

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  a     :


						def __init__(				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			parent
										_UpperCAmelCase				:						List[Any]       						=			13
										_UpperCAmelCase				:						List[Any]       						=			7
										_UpperCAmelCase				:						int       						=			True
										_UpperCAmelCase				:						List[Any]       						=			True
										_UpperCAmelCase				:						Union[str, Any]       						=			True
										_UpperCAmelCase				:						Optional[int]       						=			True
										_UpperCAmelCase				:						Tuple       						=			True
										_UpperCAmelCase				:						str       						=			99
										_UpperCAmelCase				:						int       						=			32
										_UpperCAmelCase				:						Optional[Any]       						=			4
										_UpperCAmelCase				:						List[Any]       						=			2
										_UpperCAmelCase				:						str       						=			4
										_UpperCAmelCase				:						Any       						=			37
										_UpperCAmelCase				:						List[Any]       						=			"gelu"
										_UpperCAmelCase				:						List[str]       						=			0.1
										_UpperCAmelCase				:						Tuple       						=			0.1
										_UpperCAmelCase				:						List[str]       						=			512
										_UpperCAmelCase				:						str       						=			16
										_UpperCAmelCase				:						Optional[int]       						=			2
										_UpperCAmelCase				:						Union[str, Any]       						=			0.02
										_UpperCAmelCase				:						List[str]       						=			3
										_UpperCAmelCase				:						Optional[Any]       						=			4
										_UpperCAmelCase				:						Optional[Any]       						=			None
										_UpperCAmelCase				:						str       						=			self.vocab_size - 1
										_UpperCAmelCase				:						Any       						=			self.vocab_size - 1
										_UpperCAmelCase				:						List[str]       						=			self.vocab_size - 1


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size		)

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

										_UpperCAmelCase				:						List[str]       						=			None
										if self.use_token_type_ids:
														_UpperCAmelCase				:						Dict       						=			ids_tensor([self.batch_size, self.seq_length]  ,    self.type_vocab_size		)

										_UpperCAmelCase				:						str       						=			None
										if self.use_mc_token_ids:
														_UpperCAmelCase				:						List[str]       						=			ids_tensor([self.batch_size, self.num_choices]  ,    self.seq_length		)

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

										_UpperCAmelCase				:						int       						=			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=A_  ,    )

										_UpperCAmelCase				:						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 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    *A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			TFGPTJModel(config=A_		)
										_UpperCAmelCase				:						Any       						=			{
										    "input_ids": input_ids,
										    "attention_mask": input_mask,
										    "token_type_ids": token_type_ids,
										}
										_UpperCAmelCase				:						Dict       						=			model(A_		)

										_UpperCAmelCase				:						Optional[int]       						=			[input_ids, None, input_mask]  # None is the input for 'past'
										_UpperCAmelCase				:						Optional[int]       						=			model(A_		)

										_UpperCAmelCase				:						List[str]       						=			model(A_		)

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


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    *A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			TFGPTJModel(config=A_		)

										# first forward pass
										_UpperCAmelCase				:						Optional[int]       						=			model(A_  ,    token_type_ids=A_  ,    use_cache=A_		)
										_UpperCAmelCase				:						Union[str, Any]       						=			model(A_  ,    token_type_ids=A_		)
										_UpperCAmelCase				:						List[Any]       						=			model(A_  ,    token_type_ids=A_  ,    use_cache=A_		)

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

										_UpperCAmelCase						,							_UpperCAmelCase				:						Optional[Any]       						=			outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										_UpperCAmelCase				:						Optional[Any]       						=			ids_tensor((self.batch_size, 1)  ,    config.vocab_size		)
										_UpperCAmelCase				:						Optional[int]       						=			ids_tensor([self.batch_size, 1]  ,    self.type_vocab_size		)

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

										_UpperCAmelCase				:						Optional[int]       						=			model(A_  ,    token_type_ids=A_		)["last_hidden_state"]
										_UpperCAmelCase				:						Optional[int]       						=			model(A_  ,    token_type_ids=A_  ,    past_key_values=A_		)[
										    "last_hidden_state"
										]

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

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


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    *A_		):







										'''simple docstring'''


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

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

										# first forward pass
										_UpperCAmelCase						,							_UpperCAmelCase				:						Dict       						=			model(A_  ,    attention_mask=A_		).to_tuple()

										# create hypothetical next token and extent to next_input_ids
										_UpperCAmelCase				:						int       						=			ids_tensor((self.batch_size, 1)  ,    config.vocab_size		)

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

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

										# get two different outputs
										_UpperCAmelCase				:						Any       						=			model(A_  ,    attention_mask=A_		)["last_hidden_state"]
										_UpperCAmelCase				:						Dict       						=			model(A_  ,    past_key_values=A_  ,    attention_mask=A_		)[
										    "last_hidden_state"
										]

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

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


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    *A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			TFGPTJModel(config=A_		)

										_UpperCAmelCase				:						List[str]       						=			input_ids[:1, :]
										_UpperCAmelCase				:						Dict       						=			input_mask[:1, :]
										_UpperCAmelCase				:						int       						=			token_type_ids[:1, :]
										_UpperCAmelCase				:						int       						=			1

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

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

										# create hypothetical next token and extent to next_input_ids
										_UpperCAmelCase				:						Optional[int]       						=			ids_tensor((self.batch_size, 3)  ,    config.vocab_size		)
										_UpperCAmelCase				:						Dict       						=			ids_tensor((self.batch_size, 3)  ,    2		)
										_UpperCAmelCase				:						Any       						=			ids_tensor((self.batch_size, 3)  ,    self.type_vocab_size		)

										# append to next input_ids and token_type_ids
										_UpperCAmelCase				:						Dict       						=			tf.concat([input_ids, next_tokens]  ,    axis=-1		)
										_UpperCAmelCase				:						Optional[Any]       						=			tf.concat([input_mask, next_attn_mask]  ,    axis=-1		)
										_UpperCAmelCase				:						Tuple       						=			tf.concat([token_type_ids, next_token_types]  ,    axis=-1		)

										_UpperCAmelCase				:						Optional[int]       						=			model(
										    A_  ,    token_type_ids=A_  ,    attention_mask=A_		)["last_hidden_state"]
										_UpperCAmelCase				:						List[str]       						=			model(
										    A_  ,    token_type_ids=A_  ,    attention_mask=A_  ,    past_key_values=A_  ,    )["last_hidden_state"]
										self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]		)

										# select random slice
										_UpperCAmelCase				:						Any       						=			int(ids_tensor((1,)  ,    shape_list(A_		)[-1]		)		)
										_UpperCAmelCase				:						Optional[int]       						=			output_from_no_past[:, -3:, random_slice_idx]
										_UpperCAmelCase				:						str       						=			output_from_past[:, :, random_slice_idx]

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


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_  ,    *A_		):







										'''simple docstring'''


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





						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			self.prepare_config_and_inputs()

										(
										    (
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							(
										    _UpperCAmelCase
										)						,							
										)				:						Dict       						=			config_and_inputs

										_UpperCAmelCase				:						str       						=			{
										    "input_ids": input_ids,
										    "token_type_ids": token_type_ids,
										    "attention_mask": input_mask,
										}
										return config, inputs_dict







@require_tf
class  a     (		UpperCAmelCase							,       UpperCAmelCase							,       UpperCAmelCase							,       unittest.TestCase ):
						_lowercase			  =	(
						    (TFGPTJForCausalLM, TFGPTJForSequenceClassification, TFGPTJForQuestionAnswering, TFGPTJModel)
						    if is_tf_available()
						    else ()
						)

						_lowercase			  =	(TFGPTJForCausalLM,) if is_tf_available() else ()
						_lowercase			  =	(
						    {
						        "feature-extraction": TFGPTJModel,
						        "question-answering": TFGPTJForQuestionAnswering,
						        "text-classification": TFGPTJForSequenceClassification,
						        "text-generation": TFGPTJForCausalLM,
						        "zero-shot": TFGPTJForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_lowercase			  =	False
						_lowercase			  =	False
						_lowercase			  =	False
						_lowercase			  =	False


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_		):







										'''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 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			TFGPTJModelTester(self		)
										_UpperCAmelCase				:						str       						=			ConfigTester(self  ,    config_class=A_  ,    n_embd=37		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model(*A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model_past(*A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model_attention_mask_past(*A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Dict       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model_past_large_inputs(*A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_lm_head_model(*A_		)


						@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 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B"  ,    from_pt=A_		)
										self.assertIsNotNone(A_		)





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







										'''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  a     (		unittest.TestCase ):


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"  ,    from_pt=A_		)
										_UpperCAmelCase				:						int       						=			tf.convert_to_tensor([[464, 3290]]  ,    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
										_UpperCAmelCase				:						Any       						=			[464, 3290, 318, 257, 582, 338, 1266, 1545, 13, 632, 318, 257, 9112, 15185, 11, 290, 340, 318, 257, 1545]
										# fmt: on
										_UpperCAmelCase				:						List[Any]       						=			model.generate(A_  ,    do_sample=A_		)
										self.assertListEqual(output_ids[0].numpy().tolist()  ,    A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"  ,    revision="float16"		)
										_UpperCAmelCase				:						Dict       						=			TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"  ,    revision="float16"  ,    from_pt=A_		)

										_UpperCAmelCase				:						str       						=			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"		):
														_UpperCAmelCase				:						List[str]       						=			model.generate(**A_  ,    do_sample=A_  ,    seed=[42, 0]		)
										_UpperCAmelCase				:						Tuple       						=			tokenizer.decode(output_ids[0]  ,    skip_special_tokens=A_		)

										_UpperCAmelCase				:						Optional[Any]       						=			"Today is a nice day and I’m going to go for a walk. I’"
										self.assertEqual(A_  ,    A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			TFGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"  ,    revision="float16"  ,    from_pt=A_		)
										_UpperCAmelCase				:						Dict       						=			AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B"  ,    revision="float16"		)

										_UpperCAmelCase				:						List[Any]       						=			"left"

										# Define PAD Token = EOS Token = 50256
										_UpperCAmelCase				:						Optional[Any]       						=			tokenizer.eos_token
										_UpperCAmelCase				:						int       						=			model.config.eos_token_id

										# use different length sentences to test batching
										_UpperCAmelCase				:						int       						=			[
										    "Hello, my dog is a little",
										    "Today, I",
										]
										_UpperCAmelCase				:						str       						=			[
										    "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 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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

										_UpperCAmelCase				:						List[Any]       						=			tokenizer(A_  ,    return_tensors="tf"  ,    padding=A_		)
										_UpperCAmelCase				:						List[Any]       						=			model.generate(**A_  ,    do_sample=A_  ,    num_beams=2		)
										_UpperCAmelCase				:						List[str]       						=			tokenizer.batch_decode(A_  ,    skip_special_tokens=A_		)
										self.assertListEqual(A_  ,    A_		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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

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





						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase						,							_UpperCAmelCase				:						List[Any]       						=			self._get_beam_search_test_objects()

										_UpperCAmelCase				:						int       						=			tokenizer(A_  ,    return_tensors="tf"  ,    padding=A_		)
										_UpperCAmelCase				:						List[str]       						=			tf.function(model.generate  ,    jit_compile=A_		)
										_UpperCAmelCase				:						Optional[Any]       						=			xla_generate(**A_  ,    do_sample=A_  ,    num_beams=2		)
										_UpperCAmelCase				:						Dict       						=			tokenizer.batch_decode(A_  ,    skip_special_tokens=A_		)
										self.assertListEqual(A_  ,    A_		)





from __future__ import annotations

SCREAMING_SNAKE_CASE_						=					'Alexander Joslin'

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     int       )      ->				int:

				if number <= 0:
								_UpperCAmelCase				:						Optional[Any]       						=			F'n must be greater than 0. Got n = {number}'
								raise ValueError(lowerCAmelCase       )

				_UpperCAmelCase				:						Dict       						=			[number + 1] * (number + 1)

				# starting position
				_UpperCAmelCase				:						Dict       						=			0
				for i in range(1     ,					lowerCAmelCase       ):
								_UpperCAmelCase				:						int       						=			min(table[i + 1]     ,					table[i] + 1       )
								# check if out of bounds
								if i * 2 <= number:
												_UpperCAmelCase				:						int       						=			min(table[i * 2]     ,					table[i] + 1       )
								# check if out of bounds
								if i * 3 <= number:
												_UpperCAmelCase				:						int       						=			min(table[i * 3]     ,					table[i] + 1       )
				return table[number]


if __name__ == "__main__":
					import doctest

					doctest.testmod()


