




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:
    lowerCamelCase_  						=   False
class       __a					:


     """simple docstring"""


     def __init__(	self			: Union[str, Any]				,_UpperCamelCase			: Optional[int]				,_UpperCamelCase			: Optional[Any]=1_4				,_UpperCamelCase			: Dict=7				,_UpperCamelCase			: Optional[int]=True				,_UpperCamelCase			: Dict=False				,_UpperCamelCase			: List[str]=True				,_UpperCamelCase			: Union[str, Any]=True				,_UpperCamelCase			: List[Any]=True				,_UpperCamelCase			: str=9_9				,_UpperCamelCase			: Optional[Any]=3_2				,_UpperCamelCase			: int=5				,_UpperCamelCase			: List[str]=3_7				,_UpperCamelCase			: Tuple="gelu"				,_UpperCamelCase			: str=0.1				,_UpperCamelCase			: Any=0.1				,_UpperCamelCase			: Any=5_1_2				,_UpperCamelCase			: Dict=1_6				,_UpperCamelCase			: Dict=2				,_UpperCamelCase			: int=3				,_UpperCamelCase			: Any=4				,_UpperCamelCase			: List[Any]=None				,)		->   List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=parent
         SCREAMING_SNAKE_CASE__								=batch_size
         SCREAMING_SNAKE_CASE__								=seq_length
         SCREAMING_SNAKE_CASE__								=is_training
         SCREAMING_SNAKE_CASE__								=use_token_type_ids
         SCREAMING_SNAKE_CASE__								=use_input_mask
         SCREAMING_SNAKE_CASE__								=use_labels
         SCREAMING_SNAKE_CASE__								=use_mc_token_ids
         SCREAMING_SNAKE_CASE__								=vocab_size
         SCREAMING_SNAKE_CASE__								=hidden_size
         SCREAMING_SNAKE_CASE__								=num_hidden_layers
         SCREAMING_SNAKE_CASE__								=intermediate_size
         SCREAMING_SNAKE_CASE__								=hidden_act
         SCREAMING_SNAKE_CASE__								=hidden_dropout_prob
         SCREAMING_SNAKE_CASE__								=attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE__								=max_position_embeddings
         SCREAMING_SNAKE_CASE__								=type_vocab_size
         SCREAMING_SNAKE_CASE__								=type_sequence_label_size
         SCREAMING_SNAKE_CASE__								=num_labels
         SCREAMING_SNAKE_CASE__								=num_choices
         SCREAMING_SNAKE_CASE__								=scope
         SCREAMING_SNAKE_CASE__								=vocab_size - 1
         SCREAMING_SNAKE_CASE__								=vocab_size - 1
         SCREAMING_SNAKE_CASE__								=vocab_size - 1




     def   __A				(	self			: Union[str, Any]						)		->   Dict:


         '''simple docstring'''





         return RwkvConfig.from_pretrained("""sgugger/rwkv-4-pile-7b"""						)




     def   __A				(	self			: int				,_UpperCamelCase			: Tuple=False				,_UpperCamelCase			: List[str]=False				,_UpperCamelCase			: int=False						)		->   int:


         '''simple docstring'''





         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
         if self.use_mc_token_ids:
             SCREAMING_SNAKE_CASE__								=ids_tensor([self.batch_size, self.num_choices]				,self.seq_length						)

         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__								=self.get_config(
             gradient_checkpointing=_UpperCamelCase				,scale_attn_by_inverse_layer_idx=_UpperCamelCase				,reorder_and_upcast_attn=_UpperCamelCase				,)

         return (
             config,
             input_ids,
             input_mask,
             None,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )




     def   __A				(	self			: Any				,_UpperCamelCase			: List[str]=False				,_UpperCamelCase			: Tuple=False				,_UpperCamelCase			: List[Any]=False						)		->   List[Any]:


         '''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=_UpperCamelCase				,bos_token_id=self.bos_token_id				,eos_token_id=self.eos_token_id				,pad_token_id=self.pad_token_id				,gradient_checkpointing=_UpperCamelCase				,scale_attn_by_inverse_layer_idx=_UpperCamelCase				,reorder_and_upcast_attn=_UpperCamelCase				,)




     def   __A				(	self			: Union[str, Any]						)		->   List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.get_config()
         SCREAMING_SNAKE_CASE__								=3_0_0
         return config




     def   __A				(	self			: Union[str, Any]						)		->   str:


         '''simple docstring'''





         (
             (
             SCREAMING_SNAKE_CASE__
         )			,		(
             SCREAMING_SNAKE_CASE__
         )			,		(
             SCREAMING_SNAKE_CASE__
         )			,		(
             SCREAMING_SNAKE_CASE__
         )			,		(
             SCREAMING_SNAKE_CASE__
         )			,		(
             SCREAMING_SNAKE_CASE__
         )			,		(
             SCREAMING_SNAKE_CASE__
         )			,		(
             SCREAMING_SNAKE_CASE__
         )			,		(
             SCREAMING_SNAKE_CASE__
         )			,		
         )								=self.prepare_config_and_inputs()

         SCREAMING_SNAKE_CASE__								=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
         SCREAMING_SNAKE_CASE__								=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   __A				(	self			: List[Any]				,_UpperCamelCase			: Optional[Any]				,_UpperCamelCase			: List[Any]				,_UpperCamelCase			: Union[str, Any]				,_UpperCamelCase			: str				,_UpperCamelCase			: Union[str, Any]				,*_UpperCamelCase			: Dict						)		->   Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=True
         SCREAMING_SNAKE_CASE__								=RwkvModel(config=_UpperCamelCase						)
         model.to(_UpperCamelCase						)
         model.eval()

         SCREAMING_SNAKE_CASE__								=model(_UpperCamelCase						)

         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   __A				(	self			: Dict				,_UpperCamelCase			: Any				,_UpperCamelCase			: Dict				,_UpperCamelCase			: Tuple				,_UpperCamelCase			: Union[str, Any]				,_UpperCamelCase			: Union[str, Any]				,*_UpperCamelCase			: List[Any]						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=RwkvForCausalLM(_UpperCamelCase						)
         model.to(_UpperCamelCase						)
         model.eval()

         SCREAMING_SNAKE_CASE__								=model(_UpperCamelCase				,labels=_UpperCamelCase						)
         self.parent.assertEqual(result.loss.shape				,()						)
         self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.seq_length, self.vocab_size)						)




     def   __A				(	self			: Any				,_UpperCamelCase			: Optional[int]				,_UpperCamelCase			: List[Any]				,_UpperCamelCase			: int				,_UpperCamelCase			: Optional[Any]				,_UpperCamelCase			: List[Any]				,*_UpperCamelCase			: Tuple						)		->   Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=RwkvModel(config=_UpperCamelCase						)
         model.to(_UpperCamelCase						)
         model.eval()

         SCREAMING_SNAKE_CASE__								=model(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=outputs.last_hidden_state

         SCREAMING_SNAKE_CASE__								=model(input_ids[:, :2]						)
         SCREAMING_SNAKE_CASE__								=outputs.last_hidden_state

         # Using the state computed on the first inputs, we will get the same output
         SCREAMING_SNAKE_CASE__								=model(input_ids[:, 2:]				,state=outputs.state						)
         SCREAMING_SNAKE_CASE__								=outputs.last_hidden_state

         self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two]				,dim=1						)				,_UpperCamelCase				,atol=1e-5						)						)




     def   __A				(	self			: Tuple				,_UpperCamelCase			: Union[str, Any]				,_UpperCamelCase			: List[Any]				,_UpperCamelCase			: Tuple				,_UpperCamelCase			: Optional[int]				,_UpperCamelCase			: Dict				,*_UpperCamelCase			: int				,_UpperCamelCase			: Union[str, Any]=False						)		->   List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=RwkvForCausalLM(_UpperCamelCase						)
         model.to(_UpperCamelCase						)
         if gradient_checkpointing:
             model.gradient_checkpointing_enable()

         SCREAMING_SNAKE_CASE__								=model(_UpperCamelCase				,labels=_UpperCamelCase						)
         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   __A				(	self			: Optional[Any]						)		->   Dict:


         '''simple docstring'''





         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__
         )			,		(
             SCREAMING_SNAKE_CASE__
         )			,		(
             SCREAMING_SNAKE_CASE__
         )			,		
         )								=config_and_inputs

         SCREAMING_SNAKE_CASE__								={"""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       __a					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     unittest.TestCase     ):


     """simple docstring"""


     _A				:    List[str]									=  (RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
     _A				:    List[str]									=  (
         {"feature-extraction": RwkvModel, "text-generation": RwkvForCausalLM} if is_torch_available() else {}
     )
     # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
     _A				:    Tuple									=  False
     _A				:    Optional[int]									=  False
     _A				:    List[Any]									=  False
     _A				:    Optional[Any]									=  False
     _A				:    Optional[int]									=  False  # Rwkv does not support head masking




     def   __A				(	self			: Dict						)		->   List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=RwkvModelTester(self						)
         SCREAMING_SNAKE_CASE__								=ConfigTester(
             self				,config_class=_UpperCamelCase				,n_embd=3_7				,common_properties=["""hidden_size""", """num_hidden_layers"""]						)




     def   __A				(	self			: List[str]				,_UpperCamelCase			: Tuple				,_UpperCamelCase			: Dict				,_UpperCamelCase			: Dict=None						)		->   str:


         '''simple docstring'''





         if isinstance(_UpperCamelCase				,torch.Tensor						):
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=member.max().item(), member.min().item()
         elif isinstance(_UpperCamelCase				,_UpperCamelCase						) or isinstance(_UpperCamelCase				,_UpperCamelCase						):
             SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=max(_UpperCamelCase						), min(_UpperCamelCase						)

         if not isinstance(_UpperCamelCase				,_UpperCamelCase						):
             raise TypeError("""container should be a list or tuple"""						)
         elif len(_UpperCamelCase						) != 2:
             raise ValueError("""container should have 2 elements"""						)

         SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=container

         SCREAMING_SNAKE_CASE__								=(min_value >= expected_min) and (max_value <= expected_max)

         if not is_inside_interval:
             SCREAMING_SNAKE_CASE__								="""%s not found in %s""" % (safe_repr(_UpperCamelCase						), safe_repr(_UpperCamelCase						))
             self.fail(self._formatMessage(_UpperCamelCase				,_UpperCamelCase						)						)




     def   __A				(	self			: Tuple						)		->   Any:


         '''simple docstring'''





         self.config_tester.run_common_tests()




     def   __A				(	self			: Dict						)		->   Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_rwkv_model(*_UpperCamelCase						)




     def   __A				(	self			: int						)		->   Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_causl_lm(*_UpperCamelCase						)




     def   __A				(	self			: Optional[Any]						)		->   Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_state_equivalency(*_UpperCamelCase						)




     def   __A				(	self			: Optional[Any]						)		->   List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__								=model_class(config=_UpperCamelCase						)
             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   __A				(	self			: str						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=self.model_tester.prepare_config_and_inputs_for_common()
         SCREAMING_SNAKE_CASE__								=True

         SCREAMING_SNAKE_CASE__								=getattr(self.model_tester				,"""seq_length"""				,_UpperCamelCase						)

         for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__								=True
             SCREAMING_SNAKE_CASE__								=False
             SCREAMING_SNAKE_CASE__								=True
             SCREAMING_SNAKE_CASE__								=model_class(_UpperCamelCase						)
             model.to(_UpperCamelCase						)
             model.eval()

             SCREAMING_SNAKE_CASE__								=self._prepare_for_class(_UpperCamelCase				,_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=inputs["""input_ids"""].shape[0]
             with torch.no_grad():
                 SCREAMING_SNAKE_CASE__								=model(**_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=outputs.attentions
             self.assertEqual(len(_UpperCamelCase						)				,self.model_tester.num_hidden_layers						)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             SCREAMING_SNAKE_CASE__								=True
             SCREAMING_SNAKE_CASE__								=model_class(_UpperCamelCase						)
             model.to(_UpperCamelCase						)
             model.eval()

             SCREAMING_SNAKE_CASE__								=self._prepare_for_class(_UpperCamelCase				,_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=inputs["""input_ids"""].shape[0]
             with torch.no_grad():
                 SCREAMING_SNAKE_CASE__								=model(**_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=outputs.attentions
             self.assertEqual(len(_UpperCamelCase						)				,self.model_tester.num_hidden_layers						)

             self.assertListEqual(
                 list(attentions[0].shape[-3:]						)				,[batch_size, seq_len, config.hidden_size]				,)
             SCREAMING_SNAKE_CASE__								=len(_UpperCamelCase						)

             # Check attention is always last and order is fine
             SCREAMING_SNAKE_CASE__								=True
             SCREAMING_SNAKE_CASE__								=True
             SCREAMING_SNAKE_CASE__								=model_class(_UpperCamelCase						)
             model.to(_UpperCamelCase						)
             model.eval()

             SCREAMING_SNAKE_CASE__								=self._prepare_for_class(_UpperCamelCase				,_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__								=inputs["""input_ids"""].shape[0]
             with torch.no_grad():
                 SCREAMING_SNAKE_CASE__								=model(**_UpperCamelCase						)

             SCREAMING_SNAKE_CASE__								=1
             self.assertEqual(out_len + added_hidden_states				,len(_UpperCamelCase						)						)

             SCREAMING_SNAKE_CASE__								=outputs.attentions

             self.assertEqual(len(_UpperCamelCase						)				,self.model_tester.num_hidden_layers						)
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]						)				,[batch_size, seq_len, config.hidden_size]				,)




     @slow
     def   __A				(	self			: Any						)		->   Any:


         '''simple docstring'''





         for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE__								=RwkvModel.from_pretrained(_UpperCamelCase						)
             self.assertIsNotNone(_UpperCamelCase						)






@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0							,     reason="See https://github.com/huggingface/transformers/pull/24204"     )
@slow
class       __a					( unittest.TestCase     ):


     """simple docstring"""


     def   __A				(	self			: Optional[int]						)		->   Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								="""RWKV/rwkv-4-169m-pile"""
         SCREAMING_SNAKE_CASE__								=AutoTokenizer.from_pretrained(self.model_id						)




     def   __A				(	self			: Any						)		->   Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								="""Hello my name is Jasmine and I am a newbie to the"""
         SCREAMING_SNAKE_CASE__								=RwkvForCausalLM.from_pretrained(self.model_id						).to(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=self.tokenizer("""Hello my name is"""				,return_tensors="""pt"""						).input_ids.to(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=model.generate(_UpperCamelCase				,max_new_tokens=1_0						)
         SCREAMING_SNAKE_CASE__								=self.tokenizer.decode(output[0].tolist()						)

         self.assertEqual(_UpperCamelCase				,_UpperCamelCase						)




     def   __A				(	self			: List[Any]						)		->   Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								="""Hello my name is Jasmine and I am a newbie to the"""

         SCREAMING_SNAKE_CASE__								=self.tokenizer("""Hello my name is"""				,return_tensors="""pt"""						).input_ids.to(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__								=RwkvForCausalLM.from_pretrained(self.model_id				,torch_dtype=torch.bfloataa						).to(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__								=model.generate(_UpperCamelCase				,max_new_tokens=1_0						)
         SCREAMING_SNAKE_CASE__								=self.tokenizer.decode(output[0].tolist()						)

         self.assertEqual(_UpperCamelCase				,_UpperCamelCase						)












from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any
class       __a					:


     """simple docstring"""


     _A				:    List[Any]									=  ("_front", "_back", "_len")
     @dataclass
     class       __a					:


          """simple docstring"""


          _A				:    Any									=  None
          _A				:    Deque._Node | None									=  None
          _A				:    Deque._Node | None									=  None






     class       __a					:


          """simple docstring"""


          _A				:    List[str]									=  ("_cur",)




          def __init__(	self			: int				,_UpperCamelCase			: Deque._Node | None						)		->   None:


              '''simple docstring'''





              SCREAMING_SNAKE_CASE__								=cur




          def __iter__(	self			: Optional[Any]						)		->   Deque._Iterator:


              '''simple docstring'''





              return self




          def   __A				(	self			: Union[str, Any]						)		->   Any:


              '''simple docstring'''





              if self._cur is None:
                  # finished iterating
                  raise StopIteration
              SCREAMING_SNAKE_CASE__								=self._cur.val
              SCREAMING_SNAKE_CASE__								=self._cur.next_node

              return val






     def __init__(	self			: Union[str, Any]				,_UpperCamelCase			: Iterable[Any] | None = None						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=None
         SCREAMING_SNAKE_CASE__								=0

         if iterable is not None:
             # append every value to the deque
             for val in iterable:
                 self.append(_UpperCamelCase						)




     def   __A				(	self			: Optional[Any]				,_UpperCamelCase			: Any						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self._Node(_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase						)
         if self.is_empty():
             # front = back
             SCREAMING_SNAKE_CASE__								=SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=1
         else:
             # connect nodes
             SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=self._back
             SCREAMING_SNAKE_CASE__								=node  # assign new back to the new node

             self._len += 1

             # make sure there were no errors
             assert not self.is_empty(), "Error on appending value."




     def   __A				(	self			: Any				,_UpperCamelCase			: Any						)		->   None:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self._Node(_UpperCamelCase				,_UpperCamelCase				,_UpperCamelCase						)
         if self.is_empty():
             # front = back
             SCREAMING_SNAKE_CASE__								=SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=1
         else:
             # connect nodes
             SCREAMING_SNAKE_CASE__								=self._front
             SCREAMING_SNAKE_CASE__								=node
             SCREAMING_SNAKE_CASE__								=node  # assign new front to the new node

             self._len += 1

             # make sure there were no errors
             assert not self.is_empty(), "Error on appending value."




     def   __A				(	self			: List[Any]				,_UpperCamelCase			: Iterable[Any]						)		->   None:


         '''simple docstring'''





         for val in iterable:
             self.append(_UpperCamelCase						)




     def   __A				(	self			: str				,_UpperCamelCase			: Iterable[Any]						)		->   None:


         '''simple docstring'''





         for val in iterable:
             self.appendleft(_UpperCamelCase						)




     def   __A				(	self			: str						)		->   Any:


         '''simple docstring'''





         assert not self.is_empty(), "Deque is empty."

         SCREAMING_SNAKE_CASE__								=self._back
         SCREAMING_SNAKE_CASE__								=self._back.prev_node  # set new back
         # drop the last node - python will deallocate memory automatically
         SCREAMING_SNAKE_CASE__								=None

         self._len -= 1

         return topop.val




     def   __A				(	self			: Union[str, Any]						)		->   Any:


         '''simple docstring'''





         assert not self.is_empty(), "Deque is empty."

         SCREAMING_SNAKE_CASE__								=self._front
         SCREAMING_SNAKE_CASE__								=self._front.next_node  # set new front and drop the first node
         SCREAMING_SNAKE_CASE__								=None

         self._len -= 1

         return topop.val




     def   __A				(	self			: Tuple						)		->   bool:


         '''simple docstring'''





         return self._front is None




     def __len__(	self			: Optional[int]						)		->   int:


         '''simple docstring'''





         return self._len




     def __eq__(	self			: Union[str, Any]				,_UpperCamelCase			: object						)		->   bool:


         '''simple docstring'''





         if not isinstance(_UpperCamelCase				,_UpperCamelCase						):
             return NotImplemented

         SCREAMING_SNAKE_CASE__								=self._front
         SCREAMING_SNAKE_CASE__								=other._front

         # if the length of the dequeues are not the same, they are not equal
         if len(self						) != len(_UpperCamelCase						):
             return False

         while me is not None and oth is not None:
             # compare every value
             if me.val != oth.val:
                 return False
             SCREAMING_SNAKE_CASE__								=me.next_node
             SCREAMING_SNAKE_CASE__								=oth.next_node

         return True




     def __iter__(	self			: Optional[Any]						)		->   Deque._Iterator:


         '''simple docstring'''





         return Deque._Iterator(self._front						)




     def __repr__(	self			: Optional[Any]						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=[]
         SCREAMING_SNAKE_CASE__								=self._front
         while aux is not None:
             # append the values in a list to display
             values_list.append(aux.val						)
             SCREAMING_SNAKE_CASE__								=aux.next_node

         return f"""[{", ".join(repr(_UpperCamelCase						) for val in values_list						)}]"""


if __name__ == "__main__":
    import doctest

    doctest.testmod()






