
import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
			import tensorflow as tf

			from transformers import (
			    TFEfficientFormerForImageClassification,
			    TFEfficientFormerForImageClassificationWithTeacher,
			    TFEfficientFormerModel,
			)
			from transformers.models.efficientformer.modeling_tf_efficientformer import (
			    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)


if is_vision_available():
			from PIL import Image

			from transformers import EfficientFormerImageProcessor


class        lowercase      :



		def __init__(				self       ,   A_       ,   A_ = 13       ,   A_ = 64       ,   A_ = 2       ,   A_ = 3       ,   A_ = 3       ,   A_ = True       ,   A_ = True       ,   A_ = 128       ,   A_=[16, 32, 64, 128]       ,   A_ = 7       ,   A_ = 4       ,   A_ = 37       ,   A_ = "gelu"       ,   A_ = 0.1       ,   A_ = 0.1       ,   A_ = 10       ,   A_ = 0.02       ,   A_ = 2       ,   A_ = 1       ,   A_ = 128       ,   A_ = [2, 2, 2, 2]       ,   A_ = 2       ,   A_ = 2       ,   )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  parent
				UpperCamelCase						=  batch_size
				UpperCamelCase						=  image_size
				UpperCamelCase						=  patch_size
				UpperCamelCase						=  num_channels
				UpperCamelCase						=  is_training
				UpperCamelCase						=  use_labels
				UpperCamelCase						=  hidden_size
				UpperCamelCase						=  num_hidden_layers
				UpperCamelCase						=  num_attention_heads
				UpperCamelCase						=  intermediate_size
				UpperCamelCase						=  hidden_act
				UpperCamelCase						=  hidden_dropout_prob
				UpperCamelCase						=  attention_probs_dropout_prob
				UpperCamelCase						=  type_sequence_label_size
				UpperCamelCase						=  initializer_range
				UpperCamelCase						=  encoder_stride
				UpperCamelCase						=  num_attention_outputs
				UpperCamelCase						=  embed_dim
				UpperCamelCase						=  embed_dim + 1
				UpperCamelCase						=  resolution
				UpperCamelCase						=  depths
				UpperCamelCase						=  hidden_sizes
				UpperCamelCase						=  dim
				UpperCamelCase						=  mlp_expansion_ratio



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

				UpperCamelCase						=  None
				if self.use_labels:
						UpperCamelCase						=  ids_tensor([self.batch_size]       ,   self.type_sequence_label_size )

				UpperCamelCase						=  self.get_config()

				return config, pixel_values, labels



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				return EfficientFormerConfig(
				    image_size=self.image_size       ,   patch_size=self.patch_size       ,   num_channels=self.num_channels       ,   hidden_size=self.hidden_size       ,   num_hidden_layers=self.num_hidden_layers       ,   num_attention_heads=self.num_attention_heads       ,   intermediate_size=self.intermediate_size       ,   hidden_act=self.hidden_act       ,   hidden_dropout_prob=self.hidden_dropout_prob       ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,   is_decoder=A_       ,   initializer_range=self.initializer_range       ,   encoder_stride=self.encoder_stride       ,   resolution=self.resolution       ,   depths=self.depths       ,   hidden_sizes=self.hidden_sizes       ,   dim=self.dim       ,   mlp_expansion_ratio=self.mlp_expansion_ratio       ,   )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  TFEfficientFormerModel(config=A_ )
				UpperCamelCase						=  model(A_       ,   training=A_ )
				self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size) )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  self.type_sequence_label_size
				UpperCamelCase						=  TFEfficientFormerForImageClassification(A_ )
				UpperCamelCase						=  model(A_       ,   labels=A_       ,   training=A_ )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.type_sequence_label_size) )

				# test greyscale images
				UpperCamelCase						=  1
				UpperCamelCase						=  TFEfficientFormerForImageClassification(A_ )

				UpperCamelCase						=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
				UpperCamelCase						=  model(A_       ,   labels=A_ )
				self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.type_sequence_label_size) )



		def __UpperCamelCase				(				self )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  config_and_inputs
				UpperCamelCase						=  {'pixel_values': pixel_values}
				return config, inputs_dict






@require_tf
class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):
		__lowercase : List[Any]			  =       (
		    (
		        TFEfficientFormerModel,
		        TFEfficientFormerForImageClassificationWithTeacher,
		        TFEfficientFormerForImageClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		__lowercase : Optional[int]			  =       (
		    {
		        "feature-extraction": TFEfficientFormerModel,
		        "image-classification": (
		            TFEfficientFormerForImageClassification,
		            TFEfficientFormerForImageClassificationWithTeacher,
		        ),
		    }
		    if is_tf_available()
		    else {}
		)

		__lowercase : List[Any]			  =       False

		__lowercase : int			  =       False
		__lowercase : int			  =       False
		__lowercase : str			  =       False
		__lowercase : Dict			  =       False



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  TFEfficientFormerModelTester(self )
				UpperCamelCase						=  ConfigTester(
				    self       ,   config_class=A_       ,   has_text_modality=A_       ,   hidden_size=37 )



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		@unittest.skip(reason='EfficientFormer does not use inputs_embeds' )
		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				pass



		@unittest.skip(reason='EfficientFormer does not support input and output embeddings' )
		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase						=  model_class(A_ )
						UpperCamelCase						=  inspect.signature(model.call )
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						UpperCamelCase						=  [*signature.parameters.keys()]

						UpperCamelCase						=  ['pixel_values']
						self.assertListEqual(arg_names[:1]       ,   A_ )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				def check_hidden_states_output(A_       ,   A_       ,   A_ ):
						UpperCamelCase						=  model_class(A_ )

						UpperCamelCase						=  model(**self._prepare_for_class(A_       ,   A_ )       ,   training=A_ )
						UpperCamelCase						=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						UpperCamelCase						=  getattr(
						    self.model_tester       ,   'expected_num_hidden_layers'       ,   self.model_tester.num_hidden_layers + 1 )
						self.assertEqual(len(A_ )       ,   A_ )

						if hasattr(self.model_tester       ,   'encoder_seq_length' ):
								UpperCamelCase						=  self.model_tester.encoder_seq_length
								if hasattr(self.model_tester       ,   'chunk_length' ) and self.model_tester.chunk_length > 1:
										UpperCamelCase						=  seq_length * self.model_tester.chunk_length
						else:
								UpperCamelCase						=  self.model_tester.seq_length

						self.assertListEqual(
						    list(hidden_states[-1].shape[-2:] )       ,   [seq_length, self.model_tester.hidden_size]       ,   )

						if config.is_encoder_decoder:
								UpperCamelCase						=  outputs.decoder_hidden_states

								self.asseretIsInstance(A_       ,   (list, tuple) )
								self.assertEqual(len(A_ )       ,   A_ )
								UpperCamelCase						=  getattr(self.model_tester       ,   'seq_length'       ,   A_ )
								UpperCamelCase						=  getattr(self.model_tester       ,   'decoder_seq_length'       ,   A_ )

								self.assertListEqual(
								    list(hidden_states[-1].shape[-2:] )       ,   [decoder_seq_length, self.model_tester.hidden_size]       ,   )

				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase						=  True
						check_hidden_states_output(A_       ,   A_       ,   A_ )

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						UpperCamelCase						=  True

						check_hidden_states_output(A_       ,   A_       ,   A_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_=False )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  super()._prepare_for_class(A_       ,   A_       ,   return_labels=A_ )

				if return_labels:
						if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
								del inputs_dict["labels"]

				return inputs_dict



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_ )



		@unittest.skip(reason='EfficientFormer does not implement masked image modeling yet' )
		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*A_ )



		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_ )



		@slow
		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase						=  TFEfficientFormerModel.from_pretrained(A_ )
						self.assertIsNotNone(A_ )



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()
				UpperCamelCase						=  True

				UpperCamelCase						=  getattr(self.model_tester       ,   'seq_length'       ,   A_ )
				UpperCamelCase						=  getattr(self.model_tester       ,   'encoder_seq_length'       ,   A_ )
				UpperCamelCase						=  getattr(self.model_tester       ,   'key_length'       ,   A_ )
				UpperCamelCase						=  getattr(self.model_tester       ,   'chunk_length'       ,   A_ )

				if chunk_length is not None and hasattr(self.model_tester       ,   'num_hashes' ):
						UpperCamelCase						=  encoder_seq_length * self.model_tester.num_hashes

				for model_class in self.all_model_classes:
						UpperCamelCase						=  True
						UpperCamelCase						=  False
						UpperCamelCase						=  True
						UpperCamelCase						=  model_class(A_ )

						UpperCamelCase						=  model(**self._prepare_for_class(A_       ,   A_ )       ,   training=A_ )
						UpperCamelCase						=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
						self.assertEqual(len(A_ )       ,   self.model_tester.num_attention_outputs )

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						UpperCamelCase						=  True
						UpperCamelCase						=  model_class(A_ )
						UpperCamelCase						=  model(**self._prepare_for_class(A_       ,   A_ )       ,   training=A_ )

						UpperCamelCase						=  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
						self.assertEqual(len(A_ )       ,   self.model_tester.num_attention_outputs )

						if chunk_length is not None:
								self.assertListEqual(
								    list(attentions[0].shape[-4:] )       ,   [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]       ,   )
						else:
								self.assertListEqual(
								    list(attentions[0].shape[-3:] )       ,   [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,   )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						# Prepare our model
						UpperCamelCase						=  model_class(A_ )
						# These are maximally general inputs for the model, with multiple None dimensions
						# Hopefully this will catch any conditionals that fail for flexible shapes
						UpperCamelCase						=  {
						    key: tf.keras.Input(shape=val.shape[1:]       ,   dtype=val.dtype       ,   name=A_ )
						    for key, val in model.input_signature.items()
						    if key in model.dummy_inputs
						}
						UpperCamelCase						=  model(A_ )
						self.assertTrue(outputs_dict is not None )






def    A (      )     ->						Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase						=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
		return image


@require_tf
@require_vision
class        lowercase      (	unittest.TestCase     ):



		@cached_property
		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return (
				    EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300' )
				    if is_vision_available()
				    else None
				)



		@slow
		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300' )
				UpperCamelCase						=  self.default_image_processor
				UpperCamelCase						=  prepare_img()
				UpperCamelCase						=  image_processor(images=A_       ,   return_tensors='tf' )
				# forward pass
				UpperCamelCase						=  model(**A_       ,   training=A_ )
				# verify the logits
				UpperCamelCase						=  tf.TensorShape((1, 1_000) )
				self.assertEqual(outputs.logits.shape       ,   A_ )
				UpperCamelCase						=  tf.constant([-0.0555, 0.4825, -0.0852] )
				self.assertTrue(np.allclose(outputs.logits[0, :3]       ,   A_       ,   atol=1e-4 ) )



		@slow
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				UpperCamelCase						=  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
				    'snap-research/efficientformer-l1-300' )
				UpperCamelCase						=  self.default_image_processor
				UpperCamelCase						=  prepare_img()
				UpperCamelCase						=  image_processor(images=A_       ,   return_tensors='tf' )
				# forward pass
				UpperCamelCase						=  model(**A_       ,   training=A_ )
				# verify the logits
				UpperCamelCase						=  tf.TensorShape((1, 1_000) )
				self.assertEqual(outputs.logits.shape       ,   A_ )
				UpperCamelCase						=  tf.constant([-0.1312, 0.4353, -1.0499] )
				self.assertTrue(np.allclose(outputs.logits[0, :3]       ,   A_       ,   atol=1e-4 ) )


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


class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



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



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  0
				UpperCamelCase						=  False
				while not completed:
						if counter == 1:
								self.reset()
						UpperCamelCase						=  self.advance()
						if not self.does_advance(A_ ):
								raise Exception(
								    'Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.' )

						UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  self.update(A_ )
						counter += 1

						if counter > 10_000:
								raise Exception('update() does not fulfill the constraint.' )

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



		@abstractmethod
		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )



		@abstractmethod
		def __UpperCamelCase				(				self       ,   A_ )						->      str:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )



		@abstractmethod
		def __UpperCamelCase				(				self       ,   A_ )						->      int:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )



		@abstractmethod
		def __UpperCamelCase				(				self )						->      Any:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )



		@abstractmethod
		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )



		@abstractmethod
		def __UpperCamelCase				(				self       ,   A_=False )						->      int:
				"""simple docstring"""
				raise NotImplementedError(
				    F'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_ )						->      Any:
				"""simple docstring"""
				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}.''' )

				UpperCamelCase						=  token_ids

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



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



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				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 __UpperCamelCase				(				self       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				if not isinstance(A_       ,   A_ ):
						raise ValueError(F'''`token_id` has to be an `int`, but is {token_id} of type {type(A_ )}''' )

				UpperCamelCase						=  False
				UpperCamelCase						=  False
				UpperCamelCase						=  False

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



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  False
				UpperCamelCase						=  0



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				return self.seqlen - (self.fulfilled_idx + 1)



		def __UpperCamelCase				(				self       ,   A_=False )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  PhrasalConstraint(self.token_ids )

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

				return new_constraint






class        lowercase      :



		def __init__(				self       ,   A_       ,   A_=True )						->      List[Any]:
				"""simple docstring"""
				UpperCamelCase						=  max([len(A_ ) for one in nested_token_ids] )

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

								UpperCamelCase						=  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}.''' )

				UpperCamelCase						=  root



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.trie

				for current_token in current_seq:
						UpperCamelCase						=  start[current_token]

				UpperCamelCase						=  list(start.keys() )

				return next_tokens



		def __UpperCamelCase				(				self       ,   A_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.next_tokens(A_ )

				return len(A_ ) == 0



		def __UpperCamelCase				(				self       ,   A_ )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  list(root.values() )
				if len(A_ ) == 0:
						return 1
				else:
						return sum([self.count_leaves(A_ ) for nn in next_nodes] )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  self.count_leaves(A_ )
				return len(A_ ) != leaf_count






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



		def __init__(				self       ,   A_ )						->      str:
				"""simple docstring"""
				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}.''' )

				UpperCamelCase						=  DisjunctiveTrie(A_ )
				UpperCamelCase						=  nested_token_ids

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



		def __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.trie.next_tokens(self.current_seq )

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



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				if not isinstance(A_       ,   A_ ):
						raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(A_ )}''' )

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

				return token_id in next_tokens



		def __UpperCamelCase				(				self       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				if not isinstance(A_       ,   A_ ):
						raise ValueError(F'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(A_ )}''' )

				UpperCamelCase						=  False
				UpperCamelCase						=  False
				UpperCamelCase						=  False

				if self.does_advance(A_ ):
						self.current_seq.append(A_ )
						UpperCamelCase						=  True
				else:
						UpperCamelCase						=  True
						self.reset()

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

				return stepped, completed, reset



		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				UpperCamelCase						=  False
				UpperCamelCase						=  []



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				if self.completed:
						# since this can be completed without reaching max height
						return 0
				else:
						return self.seqlen - len(self.current_seq )



		def __UpperCamelCase				(				self       ,   A_=False )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  DisjunctiveConstraint(self.token_ids )

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

				return new_constraint






class        lowercase      :



		def __init__(				self       ,   A_ )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  constraints

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

				self.init_state()



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  []
				UpperCamelCase						=  None
				UpperCamelCase						=  [constraint.copy(stateful=A_ ) for constraint in self.constraints]



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  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 __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  []
				if self.inprogress_constraint is None:
						for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
								UpperCamelCase						=  constraint.advance()
								if isinstance(A_       ,   A_ ):
										token_list.append(A_ )
								elif isinstance(A_       ,   A_ ):
										token_list.extend(A_ )
				else:
						UpperCamelCase						=  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 __UpperCamelCase				(				self       ,   A_ )						->      Any:
				"""simple docstring"""
				self.init_state()

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

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



		def __UpperCamelCase				(				self       ,   A_ )						->      int:
				"""simple docstring"""
				if not isinstance(A_       ,   A_ ):
						raise ValueError(F'''`token_id` should be an `int`, but is `{token_id}`.''' )

				UpperCamelCase			,						UpperCamelCase						=  False, False

				if self.completed:
						UpperCamelCase						=  True
						UpperCamelCase						=  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						=  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_ ) )
								UpperCamelCase						=  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						=  None

								if len(self.pending_constraints ) == 0:
										# we're done!
										UpperCamelCase						=  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_ ):
										UpperCamelCase			,						UpperCamelCase			,						UpperCamelCase						=  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_ )
												UpperCamelCase						=  None

										if not complete and stepped:
												UpperCamelCase						=  pending_constraint

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

												UpperCamelCase						=  (
												    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						=  True

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

				return complete, stepped



		def __UpperCamelCase				(				self       ,   A_=True )						->      Tuple:
				"""simple docstring"""
				UpperCamelCase						=  ConstraintListState(self.constraints )  # we actually never though self.constraints objects
				# throughout this process. So it's at initialization state.

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

				return new_state
