code: 
  
  
  '''simple docstring'''
  
  import unittest
  from unittest.util import safe_repr
  
  from transformers import AutoTokenizer, RwkvConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
  							import torch
  
  							from transformers import (
  							    RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
  							    RwkvForCausalLM,
  							    RwkvModel,
  							)
  							from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
  else:
  							__lowercase:		Tuple							=     False
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
  	def __init__( self :	List[Any],					a_ :	List[Any],					a_ :	List[str]=14,					a_ :	int=7,					a_ :	Union[str, Any]=True,					a_ :	Optional[int]=False,					a_ :	Optional[Any]=True,					a_ :	List[str]=True,					a_ :	Any=True,					a_ :	List[str]=99,					a_ :	Optional[Any]=32,					a_ :	int=5,					a_ :	Optional[Any]=37,					a_ :	Union[str, Any]="gelu",					a_ :	Any=0.1,					a_ :	List[Any]=0.1,					a_ :	Union[str, Any]=512,					a_ :	str=16,					a_ :	List[str]=2,					a_ :	Optional[int]=3,					a_ :	Tuple=4,					a_ :	Tuple=None,					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						parent
  				UpperCamelCase__ 						=						batch_size
  				UpperCamelCase__ 						=						seq_length
  				UpperCamelCase__ 						=						is_training
  				UpperCamelCase__ 						=						use_token_type_ids
  				UpperCamelCase__ 						=						use_input_mask
  				UpperCamelCase__ 						=						use_labels
  				UpperCamelCase__ 						=						use_mc_token_ids
  				UpperCamelCase__ 						=						vocab_size
  				UpperCamelCase__ 						=						hidden_size
  				UpperCamelCase__ 						=						num_hidden_layers
  				UpperCamelCase__ 						=						intermediate_size
  				UpperCamelCase__ 						=						hidden_act
  				UpperCamelCase__ 						=						hidden_dropout_prob
  				UpperCamelCase__ 						=						attention_probs_dropout_prob
  				UpperCamelCase__ 						=						max_position_embeddings
  				UpperCamelCase__ 						=						type_vocab_size
  				UpperCamelCase__ 						=						type_sequence_label_size
  				UpperCamelCase__ 						=						num_labels
  				UpperCamelCase__ 						=						num_choices
  				UpperCamelCase__ 						=						scope
  				UpperCamelCase__ 						=						vocab_size - 1
  				UpperCamelCase__ 						=						vocab_size - 1
  				UpperCamelCase__ 						=						vocab_size - 1
  
  	def 		lowercase_						( self :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b"					)
  
  	def 		lowercase_						( self :	Any,					a_ :	List[str]=False,					a_ :	str=False,					a_ :	Dict=False					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						ids_tensor([self.batch_size, self.seq_length],					self.vocab_size					)
  
  				UpperCamelCase__ 						=						None
  				if self.use_input_mask:
  							UpperCamelCase__ 						=						random_attention_mask([self.batch_size, self.seq_length]					)
  
  				UpperCamelCase__ 						=						None
  				if self.use_token_type_ids:
  							UpperCamelCase__ 						=						ids_tensor([self.batch_size, self.seq_length],					self.type_vocab_size					)
  
  				UpperCamelCase__ 						=						None
  				if self.use_mc_token_ids:
  							UpperCamelCase__ 						=						ids_tensor([self.batch_size, self.num_choices],					self.seq_length					)
  
  				UpperCamelCase__ 						=						None
  				UpperCamelCase__ 						=						None
  				UpperCamelCase__ 						=						None
  				if self.use_labels:
  							UpperCamelCase__ 						=						ids_tensor([self.batch_size],					self.type_sequence_label_size					)
  							UpperCamelCase__ 						=						ids_tensor([self.batch_size, self.seq_length],					self.num_labels					)
  							UpperCamelCase__ 						=						ids_tensor([self.batch_size],					self.num_choices					)
  
  				UpperCamelCase__ 						=						self.get_config(
  				    gradient_checkpointing=lowerCAmelCase_,					scale_attn_by_inverse_layer_idx=lowerCAmelCase_,					reorder_and_upcast_attn=lowerCAmelCase_,					)
  
  				return (
  				    config,
  				    input_ids,
  				    input_mask,
  				    None,
  				    token_type_ids,
  				    mc_token_ids,
  				    sequence_labels,
  				    token_labels,
  				    choice_labels,
  				)
  
  	def 		lowercase_						( self :	str,					a_ :	List[str]=False,					a_ :	Any=False,					a_ :	Optional[Any]=False					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return RwkvConfig(
  				    vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					intermediate_size=self.intermediate_size,					activation_function=self.hidden_act,					resid_pdrop=self.hidden_dropout_prob,					attn_pdrop=self.attention_probs_dropout_prob,					n_positions=self.max_position_embeddings,					type_vocab_size=self.type_vocab_size,					use_cache=lowerCAmelCase_,					bos_token_id=self.bos_token_id,					eos_token_id=self.eos_token_id,					pad_token_id=self.pad_token_id,					gradient_checkpointing=lowerCAmelCase_,					scale_attn_by_inverse_layer_idx=lowerCAmelCase_,					reorder_and_upcast_attn=lowerCAmelCase_,					)
  
  	def 		lowercase_						( self :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.get_config()
  				UpperCamelCase__ 						=						300
  				return config
  
  	def 		lowercase_						( self :	Dict					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				(
  				    (
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		
  				) 						=						self.prepare_config_and_inputs()
  
  				UpperCamelCase__ 						=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
  				UpperCamelCase__ 						=						ids_tensor([self.batch_size, self.seq_length],					vocab_size=2					)
  
  				return (
  				    config,
  				    input_ids,
  				    input_mask,
  				    head_mask,
  				    token_type_ids,
  				    sequence_labels,
  				    token_labels,
  				    choice_labels,
  				    encoder_hidden_states,
  				    encoder_attention_mask,
  				)
  
  	def 		lowercase_						( self :	str,					a_ :	Optional[int],					a_ :	List[str],					a_ :	Any,					a_ :	Any,					a_ :	Tuple,					*a_ :	Dict					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						True
  				UpperCamelCase__ 						=						RwkvModel(config=lowerCAmelCase_					)
  				model.to(lowerCAmelCase_					)
  				model.eval()
  
  				UpperCamelCase__ 						=						model(lowerCAmelCase_					)
  
  				self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)					)
  				self.parent.assertEqual(len(result.hidden_states					),					config.num_hidden_layers + 1					)
  
  	def 		lowercase_						( self :	Optional[int],					a_ :	List[str],					a_ :	Optional[int],					a_ :	Dict,					a_ :	Optional[Any],					a_ :	Optional[Any],					*a_ :	List[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						RwkvForCausalLM(lowerCAmelCase_					)
  				model.to(lowerCAmelCase_					)
  				model.eval()
  
  				UpperCamelCase__ 						=						model(lowerCAmelCase_,					labels=lowerCAmelCase_					)
  				self.parent.assertEqual(result.loss.shape,					()					)
  				self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)					)
  
  	def 		lowercase_						( self :	Dict,					a_ :	Optional[Any],					a_ :	Dict,					a_ :	Optional[int],					a_ :	Optional[int],					a_ :	int,					*a_ :	Tuple					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						RwkvModel(config=lowerCAmelCase_					)
  				model.to(lowerCAmelCase_					)
  				model.eval()
  
  				UpperCamelCase__ 						=						model(lowerCAmelCase_					)
  				UpperCamelCase__ 						=						outputs.last_hidden_state
  
  				UpperCamelCase__ 						=						model(input_ids[:, :2]					)
  				UpperCamelCase__ 						=						outputs.last_hidden_state
  
  				# Using the state computed on the first inputs, we will get the same output
  				UpperCamelCase__ 						=						model(input_ids[:, 2:],					state=outputs.state					)
  				UpperCamelCase__ 						=						outputs.last_hidden_state
  
  				self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two],					dim=1					),					lowerCAmelCase_,					atol=1e-5					)					)
  
  	def 		lowercase_						( self :	Union[str, Any],					a_ :	int,					a_ :	Tuple,					a_ :	List[Any],					a_ :	List[str],					a_ :	Union[str, Any],					*a_ :	int,					a_ :	str=False					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						RwkvForCausalLM(lowerCAmelCase_					)
  				model.to(lowerCAmelCase_					)
  				if gradient_checkpointing:
  							model.gradient_checkpointing_enable()
  
  				UpperCamelCase__ 						=						model(lowerCAmelCase_,					labels=lowerCAmelCase_					)
  				self.parent.assertEqual(result.loss.shape,					()					)
  				self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)					)
  				result.loss.backward()
  
  	def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.prepare_config_and_inputs()
  
  				(
  				    (
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		(
  				    UpperCamelCase__
  				)						,		
  				) 						=						config_and_inputs
  
  				UpperCamelCase__ 						=						{"input_ids": input_ids}
  
  				return config, inputs_dict
  
  
  
  
  
  
  
  @unittest.skipIf(
      not is_torch_greater_or_equal_than_2_0		,			reason='See https://github.com/huggingface/transformers/pull/24204')
  @require_torch
  class  UpperCAmelCase (       lowerCAmelCase__		,			lowerCAmelCase__		,			lowerCAmelCase__		,			unittest.TestCase):
  	_lowerCamelCase :					str    					=					(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
  	_lowerCamelCase :					Union[str, Any]    					=					(
  	    {"""feature-extraction""": RwkvModel, """text-generation""": RwkvForCausalLM} if is_torch_available() else {}
  	)
  	# all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
  	_lowerCamelCase :					Optional[Any]    					=					False
  	_lowerCamelCase :					Optional[Any]    					=					False
  	_lowerCamelCase :					int    					=					False
  	_lowerCamelCase :					List[Any]    					=					False
  	_lowerCamelCase :					Tuple    					=					False  # Rwkv does not support head masking
  
  	def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						RwkvModelTester(self					)
  				UpperCamelCase__ 						=						ConfigTester(
  				    self,					config_class=lowerCAmelCase_,					n_embd=37,					common_properties=["hidden_size", "num_hidden_layers"]					)
  
  	def 		lowercase_						( self :	Optional[int],					a_ :	List[Any],					a_ :	str,					a_ :	int=None					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if isinstance(lowerCAmelCase_,					torch.Tensor					):
  							UpperCamelCase__						,		UpperCamelCase__ 						=						member.max().item(), member.min().item()
  				elif isinstance(lowerCAmelCase_,					lowerCAmelCase_					) or isinstance(lowerCAmelCase_,					lowerCAmelCase_					):
  							UpperCamelCase__						,		UpperCamelCase__ 						=						max(lowerCAmelCase_					), min(lowerCAmelCase_					)
  
  				if not isinstance(lowerCAmelCase_,					lowerCAmelCase_					):
  							raise TypeError("container should be a list or tuple"					)
  				elif len(lowerCAmelCase_					) != 2:
  							raise ValueError("container should have 2 elements"					)
  
  				UpperCamelCase__						,		UpperCamelCase__ 						=						container
  
  				UpperCamelCase__ 						=						(min_value >= expected_min) and (max_value <= expected_max)
  
  				if not is_inside_interval:
  							UpperCamelCase__ 						=						"%s not found in %s" % (safe_repr(lowerCAmelCase_					), safe_repr(lowerCAmelCase_					))
  							self.fail(self._formatMessage(lowerCAmelCase_,					lowerCAmelCase_					)					)
  
  	def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				self.config_tester.run_common_tests()
  
  	def 		lowercase_						( self :	Dict					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_rwkv_model(*lowerCAmelCase_					)
  
  	def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_causl_lm(*lowerCAmelCase_					)
  
  	def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_state_equivalency(*lowerCAmelCase_					)
  
  	def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__						,		UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs_for_common()
  
  				for model_class in self.all_model_classes:
  							UpperCamelCase__ 						=						model_class(config=lowerCAmelCase_					)
  							for name, param in model.named_parameters():
  										if "time_decay" in name:
  													if param.requires_grad:
  																self.assertTrue(param.data.max().item() == 3.0					)
  																self.assertTrue(param.data.min().item() == -5.0					)
  										elif "time_first" in name:
  													if param.requires_grad:
  																# check if it's a ones like
  																self.assertTrue(torch.allclose(param.data,					torch.ones_like(param.data					),					atol=1e-5,					rtol=1e-5					)					)
  										elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]					):
  													if param.requires_grad:
  																self.assertInterval(
  																    param.data,					[0.0, 1.0],					msg=f'Parameter {name} of model {model_class} seems not properly initialized',					)
  										elif "time_mix_value" in name:
  													if param.requires_grad:
  																self.assertInterval(
  																    param.data,					[0.0, 1.3],					msg=f'Parameter {name} of model {model_class} seems not properly initialized',					)
  
  	def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__						,		UpperCamelCase__ 						=						self.model_tester.prepare_config_and_inputs_for_common()
  				UpperCamelCase__ 						=						True
  
  				UpperCamelCase__ 						=						getattr(self.model_tester,					"seq_length",					lowerCAmelCase_					)
  
  				for model_class in self.all_model_classes:
  							UpperCamelCase__ 						=						True
  							UpperCamelCase__ 						=						False
  							UpperCamelCase__ 						=						True
  							UpperCamelCase__ 						=						model_class(lowerCAmelCase_					)
  							model.to(lowerCAmelCase_					)
  							model.eval()
  
  							UpperCamelCase__ 						=						self._prepare_for_class(lowerCAmelCase_,					lowerCAmelCase_					)
  							UpperCamelCase__ 						=						inputs["input_ids"].shape[0]
  							with torch.no_grad():
  										UpperCamelCase__ 						=						model(**lowerCAmelCase_					)
  							UpperCamelCase__ 						=						outputs.attentions
  							self.assertEqual(len(lowerCAmelCase_					),					self.model_tester.num_hidden_layers					)
  
  							# check that output_attentions also work using config
  							del inputs_dict["output_attentions"]
  							UpperCamelCase__ 						=						True
  							UpperCamelCase__ 						=						model_class(lowerCAmelCase_					)
  							model.to(lowerCAmelCase_					)
  							model.eval()
  
  							UpperCamelCase__ 						=						self._prepare_for_class(lowerCAmelCase_,					lowerCAmelCase_					)
  							UpperCamelCase__ 						=						inputs["input_ids"].shape[0]
  							with torch.no_grad():
  										UpperCamelCase__ 						=						model(**lowerCAmelCase_					)
  							UpperCamelCase__ 						=						outputs.attentions
  							self.assertEqual(len(lowerCAmelCase_					),					self.model_tester.num_hidden_layers					)
  
  							self.assertListEqual(
  							    list(attentions[0].shape[-3:]					),					[batch_size, seq_len, config.hidden_size],					)
  							UpperCamelCase__ 						=						len(lowerCAmelCase_					)
  
  							# Check attention is always last and order is fine
  							UpperCamelCase__ 						=						True
  							UpperCamelCase__ 						=						True
  							UpperCamelCase__ 						=						model_class(lowerCAmelCase_					)
  							model.to(lowerCAmelCase_					)
  							model.eval()
  
  							UpperCamelCase__ 						=						self._prepare_for_class(lowerCAmelCase_,					lowerCAmelCase_					)
  							UpperCamelCase__ 						=						inputs["input_ids"].shape[0]
  							with torch.no_grad():
  										UpperCamelCase__ 						=						model(**lowerCAmelCase_					)
  
  							UpperCamelCase__ 						=						1
  							self.assertEqual(out_len + added_hidden_states,					len(lowerCAmelCase_					)					)
  
  							UpperCamelCase__ 						=						outputs.attentions
  
  							self.assertEqual(len(lowerCAmelCase_					),					self.model_tester.num_hidden_layers					)
  							self.assertListEqual(
  							    list(self_attentions[0].shape[-3:]					),					[batch_size, seq_len, config.hidden_size],					)
  
  	@slow
  	def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  							UpperCamelCase__ 						=						RwkvModel.from_pretrained(lowerCAmelCase_					)
  							self.assertIsNotNone(lowerCAmelCase_					)
  
  
  
  
  
  
  
  @unittest.skipIf(
      not is_torch_greater_or_equal_than_2_0		,			reason='See https://github.com/huggingface/transformers/pull/24204')
  @slow
  class  UpperCAmelCase (       unittest.TestCase):
  
  	def 		lowercase_						( self :	Dict					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						"RWKV/rwkv-4-169m-pile"
  				UpperCamelCase__ 						=						AutoTokenizer.from_pretrained(self.model_id					)
  
  	def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						"Hello my name is Jasmine and I am a newbie to the"
  				UpperCamelCase__ 						=						RwkvForCausalLM.from_pretrained(self.model_id					).to(lowerCAmelCase_					)
  
  				UpperCamelCase__ 						=						self.tokenizer("Hello my name is",					return_tensors="pt"					).input_ids.to(lowerCAmelCase_					)
  				UpperCamelCase__ 						=						model.generate(lowerCAmelCase_,					max_new_tokens=10					)
  				UpperCamelCase__ 						=						self.tokenizer.decode(output[0].tolist()					)
  
  				self.assertEqual(lowerCAmelCase_,					lowerCAmelCase_					)
  
  	def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						"Hello my name is Jasmine and I am a newbie to the"
  
  				UpperCamelCase__ 						=						self.tokenizer("Hello my name is",					return_tensors="pt"					).input_ids.to(lowerCAmelCase_					)
  				UpperCamelCase__ 						=						RwkvForCausalLM.from_pretrained(self.model_id,					torch_dtype=torch.bfloataa					).to(lowerCAmelCase_					)
  
  				UpperCamelCase__ 						=						model.generate(lowerCAmelCase_,					max_new_tokens=10					)
  				UpperCamelCase__ 						=						self.tokenizer.decode(output[0].tolist()					)
  
  				self.assertEqual(lowerCAmelCase_,					lowerCAmelCase_					)
style_context: 
  
  
  '''simple docstring'''
  
  from dataclasses import dataclass
  
  __lowercase:		List[str]							=     "0.1"
  __lowercase:		Union[str, Any]							=     "Lucia Harcekova"
  
  
  
  
  
  
  
  @dataclass
  class  UpperCAmelCase :
   _lowerCamelCase :					int
   _lowerCamelCase :					int
   _lowerCamelCase :					str
  
   def __repr__( self :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return f'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
   def __init__( self :	Optional[int],					a_ :	int = 13,					a_ :	int = 6					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						window_size
      UpperCamelCase__ 						=						lookahead_buffer_size
      UpperCamelCase__ 						=						self.window_size - self.lookahead_buffer_size
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      UpperCamelCase__ 						=						""
  
      # while there are still characters in text to compress
      while text:
         # find the next encoding phrase
         # - triplet with offset, length, indicator (the next encoding character)
         UpperCamelCase__ 						=						self._find_encoding_token(a_,					a_					)
  
         # update the search buffer:
         # - add new characters from text into it
         # - check if size exceed the max search buffer size, if so, drop the
         #   oldest elements
         search_buffer += text[: token.length + 1]
         if len(a_					) > self.search_buffer_size:
            UpperCamelCase__ 						=						search_buffer[-self.search_buffer_size :]
  
         # update the text
         UpperCamelCase__ 						=						text[token.length + 1 :]
  
         # append the token to output
         output.append(a_					)
  
      return output
  
   def 		lowercase_						( self :	List[Any],					a_ :	list[Token]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						""
  
      for token in tokens:
         for _ in range(token.length					):
            output += output[-token.offset]
         output += token.indicator
  
      return output
  
   def 		lowercase_						( self :	Any,					a_ :	str,					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text:
         raise ValueError("We need some text to work with."					)
  
      # Initialise result parameters to default values
      UpperCamelCase__						,		UpperCamelCase__ 						=						0, 0
  
      if not search_buffer:
         return Token(a_,					a_,					text[length]					)
  
      for i, character in enumerate(a_					):
         UpperCamelCase__ 						=						len(a_					) - i
         if character == text[0]:
            UpperCamelCase__ 						=						self._match_length_from_index(a_,					a_,					0,					a_					)
            # if the found length is bigger than the current or if it's equal,
            # which means it's offset is smaller: update offset and length
            if found_length >= length:
               UpperCamelCase__						,		UpperCamelCase__ 						=						found_offset, found_length
  
      return Token(a_,					a_,					text[length]					)
  
   def 		lowercase_						( self :	Optional[int],					a_ :	str,					a_ :	str,					a_ :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not text or text[text_index] != window[window_index]:
         return 0
      return 1 + self._match_length_from_index(
          a_,					window + text[text_index],					text_index + 1,					window_index + 1					)
  
  
  if __name__ == "__main__":
         from doctest import testmod
  
         testmod()
         # Initialize compressor class
         __lowercase:		Optional[Any]							=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
         # Example
         __lowercase:		Union[str, Any]							=     "cabracadabrarrarrad"
         __lowercase:		Optional[int]							=     lzaa_compressor.compress(TEXT)
         print(lzaa_compressor.compress("ababcbababaa"))
         __lowercase:		Optional[int]							=     lzaa_compressor.decompress(compressed_text)
         assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."