




from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase=None	,      UpperCamelCase=None      )     ->	List[str]:
  if attention_mask is None:
    lowerCAmelCase__				:  Optional[Any]		       =     tf.cast(tf.math.not_equal(UpperCamelCase	,      config.pad_token_id      )	,      tf.inta      )
  return {"input_ids": input_ids, "attention_mask": attention_mask}






@require_tf
class  _lowerCAmelCase     :
      A__		     =		OPTConfig
      A__		     =		{}
      A__		     =		'gelu'

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase=13      ,				__UpperCAmelCase=7      ,				__UpperCAmelCase=True      ,				__UpperCAmelCase=False      ,				__UpperCAmelCase=99      ,				__UpperCAmelCase=16      ,				__UpperCAmelCase=2      ,				__UpperCAmelCase=4      ,				__UpperCAmelCase=4      ,				__UpperCAmelCase="gelu"      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=0.1      ,				__UpperCAmelCase=20      ,				__UpperCAmelCase=2      ,				__UpperCAmelCase=1      ,				__UpperCAmelCase=0      ,				__UpperCAmelCase=16      ,				__UpperCAmelCase=16      ,				):
        lowerCAmelCase__				:  Dict		       =     parent
        lowerCAmelCase__				:  List[Any]		       =     batch_size
        lowerCAmelCase__				:  str		       =     seq_length
        lowerCAmelCase__				:  int		       =     is_training
        lowerCAmelCase__				:  Optional[int]		       =     use_labels
        lowerCAmelCase__				:  int		       =     vocab_size
        lowerCAmelCase__				:  Optional[Any]		       =     hidden_size
        lowerCAmelCase__				:  str		       =     num_hidden_layers
        lowerCAmelCase__				:  Optional[Any]		       =     num_attention_heads
        lowerCAmelCase__				:  List[str]		       =     intermediate_size
        lowerCAmelCase__				:  List[Any]		       =     hidden_act
        lowerCAmelCase__				:  Any		       =     hidden_dropout_prob
        lowerCAmelCase__				:  Any		       =     attention_probs_dropout_prob
        lowerCAmelCase__				:  Optional[int]		       =     max_position_embeddings
        lowerCAmelCase__				:  Tuple		       =     eos_token_id
        lowerCAmelCase__				:  Tuple		       =     pad_token_id
        lowerCAmelCase__				:  Tuple		       =     bos_token_id
        lowerCAmelCase__				:  Union[str, Any]		       =     embed_dim
        lowerCAmelCase__				:  Optional[int]		       =     word_embed_proj_dim
        lowerCAmelCase__				:  int		       =     False

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Union[str, Any]		       =     ids_tensor([self.batch_size, self.seq_length - 1]      ,				self.vocab_size		)
        lowerCAmelCase__				:  int		       =     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)      ,				1		)
        lowerCAmelCase__				:  Tuple		       =     tf.concat([input_ids, eos_tensor]      ,				axis=1		)

        lowerCAmelCase__				:  str		       =     self.config_cls(
            vocab_size=self.vocab_size      ,				hidden_size=self.hidden_size      ,				num_hidden_layers=self.num_hidden_layers      ,				num_attention_heads=self.num_attention_heads      ,				ffn_dim=self.intermediate_size      ,				dropout=self.hidden_dropout_prob      ,				attention_dropout=self.attention_probs_dropout_prob      ,				max_position_embeddings=self.max_position_embeddings      ,				eos_token_id=self.eos_token_id      ,				bos_token_id=self.bos_token_id      ,				pad_token_id=self.pad_token_id      ,				embed_dim=self.embed_dim      ,				word_embed_proj_dim=self.word_embed_proj_dim      ,				is_encoder_decoder=__UpperCAmelCase      ,				**self.config_updates      ,				)
        lowerCAmelCase__				:  Optional[Any]		       =     prepare_opt_inputs_dict(__UpperCAmelCase      ,				__UpperCAmelCase		)
        return config, inputs_dict







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Any		       =     TFOPTModel(config=__UpperCAmelCase		)
        lowerCAmelCase__				:  Tuple		       =     inputs_dict['''input_ids''']

        lowerCAmelCase__				:  str		       =     input_ids[:1, :]
        lowerCAmelCase__				:  str		       =     inputs_dict['''attention_mask'''][:1, :]
        lowerCAmelCase__				:  List[str]		       =     1

        # first forward pass
        lowerCAmelCase__				:  str		       =     model(__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase      ,				use_cache=__UpperCAmelCase		)

        lowerCAmelCase__       ,			lowerCAmelCase__				:  List[Any]		       =     outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        lowerCAmelCase__				:  Optional[int]		       =     ids_tensor((self.batch_size, 3)      ,				config.vocab_size		)
        lowerCAmelCase__				:  List[Any]		       =     tf.cast(ids_tensor((self.batch_size, 3)      ,				2		)      ,				tf.inta		)

        # append to next input_ids and
        lowerCAmelCase__				:  Tuple		       =     tf.concat([input_ids, next_tokens]      ,				axis=-1		)
        lowerCAmelCase__				:  Any		       =     tf.concat([attention_mask, next_attn_mask]      ,				axis=-1		)

        lowerCAmelCase__				:  Dict		       =     model(__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase		)[0]
        lowerCAmelCase__				:  Any		       =     model(__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase      ,				past_key_values=__UpperCAmelCase		)[0]

        self.parent.assertEqual(next_tokens.shape[1]      ,				output_from_past.shape[1]		)

        # select random slice
        lowerCAmelCase__				:  Union[str, Any]		       =     int(ids_tensor((1,)      ,				output_from_past.shape[-1]		)		)
        lowerCAmelCase__				:  Tuple		       =     output_from_no_past[:, -3:, random_slice_idx]
        lowerCAmelCase__				:  Tuple		       =     output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(__UpperCAmelCase      ,				__UpperCAmelCase      ,				rtol=1e-3		)






@require_tf
class  _lowerCAmelCase     (  _lowercase    ,			_lowercase    ,			unittest.TestCase	):
      A__		     =		(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
      A__		     =		(TFOPTForCausalLM,) if is_tf_available() else ()
      A__		     =		(
          {'feature-extraction': TFOPTModel, 'text-generation': TFOPTForCausalLM} if is_tf_available() else {}
      )
      A__		     =		False
      A__		     =		False
      A__		     =		False
      A__		     =		10

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Union[str, Any]		       =     TFOPTModelTester(self		)
        lowerCAmelCase__				:  int		       =     ConfigTester(self      ,				config_class=__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        self.config_tester.run_common_tests()

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Tuple		       =     self.model_tester.prepare_config_and_inputs_for_common()

        def _get_word_embedding_weight(__UpperCAmelCase      ,				__UpperCAmelCase		):
          if hasattr(__UpperCAmelCase      ,				'''weight'''		):
            return embedding_layer.weight
          else:
            # Here we build the word embeddings weights if not exists.
            # And then we retry to get the attribute once built.
            model.build()
            if hasattr(__UpperCAmelCase      ,				'''weight'''		):
              return embedding_layer.weight
            else:
              return None

        for model_class in self.all_model_classes:
          for size in [config.vocab_size - 10, config.vocab_size + 10]:
            # build the embeddings
            lowerCAmelCase__				:  Dict		       =     model_class(config=__UpperCAmelCase		)
            lowerCAmelCase__				:  Optional[Any]		       =     _get_word_embedding_weight(__UpperCAmelCase      ,				model.get_input_embeddings()		)
            lowerCAmelCase__				:  Optional[int]		       =     _get_word_embedding_weight(__UpperCAmelCase      ,				model.get_output_embeddings()		)

            # reshape the embeddings
            model.resize_token_embeddings(__UpperCAmelCase		)
            lowerCAmelCase__				:  Tuple		       =     _get_word_embedding_weight(__UpperCAmelCase      ,				model.get_input_embeddings()		)
            lowerCAmelCase__				:  List[Any]		       =     _get_word_embedding_weight(__UpperCAmelCase      ,				model.get_output_embeddings()		)

            # check that the resized embeddings size matches the desired size.
            lowerCAmelCase__				:  Any		       =     size if size is not None else config.vocab_size

            self.assertEqual(new_input_embeddings.shape[0]      ,				__UpperCAmelCase		)

            # check that weights remain the same after resizing
            lowerCAmelCase__				:  Tuple		       =     True
            for pa, pa in zip(old_input_embeddings.value()      ,				new_input_embeddings.value()		):
              if tf.math.reduce_sum(tf.math.abs(pa - pa		)		) > 0:
                lowerCAmelCase__				:  List[Any]		       =     False
            self.assertTrue(__UpperCAmelCase		)

            if old_output_embeddings is not None and new_output_embeddings is not None:
              self.assertEqual(new_output_embeddings.shape[0]      ,				__UpperCAmelCase		)

              lowerCAmelCase__				:  int		       =     True
              for pa, pa in zip(old_output_embeddings.value()      ,				new_output_embeddings.value()		):
                if tf.math.reduce_sum(tf.math.abs(pa - pa		)		) > 0:
                  lowerCAmelCase__				:  int		       =     False
              self.assertTrue(__UpperCAmelCase		)



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	Tuple:
  return tf.constant(UpperCamelCase	,      dtype=tf.intaa      )






@require_tf
class  _lowerCAmelCase     (  unittest.TestCase	):
      A__		     =		99

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[Any]		       =     tf.ones((4, 1)      ,				dtype=tf.intaa		) * 2
        lowerCAmelCase__				:  Optional[Any]		       =     tf.concat([ids_tensor((4, 6)      ,				self.vocab_size - 3		) + 3, eos_column_vector]      ,				axis=1		)
        lowerCAmelCase__				:  Tuple		       =     input_ids.shape[0]
        lowerCAmelCase__				:  Optional[Any]		       =     OPTConfig(
            vocab_size=self.vocab_size      ,				hidden_size=24      ,				num_hidden_layers=2      ,				num_attention_heads=2      ,				ffn_dim=32      ,				max_position_embeddings=48      ,				eos_token_id=2      ,				pad_token_id=1      ,				bos_token_id=0      ,				)
        return config, input_ids, batch_size






@require_sentencepiece
@require_tf
class  _lowerCAmelCase     (  unittest.TestCase	):

      @slow
      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Optional[int]		       =     TFOPTModel.from_pretrained('''facebook/opt-350m'''		)
        lowerCAmelCase__				:  List[str]		       =     _long_tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		)
        lowerCAmelCase__				:  str		       =     tf.not_equal(__UpperCAmelCase      ,				model.config.pad_token_id		)
        with tf.GradientTape():
          lowerCAmelCase__				:  Dict		       =     model(input_ids=__UpperCAmelCase      ,				attention_mask=__UpperCAmelCase		).last_hidden_state
        lowerCAmelCase__				:  int		       =     (1, 11, 512)
        self.assertEqual(output.shape      ,				__UpperCAmelCase		)
        lowerCAmelCase__				:  Dict		       =     tf.constant(
            [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]		)
        self.assertTrue(np.allclose(output[:, :3, :3]      ,				__UpperCAmelCase      ,				atol=4e-3		)		)

        lowerCAmelCase__				:  Any		       =     tf.function(__UpperCAmelCase      ,				jit_compile=__UpperCAmelCase		)
        lowerCAmelCase__				:  Tuple		       =     xla_generate(__UpperCAmelCase      ,				__UpperCAmelCase		)[0]
        self.assertTrue(np.allclose(output[:, :3, :3]      ,				__UpperCAmelCase      ,				atol=4e-2		)		)






@require_tf
@slow
class  _lowerCAmelCase     (  unittest.TestCase	):

      def 	__magic_name__(   self		):
        super().setUp()
        lowerCAmelCase__				:  str		       =     '''facebook/opt-350m'''







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[str]		       =     TFOPTForCausalLM.from_pretrained(self.path_model		)
        lowerCAmelCase__				:  List[Any]		       =     GPTaTokenizer.from_pretrained(self.path_model		)

        lowerCAmelCase__				:  Any		       =     [
            '''Today is a beautiful day and I want to''',
            '''In the city of''',
            '''Paris is the capital of France and''',
            '''Computers and mobile phones have taken''',
        ]
        # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
        lowerCAmelCase__				:  List[str]		       =     tokenizer(__UpperCAmelCase      ,				return_tensors='''tf'''      ,				padding=__UpperCAmelCase      ,				add_special_tokens=__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[int]		       =     tf.math.reduce_mean(model(inputs.input_ids      ,				attention_mask=inputs.attention_mask		)[0]      ,				axis=-1		)
        lowerCAmelCase__				:  str		       =     tf.constant(
            [
                [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
                [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
                [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
                [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
            ]		)
        self.assertTrue(np.allclose(__UpperCAmelCase      ,				__UpperCAmelCase      ,				atol=1e-4		)		)

        lowerCAmelCase__				:  Tuple		       =     tf.function(__UpperCAmelCase      ,				jit_compile=__UpperCAmelCase		)
        lowerCAmelCase__				:  Tuple		       =     tf.math.reduce_mean(xla_generate(inputs.input_ids      ,				attention_mask=inputs.attention_mask		)[0]      ,				axis=-1		)
        self.assertTrue(np.allclose(__UpperCAmelCase      ,				__UpperCAmelCase      ,				atol=1e-4		)		)






@require_tf
@slow
class  _lowerCAmelCase     (  unittest.TestCase	):

      @property
      def 	__magic_name__(   self		):
        return [
            "Today is a beautiful day and I want",
            "In the city of",
            "Paris is the capital of France and",
            "Computers and mobile phones have taken",
        ]

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  Tuple		       =     '''facebook/opt-125m'''

        lowerCAmelCase__				:  str		       =     [
            '''Today is a beautiful day and I want to''',
            '''In the city of New York, the city''',
            '''Paris is the capital of France and the capital''',
            '''Computers and mobile phones have taken over the''',
        ]

        lowerCAmelCase__				:  Optional[Any]		       =     []
        lowerCAmelCase__				:  str		       =     GPTaTokenizer.from_pretrained(__UpperCAmelCase		)
        lowerCAmelCase__				:  List[str]		       =     TFOPTForCausalLM.from_pretrained(__UpperCAmelCase		)

        for prompt in self.prompts:
          lowerCAmelCase__				:  Optional[int]		       =     tokenizer(__UpperCAmelCase      ,				return_tensors='''tf'''		).input_ids

          lowerCAmelCase__				:  Dict		       =     model.generate(__UpperCAmelCase      ,				max_length=10		)

          lowerCAmelCase__				:  Tuple		       =     tokenizer.batch_decode(__UpperCAmelCase      ,				skip_special_tokens=__UpperCAmelCase		)
          predicted_outputs += generated_string

        self.assertListEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)

      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  str		       =     '''facebook/opt-350m'''

        lowerCAmelCase__				:  Tuple		       =     GPTaTokenizer.from_pretrained(__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     TFOPTForCausalLM.from_pretrained(__UpperCAmelCase		)

        lowerCAmelCase__				:  Dict		       =     '''left'''

        # use different length sentences to test batching
        lowerCAmelCase__				:  str		       =     [
            '''Hello, my dog is a little''',
            '''Today, I''',
        ]

        lowerCAmelCase__				:  Tuple		       =     tokenizer(__UpperCAmelCase      ,				return_tensors='''tf'''      ,				padding=__UpperCAmelCase		)
        lowerCAmelCase__				:  Dict		       =     inputs['''input_ids''']

        lowerCAmelCase__				:  Optional[int]		       =     model.generate(input_ids=__UpperCAmelCase      ,				attention_mask=inputs['''attention_mask''']		)

        lowerCAmelCase__				:  Union[str, Any]		       =     tokenizer(sentences[0]      ,				return_tensors='''tf'''		).input_ids
        lowerCAmelCase__				:  str		       =     model.generate(input_ids=__UpperCAmelCase		)

        lowerCAmelCase__				:  List[str]		       =     inputs_non_padded.shape[-1] - tf.math.reduce_sum(
            tf.cast(inputs['''attention_mask'''][-1]      ,				tf.intaa		)		)
        lowerCAmelCase__				:  Tuple		       =     tokenizer(sentences[1]      ,				return_tensors='''tf'''		).input_ids
        lowerCAmelCase__				:  int		       =     model.generate(input_ids=__UpperCAmelCase      ,				max_length=model.config.max_length - num_paddings		)

        lowerCAmelCase__				:  Optional[Any]		       =     tokenizer.batch_decode(__UpperCAmelCase      ,				skip_special_tokens=__UpperCAmelCase		)
        lowerCAmelCase__				:  int		       =     tokenizer.decode(output_non_padded[0]      ,				skip_special_tokens=__UpperCAmelCase		)
        lowerCAmelCase__				:  Dict		       =     tokenizer.decode(output_padded[0]      ,				skip_special_tokens=__UpperCAmelCase		)

        lowerCAmelCase__				:  Any		       =     [
            '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
            '''Today, I was in the middle of a conversation with a friend about the''',
        ]
        self.assertListEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)
        self.assertListEqual(__UpperCAmelCase      ,				[non_padded_sentence, padded_sentence]		)







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     '''facebook/opt-350m'''

        lowerCAmelCase__				:  Optional[int]		       =     [
            '''Today is a beautiful day and I want to''',
            '''In the city of San Francisco, the city''',
            '''Paris is the capital of France and the capital''',
            '''Computers and mobile phones have taken over the''',
        ]

        lowerCAmelCase__				:  List[str]		       =     []
        lowerCAmelCase__				:  int		       =     GPTaTokenizer.from_pretrained(__UpperCAmelCase		)
        lowerCAmelCase__				:  Optional[Any]		       =     TFOPTForCausalLM.from_pretrained(__UpperCAmelCase		)

        for prompt in self.prompts:
          lowerCAmelCase__				:  Optional[Any]		       =     tokenizer(__UpperCAmelCase      ,				return_tensors='''tf'''		).input_ids

          lowerCAmelCase__				:  Dict		       =     model.generate(__UpperCAmelCase      ,				max_length=10		)

          lowerCAmelCase__				:  Union[str, Any]		       =     tokenizer.batch_decode(__UpperCAmelCase      ,				skip_special_tokens=__UpperCAmelCase		)
          predicted_outputs += generated_string

        self.assertListEqual(__UpperCAmelCase      ,				__UpperCAmelCase		)







from collections.abc import Callable






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase = None		):
        # Stores actual heap items.
        lowerCAmelCase__				:  list		       =     []
        # Stores indexes of each item for supporting updates and deletion.
        lowerCAmelCase__				:  dict		       =     {}
        # Stores current size of heap.
        lowerCAmelCase__				:  Any		       =     0
        # Stores function used to evaluate the score of an item on which basis ordering
        # will be done.
        lowerCAmelCase__				:  Union[str, Any]		       =     key or (lambda __UpperCAmelCase		: x)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        return int((i - 1) / 2		) if i > 0 else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     int(2 * i + 1		)
        return left if 0 < left < self.size else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     int(2 * i + 2		)
        return right if 0 < right < self.size else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  List[str]		       =     (
            self.pos_map[self.arr[j][0]],
            self.pos_map[self.arr[i][0]],
        )
        # Then swap the items in the list.
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Any		       =     self.arr[j], self.arr[i]

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        return self.arr[i][1] < self.arr[j][1]

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[int]		       =     self._left(__UpperCAmelCase		)
        lowerCAmelCase__				:  Any		       =     self._right(__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     i

        if left is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  Dict		       =     left
        if right is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  List[str]		       =     right

        return valid_parent

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     self._parent(__UpperCAmelCase		)
        while parent is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          self._swap(__UpperCAmelCase      ,				__UpperCAmelCase		)
          lowerCAmelCase__       ,			lowerCAmelCase__				:  Dict		       =     parent, self._parent(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  str		       =     self._get_valid_parent(__UpperCAmelCase		)
        while valid_parent != index:
          self._swap(__UpperCAmelCase      ,				__UpperCAmelCase		)
          lowerCAmelCase__       ,			lowerCAmelCase__				:  List[Any]		       =     valid_parent, self._get_valid_parent(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        if item not in self.pos_map:
          return
        lowerCAmelCase__				:  Tuple		       =     self.pos_map[item]
        lowerCAmelCase__				:  Any		       =     [item, self.key(__UpperCAmelCase		)]
        # Make sure heap is right in both up and down direction.
        # Ideally only one of them will make any change.
        self._heapify_up(__UpperCAmelCase		)
        self._heapify_down(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        if item not in self.pos_map:
          return
        lowerCAmelCase__				:  Tuple		       =     self.pos_map[item]
        del self.pos_map[item]
        lowerCAmelCase__				:  Tuple		       =     self.arr[self.size - 1]
        lowerCAmelCase__				:  int		       =     index
        self.size -= 1
        # Make sure heap is right in both up and down direction. Ideally only one
        # of them will make any change- so no performance loss in calling both.
        if self.size > index:
          self._heapify_up(__UpperCAmelCase		)
          self._heapify_down(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[int]		       =     len(self.arr		)
        if arr_len == self.size:
          self.arr.append([item, self.key(__UpperCAmelCase		)]		)
        else:
          lowerCAmelCase__				:  str		       =     [item, self.key(__UpperCAmelCase		)]
        lowerCAmelCase__				:  Optional[Any]		       =     self.size
        self.size += 1
        self._heapify_up(self.size - 1		)

      def 	__magic_name__(   self		):
        return self.arr[0] if self.size else None







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.get_top()
        if top_item_tuple:
          self.delete_item(top_item_tuple[0]		)
        return top_item_tuple



def      __lowerCAmelCase			(					)     ->	None:
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()

