code: 
  
  
  
  
  
  from __future__ import annotations
  
  import copy
  import os
  import tempfile
  from importlib import import_module
  from math import isnan
  
  from transformers import is_tf_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow
  
  from ..test_modeling_tf_common import ids_tensor
  
  
  if is_tf_available():
       import numpy as np
       import tensorflow as tf
  
       from transformers import (
           TF_MODEL_FOR_CAUSAL_LM_MAPPING,
           TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
           TF_MODEL_FOR_MASKED_LM_MAPPING,
           TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
           TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
           TF_MODEL_FOR_PRETRAINING_MAPPING,
           TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
           TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
           TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
           TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
           TFSharedEmbeddings,
       )
  
       if _tf_gpu_memory_limit is not None:
            _UpperCamelCase       = tf.config.list_physical_devices('''GPU''')
            for gpu in gpus:
                 # Restrict TensorFlow to only allocate x GB of memory on the GPUs
                 try:
                      tf.config.set_logical_device_configuration(
                          gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
                      )
                      _UpperCamelCase       = tf.config.list_logical_devices('''GPU''')
                      print('''Logical GPUs''', logical_gpus)
                 except RuntimeError as e:
                      # Virtual devices must be set before GPUs have been initialized
                      print(e)
  
  
  
  
  @require_tf
  class 				_lowerCamelCase :
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        UpperCAmelCase_						:      int       =None
        UpperCAmelCase_						:      Union[str, Any]       =()
        UpperCAmelCase_						:      List[str]       =()
        UpperCAmelCase_						:      Optional[Any]       =True
        UpperCAmelCase_						:      Optional[Any]       =True
        UpperCAmelCase_						:      Any       =True
        UpperCAmelCase_						:      int       =False
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=False   )       ->   dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Any        =       copy.deepcopy(UpperCAmelCase   )
  
          if model_class in get_values(UpperCAmelCase   ):
            __snake_case   :				Optional[int]        =       {
                k: tf.tile(tf.expand_dims(UpperCAmelCase				,						1   )				,						(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)   )
                if isinstance(UpperCAmelCase				,						tf.Tensor   ) and v.ndim > 0
                else v
                for k, v in inputs_dict.items()
            }
  
          if return_labels:
            if model_class in get_values(UpperCAmelCase   ):
              __snake_case   :				List[str]        =       tf.ones(self.model_tester.batch_size				,						dtype=tf.intaa   )
            elif model_class in get_values(UpperCAmelCase   ):
              __snake_case   :				List[Any]        =       tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa   )
              __snake_case   :				Union[str, Any]        =       tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa   )
            elif model_class in [
                *get_values(UpperCAmelCase   ),
                *get_values(UpperCAmelCase   ),
            ]:
              __snake_case   :				int        =       tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa   )
            elif model_class in get_values(UpperCAmelCase   ):
              __snake_case   :				Any        =       tf.zeros(self.model_tester.batch_size				,						dtype=tf.intaa   )
            elif model_class in [
                *get_values(UpperCAmelCase   ),
                *get_values(UpperCAmelCase   ),
                *get_values(UpperCAmelCase   ),
                *get_values(UpperCAmelCase   ),
                *get_values(UpperCAmelCase   ),
            ]:
              __snake_case   :				Optional[Any]        =       tf.zeros(
                  (self.model_tester.batch_size, self.model_tester.seq_length)				,						dtype=tf.intaa   )
          return inputs_dict
  
  
  
  
  
  
  
        @slow
        def 						UpperCAmelCase		( self   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case    ,   __snake_case   :				List[str]        =       self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
            __snake_case   :				str        =       self._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase   )
            __snake_case   :				List[str]        =       model_class(UpperCAmelCase   )
  
            @tf.function
            def run_in_graph_mode():
              return model(UpperCAmelCase   )
  
            __snake_case   :				int        =       run_in_graph_mode()
            self.assertIsNotNone(UpperCAmelCase   )
  
  
  
  
  
  
  
        @slow
        def 						UpperCAmelCase		( self   )       ->   int:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case    ,   __snake_case   :				List[str]        =       self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
            __snake_case   :				str        =       self._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase   )
            __snake_case   :				Union[str, Any]        =       model_class(UpperCAmelCase   )
  
            @tf.function(experimental_compile=UpperCAmelCase   )
            def run_in_graph_mode():
              return model(UpperCAmelCase   )
  
            __snake_case   :				int        =       run_in_graph_mode()
            self.assertIsNotNone(UpperCAmelCase   )
  
  
  
  
  
  
  
        @slow
        def 						UpperCAmelCase		( self   )       ->   List[Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case    ,   __snake_case   :				Optional[int]        =       self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes[:2]:
            __snake_case   :				List[Any]        =       model_class(UpperCAmelCase   )
            if getattr(UpperCAmelCase				,						"hf_compute_loss"				,						UpperCAmelCase   ):
              # Test that model correctly compute the loss with kwargs
              __snake_case   :				List[Any]        =       self._prepare_for_class(inputs_dict.copy()				,						UpperCAmelCase				,						return_labels=UpperCAmelCase   )
              # Is there a better way to remove these decoder inputs?
              __snake_case   :				int        =       {
                  key: val
                  for key, val in prepared_for_class.items()
                  if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
              }
  
              __snake_case   :				Dict        =       {
                  "labels",
                  "label",
                  "label_ids",
                  "start_positions",
                  "start_position",
                  "end_positions",
                  "end_position",
                  "next_sentence_label",
              }
              __snake_case   :				str        =       possible_label_cols.intersection(set(UpperCAmelCase   )   )
              self.assertGreater(len(UpperCAmelCase   )				,						0				,						msg="No matching label names found!"   )
              __snake_case   :				Optional[Any]        =       {key: val for key, val in prepared_for_class.items() if key in label_names}
              __snake_case   :				int        =       {key: val for key, val in prepared_for_class.items() if key not in label_names}
              self.assertGreater(len(UpperCAmelCase   )				,						0   )
  
              # Make sure it works with XLA!
              model.compile(optimizer=tf.keras.optimizers.SGD(0.0   )				,						jit_compile=UpperCAmelCase   )
              # Make sure the model fits without crashing regardless of where we pass the labels
              __snake_case   :				str        =       model.fit(
                  UpperCAmelCase				,						validation_data=UpperCAmelCase				,						steps_per_epoch=1				,						validation_steps=1				,						shuffle=UpperCAmelCase				,						verbose=0				,						)
              __snake_case   :				List[str]        =       history.history["loss"][0]
              self.assertTrue(not isnan(UpperCAmelCase   )   )
              __snake_case   :				str        =       history.history["val_loss"][0]
              self.assertTrue(not isnan(UpperCAmelCase   )   )
  
              # Now test it with separate labels, to make sure that path works in XLA too.
              __snake_case   :				str        =       model_class(UpperCAmelCase   )
              model.compile(optimizer=tf.keras.optimizers.SGD(0.0   )				,						jit_compile=UpperCAmelCase   )
              __snake_case   :				List[Any]        =       model.fit(
                  UpperCAmelCase				,						UpperCAmelCase				,						validation_data=(inputs_minus_labels, labels)				,						steps_per_epoch=1				,						validation_steps=1				,						shuffle=UpperCAmelCase				,						verbose=0				,						)
  
              __snake_case   :				str        =       history.history["loss"][0]
              self.assertTrue(not isnan(UpperCAmelCase   )   )
              __snake_case   :				List[Any]        =       history.history["val_loss"][0]
              self.assertTrue(not isnan(UpperCAmelCase   )   )
  
  
  
  
  
  
  
        @slow
        def 						UpperCAmelCase		( self   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case    ,   __snake_case   :				Union[str, Any]        =       self.model_tester.prepare_config_and_inputs_for_common()
          __snake_case   :				Dict        =       True
          __snake_case   :				Any        =       True
  
          if hasattr(UpperCAmelCase				,						"use_cache"   ):
            __snake_case   :				Any        =       True
  
          __snake_case   :				int        =       getattr(self.model_tester				,						"encoder_seq_length"				,						self.model_tester.seq_length   )
          __snake_case   :				Tuple        =       getattr(self.model_tester				,						"key_length"				,						UpperCAmelCase   )
  
          for model_class in self.all_model_classes[:2]:
            __snake_case   :				Dict        =       self._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase   )
            __snake_case   :				int        =       model_class(UpperCAmelCase   )
            model.build()
            __snake_case   :				str        =       len(model(UpperCAmelCase   )   )
  
            for key in list(class_inputs_dict.keys()   ):
              # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
              if key not in model.input_signature:
                del class_inputs_dict[key]
              # Check it's a tensor, in case the inputs dict has some bools in it too
              elif isinstance(class_inputs_dict[key]				,						tf.Tensor   ) and class_inputs_dict[key].dtype.is_integer:
                __snake_case   :				List[str]        =       tf.cast(class_inputs_dict[key]				,						tf.intaa   )
  
            if set(class_inputs_dict.keys()   ) != set(model.input_signature.keys()   ):
              continue  # Some models have inputs that the preparation functions don't create, we skip those
  
            with tempfile.TemporaryDirectory() as tmpdirname:
              model.save_pretrained(UpperCAmelCase				,						saved_model=UpperCAmelCase   )
              __snake_case   :				str        =       os.path.join(UpperCAmelCase				,						"saved_model"				,						"1"   )
              __snake_case   :				Tuple        =       tf.keras.models.load_model(UpperCAmelCase   )
              __snake_case   :				Tuple        =       model(UpperCAmelCase   )
  
              if self.is_encoder_decoder:
                __snake_case   :				int        =       outputs["encoder_hidden_states"]
                __snake_case   :				str        =       outputs["encoder_attentions"]
              else:
                __snake_case   :				int        =       outputs["hidden_states"]
                __snake_case   :				Optional[Any]        =       outputs["attentions"]
  
              self.assertEqual(len(UpperCAmelCase   )				,						UpperCAmelCase   )
  
              __snake_case   :				int        =       getattr(
                  self.model_tester				,						"expected_num_hidden_layers"				,						self.model_tester.num_hidden_layers + 1   )
  
              self.assertEqual(len(UpperCAmelCase   )				,						UpperCAmelCase   )
              self.assertListEqual(
                  list(output_hidden_states[0].shape[-2:]   )				,						[self.model_tester.seq_length, self.model_tester.hidden_size]				,						)
  
              self.assertEqual(len(UpperCAmelCase   )				,						self.model_tester.num_hidden_layers   )
              self.assertListEqual(
                  list(output_attentions[0].shape[-3:]   )				,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]				,						)
  
  
  
  
  
  
  
        @slow
        def 						UpperCAmelCase		( self   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          tf.keras.mixed_precision.set_global_policy("mixed_float16"   )
  
          # try/finally block to ensure subsequent tests run in float32
          try:
            __snake_case    ,   __snake_case   :				Optional[int]        =       self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes[:2]:
              __snake_case   :				List[str]        =       self._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase   )
              __snake_case   :				Optional[Any]        =       model_class(UpperCAmelCase   )
              __snake_case   :				List[str]        =       model(UpperCAmelCase   )
  
              self.assertIsNotNone(UpperCAmelCase   )
          finally:
            tf.keras.mixed_precision.set_global_policy("float32"   )
  
  
  
  
  
  
  
        @slow
        def 						UpperCAmelCase		( self   )       ->   Union[str, Any]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case    ,   __snake_case   :				Dict        =       self.model_tester.prepare_config_and_inputs_for_common()
          # head_mask and decoder_head_mask has different shapes than other input args
          if "head_mask" in inputs_dict:
            del inputs_dict["head_mask"]
          if "decoder_head_mask" in inputs_dict:
            del inputs_dict["decoder_head_mask"]
          if "cross_attn_head_mask" in inputs_dict:
            del inputs_dict["cross_attn_head_mask"]
          __snake_case   :				str        =       {
              module_member
              for model_class in self.all_model_classes
              for module in (import_module(model_class.__module__   ),)
              for module_member_name in dir(UpperCAmelCase   )
              if module_member_name.endswith("MainLayer"   )
              for module_member in (getattr(UpperCAmelCase				,						UpperCAmelCase   ),)
              if isinstance(UpperCAmelCase				,						UpperCAmelCase   )
              and tf.keras.layers.Layer in module_member.__bases__
              and getattr(UpperCAmelCase				,						"_keras_serializable"				,						UpperCAmelCase   )
          }
  
          for main_layer_class in tf_main_layer_classes:
            # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
            if "T5" in main_layer_class.__name__:
              # Take the same values than in TFT5ModelTester for this shared layer
              __snake_case   :				Tuple        =       TFSharedEmbeddings(self.model_tester.vocab_size				,						self.model_tester.hidden_size				,						name="shared"   )
              __snake_case   :				List[str]        =       False
              __snake_case   :				List[Any]        =       main_layer_class(UpperCAmelCase				,						embed_tokens=UpperCAmelCase   )
            else:
              __snake_case   :				Any        =       main_layer_class(UpperCAmelCase   )
  
            __snake_case   :				str        =       {
                name: tf.keras.Input(tensor.shape[1:]				,						dtype=tensor.dtype   ) for name, tensor in inputs_dict.items()
            }
  
            if hasattr(self.model_tester				,						"num_labels"   ):
              __snake_case   :				List[Any]        =       self.model_tester.num_labels
            else:
              __snake_case   :				Tuple        =       2
  
            __snake_case   :				List[str]        =       tf.data.Dataset.from_tensor_slices(
                (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)   ))   ).batch(1   )
  
            __snake_case   :				Any        =       main_layer(UpperCAmelCase   )[0]
            __snake_case   :				str        =       tf.keras.layers.Dense(UpperCAmelCase				,						activation="softmax"				,						name="outputs"   )(UpperCAmelCase   )
            __snake_case   :				str        =       tf.keras.models.Model(inputs=UpperCAmelCase				,						outputs=[outputs]   )
  
            model.compile(loss="binary_crossentropy"				,						optimizer="adam"				,						metrics=["binary_accuracy"]   )
            model.fit(UpperCAmelCase				,						epochs=1   )
  
            with tempfile.TemporaryDirectory() as tmpdirname:
              __snake_case   :				Any        =       os.path.join(UpperCAmelCase				,						"keras_model.h5"   )
              model.save(UpperCAmelCase   )
              if "T5" in main_layer_class.__name__:
                __snake_case   :				Optional[Any]        =       tf.keras.models.load_model(
                    UpperCAmelCase				,						custom_objects={
                        main_layer_class.__name__: main_layer_class,
                        "TFSharedEmbeddings": TFSharedEmbeddings,
                    }				,						)
              else:
                __snake_case   :				Dict        =       tf.keras.models.load_model(
                    UpperCAmelCase				,						custom_objects={main_layer_class.__name__: main_layer_class}   )
              assert isinstance(UpperCAmelCase				,						tf.keras.Model   )
              model(UpperCAmelCase   )
  
  
  
  
  
  
  
        @slow
        def 						UpperCAmelCase		( self   )       ->   int:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case    ,   __snake_case   :				int        =       self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes[:2]:
            __snake_case   :				Dict        =       model_class(UpperCAmelCase   )
  
            __snake_case   :				str        =       copy.deepcopy(UpperCAmelCase   )
  
            if not self.is_encoder_decoder:
              __snake_case   :				int        =       inputs["input_ids"]
              del inputs["input_ids"]
            else:
              __snake_case   :				Dict        =       inputs["input_ids"]
              __snake_case   :				Any        =       inputs.get("decoder_input_ids"				,						UpperCAmelCase   )
              del inputs["input_ids"]
              inputs.pop("decoder_input_ids"				,						UpperCAmelCase   )
  
            if not self.is_encoder_decoder:
              __snake_case   :				List[str]        =       model.get_input_embeddings()(UpperCAmelCase   )
            else:
              __snake_case   :				Optional[int]        =       model.get_input_embeddings()(UpperCAmelCase   )
              __snake_case   :				str        =       model.get_input_embeddings()(UpperCAmelCase   )
  
            __snake_case   :				Dict        =       self._prepare_for_class(UpperCAmelCase				,						UpperCAmelCase   )
  
            @tf.function
            def run_in_graph_mode():
              return model(UpperCAmelCase   )
  
            __snake_case   :				int        =       run_in_graph_mode()
            self.assertIsNotNone(UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				int        =       []
          if model.config.bos_token_id is not None:
            special_tokens.append(model.config.bos_token_id   )
          if model.config.pad_token_id is not None:
            special_tokens.append(model.config.pad_token_id   )
          if model.config.eos_token_id is not None:
            special_tokens.append(model.config.eos_token_id   )
  
          # create random bad tokens that are not special tokens
          __snake_case   :				Optional[Any]        =       []
          while len(UpperCAmelCase   ) < num_bad_tokens:
            __snake_case   :				Any        =       tf.squeeze(ids_tensor((1, 1)				,						self.model_tester.vocab_size   )				,						0   ).numpy()[0]
            if token not in special_tokens:
              bad_tokens.append(UpperCAmelCase   )
          return bad_tokens
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   Any:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          for token_id in output_ids[0].numpy().tolist():
            self.assertGreaterEqual(UpperCAmelCase				,						0   )
            self.assertLess(UpperCAmelCase				,						self.model_tester.vocab_size   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   List[str]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          for bad_word_ids in bad_words_ids:
            # for all slices in batch
            for generated_ids_slice in generated_ids:
              # for all word idx
              for i in range(len(UpperCAmelCase   )				,						len(UpperCAmelCase   )   ):
                # if tokens match
                if generated_ids_slice[i - len(UpperCAmelCase   ) : i] == bad_word_ids:
                  return True
          return False
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Callable
  from typing import Generic, TypeVar
  
  _UpperCamelCase       = TypeVar('''T''')
  _UpperCamelCase       = TypeVar('''U''')
  
  
  
  
  class 				_lowerCamelCase (    Generic[T, U]    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   Dict:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Any        =       key
          __snake_case   :				Tuple        =       val
          __snake_case   :				int        =       0
          __snake_case   :				DoubleLinkedListNode[T, U] | None        =       None
          __snake_case   :				DoubleLinkedListNode[T, U] | None        =       None
  
  
  
  
  
  
  
        def __repr__( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
              self.key				,						self.val				,						self.freq				,						self.next is not None				,						self.prev is not None   )
  
  
  
  
  class 				_lowerCamelCase (    Generic[T, U]    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        def __init__( self   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				DoubleLinkedListNode[T, U]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
          __snake_case   :				DoubleLinkedListNode[T, U]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
          __snake_case    ,   __snake_case   :				Any        =       self.rear, self.head
  
  
  
  
  
  
  
        def __repr__( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Union[str, Any]        =       ["DoubleLinkedList"]
          __snake_case   :				List[Any]        =       self.head
          while node.next is not None:
            rep.append(str(UpperCAmelCase   )   )
            __snake_case   :				List[Any]        =       node.next
          rep.append(str(self.rear   )   )
          return ",\n    ".join(UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				Dict        =       self.rear.prev
  
          # All nodes other than self.head are guaranteed to have non-None previous
          assert previous is not None
  
          __snake_case   :				int        =       node
          __snake_case   :				List[str]        =       previous
          __snake_case   :				Optional[int]        =       node
          __snake_case   :				int        =       self.rear
          node.freq += 1
          self._position_node(UpperCAmelCase   )
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          while node.prev is not None and node.prev.freq > node.freq:
            # swap node with previous node
            __snake_case   :				List[Any]        =       node.prev
  
            __snake_case   :				Dict        =       previous_node.prev
            __snake_case   :				Union[str, Any]        =       node.prev
            __snake_case   :				Union[str, Any]        =       previous_node
            __snake_case   :				int        =       node
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   DoubleLinkedListNode[T, U] | None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if node.prev is None or node.next is None:
            return None
  
          __snake_case   :				Optional[int]        =       node.next
          __snake_case   :				Optional[Any]        =       node.prev
          __snake_case   :				Optional[int]        =       None
          __snake_case   :				Any        =       None
          return node
  
  
  
  
  class 				_lowerCamelCase (    Generic[T, U]    ):
  
  
  
        """simple docstring"""
  
  
  
  
  
  
        UpperCAmelCase_						:      dict[Callable[[T], U], LFUCache[T, U]]       ={}
  
  
  
  
  
  
  
        def __init__( self				,						UpperCAmelCase   )       ->   Tuple:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          __snake_case   :				DoubleLinkedList[T, U]        =       DoubleLinkedList()
          __snake_case   :				str        =       capacity
          __snake_case   :				str        =       0
          __snake_case   :				Optional[int]        =       0
          __snake_case   :				Any        =       0
          __snake_case   :				dict[T, DoubleLinkedListNode[T, U]]        =       {}
  
  
  
  
  
  
  
        def __repr__( self   )       ->   str:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return (
              F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
              F"""capacity={self.capacity}, current_size={self.num_keys})"""
          )
  
  
  
  
  
  
  
        def __contains__( self				,						UpperCAmelCase   )       ->   bool:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          return key in self.cache
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase   )       ->   U | None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if key in self.cache:
            self.hits += 1
            __snake_case   :				DoubleLinkedListNode[T, U]        =       self.cache[key]
            __snake_case   :				Union[str, Any]        =       self.list.remove(self.cache[key]   )
            assert node == value_node
  
            # node is guaranteed not None because it is in self.cache
            assert node is not None
            self.list.add(UpperCAmelCase   )
            return node.val
          self.miss += 1
          return None
  
  
  
  
  
  
  
        def 						UpperCAmelCase		( self				,						UpperCAmelCase				,						UpperCAmelCase   )       ->   None:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          if key not in self.cache:
            if self.num_keys >= self.capacity:
              # delete first node when over capacity
              __snake_case   :				Optional[Any]        =       self.list.head.next
  
              # guaranteed to have a non-None first node when num_keys > 0
              # explain to type checker via assertions
              assert first_node is not None
              assert first_node.key is not None
              assert self.list.remove(UpperCAmelCase   ) is not None
              # first_node guaranteed to be in list
  
              del self.cache[first_node.key]
              self.num_keys -= 1
            __snake_case   :				Union[str, Any]        =       DoubleLinkedListNode(UpperCAmelCase				,						UpperCAmelCase   )
            self.list.add(self.cache[key]   )
            self.num_keys += 1
  
          else:
            __snake_case   :				Union[str, Any]        =       self.list.remove(self.cache[key]   )
            assert node is not None  # node guaranteed to be in list
            __snake_case   :				Optional[Any]        =       value
            self.list.add(UpperCAmelCase   )
  
  
  
  
  
  
  
        @classmethod
        def 						UpperCAmelCase		( cls				,						UpperCAmelCase = 128   )       ->   Callable[[Callable[[T], U]], Callable[..., U]]:
  
  
  
  
  
  
          '''simple docstring'''
  
  
  
  
  
  
          def cache_decorator_inner(UpperCAmelCase   ) -> Callable[..., U]:
            def cache_decorator_wrapper(*UpperCAmelCase   ) -> U:
              if func not in cls.decorator_function_to_instance_map:
                __snake_case   :				str        =       LFUCache(UpperCAmelCase   )
  
              __snake_case   :				Dict        =       cls.decorator_function_to_instance_map[func].get(args[0]   )
              if result is None:
                __snake_case   :				Optional[int]        =       func(*UpperCAmelCase   )
                cls.decorator_function_to_instance_map[func].put(args[0]				,						UpperCAmelCase   )
              return result
  
            def cache_info() -> LFUCache[T, U]:
              return cls.decorator_function_to_instance_map[func]
  
            setattr(UpperCAmelCase				,						"cache_info"				,						UpperCAmelCase   )  # noqa: B010
  
            return cache_decorator_wrapper
  
          return cache_decorator_inner
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
  