CASE						= n_head
						SCREAMING_SNAKE_CASE						= d_head
						SCREAMING_SNAKE_CASE						= d_inner
						SCREAMING_SNAKE_CASE						= hidden_act
						SCREAMING_SNAKE_CASE						= hidden_dropout
						SCREAMING_SNAKE_CASE						= attention_dropout
						SCREAMING_SNAKE_CASE						= activation_dropout
						SCREAMING_SNAKE_CASE						= max_position_embeddings
						SCREAMING_SNAKE_CASE						= type_vocab_size
						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= num_labels
						SCREAMING_SNAKE_CASE						= num_choices
						SCREAMING_SNAKE_CASE						= scope
						SCREAMING_SNAKE_CASE						= initializer_std

						# Used in the tests to check the size of the first attention layer
						SCREAMING_SNAKE_CASE						= n_head
						# Used in the tests to check the size of the first hidden state
						SCREAMING_SNAKE_CASE						= self.d_model
						# Used in the tests to check the number of output hidden states/attentions
						SCREAMING_SNAKE_CASE						= sum(self.block_sizes) + (0 if base else self.num_decoder_layers)
						# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
						# the last hidden state of the first block (which is the first hidden state of the decoder).
						if not base:
									SCREAMING_SNAKE_CASE						= self.num_hidden_layers + 2






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size)

						SCREAMING_SNAKE_CASE						= None
						if self.use_input_mask:
									SCREAMING_SNAKE_CASE						= random_attention_mask([self.batch_size, self.seq_length])

						SCREAMING_SNAKE_CASE						= None
						if self.use_token_type_ids:
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length]		,					self.type_vocab_size)

						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= None
						if self.use_labels:
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size]		,					self.type_sequence_label_size)
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length]		,					self.num_labels)
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size]		,					self.num_choices)

						SCREAMING_SNAKE_CASE						= FunnelConfig(
						    vocab_size=self.vocab_size		,					block_sizes=self.block_sizes		,					num_decoder_layers=self.num_decoder_layers		,					d_model=self.d_model		,					n_head=self.n_head		,					d_head=self.d_head		,					d_inner=self.d_inner		,					hidden_act=self.hidden_act		,					hidden_dropout=self.hidden_dropout		,					attention_dropout=self.attention_dropout		,					activation_dropout=self.activation_dropout		,					max_position_embeddings=self.max_position_embeddings		,					type_vocab_size=self.type_vocab_size		,					initializer_std=self.initializer_std		,					)

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= TFFunnelModel(config=__A)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
						SCREAMING_SNAKE_CASE						= model(__A)

						SCREAMING_SNAKE_CASE						= [input_ids, input_mask]
						SCREAMING_SNAKE_CASE						= model(__A)

						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.d_model))

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= TFFunnelModel(config=__A)
						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.d_model))

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= TFFunnelModel(config=__A)
						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.d_model))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Any:
						SCREAMING_SNAKE_CASE						= TFFunnelBaseModel(config=__A)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
						SCREAMING_SNAKE_CASE						= model(__A)

						SCREAMING_SNAKE_CASE						= [input_ids, input_mask]
						SCREAMING_SNAKE_CASE						= model(__A)

						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, 2, self.d_model))

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= TFFunnelBaseModel(config=__A)
						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, 3, self.d_model))

						SCREAMING_SNAKE_CASE						= False
						SCREAMING_SNAKE_CASE						= TFFunnelBaseModel(config=__A)
						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, 2, self.d_model))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= TFFunnelForPreTraining(config=__A)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= TFFunnelForMaskedLM(config=__A)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.num_labels
						SCREAMING_SNAKE_CASE						= TFFunnelForSequenceClassification(config=__A)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_labels))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.num_choices
						SCREAMING_SNAKE_CASE						= TFFunnelForMultipleChoice(config=__A)
						SCREAMING_SNAKE_CASE						= tf.tile(tf.expand_dims(__A		,					1)		,					(1, self.num_choices, 1))
						SCREAMING_SNAKE_CASE						= tf.tile(tf.expand_dims(__A		,					1)		,					(1, self.num_choices, 1))
						SCREAMING_SNAKE_CASE						= tf.tile(tf.expand_dims(__A		,					1)		,					(1, self.num_choices, 1))
						SCREAMING_SNAKE_CASE						= {
						    'input_ids': multiple_choice_inputs_ids,
						    'attention_mask': multiple_choice_input_mask,
						    'token_type_ids': multiple_choice_token_type_ids,
						}
						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_choices))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.num_labels
						SCREAMING_SNAKE_CASE						= TFFunnelForTokenClassification(config=__A)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= TFFunnelForQuestionAnswering(config=__A)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
						SCREAMING_SNAKE_CASE						= model(__A)
						self.parent.assertEqual(result.start_logits.shape		,					(self.batch_size, self.seq_length))
						self.parent.assertEqual(result.end_logits.shape		,					(self.batch_size, self.seq_length))







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE				
						)				,						(
						    SCREAMING_SNAKE_CASE				
						)				,						(
						    SCREAMING_SNAKE_CASE				
						)				,						(
						    SCREAMING_SNAKE_CASE				
						)				,						(
						    SCREAMING_SNAKE_CASE				
						)				,						(
						    SCREAMING_SNAKE_CASE				
						)				,						(
						    SCREAMING_SNAKE_CASE				
						)				,										
						)						= config_and_inputs
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
						return config, inputs_dict



@require_tf
class 		_snake_case  (  __lowercase   ,    __lowercase   ,    unittest.TestCase					):
			_lowercase		:    Tuple			     =							(
			    (
			        TFFunnelModel,
			        TFFunnelForMaskedLM,
			        TFFunnelForPreTraining,
			        TFFunnelForQuestionAnswering,
			        TFFunnelForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			_lowercase		:    Union[str, Any]			     =							(
			    {
			        'feature-extraction': (TFFunnelBaseModel, TFFunnelModel),
			        'fill-mask': TFFunnelForMaskedLM,
			        'question-answering': TFFunnelForQuestionAnswering,
			        'text-classification': TFFunnelForSequenceClassification,
			        'token-classification': TFFunnelForTokenClassification,
			        'zero-shot': TFFunnelForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			_lowercase		:    Tuple			     =							False
			_lowercase		:    Union[str, Any]			     =							False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= TFFunnelModelTester(self)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=__A)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						self.config_tester.run_common_tests()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__A)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__A)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__A)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__A)



@require_tf
class 		_snake_case  (  __lowercase   ,    unittest.TestCase					):
			_lowercase		:    Tuple			     =							(
			    (TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
			)
			_lowercase		:    List[Any]			     =							False
			_lowercase		:    Optional[Any]			     =							False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= TFFunnelModelTester(self		,					base=__A)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=__A)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						self.config_tester.run_common_tests()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_base_model(*__A)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__A)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*__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




