



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:
														__SCREAMING_SNAKE_CASE     =	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)]
																												)
																												__SCREAMING_SNAKE_CASE     =	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'''
						a__ =	None
						a__ =	()
						a__ =	()
						a__ =	True
						a__ =	True
						a__ =	True
						a__ =	False







						def       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      Union[str, Any]						,						__lowerCamelCase   :      List[Any]=False			)   ->					dict:
									A				:       Optional[Any]														=    copy.deepcopy(__lowerCamelCase			)

									if model_class in get_values(__lowerCamelCase			):
												A				:       Dict														=    {
												    k: tf.tile(tf.expand_dims(__lowerCamelCase						,						1			)						,						(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)			)
												    if isinstance(__lowerCamelCase						,						tf.Tensor			) and v.ndim > 0
												    else v
												    for k, v in inputs_dict.items()
												}

									if return_labels:
												if model_class in get_values(__lowerCamelCase			):
															A				:       Optional[Any]														=    tf.ones(self.model_tester.batch_size						,						dtype=tf.intaa			)
												elif model_class in get_values(__lowerCamelCase			):
															A				:       str														=    tf.zeros(self.model_tester.batch_size						,						dtype=tf.intaa			)
															A				:       Union[str, Any]														=    tf.zeros(self.model_tester.batch_size						,						dtype=tf.intaa			)
												elif model_class in [
												    *get_values(__lowerCamelCase			),
												    *get_values(__lowerCamelCase			),
												]:
															A				:       Optional[Any]														=    tf.zeros(self.model_tester.batch_size						,						dtype=tf.intaa			)
												elif model_class in get_values(__lowerCamelCase			):
															A				:       Any														=    tf.zeros(self.model_tester.batch_size						,						dtype=tf.intaa			)
												elif model_class in [
												    *get_values(__lowerCamelCase			),
												    *get_values(__lowerCamelCase			),
												    *get_values(__lowerCamelCase			),
												    *get_values(__lowerCamelCase			),
												    *get_values(__lowerCamelCase			),
												]:
															A				:       List[Any]														=    tf.zeros(
															    (self.model_tester.batch_size, self.model_tester.seq_length)						,						dtype=tf.intaa			)
									return inputs_dict







						@slow
						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Optional[Any]:
									A							,			A				:       List[str]														=    self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
												A				:       Union[str, Any]														=    self._prepare_for_class(__lowerCamelCase						,						__lowerCamelCase			)
												A				:       Union[str, Any]														=    model_class(__lowerCamelCase			)

												@tf.function
												def run_in_graph_mode():
															return model(__lowerCamelCase			)

												A				:       Union[str, Any]														=    run_in_graph_mode()
												self.assertIsNotNone(__lowerCamelCase			)







						@slow
						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[int]:
									A							,			A				:       Dict														=    self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
												A				:       List[str]														=    self._prepare_for_class(__lowerCamelCase						,						__lowerCamelCase			)
												A				:       List[Any]														=    model_class(__lowerCamelCase			)

												@tf.function(experimental_compile=__lowerCamelCase			)
												def run_in_graph_mode():
															return model(__lowerCamelCase			)

												A				:       Union[str, Any]														=    run_in_graph_mode()
												self.assertIsNotNone(__lowerCamelCase			)







						@slow
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Union[str, Any]:
									# This is a copy of the test_keras_fit method, but we use XLA compilation instead of eager
									A							,			A				:       Union[str, Any]														=    self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes[:2]:
												A				:       List[str]														=    model_class(__lowerCamelCase			)
												if getattr(__lowerCamelCase						,						"hf_compute_loss"						,						__lowerCamelCase			):
															# Test that model correctly compute the loss with kwargs
															A				:       Dict														=    self._prepare_for_class(inputs_dict.copy()						,						__lowerCamelCase						,						return_labels=__lowerCamelCase			)
															# Is there a better way to remove these decoder inputs?
															A				:       Any														=    {
															    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")
															}

															A				:       Tuple														=    {
															    "labels",
															    "label",
															    "label_ids",
															    "start_positions",
															    "start_position",
															    "end_positions",
															    "end_position",
															    "next_sentence_label",
															}
															A				:       int														=    possible_label_cols.intersection(set(__lowerCamelCase			)			)
															self.assertGreater(len(__lowerCamelCase			)						,						0						,						msg="No matching label names found!"			)
															A				:       Dict														=    {key: val for key, val in prepared_for_class.items() if key in label_names}
															A				:       List[str]														=    {key: val for key, val in prepared_for_class.items() if key not in label_names}
															self.assertGreater(len(__lowerCamelCase			)						,						0			)

															# Make sure it works with XLA!
															model.compile(optimizer=tf.keras.optimizers.SGD(0.0			)						,						jit_compile=__lowerCamelCase			)
															# Make sure the model fits without crashing regardless of where we pass the labels
															A				:       Tuple														=    model.fit(
															    __lowerCamelCase						,						validation_data=__lowerCamelCase						,						steps_per_epoch=1						,						validation_steps=1						,						shuffle=__lowerCamelCase						,						verbose=0						,						)
															A				:       Union[str, Any]														=    history.history["loss"][0]
															self.assertTrue(not isnan(__lowerCamelCase			)			)
															A				:       List[str]														=    history.history["val_loss"][0]
															self.assertTrue(not isnan(__lowerCamelCase			)			)

															# Now test it with separate labels, to make sure that path works in XLA too.
															A				:       Union[str, Any]														=    model_class(__lowerCamelCase			)
															model.compile(optimizer=tf.keras.optimizers.SGD(0.0			)						,						jit_compile=__lowerCamelCase			)
															A				:       Optional[Any]														=    model.fit(
															    __lowerCamelCase						,						__lowerCamelCase						,						validation_data=(inputs_minus_labels, labels)						,						steps_per_epoch=1						,						validation_steps=1						,						shuffle=__lowerCamelCase						,						verbose=0						,						)

															A				:       Dict														=    history.history["loss"][0]
															self.assertTrue(not isnan(__lowerCamelCase			)			)
															A				:       List[str]														=    history.history["val_loss"][0]
															self.assertTrue(not isnan(__lowerCamelCase			)			)







						@slow
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Tuple:
									A							,			A				:       str														=    self.model_tester.prepare_config_and_inputs_for_common()
									A				:       Dict														=    True
									A				:       List[str]														=    True

									if hasattr(__lowerCamelCase						,						"use_cache"			):
												A				:       Optional[int]														=    True

									A				:       List[Any]														=    getattr(self.model_tester						,						"encoder_seq_length"						,						self.model_tester.seq_length			)
									A				:       int														=    getattr(self.model_tester						,						"key_length"						,						__lowerCamelCase			)

									for model_class in self.all_model_classes[:2]:
												A				:       Tuple														=    self._prepare_for_class(__lowerCamelCase						,						__lowerCamelCase			)
												A				:       Union[str, Any]														=    model_class(__lowerCamelCase			)
												model.build()
												A				:       str														=    len(model(__lowerCamelCase			)			)

												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:
																		A				:       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(__lowerCamelCase						,						saved_model=__lowerCamelCase			)
															A				:       str														=    os.path.join(__lowerCamelCase						,						"saved_model"						,						"1"			)
															A				:       Any														=    tf.keras.models.load_model(__lowerCamelCase			)
															A				:       Tuple														=    model(__lowerCamelCase			)

															if self.is_encoder_decoder:
																		A				:       Optional[Any]														=    outputs["encoder_hidden_states"]
																		A				:       List[Any]														=    outputs["encoder_attentions"]
															else:
																		A				:       Optional[Any]														=    outputs["hidden_states"]
																		A				:       List[Any]														=    outputs["attentions"]

															self.assertEqual(len(__lowerCamelCase			)						,						__lowerCamelCase			)

															A				:       List[Any]														=    getattr(
															    self.model_tester						,						"expected_num_hidden_layers"						,						self.model_tester.num_hidden_layers + 1			)

															self.assertEqual(len(__lowerCamelCase			)						,						__lowerCamelCase			)
															self.assertListEqual(
															    list(output_hidden_states[0].shape[-2:]			)						,						[self.model_tester.seq_length, self.model_tester.hidden_size]						,						)

															self.assertEqual(len(__lowerCamelCase			)						,						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       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Optional[Any]:
									tf.keras.mixed_precision.set_global_policy("mixed_float16"			)

									# try/finally block to ensure subsequent tests run in float32
									try:
												A							,			A				:       Optional[int]														=    self.model_tester.prepare_config_and_inputs_for_common()
												for model_class in self.all_model_classes[:2]:
															A				:       Optional[Any]														=    self._prepare_for_class(__lowerCamelCase						,						__lowerCamelCase			)
															A				:       Optional[Any]														=    model_class(__lowerCamelCase			)
															A				:       Dict														=    model(__lowerCamelCase			)

															self.assertIsNotNone(__lowerCamelCase			)
									finally:
												tf.keras.mixed_precision.set_global_policy("float32"			)







						@slow
						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					str:
									A							,			A				:       List[Any]														=    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"]
									A				:       Any														=    {
									    module_member
									    for model_class in self.all_model_classes
									    for module in (import_module(model_class.__module__			),)
									    for module_member_name in dir(__lowerCamelCase			)
									    if module_member_name.endswith("MainLayer"			)
									    for module_member in (getattr(__lowerCamelCase						,						__lowerCamelCase			),)
									    if isinstance(__lowerCamelCase						,						__lowerCamelCase			)
									    and tf.keras.layers.Layer in module_member.__bases__
									    and getattr(__lowerCamelCase						,						"_keras_serializable"						,						__lowerCamelCase			)
									}

									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
															A				:       int														=    TFSharedEmbeddings(self.model_tester.vocab_size						,						self.model_tester.hidden_size						,						name="shared"			)
															A				:       Optional[Any]														=    False
															A				:       Tuple														=    main_layer_class(__lowerCamelCase						,						embed_tokens=__lowerCamelCase			)
												else:
															A				:       Union[str, Any]														=    main_layer_class(__lowerCamelCase			)

												A				:       Union[str, Any]														=    {
												    name: tf.keras.Input(tensor.shape[1:]						,						dtype=tensor.dtype			) for name, tensor in inputs_dict.items()
												}

												if hasattr(self.model_tester						,						"num_labels"			):
															A				:       Union[str, Any]														=    self.model_tester.num_labels
												else:
															A				:       int														=    2

												A				:       List[Any]														=    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			)

												A				:       Any														=    main_layer(__lowerCamelCase			)[0]
												A				:       List[str]														=    tf.keras.layers.Dense(__lowerCamelCase						,						activation="softmax"						,						name="outputs"			)(__lowerCamelCase			)
												A				:       Optional[Any]														=    tf.keras.models.Model(inputs=__lowerCamelCase						,						outputs=[outputs]			)

												model.compile(loss="binary_crossentropy"						,						optimizer="adam"						,						metrics=["binary_accuracy"]			)
												model.fit(__lowerCamelCase						,						epochs=1			)

												with tempfile.TemporaryDirectory() as tmpdirname:
															A				:       List[Any]														=    os.path.join(__lowerCamelCase						,						"keras_model.h5"			)
															model.save(__lowerCamelCase			)
															if "T5" in main_layer_class.__name__:
																		A				:       str														=    tf.keras.models.load_model(
																		    __lowerCamelCase						,						custom_objects={
																		        main_layer_class.__name__: main_layer_class,
																		        "TFSharedEmbeddings": TFSharedEmbeddings,
																		    }						,						)
															else:
																		A				:       int														=    tf.keras.models.load_model(
																		    __lowerCamelCase						,						custom_objects={main_layer_class.__name__: main_layer_class}			)
															assert isinstance(__lowerCamelCase						,						tf.keras.Model			)
															model(__lowerCamelCase			)







						@slow
						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Union[str, Any]:
									A							,			A				:       Any														=    self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes[:2]:
												A				:       Dict														=    model_class(__lowerCamelCase			)

												A				:       str														=    copy.deepcopy(__lowerCamelCase			)

												if not self.is_encoder_decoder:
															A				:       Optional[Any]														=    inputs["input_ids"]
															del inputs["input_ids"]
												else:
															A				:       List[Any]														=    inputs["input_ids"]
															A				:       Union[str, Any]														=    inputs.get("decoder_input_ids"						,						__lowerCamelCase			)
															del inputs["input_ids"]
															inputs.pop("decoder_input_ids"						,						__lowerCamelCase			)

												if not self.is_encoder_decoder:
															A				:       Any														=    model.get_input_embeddings()(__lowerCamelCase			)
												else:
															A				:       int														=    model.get_input_embeddings()(__lowerCamelCase			)
															A				:       Optional[int]														=    model.get_input_embeddings()(__lowerCamelCase			)

												A				:       List[Any]														=    self._prepare_for_class(__lowerCamelCase						,						__lowerCamelCase			)

												@tf.function
												def run_in_graph_mode():
															return model(__lowerCamelCase			)

												A				:       Union[str, Any]														=    run_in_graph_mode()
												self.assertIsNotNone(__lowerCamelCase			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      Tuple			)   ->					Optional[Any]:
									# special tokens cannot be bad tokens
									A				:       str														=    []
									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
									A				:       Union[str, Any]														=    []
									while len(__lowerCamelCase			) < num_bad_tokens:
												A				:       Any														=    tf.squeeze(ids_tensor((1, 1)						,						self.model_tester.vocab_size			)						,						0			).numpy()[0]
												if token not in special_tokens:
															bad_tokens.append(__lowerCamelCase			)
									return bad_tokens







						def       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      Union[str, Any]			)   ->					int:
									for token_id in output_ids[0].numpy().tolist():
												self.assertGreaterEqual(__lowerCamelCase						,						0			)
												self.assertLess(__lowerCamelCase						,						self.model_tester.vocab_size			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      int						,						__lowerCamelCase   :      int			)   ->					List[str]:
									# for all bad word tokens
									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(__lowerCamelCase			)						,						len(__lowerCamelCase			)			):
																		# if tokens match
																		if generated_ids_slice[i - len(__lowerCamelCase			) : i] == bad_word_ids:
																					return True
									return False




class 						lowerCamelCase_     :






						'''simple docstring'''







						def __init__(						self   :      Optional[int]						,						__lowerCamelCase   :      List[Any]			)   ->					Tuple:
									A				:       Dict														=    val
									# Number of nodes in left subtree
									A				:       List[Any]														=    0
									A				:       List[str]														=    None
									A				:       Union[str, Any]														=    None
									A				:       Dict														=    None







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      Union[str, Any]			)   ->					Optional[Any]:
									assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

									if self.val < other.val:
												A				:       List[str]														=    self.right
												A				:       Any														=    None
												if self.right:
															A				:       Dict														=    other
												A				:       Optional[int]														=    other
												A				:       int														=    self.left_tree_size * 2 + 1
												return self
									else:
												A				:       Optional[Any]														=    other.right
												A				:       Dict														=    None
												if other.right:
															A				:       str														=    self
												A				:       Any														=    self
												A				:       int														=    other.left_tree_size * 2 + 1
												return other







class 						lowerCamelCase_     :






						'''simple docstring'''







						def __init__(						self   :      Optional[int]						,						__lowerCamelCase   :      Optional[Any]=None						,						__lowerCamelCase   :      Union[str, Any]=None						,						__lowerCamelCase   :      Union[str, Any]=0			)   ->					Optional[Any]:
									A				:       List[str]														=    heap_size
									A				:       Optional[int]														=    bottom_root
									A				:       str														=    min_node







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]						,						__lowerCamelCase   :      Optional[int]			)   ->					Optional[int]:

									# Empty heaps corner cases
									if other.size == 0:
												return None
									if self.size == 0:
												A				:       int														=    other.size
												A				:       Dict														=    other.bottom_root
												A				:       Tuple														=    other.min_node
												return None
									# Update size
									A				:       Optional[Any]														=    self.size + other.size

									# Update min.node
									if self.min_node.val > other.min_node.val:
												A				:       Tuple														=    other.min_node
									# Merge

									# Order roots by left_subtree_size
									A				:       Tuple														=    []
									A							,			A				:       List[str]														=    self.bottom_root, other.bottom_root
									while i or j:
												if i and ((not j) or i.left_tree_size < j.left_tree_size):
															combined_roots_list.append((i, True)			)
															A				:       List[Any]														=    i.parent
												else:
															combined_roots_list.append((j, False)			)
															A				:       Optional[Any]														=    j.parent
        # Insert links between them
									for i in range(len(__lowerCamelCase			) - 1			):
												if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
															A				:       str														=    combined_roots_list[i + 1][0]
															A				:       Any														=    combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
									A				:       Dict														=    combined_roots_list[0][0]
									while i.parent:
												if (
												    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
												) or (
												    i.left_tree_size == i.parent.left_tree_size
												    and i.left_tree_size != i.parent.parent.left_tree_size
												):
															# Neighbouring Nodes
															A				:       Union[str, Any]														=    i.left
															A				:       Tuple														=    i.parent.parent

															# Merging trees
															A				:       Optional[int]														=    i.merge_trees(i.parent			)

															# Updating links
															A				:       List[str]														=    previous_node
															A				:       int														=    next_node
															if previous_node:
																		A				:       int														=    i
															if next_node:
																		A				:       Optional[int]														=    i
												else:
															A				:       str														=    i.parent
        # Updating self.bottom_root
									while i.left:
												A				:       List[str]														=    i.left
									A				:       List[str]														=    i

									# Update other
									A				:       Optional[Any]														=    self.size
									A				:       Any														=    self.bottom_root
									A				:       Optional[Any]														=    self.min_node

									# Return the merged heap
									return self







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      int			)   ->					Optional[Any]:
									if self.size == 0:
												A				:       Dict														=    Node(__lowerCamelCase			)
												A				:       List[Any]														=    1
												A				:       Optional[Any]														=    self.bottom_root
									else:
												# Create new node
												A				:       Tuple														=    Node(__lowerCamelCase			)

												# Update size
												self.size += 1

												# update min_node
												if val < self.min_node.val:
															A				:       List[Any]														=    new_node
												# Put new_node as a bottom_root in heap
												A				:       int														=    new_node
												A				:       Tuple														=    self.bottom_root
												A				:       Any														=    new_node

												# Consecutively merge roots with same left_tree_size
												while (
												    self.bottom_root.parent
												    and self.bottom_root.left_tree_size
												    == self.bottom_root.parent.left_tree_size
												):
															# Next node
															A				:       List[Any]														=    self.bottom_root.parent.parent

															# Merge
															A				:       Optional[Any]														=    self.bottom_root.merge_trees(self.bottom_root.parent			)

															# Update Links
															A				:       List[Any]														=    next_node
															A				:       List[str]														=    None
															if next_node:
																		A				:       List[Any]														=    self.bottom_root







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									return self.min_node.val







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Tuple:
									return self.size == 0







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Optional[int]:
									A				:       List[str]														=    self.min_node.val

									# Last element in heap corner case
									if self.size == 1:
												# Update size
												A				:       Union[str, Any]														=    0

												# Update bottom root
												A				:       int														=    None

												# Update min_node
												A				:       Optional[int]														=    None

												return min_value
									# No right subtree corner case
									# The structure of the tree implies that this should be the bottom root
									# and there is at least one other root
									if self.min_node.right is None:
												# Update size
												self.size -= 1

												# Update bottom root
												A				:       List[str]														=    self.bottom_root.parent
												A				:       Dict														=    None

												# Update min_node
												A				:       List[str]														=    self.bottom_root
												A				:       Dict														=    self.bottom_root.parent
												while i:
															if i.val < self.min_node.val:
																		A				:       Optional[int]														=    i
															A				:       List[Any]														=    i.parent
												return min_value
									# General case
									# Find the BinomialHeap of the right subtree of min_node
									A				:       List[str]														=    self.min_node.right
									A				:       List[Any]														=    None
									A				:       Tuple														=    bottom_of_new
									A				:       Optional[Any]														=    1

									# Size, min_node and bottom_root
									while bottom_of_new.left:
												A				:       List[str]														=    size_of_new * 2 + 1
												A				:       int														=    bottom_of_new.left
												if bottom_of_new.val < min_of_new.val:
															A				:       int														=    bottom_of_new
        # Corner case of single root on top left path
									if (not self.min_node.left) and (not self.min_node.parent):
												A				:       Optional[Any]														=    size_of_new
												A				:       List[str]														=    bottom_of_new
												A				:       Optional[int]														=    min_of_new
												# print("Single root, multiple nodes case")
												return min_value
									# Remaining cases
									# Construct heap of right subtree
									A				:       List[str]														=    BinomialHeap(
									    bottom_root=__lowerCamelCase						,						min_node=__lowerCamelCase						,						heap_size=__lowerCamelCase			)

									# Update size
									A				:       Tuple														=    self.size - 1 - size_of_new

									# Neighbour nodes
									A				:       List[str]														=    self.min_node.left
									A				:       List[str]														=    self.min_node.parent

									# Initialize new bottom_root and min_node
									A				:       List[Any]														=    previous_node or next_node
									A				:       int														=    next_node

									# Update links of previous_node and search below for new min_node and
									# bottom_root
									if previous_node:
												A				:       Optional[int]														=    next_node

												# Update bottom_root and search for min_node below
												A				:       Optional[Any]														=    previous_node
												A				:       Dict														=    previous_node
												while self.bottom_root.left:
															A				:       List[str]														=    self.bottom_root.left
															if self.bottom_root.val < self.min_node.val:
																		A				:       Optional[Any]														=    self.bottom_root
									if next_node:
												A				:       Any														=    previous_node

												# Search for new min_node above min_node
												A				:       int														=    next_node
												while i:
															if i.val < self.min_node.val:
																		A				:       Optional[int]														=    i
															A				:       Any														=    i.parent
        # Merge heaps
									self.merge_heaps(__lowerCamelCase			)

									return min_value







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Optional[Any]:
									A				:       Tuple														=    self.bottom_root
									while top_root.parent:
												A				:       Optional[int]														=    top_root.parent
									# preorder
									A				:       Union[str, Any]														=    []
									self.__traversal(__lowerCamelCase						,						__lowerCamelCase			)
									return heap_pre_order







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]						,						__lowerCamelCase   :      List[Any]						,						__lowerCamelCase   :      Optional[int]						,						__lowerCamelCase   :      Any=0			)   ->					List[Any]:
									if curr_node:
												preorder.append((curr_node.val, level)			)
												self.__traversal(curr_node.left						,						__lowerCamelCase						,						level + 1			)
												self.__traversal(curr_node.right						,						__lowerCamelCase						,						level + 1			)
									else:
												preorder.append(("#", level)			)







						def __str__(						self   :      List[str]			)   ->					Optional[Any]:
									if self.is_empty():
												return ""
									A				:       Optional[int]														=    self.pre_order()

									return "\n".join(("-" * level + str(__lowerCamelCase			)) for value, level in preorder_heap			)


# Unit Tests
if __name__ == "__main__":
							import doctest

							doctest.testmod()