




"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, GPTJConfig, is_tf_available
from transformers.testing_utils import require_tf, slow, tooslow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
from ...utils.test_modeling_tf_core import TFCoreModelTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers.models.gptj.modeling_tf_gptj import (
		    TFGPTJForCausalLM,
		    TFGPTJForQuestionAnswering,
		    TFGPTJForSequenceClassification,
		    TFGPTJModel,
		    shape_list,
		)

class 							lowercase   :







				def __init__(				self :       List[str]   ,  _lowerCamelCase :       List[str]							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		parent
					A_	:					Dict						 =		13
					A_	:					List[Any]						 =		7
					A_	:					Optional[Any]						 =		True
					A_	:					str						 =		True
					A_	:					List[str]						 =		True
					A_	:					List[str]						 =		True
					A_	:					List[Any]						 =		True
					A_	:					Any						 =		99
					A_	:					Any						 =		32
					A_	:					Optional[Any]						 =		4
					A_	:					Union[str, Any]						 =		2
					A_	:					List[str]						 =		4
					A_	:					Union[str, Any]						 =		37
					A_	:					Dict						 =		'''gelu'''
					A_	:					int						 =		0.1
					A_	:					Union[str, Any]						 =		0.1
					A_	:					Tuple						 =		5_12
					A_	:					Tuple						 =		16
					A_	:					Optional[int]						 =		2
					A_	:					int						 =		0.02
					A_	:					Optional[int]						 =		3
					A_	:					int						 =		4
					A_	:					int						 =		None
					A_	:					int						 =		self.vocab_size - 1
					A_	:					str						 =		self.vocab_size - 1
					A_	:					List[Any]						 =		self.vocab_size - 1







				def        a_	(				self :       int							):



					"""simple docstring"""

					A_	:					Any						 =		ids_tensor([self.batch_size, self.seq_length]   ,  self.vocab_size							)

					A_	:					Optional[int]						 =		None
					if self.use_input_mask:
						A_	:					Optional[Any]						 =		random_attention_mask([self.batch_size, self.seq_length]							)

					A_	:					Optional[int]						 =		None
					if self.use_token_type_ids:
						A_	:					int						 =		ids_tensor([self.batch_size, self.seq_length]   ,  self.type_vocab_size							)

					A_	:					Optional[Any]						 =		None
					if self.use_mc_token_ids:
						A_	:					Union[str, Any]						 =		ids_tensor([self.batch_size, self.num_choices]   ,  self.seq_length							)

					A_	:					List[Any]						 =		None
					A_	:					int						 =		None
					A_	:					Dict						 =		None
					if self.use_labels:
						A_	:					List[Any]						 =		ids_tensor([self.batch_size]   ,  self.type_sequence_label_size							)
						A_	:					int						 =		ids_tensor([self.batch_size, self.seq_length]   ,  self.num_labels							)
						A_	:					Union[str, Any]						 =		ids_tensor([self.batch_size]   ,  self.num_choices							)

					A_	:					Tuple						 =		GPTJConfig(
					    vocab_size=self.vocab_size   ,  n_embd=self.hidden_size   ,  n_layer=self.num_hidden_layers   ,  n_head=self.num_attention_heads   ,  intermediate_size=self.intermediate_size   ,  hidden_act=self.hidden_act   ,  hidden_dropout_prob=self.hidden_dropout_prob   ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,  n_positions=self.max_position_embeddings   ,  type_vocab_size=self.type_vocab_size   ,  initializer_range=self.initializer_range   ,  bos_token_id=self.bos_token_id   ,  eos_token_id=self.eos_token_id   ,  pad_token_id=self.pad_token_id   ,  rotary_dim=self.rotary_dim   ,  return_dict=_lowerCamelCase   ,  )

					A_	:					Optional[Any]						 =		ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,  2							)

					return (
					    config,
					    input_ids,
					    input_mask,
					    head_mask,
					    token_type_ids,
					    mc_token_ids,
					    sequence_labels,
					    token_labels,
					    choice_labels,
					)







				def        a_	(				self :       Tuple   ,  _lowerCamelCase :       Optional[Any]   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       Optional[int]   ,  _lowerCamelCase :       List[str]   ,  _lowerCamelCase :       List[Any]   ,  *_lowerCamelCase :       Tuple							):



					"""simple docstring"""

					A_	:					Union[str, Any]						 =		TFGPTJModel(config=_lowerCamelCase							)
					A_	:					Optional[int]						 =		{
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}
					A_	:					Tuple						 =		model(_lowerCamelCase							)

					A_	:					Tuple						 =		[input_ids, None, input_mask]  # None is the input for 'past'
					A_	:					Tuple						 =		model(_lowerCamelCase							)

					A_	:					int						 =		model(_lowerCamelCase							)

					self.parent.assertEqual(result.last_hidden_state.shape   ,  (self.batch_size, self.seq_length, self.hidden_size)							)







				def        a_	(				self :       Any   ,  _lowerCamelCase :       Dict   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       Optional[int]   ,  *_lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					Dict						 =		TFGPTJModel(config=_lowerCamelCase							)

					# first forward pass
					A_	:					Union[str, Any]						 =		model(_lowerCamelCase   ,  token_type_ids=_lowerCamelCase   ,  use_cache=_lowerCamelCase							)
					A_	:					str						 =		model(_lowerCamelCase   ,  token_type_ids=_lowerCamelCase							)
					A_	:					Dict						 =		model(_lowerCamelCase   ,  token_type_ids=_lowerCamelCase   ,  use_cache=_lowerCamelCase							)

					self.parent.assertTrue(len(_lowerCamelCase							) == len(_lowerCamelCase							)							)
					self.parent.assertTrue(len(_lowerCamelCase							) == len(_lowerCamelCase							) + 1							)

					A_ ,			A_	:					int						 =		outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					A_	:					str						 =		ids_tensor((self.batch_size, 1)   ,  config.vocab_size							)
					A_	:					Tuple						 =		ids_tensor([self.batch_size, 1]   ,  self.type_vocab_size							)

					# append to next input_ids and token_type_ids
					A_	:					List[str]						 =		tf.concat([input_ids, next_tokens]   ,  axis=-1							)
					A_	:					Dict						 =		tf.concat([token_type_ids, next_token_types]   ,  axis=-1							)

					A_	:					Optional[int]						 =		model(_lowerCamelCase   ,  token_type_ids=_lowerCamelCase							)['''last_hidden_state''']
					A_	:					List[str]						 =		model(_lowerCamelCase   ,  token_type_ids=_lowerCamelCase   ,  past_key_values=_lowerCamelCase							)[
					    '''last_hidden_state'''
					]

					# select random slice
					A_	:					Union[str, Any]						 =		int(ids_tensor((1,)   ,  shape_list(_lowerCamelCase							)[-1]							)							)
					A_	:					List[Any]						 =		output_from_no_past[:, -1, random_slice_idx]
					A_	:					Optional[int]						 =		output_from_past[:, 0, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(_lowerCamelCase   ,  _lowerCamelCase   ,  rtol=1E-6							)







				def        a_	(				self :       str   ,  _lowerCamelCase :       Optional[int]   ,  _lowerCamelCase :       Optional[int]   ,  _lowerCamelCase :       List[str]   ,  _lowerCamelCase :       List[str]   ,  _lowerCamelCase :       Any   ,  *_lowerCamelCase :       Union[str, Any]							):



					"""simple docstring"""

					A_	:					Tuple						 =		TFGPTJModel(config=_lowerCamelCase							)

					# create attention mask
					A_	:					Union[str, Any]						 =		self.seq_length // 2
					A_	:					List[Any]						 =		tf.ones((self.batch_size, half_seq_length)   ,  dtype=tf.intaa							)
					A_	:					List[Any]						 =		tf.zeros((self.batch_size, self.seq_length - half_seq_length)   ,  dtype=tf.intaa							)
					A_	:					int						 =		tf.concat([attn_mask_begin, attn_mask_end]   ,  axis=1							)

					# first forward pass
					A_ ,			A_	:					List[str]						 =		model(_lowerCamelCase   ,  attention_mask=_lowerCamelCase							).to_tuple()

					# create hypothetical next token and extent to next_input_ids
					A_	:					List[str]						 =		ids_tensor((self.batch_size, 1)   ,  config.vocab_size							)

					# change a random masked slice from input_ids
					A_	:					Optional[int]						 =		ids_tensor((1,)   ,  _lowerCamelCase							).numpy() + 1
					A_	:					Any						 =		ids_tensor((self.batch_size, self.seq_length)   ,  config.vocab_size							)
					A_	:					Tuple						 =		tf.range(self.seq_length							) == (self.seq_length - random_seq_idx_to_change)
					A_	:					Union[str, Any]						 =		tf.transpose(
					    tf.broadcast_to(tf.expand_dims(_lowerCamelCase   ,  -1							)   ,  (self.seq_length, self.batch_size)							)							)
					A_	:					Optional[Any]						 =		tf.where(_lowerCamelCase   ,  _lowerCamelCase   ,  _lowerCamelCase							)

					# append to next input_ids and attn_mask
					A_	:					List[Any]						 =		tf.concat([input_ids, next_tokens]   ,  axis=-1							)
					A_	:					Any						 =		tf.concat([attn_mask, tf.ones((shape_list(_lowerCamelCase							)[0], 1)   ,  dtype=tf.intaa							)]   ,  axis=1							)

					# get two different outputs
					A_	:					Tuple						 =		model(_lowerCamelCase   ,  attention_mask=_lowerCamelCase							)['''last_hidden_state''']
					A_	:					List[Any]						 =		model(_lowerCamelCase   ,  past_key_values=_lowerCamelCase   ,  attention_mask=_lowerCamelCase							)[
					    '''last_hidden_state'''
					]

					# select random slice
					A_	:					Tuple						 =		int(ids_tensor((1,)   ,  shape_list(_lowerCamelCase							)[-1]							)							)
					A_	:					Any						 =		output_from_no_past[:, -1, random_slice_idx]
					A_	:					Optional[Any]						 =		output_from_past[:, 0, random_slice_idx]

					# test that outputs are equal for slice
					tf.debugging.assert_near(_lowerCamelCase   ,  _lowerCamelCase   ,  rtol=1E-12							)







				def        a_	(				self :       Optional[int]   ,  _lowerCamelCase :       Tuple   ,  _lowerCamelCase :       Tuple   ,  _lowerCamelCase :       int   ,  _lowerCamelCase :       Dict   ,  _lowerCamelCase :       List[Any]   ,  *_lowerCamelCase :       Tuple							):



					"""simple docstring"""

					A_	:					List[str]						 =		TFGPTJModel(config=_lowerCamelCase							)

					A_	:					Any						 =		input_ids[:1, :]
					A_	:					Optional[Any]						 =		input_mask[:1, :]
					A_	:					List[Any]						 =		token_type_ids[:1, :]
					A_	:					List[Any]						 =		1

					# first forward pass
					A_	:					Any						 =		model(_lowerCamelCase   ,  attention_mask=_lowerCamelCase   ,  token_type_ids=_lowerCamelCase   ,  use_cache=_lowerCamelCase							)

					A_ ,			A_	:					int						 =		outputs.to_tuple()

					# create hypothetical next token and extent to next_input_ids
					A_	:					Tuple						 =		ids_tensor((self.batch_size, 3)   ,  config.vocab_size							)
					A_	:					List[str]						 =		ids_tensor((self.batch_size, 3)   ,  2							)
					A_	:					Dict						 =		ids_tensor((self.batch_size, 3)   ,  self.type_vocab_size							)

					# append to next input_ids and token_type_ids
					A_	:					Optional[int]						 =		tf.concat([input_ids, next_tokens]   ,  axis=-1							)
					A_	:					Optional[Any]						 =		tf.concat([input_mask, next_attn_mask]   ,  axis=-1							)
					A_	:					Union[str, Any]						 =		tf.concat([token_type_ids, next_token_types]   ,  axis=-1							)

					A_	:					Dict						 =		model(
					    _lowerCamelCase   ,  token_type_ids=_lowerCamelCase   ,  attention_mask=_lowerCamelCase							)['''last_hidden_state''']
					A_	:					int						 =		model(
					    _lowerCamelCase   ,  token_type_ids=_lowerCamelCase   ,  attention_mask=_lowerCamelCase   ,  past_key_values=_lowerCamelCase   ,  )['''last_hidden_state''']
					self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]							)

					# select random slice
					A_	:					Tuple						 =		int(ids_tensor((1,)   ,  shape_list(_lowerCamelCase							)[-1]							)							)
					A_	:					int						 =		output_from_no_past[:, -3:, random_slice_idx]
					A_	:					Tuple						 =		output_from_past[:, :, random_slice_idx]

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







				def        a_	(				self :       Any   ,  _lowerCamelCase :       Optional[Any]   ,  _lowerCamelCase :       Union[str, Any]   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       Optional[Any]   ,  _lowerCamelCase :       Tuple   ,  *_lowerCamelCase :       Optional[Any]							):



					"""simple docstring"""

					A_	:					List[str]						 =		TFGPTJForCausalLM(config=_lowerCamelCase							)
					A_	:					str						 =		{
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}
					A_	:					Dict						 =		model(_lowerCamelCase							)
					self.parent.assertEqual(result.logits.shape   ,  (self.batch_size, self.seq_length, self.vocab_size)							)







				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_	:					Dict						 =		self.prepare_config_and_inputs()

					(
					    (
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			(
					    A_
					) ,			
					)	:					int						 =		config_and_inputs

					A_	:					str						 =		{
					    '''input_ids''': input_ids,
					    '''token_type_ids''': token_type_ids,
					    '''attention_mask''': input_mask,
					}
					return config, inputs_dict

@require_tf
class 							lowercase   (     __UpperCAmelCase  ,						__UpperCAmelCase  ,						__UpperCAmelCase  ,						unittest.TestCase):
				__lowerCAmelCase     :     Dict								= (
				    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
				    if is_tf_available()
				    else ()
				)

				__lowerCAmelCase     :     Optional[int]								= (TFGPTJForCausalLM,) if is_tf_available() else ()
				__lowerCAmelCase     :     int								= (
				    {
				        """feature-extraction""": TFGPTJModel,
				        """question-answering""": TFGPTJForQuestionAnswering,
				        """text-classification""": TFGPTJForSequenceClassification,
				        """text-generation""": TFGPTJForCausalLM,
				        """zero-shot""": TFGPTJForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				__lowerCAmelCase     :     Tuple								= False
				__lowerCAmelCase     :     Optional[int]								= False
				__lowerCAmelCase     :     Dict								= False
				__lowerCAmelCase     :     Optional[int]								= False







				def        a_	(				self :       str   ,  _lowerCamelCase :       List[Any]   ,  _lowerCamelCase :       Any   ,  _lowerCamelCase :       Tuple   ,  _lowerCamelCase :       str   ,  _lowerCamelCase :       Union[str, Any]							):



					"""simple docstring"""

					if (
					    pipeline_test_casse_name == "QAPipelineTests"
					    and tokenizer_name is not None
					    and not tokenizer_name.endswith('''Fast'''							)
					):
						# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
						# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
						# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
						return True

					return False







				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_	:					Dict						 =		TFGPTJModelTester(self							)
					A_	:					Dict						 =		ConfigTester(self   ,  config_class=_lowerCamelCase   ,  n_embd=37							)







				def        a_	(				self :       Optional[Any]							):



					"""simple docstring"""

					self.config_tester.run_common_tests()







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



					"""simple docstring"""

					A_	:					int						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model(*_lowerCamelCase							)







				def        a_	(				self :       str							):



					"""simple docstring"""

					A_	:					List[str]						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_past(*_lowerCamelCase							)







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_	:					str						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_attention_mask_past(*_lowerCamelCase							)







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_	:					List[Any]						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_model_past_large_inputs(*_lowerCamelCase							)







				def        a_	(				self :       Tuple							):



					"""simple docstring"""

					A_	:					Dict						 =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_gptj_lm_head_model(*_lowerCamelCase							)







				@slow
				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices('''GPU'''							)							) > 0   ,  '''skip testing on GPU for now to avoid GPU OOM.'''   ,  )
				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_	:					Tuple						 =		TFGPTJModel.from_pretrained('''EleutherAI/gpt-j-6B'''   ,  from_pt=_lowerCamelCase							)
					self.assertIsNotNone(_lowerCamelCase							)







				@unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''							)
				def        a_	(				self :       int							):



					"""simple docstring"""

					super().test_resize_token_embeddings()

@require_tf
@tooslow
# Marked as @tooslow due to GPU OOM -- but still useful to run locally. Requires ~39GB of RAM.
class 							lowercase   (     unittest.TestCase):







				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					A_	:					int						 =		TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''   ,  from_pt=_lowerCamelCase							)
					A_	:					int						 =		tf.convert_to_tensor([[4_64, 32_90]]   ,  dtype=tf.intaa							)  # The dog
					# fmt: off
					# The dog is a man's best friend. It is a loyal companion, and it is a friend
					A_	:					Union[str, Any]						 =		[4_64, 32_90, 3_18, 2_57, 5_82, 3_38, 12_66, 15_45, 13, 6_32, 3_18, 2_57, 91_12, 1_51_85, 11, 2_90, 3_40, 3_18, 2_57, 15_45]
					# fmt: on
					A_	:					Optional[int]						 =		model.generate(_lowerCamelCase   ,  do_sample=_lowerCamelCase							)
					self.assertListEqual(output_ids[0].numpy().tolist()   ,  _lowerCamelCase							)







				def        a_	(				self :       Any							):



					"""simple docstring"""

					A_	:					Optional[Any]						 =		AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B'''   ,  revision='''float16'''							)
					A_	:					Dict						 =		TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''   ,  revision='''float16'''   ,  from_pt=_lowerCamelCase							)

					A_	:					Any						 =		tokenizer('''Today is a nice day and'''   ,  return_tensors='''tf'''							)
					# forces the generation to happen on CPU, to avoid GPU-related quirks
					with tf.device(''':/CPU:0'''							):
						A_	:					Optional[int]						 =		model.generate(**_lowerCamelCase   ,  do_sample=_lowerCamelCase   ,  seed=[42, 0]							)
					A_	:					Optional[int]						 =		tokenizer.decode(output_ids[0]   ,  skip_special_tokens=_lowerCamelCase							)

					A_	:					Union[str, Any]						 =		'''Today is a nice day and I’m going to go for a walk. I’'''
					self.assertEqual(_lowerCamelCase   ,  _lowerCamelCase							)







				def        a_	(				self :       Dict							):



					"""simple docstring"""

					A_	:					Tuple						 =		TFGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''   ,  revision='''float16'''   ,  from_pt=_lowerCamelCase							)
					A_	:					List[str]						 =		AutoTokenizer.from_pretrained('''EleutherAI/gpt-j-6B'''   ,  revision='''float16'''							)

					A_	:					Optional[Any]						 =		'''left'''

					# Define PAD Token = EOS Token = 50256
					A_	:					str						 =		tokenizer.eos_token
					A_	:					Tuple						 =		model.config.eos_token_id

					# use different length sentences to test batching
					A_	:					Any						 =		[
					    '''Hello, my dog is a little''',
					    '''Today, I''',
					]
					A_	:					Dict						 =		[
					    '''Hello, my dog is a little over a year old and has been diagnosed with hip dysplasia''',
					    '''Today, I’m going to be talking about a topic that’''',
					]
					return model, tokenizer, sentences, expected_output_sentences







				def        a_	(				self :       Dict							):



					"""simple docstring"""

					A_ ,			A_ ,			A_ ,			A_	:					Optional[Any]						 =		self._get_beam_search_test_objects()

					A_	:					Optional[int]						 =		tokenizer(_lowerCamelCase   ,  return_tensors='''tf'''   ,  padding=_lowerCamelCase							)
					A_	:					int						 =		model.generate(**_lowerCamelCase   ,  do_sample=_lowerCamelCase   ,  num_beams=2							)
					A_	:					Optional[Any]						 =		tokenizer.batch_decode(_lowerCamelCase   ,  skip_special_tokens=_lowerCamelCase							)
					self.assertListEqual(_lowerCamelCase   ,  _lowerCamelCase							)







				def        a_	(				self :       List[Any]							):



					"""simple docstring"""

					A_ ,			A_ ,			A_ ,			A_	:					Any						 =		self._get_beam_search_test_objects()

					A_	:					Union[str, Any]						 =		tokenizer(_lowerCamelCase   ,  return_tensors='''tf'''   ,  padding=_lowerCamelCase							)
					A_	:					str						 =		tokenizer(sentences[0]   ,  return_tensors='''tf'''							)
					A_	:					List[str]						 =		model.generate(**_lowerCamelCase   ,  do_sample=_lowerCamelCase   ,  num_beams=2							)
					A_	:					int						 =		(
					    shape_list(inputs_non_padded['''input_ids''']							)[-1]
					    - tf.reduce_sum(tf.cast(inputs['''attention_mask'''][-1]   ,  tf.intaa							)							).numpy()
					)
					A_	:					int						 =		tokenizer(sentences[1]   ,  return_tensors='''tf'''							)
					A_	:					str						 =		model.generate(
					    **_lowerCamelCase   ,  do_sample=_lowerCamelCase   ,  num_beams=2   ,  max_length=model.config.max_length - num_paddings							)
					A_	:					List[Any]						 =		tokenizer.decode(output_non_padded[0]   ,  skip_special_tokens=_lowerCamelCase							)
					A_	:					str						 =		tokenizer.decode(output_padded[0]   ,  skip_special_tokens=_lowerCamelCase							)
					self.assertListEqual(_lowerCamelCase   ,  [non_padded_sentence, padded_sentence]							)







				def        a_	(				self :       List[str]							):



					"""simple docstring"""

					A_ ,			A_ ,			A_ ,			A_	:					int						 =		self._get_beam_search_test_objects()

					A_	:					str						 =		tokenizer(_lowerCamelCase   ,  return_tensors='''tf'''   ,  padding=_lowerCamelCase							)
					A_	:					Any						 =		tf.function(model.generate   ,  jit_compile=_lowerCamelCase							)
					A_	:					int						 =		xla_generate(**_lowerCamelCase   ,  do_sample=_lowerCamelCase   ,  num_beams=2							)
					A_	:					int						 =		tokenizer.batch_decode(_lowerCamelCase   ,  skip_special_tokens=_lowerCamelCase							)
					self.assertListEqual(_lowerCamelCase   ,  _lowerCamelCase							)











"""simple docstring"""

class 							lowercase   :







				def __init__(				self :       Union[str, Any]   ,  _lowerCamelCase :       int							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		data
					A_	:					int
					A_	:					Node

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					List[str]						 =		0
	A_	:					List[str]						 =		x

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_ ,			A_	:					Any						 =		find_set(_UpperCAmelCase						), find_set(_UpperCAmelCase						)
	if x == y:
		return

	elif x.rank > y.rank:
		A_	:					Optional[int]						 =		x
	else:
		A_	:					Optional[int]						 =		y
		if x.rank == y.rank:
			y.rank += 1

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	if x != x.parent:
		A_	:					Union[str, Any]						 =		find_set(x.parent						)
	return x.parent

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					str						 =		({0, 1, 2}, {3, 4, 5})
	for s in sets:
		if node.data in s:
			return s
	A_	:					Optional[int]						 =		f"""{node.data} is not in {sets}"""
	raise ValueError(_UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					Dict						 =		[Node(_UpperCAmelCase						) for i in range(6						)]
	for v in vertex:
		make_set(_UpperCAmelCase						)

	union_set(vertex[0]	,   vertex[1]						)
	union_set(vertex[1]	,   vertex[2]						)
	union_set(vertex[3]	,   vertex[4]						)
	union_set(vertex[3]	,   vertex[5]						)

	for nodea in vertex:
		for nodea in vertex:
			if find_python_set(_UpperCAmelCase						).isdisjoint(find_python_set(_UpperCAmelCase						)						):
				assert find_set(_UpperCAmelCase						) != find_set(_UpperCAmelCase						)
			else:
				assert find_set(_UpperCAmelCase						) == find_set(_UpperCAmelCase						)


if __name__ == "__main__":
		test_disjoint_set()





