



'''simple docstring'''




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						UpperCamelCase__	:     Optional[int]    =			'platform'

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def     lowerCAmelCase_       (     _lowerCamelCase:       List[str]	,				_lowerCamelCase:       Optional[Any]	,				_lowerCamelCase:       List[str]=None	,				_lowerCamelCase:       Any=None ):







		if attention_mask is None:
				__SCREAMING_SNAKE_CASE	:		Any   	=		np.where(input_ids != config.pad_token_id	,				1	,				0 )
		return {
		    "input_ids": input_ids,
		    "attention_mask": attention_mask,
		}






@require_flax
class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			Optional[int]							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			Union[str, Any]=1_3							,					lowerCAmelCase__					:			Optional[int]=7							,					lowerCAmelCase__					:			Tuple=True							,					lowerCAmelCase__					:			Tuple=False							,					lowerCAmelCase__					:			List[Any]=9_9							,					lowerCAmelCase__					:			Tuple=1_6							,					lowerCAmelCase__					:			Tuple=2							,					lowerCAmelCase__					:			Union[str, Any]=4							,					lowerCAmelCase__					:			Optional[Any]=4							,					lowerCAmelCase__					:			Tuple="gelu"							,					lowerCAmelCase__					:			Union[str, Any]=0.1							,					lowerCAmelCase__					:			Optional[Any]=0.1							,					lowerCAmelCase__					:			Tuple=2_0							,					lowerCAmelCase__					:			Dict=2							,					lowerCAmelCase__					:			Optional[Any]=1							,					lowerCAmelCase__					:			List[Any]=0							,					lowerCAmelCase__					:			int=1_6							,					lowerCAmelCase__					:			Optional[Any]=1_6							,					lowerCAmelCase__					:			Optional[Any]=0.02							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		parent
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		batch_size
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		seq_length
					__SCREAMING_SNAKE_CASE	:		Dict   	=		is_training
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		use_labels
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		vocab_size
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		hidden_size
					__SCREAMING_SNAKE_CASE	:		int   	=		num_hidden_layers
					__SCREAMING_SNAKE_CASE	:		Any   	=		num_attention_heads
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		intermediate_size
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		hidden_act
					__SCREAMING_SNAKE_CASE	:		int   	=		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE	:		Dict   	=		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		max_position_embeddings
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		eos_token_id
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		pad_token_id
					__SCREAMING_SNAKE_CASE	:		Dict   	=		bos_token_id
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		embed_dim
					__SCREAMING_SNAKE_CASE	:		Dict   	=		word_embed_proj_dim
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		initializer_range
					__SCREAMING_SNAKE_CASE	:		str   	=		False

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,					self.vocab_size						)							,					3							,					self.vocab_size						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,					dtype=np.intaa						))							,					-1						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		OPTConfig(
					    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							,					is_encoder_decoder=__UpperCamelCase							,					word_embed_proj_dim=self.word_embed_proj_dim							,					initializer_range=self.initializer_range							,					use_cache=__UpperCamelCase							,					)
					__SCREAMING_SNAKE_CASE	:		int   	=		prepare_opt_inputs_dict(__UpperCamelCase							,					__UpperCamelCase						)
					return config, inputs_dict

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.prepare_config_and_inputs()
					return config, inputs_dict

			def UpperCamelCase__ (							self					:			List[Any]							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		2_0
					__SCREAMING_SNAKE_CASE	:		Dict   	=		model_class_name(__UpperCamelCase						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		inputs_dict["""input_ids"""]
					__SCREAMING_SNAKE_CASE	:		Any   	=		inputs_dict["""attention_mask"""]

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model.init_cache(input_ids.shape[0]							,					__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		jnp.ones((input_ids.shape[0], max_length)							,					dtype="""i4"""						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1						)[None, :]							,					(input_ids.shape[0], input_ids.shape[-1] - 1)							,					)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model(
					    input_ids[:, :-1]							,					attention_mask=__UpperCamelCase							,					past_key_values=__UpperCamelCase							,					position_ids=__UpperCamelCase							,					)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,					dtype="""i4"""						)
					__SCREAMING_SNAKE_CASE	:		int   	=		model(
					    input_ids[:, -1:]							,					attention_mask=__UpperCamelCase							,					past_key_values=outputs_cache.past_key_values							,					position_ids=__UpperCamelCase							,					)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model(__UpperCamelCase						)

					__SCREAMING_SNAKE_CASE	:		int   	=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
					self.parent.assertTrue(diff < 1E-3							,					msg=F"Max diff is {diff}"						)



			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			str							,					lowerCAmelCase__					:			Optional[int]							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		2_0
					__SCREAMING_SNAKE_CASE	:		int   	=		model_class_name(__UpperCamelCase						)

					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		(
					    inputs_dict["""input_ids"""],
					    inputs_dict["""attention_mask"""],
					)

					__SCREAMING_SNAKE_CASE	:		Any   	=		jnp.concatenate(
					    [
					        attention_mask,
					        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])						),
					    ]							,					axis=-1							,					)

					__SCREAMING_SNAKE_CASE	:		int   	=		model.init_cache(input_ids.shape[0]							,					__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		str   	=		jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1						)[None, :]							,					(input_ids.shape[0], input_ids.shape[-1] - 1)							,					)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		model(
					    input_ids[:, :-1]							,					attention_mask=__UpperCamelCase							,					past_key_values=__UpperCamelCase							,					position_ids=__UpperCamelCase							,					)
					__SCREAMING_SNAKE_CASE	:		Any   	=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,					dtype="""i4"""						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(
					    input_ids[:, -1:]							,					past_key_values=outputs_cache.past_key_values							,					attention_mask=__UpperCamelCase							,					position_ids=__UpperCamelCase							,					)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		model(__UpperCamelCase							,					attention_mask=__UpperCamelCase						)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
					self.parent.assertTrue(diff < 1E-3							,					msg=F"Max diff is {diff}"						)






@require_flax
class 						_UpperCamelCase  (							UpperCAmelCase_   , unittest.TestCase   , UpperCAmelCase_   ):


			'''simple docstring'''

			_A				:		Optional[int]         =		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
			_A				:		Tuple         =		() if is_flax_available() else ()

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		FlaxOPTModelTester(self						)

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.model_tester.prepare_config_and_inputs()
					for model_class in self.all_model_classes:
							self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase							,					__UpperCamelCase							,					__UpperCamelCase						)



			@slow
			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		model_class_name.from_pretrained("""facebook/opt-125m"""						)
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.ones((1, 1)						) * model.config.eos_token_id
							__SCREAMING_SNAKE_CASE	:		Dict   	=		model(__UpperCamelCase						)
							self.assertIsNotNone(__UpperCamelCase						)






@require_sentencepiece
@require_flax
class 						_UpperCamelCase  (							unittest.TestCase   ):


			'''simple docstring'''

			@slow
			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		FlaxOPTModel.from_pretrained("""facebook/opt-350m"""						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		model(input_ids=__UpperCamelCase						).last_hidden_state
					__SCREAMING_SNAKE_CASE	:		int   	=		(1, 1_1, 5_1_2)
					self.assertEqual(output.shape							,					__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		jnp.array(
					    [[-0.28_67, -1.92_56, -0.30_62], [-1.27_11, -0.13_37, -0.18_97], [0.41_09, 0.11_87, -1.31_42]]						)
					self.assertTrue(jnp.allclose(output[:, :3, :3]							,					__UpperCamelCase							,					atol=4E-2						)						)






@require_flax
@slow
class 						_UpperCamelCase  (							unittest.TestCase   ):


			'''simple docstring'''

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					super().setUp()
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		"""facebook/opt-350m"""



			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		FlaxOPTForCausalLM.from_pretrained(self.path_model						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		GPTaTokenizer.from_pretrained(self.path_model						)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		[
					    """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
					__SCREAMING_SNAKE_CASE	:		Any   	=		tokenizer(__UpperCamelCase							,					return_tensors="""jax"""							,					padding=__UpperCamelCase							,					add_special_tokens=__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model(inputs.input_ids							,					attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		jnp.array(
					    [
					        [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
					        [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
					        [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
					        [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
					    ]						)
					self.assertTrue(jnp.allclose(__UpperCamelCase							,					__UpperCamelCase							,					atol=4E-2						)						)

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		jax.jit(__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		str   	=		model(inputs.input_ids							,					attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
					self.assertTrue(jnp.allclose(__UpperCamelCase							,					__UpperCamelCase							,					atol=4E-2						)						)






@require_flax
@slow
class 						_UpperCamelCase  (							unittest.TestCase   ):


			'''simple docstring'''

			@property
			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					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 UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		"""facebook/opt-125m"""

					__SCREAMING_SNAKE_CASE	:		int   	=		[
					    """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""",
					]

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		[]

					__SCREAMING_SNAKE_CASE	:		Dict   	=		FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		GPTaTokenizer.from_pretrained(__UpperCamelCase						)

					for prompt in self.prompts:
							__SCREAMING_SNAKE_CASE	:		Any   	=		tokenizer(__UpperCamelCase							,					return_tensors="""jax"""						).input_ids

							__SCREAMING_SNAKE_CASE	:		Dict   	=		model.generate(__UpperCamelCase							,					max_length=1_0						)
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		generated_ids[0]

							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		tokenizer.batch_decode(__UpperCamelCase							,					skip_special_tokens=__UpperCamelCase						)
							predicted_outputs += generated_string

					self.assertListEqual(__UpperCamelCase							,					__UpperCamelCase						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		"""facebook/opt-350m"""

					__SCREAMING_SNAKE_CASE	:		str   	=		[
					    """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""",
					]

					__SCREAMING_SNAKE_CASE	:		str   	=		[]
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		GPTaTokenizer.from_pretrained(__UpperCamelCase						)

					for prompt in self.prompts:
							__SCREAMING_SNAKE_CASE	:		str   	=		tokenizer(__UpperCamelCase							,					return_tensors="""jax"""						).input_ids

							__SCREAMING_SNAKE_CASE	:		int   	=		model.generate(__UpperCamelCase							,					max_length=1_0						)
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		generated_ids[0]

							__SCREAMING_SNAKE_CASE	:		int   	=		tokenizer.batch_decode(__UpperCamelCase							,					skip_special_tokens=__UpperCamelCase						)
							predicted_outputs += generated_string

					self.assertListEqual(__UpperCamelCase							,					__UpperCamelCase						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		"""facebook/opt-125m"""
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		[
					    """Today is a beautiful day and I want to thank""",
					    """In the city of Rome Canaver Canaver Canaver Canaver""",
					]
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		GPTaTokenizer.from_pretrained(__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		tokenizer(
					    [
					        """Today is a beautiful day and I want to""",
					        """In the city of""",
					    ]							,					return_tensors="""jax"""							,					padding=__UpperCamelCase							,					)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		jax.jit(model.generate						)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		jit_generate(inputs["""input_ids"""]							,					attention_mask=inputs["""attention_mask"""]						).sequences

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		tokenizer.batch_decode(__UpperCamelCase							,					skip_special_tokens=__UpperCamelCase						)

					self.assertIsNotNone(__UpperCamelCase							,					__UpperCamelCase						)



			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Any   	=		"""facebook/opt-350m"""

					__SCREAMING_SNAKE_CASE	:		int   	=		GPTaTokenizer.from_pretrained(__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		FlaxOPTForCausalLM.from_pretrained(__UpperCamelCase						)

					__SCREAMING_SNAKE_CASE	:		str   	=		"""left"""

					# use different length sentences to test batching
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		[
					    """Hello, my dog is a little""",
					    """Today, I""",
					]

					__SCREAMING_SNAKE_CASE	:		Dict   	=		tokenizer(__UpperCamelCase							,					return_tensors="""jax"""							,					padding=__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		str   	=		inputs["""input_ids"""]

					__SCREAMING_SNAKE_CASE	:		Any   	=		model.generate(input_ids=__UpperCamelCase							,					attention_mask=inputs["""attention_mask"""]							,					trace=__UpperCamelCase						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		tokenizer(sentences[0]							,					return_tensors="""jax"""						).input_ids
					__SCREAMING_SNAKE_CASE	:		int   	=		model.generate(input_ids=__UpperCamelCase						)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].sum()
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		tokenizer(sentences[1]							,					return_tensors="""jax"""						).input_ids
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model.generate(input_ids=__UpperCamelCase							,					max_length=model.config.max_length - num_paddings						)

					__SCREAMING_SNAKE_CASE	:		int   	=		tokenizer.batch_decode(outputs[0]							,					skip_special_tokens=__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		tokenizer.decode(output_non_padded[0][0]							,					skip_special_tokens=__UpperCamelCase						)
					__SCREAMING_SNAKE_CASE	:		int   	=		tokenizer.decode(output_padded[0][0]							,					skip_special_tokens=__UpperCamelCase						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		[
					    """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]						)





'''simple docstring'''




from __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCamelCase__	:     str    =			TypeVar('''T''', bound=bool)






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			str							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		T   	=		value
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None
					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

			@property
			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					return self._value



			@staticmethod
			def UpperCamelCase__ (							lowerCAmelCase__					:			RandomizedHeapNode[T] | None							,					lowerCAmelCase__					:			RandomizedHeapNode[T] | None						):





					"""simple docstring"""






					if not roota:
							return roota

					if not roota:
							return roota

					if roota.value > roota.value:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		roota, roota

					if random.choice([True, False]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		roota.right, roota.left

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		RandomizedHeapNode.merge(roota.left							,					lowerCAmelCase__						)

					return roota






class 						_UpperCamelCase  (							Generic[T]   ):


			'''simple docstring'''

			def __init__(							self					:			Optional[int]							,					lowerCAmelCase__					:			Iterable[T] | None = ()						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		RandomizedHeapNode[T] | None   	=		None

					if data:
							for item in data:
									self.insert(lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			T						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		RandomizedHeapNode.merge(self._root							,					RandomizedHeapNode(lowerCAmelCase__						)						)

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.top()

					if self._root is None:
							return None

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		RandomizedHeapNode.merge(self._root.left							,					self._root.right						)

					return result

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					if not self._root:
							raise IndexError("""Can't get top element for the empty heap."""						)
					return self._root.value

			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		[]
					while self:
							result.append(self.pop()						)

					return result



			def __bool__(							self					:			List[Any]						):





					"""simple docstring"""






					return self._root is not None


if __name__ == "__main__":
						import doctest

						doctest.testmod()