"""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       :    Union[str, Any]       ,    _lowercase       :    List[str]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		parent
										UpperCAmelCase__						=		1_3
										UpperCAmelCase__						=		7
										UpperCAmelCase__						=		True
										UpperCAmelCase__						=		True
										UpperCAmelCase__						=		True
										UpperCAmelCase__						=		True
										UpperCAmelCase__						=		True
										UpperCAmelCase__						=		9_9
										UpperCAmelCase__						=		3_2
										UpperCAmelCase__						=		4
										UpperCAmelCase__						=		2
										UpperCAmelCase__						=		4
										UpperCAmelCase__						=		3_7
										UpperCAmelCase__						=		"""gelu"""
										UpperCAmelCase__						=		0.1
										UpperCAmelCase__						=		0.1
										UpperCAmelCase__						=		5_1_2
										UpperCAmelCase__						=		1_6
										UpperCAmelCase__						=		2
										UpperCAmelCase__						=		0.02
										UpperCAmelCase__						=		3
										UpperCAmelCase__						=		4
										UpperCAmelCase__						=		None
										UpperCAmelCase__						=		self.vocab_size - 1
										UpperCAmelCase__						=		self.vocab_size - 1
										UpperCAmelCase__						=		self.vocab_size - 1

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


										'''simple docstring'''
										UpperCAmelCase__						=		ids_tensor([self.batch_size, self.seq_length]       ,    self.vocab_size     )

										UpperCAmelCase__						=		None
										if self.use_input_mask:
																UpperCAmelCase__						=		random_attention_mask([self.batch_size, self.seq_length]     )

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

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

										UpperCAmelCase__						=		None
										UpperCAmelCase__						=		None
										UpperCAmelCase__						=		None
										if self.use_labels:
																UpperCAmelCase__						=		ids_tensor([self.batch_size]       ,    self.type_sequence_label_size     )
																UpperCAmelCase__						=		ids_tensor([self.batch_size, self.seq_length]       ,    self.num_labels     )
																UpperCAmelCase__						=		ids_tensor([self.batch_size]       ,    self.num_choices     )

										UpperCAmelCase__						=		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=_lowercase       ,    )

										UpperCAmelCase__						=		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       :    Optional[Any]       ,    _lowercase       :    List[Any]       ,    _lowercase       :    int       ,    _lowercase       :    Dict       ,    _lowercase       :    List[str]       ,    _lowercase       :    List[str]       ,    *_lowercase       :    Any     ):


										'''simple docstring'''
										UpperCAmelCase__						=		TFGPTJModel(config=_lowercase     )
										UpperCAmelCase__						=		{
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										UpperCAmelCase__						=		model(_lowercase     )

										UpperCAmelCase__						=		[input_ids, None, input_mask]  # None is the input for 'past'
										UpperCAmelCase__						=		model(_lowercase     )

										UpperCAmelCase__						=		model(_lowercase     )

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

				def        A (     self       :    List[str]       ,    _lowercase       :    Optional[Any]       ,    _lowercase       :    str       ,    _lowercase       :    List[str]       ,    _lowercase       :    Optional[Any]       ,    _lowercase       :    Any       ,    *_lowercase       :    str     ):


										'''simple docstring'''
										UpperCAmelCase__						=		TFGPTJModel(config=_lowercase     )

										# first forward pass
										UpperCAmelCase__						=		model(_lowercase       ,    token_type_ids=_lowercase       ,    use_cache=_lowercase     )
										UpperCAmelCase__						=		model(_lowercase       ,    token_type_ids=_lowercase     )
										UpperCAmelCase__						=		model(_lowercase       ,    token_type_ids=_lowercase       ,    use_cache=_lowercase     )

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

										UpperCAmelCase__	,      UpperCAmelCase__						=		outputs.to_tuple()

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

										# append to next input_ids and token_type_ids
										UpperCAmelCase__						=		tf.concat([input_ids, next_tokens]       ,    axis=-1     )
										UpperCAmelCase__						=		tf.concat([token_type_ids, next_token_types]       ,    axis=-1     )

										UpperCAmelCase__						=		model(_lowercase       ,    token_type_ids=_lowercase     )["""last_hidden_state"""]
										UpperCAmelCase__						=		model(_lowercase       ,    token_type_ids=_lowercase       ,    past_key_values=_lowercase     )[
										    """last_hidden_state"""
										]

										# select random slice
										UpperCAmelCase__						=		int(ids_tensor((1,)       ,    shape_list(_lowercase     )[-1]     )     )
										UpperCAmelCase__						=		output_from_no_past[:, -1, random_slice_idx]
										UpperCAmelCase__						=		output_from_past[:, 0, random_slice_idx]

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

				def        A (     self       :    Optional[int]       ,    _lowercase       :    str       ,    _lowercase       :    Any       ,    _lowercase       :    Tuple       ,    _lowercase       :    Optional[Any]       ,    _lowercase       :    Tuple       ,    *_lowercase       :    int     ):


										'''simple docstring'''
										UpperCAmelCase__						=		TFGPTJModel(config=_lowercase     )

										# create attention mask
										UpperCAmelCase__						=		self.seq_length // 2
										UpperCAmelCase__						=		tf.ones((self.batch_size, half_seq_length)       ,    dtype=tf.intaa     )
										UpperCAmelCase__						=		tf.zeros((self.batch_size, self.seq_length - half_seq_length)       ,    dtype=tf.intaa     )
										UpperCAmelCase__						=		tf.concat([attn_mask_begin, attn_mask_end]       ,    axis=1     )

										# first forward pass
										UpperCAmelCase__	,      UpperCAmelCase__						=		model(_lowercase       ,    attention_mask=_lowercase     ).to_tuple()

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

										# change a random masked slice from input_ids
										UpperCAmelCase__						=		ids_tensor((1,)       ,    _lowercase     ).numpy() + 1
										UpperCAmelCase__						=		ids_tensor((self.batch_size, self.seq_length)       ,    config.vocab_size     )
										UpperCAmelCase__						=		tf.range(self.seq_length     ) == (self.seq_length - random_seq_idx_to_change)
										UpperCAmelCase__						=		tf.transpose(
										    tf.broadcast_to(tf.expand_dims(_lowercase       ,    -1     )       ,    (self.seq_length, self.batch_size)     )     )
										UpperCAmelCase__						=		tf.where(_lowercase       ,    _lowercase       ,    _lowercase     )

										# append to next input_ids and attn_mask
										UpperCAmelCase__						=		tf.concat([input_ids, next_tokens]       ,    axis=-1     )
										UpperCAmelCase__						=		tf.concat([attn_mask, tf.ones((shape_list(_lowercase     )[0], 1)       ,    dtype=tf.intaa     )]       ,    axis=1     )

										# get two different outputs
										UpperCAmelCase__						=		model(_lowercase       ,    attention_mask=_lowercase     )["""last_hidden_state"""]
										UpperCAmelCase__						=		model(_lowercase       ,    past_key_values=_lowercase       ,    attention_mask=_lowercase     )[
										    """last_hidden_state"""
										]

										# select random slice
										UpperCAmelCase__						=		int(ids_tensor((1,)       ,    shape_list(_lowercase     )[-1]     )     )
										UpperCAmelCase__						=		output_from_no_past[:, -1, random_slice_idx]
										UpperCAmelCase__						=		output_from_past[:, 0, random_slice_idx]

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

				def        A (     self       :    Union[str, Any]       ,    _lowercase       :    Optional[Any]       ,    _lowercase       :    str       ,    _lowercase       :    List[Any]       ,    _lowercase       :    List[Any]       ,    _lowercase       :    Optional[Any]       ,    *_lowercase       :    List[str]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		TFGPTJModel(config=_lowercase     )

										UpperCAmelCase__						=		input_ids[:1, :]
										UpperCAmelCase__						=		input_mask[:1, :]
										UpperCAmelCase__						=		token_type_ids[:1, :]
										UpperCAmelCase__						=		1

										# first forward pass
										UpperCAmelCase__						=		model(_lowercase       ,    attention_mask=_lowercase       ,    token_type_ids=_lowercase       ,    use_cache=_lowercase     )

										UpperCAmelCase__	,      UpperCAmelCase__						=		outputs.to_tuple()

										# create hypothetical next token and extent to next_input_ids
										UpperCAmelCase__						=		ids_tensor((self.batch_size, 3)       ,    config.vocab_size     )
										UpperCAmelCase__						=		ids_tensor((self.batch_size, 3)       ,    2     )
										UpperCAmelCase__						=		ids_tensor((self.batch_size, 3)       ,    self.type_vocab_size     )

										# append to next input_ids and token_type_ids
										UpperCAmelCase__						=		tf.concat([input_ids, next_tokens]       ,    axis=-1     )
										UpperCAmelCase__						=		tf.concat([input_mask, next_attn_mask]       ,    axis=-1     )
										UpperCAmelCase__						=		tf.concat([token_type_ids, next_token_types]       ,    axis=-1     )

										UpperCAmelCase__						=		model(
										    _lowercase       ,    token_type_ids=_lowercase       ,    attention_mask=_lowercase     )["""last_hidden_state"""]
										UpperCAmelCase__						=		model(
										    _lowercase       ,    token_type_ids=_lowercase       ,    attention_mask=_lowercase       ,    past_key_values=_lowercase       ,    )["""last_hidden_state"""]
										self.parent.assertTrue(output_from_past.shape[1] == next_tokens.shape[1]     )

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

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

				def        A (     self       :    str       ,    _lowercase       :    List[str]       ,    _lowercase       :    str       ,    _lowercase       :    Tuple       ,    _lowercase       :    Dict       ,    _lowercase       :    Any       ,    *_lowercase       :    str     ):


										'''simple docstring'''
										UpperCAmelCase__						=		TFGPTJForCausalLM(config=_lowercase     )
										UpperCAmelCase__						=		{
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										UpperCAmelCase__						=		model(_lowercase     )
										self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.seq_length, self.vocab_size)     )

				def        A (     self       :    Any     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.prepare_config_and_inputs()

										(
										    (
										    UpperCAmelCase__
										)	,      (
										    UpperCAmelCase__
										)	,      (
										    UpperCAmelCase__
										)	,      (
										    UpperCAmelCase__
										)	,      (
										    UpperCAmelCase__
										)	,      (
										    UpperCAmelCase__
										)	,      (
										    UpperCAmelCase__
										)	,      (
										    UpperCAmelCase__
										)	,      (
										    UpperCAmelCase__
										)	,      
										)						=		config_and_inputs

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


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

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

				def        A (     self       :    str       ,    _lowercase       :    List[str]       ,    _lowercase       :    List[str]       ,    _lowercase       :    List[str]       ,    _lowercase       :    Dict       ,    _lowercase       :    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       :    Dict     ):


										'''simple docstring'''
										UpperCAmelCase__						=		TFGPTJModelTester(self     )
										UpperCAmelCase__						=		ConfigTester(self       ,    config_class=_lowercase       ,    n_embd=3_7     )

				def        A (     self       :    List[Any]     ):


										'''simple docstring'''
										self.config_tester.run_common_tests()

				def        A (     self       :    Tuple     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model(*_lowercase     )

				def        A (     self       :    str     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model_past(*_lowercase     )

				def        A (     self       :    Dict     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model_attention_mask_past(*_lowercase     )

				def        A (     self       :    Dict     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_model_past_large_inputs(*_lowercase     )

				def        A (     self       :    Optional[int]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_gptj_lm_head_model(*_lowercase     )

				@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       :    List[str]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		TFGPTJModel.from_pretrained("""EleutherAI/gpt-j-6B"""       ,    from_pt=_lowercase     )
										self.assertIsNotNone(_lowercase     )

				@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""     )
				def        A (     self       :    Optional[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       :    Optional[Any]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""       ,    from_pt=_lowercase     )
										UpperCAmelCase__						=		tf.convert_to_tensor([[4_6_4, 3_2_9_0]]       ,    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__						=		[4_6_4, 3_2_9_0, 3_1_8, 2_5_7, 5_8_2, 3_3_8, 1_2_6_6, 1_5_4_5, 1_3, 6_3_2, 3_1_8, 2_5_7, 9_1_1_2, 1_5_1_8_5, 1_1, 2_9_0, 3_4_0, 3_1_8, 2_5_7, 1_5_4_5]
										# fmt: on
										UpperCAmelCase__						=		model.generate(_lowercase       ,    do_sample=_lowercase     )
										self.assertListEqual(output_ids[0].numpy().tolist()       ,    _lowercase     )

				def        A (     self       :    Optional[Any]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""       ,    revision="""float16"""     )
										UpperCAmelCase__						=		TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""       ,    revision="""float16"""       ,    from_pt=_lowercase     )

										UpperCAmelCase__						=		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__						=		model.generate(**_lowercase       ,    do_sample=_lowercase       ,    seed=[4_2, 0]     )
										UpperCAmelCase__						=		tokenizer.decode(output_ids[0]       ,    skip_special_tokens=_lowercase     )

										UpperCAmelCase__						=		"""Today is a nice day and I’m going to go for a walk. I’"""
										self.assertEqual(_lowercase       ,    _lowercase     )

				def        A (     self       :    Optional[int]     ):


										'''simple docstring'''
										UpperCAmelCase__						=		TFGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""       ,    revision="""float16"""       ,    from_pt=_lowercase     )
										UpperCAmelCase__						=		AutoTokenizer.from_pretrained("""EleutherAI/gpt-j-6B"""       ,    revision="""float16"""     )

										UpperCAmelCase__						=		"""left"""

										# Define PAD Token = EOS Token = 50256
										UpperCAmelCase__						=		tokenizer.eos_token
										UpperCAmelCase__						=		model.config.eos_token_id

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


										'''simple docstring'''
										# Confirms that we get the expected results with left-padded beam search
										UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__						=		self._get_beam_search_test_objects()

										UpperCAmelCase__						=		tokenizer(_lowercase       ,    return_tensors="""tf"""       ,    padding=_lowercase     )
										UpperCAmelCase__						=		model.generate(**_lowercase       ,    do_sample=_lowercase       ,    num_beams=2     )
										UpperCAmelCase__						=		tokenizer.batch_decode(_lowercase       ,    skip_special_tokens=_lowercase     )
										self.assertListEqual(_lowercase       ,    _lowercase     )

				def        A (     self       :    Tuple     ):


										'''simple docstring'''
										# Confirms that left-padding is working properly
										UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__						=		self._get_beam_search_test_objects()

										UpperCAmelCase__						=		tokenizer(_lowercase       ,    return_tensors="""tf"""       ,    padding=_lowercase     )
										UpperCAmelCase__						=		tokenizer(sentences[0]       ,    return_tensors="""tf"""     )
										UpperCAmelCase__						=		model.generate(**_lowercase       ,    do_sample=_lowercase       ,    num_beams=2     )
										UpperCAmelCase__						=		(
										    shape_list(inputs_non_padded["""input_ids"""]     )[-1]
										    - tf.reduce_sum(tf.cast(inputs["""attention_mask"""][-1]       ,    tf.intaa     )     ).numpy()
										)
										UpperCAmelCase__						=		tokenizer(sentences[1]       ,    return_tensors="""tf"""     )
										UpperCAmelCase__						=		model.generate(
										    **_lowercase       ,    do_sample=_lowercase       ,    num_beams=2       ,    max_length=model.config.max_length - num_paddings     )
										UpperCAmelCase__						=		tokenizer.decode(output_non_padded[0]       ,    skip_special_tokens=_lowercase     )
										UpperCAmelCase__						=		tokenizer.decode(output_padded[0]       ,    skip_special_tokens=_lowercase     )
										self.assertListEqual(_lowercase       ,    [non_padded_sentence, padded_sentence]     )

				def        A (     self       :    str     ):


										'''simple docstring'''
										# Confirms that XLA is working properly
										UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__	,      UpperCAmelCase__						=		self._get_beam_search_test_objects()

										UpperCAmelCase__						=		tokenizer(_lowercase       ,    return_tensors="""tf"""       ,    padding=_lowercase     )
										UpperCAmelCase__						=		tf.function(model.generate       ,    jit_compile=_lowercase     )
										UpperCAmelCase__						=		xla_generate(**_lowercase       ,    do_sample=_lowercase       ,    num_beams=2     )
										UpperCAmelCase__						=		tokenizer.batch_decode(_lowercase       ,    skip_special_tokens=_lowercase     )
										self.assertListEqual(_lowercase       ,    _lowercase     )


"""simple docstring"""







from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
UpperCamelCase_       :       Optional[int]							   =		load_iris()
iris.keys()


print(F"""Target names: \n {iris.target_names} """)
print(F"""\n Features: \n {iris.feature_names}""")

# Train set e Test set
UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_       :       int							   =		train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

UpperCamelCase_       :       str							   =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
UpperCamelCase_       :       Union[str, Any]							   =		[[1, 2, 1, 4], [2, 3, 4, 5]]

UpperCamelCase_       :       Union[str, Any]							   =		knn.predict(X_new)

print(
    F"""\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"""
    F""" {iris["target_names"][prediction]}"""
)

