
import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

__SCREAMING_SNAKE_CASE			:   List[str]         =     logging.getLogger()
__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     torch.cuda.is_available()
__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     {
    'max_tokens_per_batch': None,
    'supervise_forward': True,
    'normalize_hidden': True,
    'label_smoothing': 0.2,
    'eval_max_gen_length': None,
    'eval_beams': 1,
    'val_metric': 'loss',
    'save_top_k': 1,
    'adafactor': True,
    'early_stopping_patience': 2,
    'logger_name': 'default',
    'length_penalty': 0.5,
    'cache_dir': '',
    'task': 'summarization',
    'num_workers': 2,
    'alpha_hid': 0,
    'freeze_embeds': True,
    'enc_only': False,
    'tgt_suffix': '',
    'resume_from_checkpoint': None,
    'sortish_sampler': True,
    'student_decoder_layers': 1,
    'val_check_interval': 1.0,
    'output_dir': '',
    'fp16': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    'no_teacher': False,
    'fp16_opt_level': 'O1',
    'gpus': 1 if CUDA_AVAILABLE else 0,
    'n_tpu_cores': 0,
    'max_grad_norm': 1.0,
    'do_train': True,
    'do_predict': True,
    'accumulate_grad_batches': 1,
    'server_ip': '',
    'server_port': '',
    'seed': 42,
    'model_name_or_path': 'sshleifer/bart-tiny-random',
    'config_name': '',
    'tokenizer_name': 'facebook/bart-large',
    'do_lower_case': False,
    'learning_rate': 0.3,
    'lr_scheduler': 'linear',
    'weight_decay': 0.0,
    'adam_epsilon': 1E-0_8,
    'warmup_steps': 0,
    'max_epochs': 1,
    'train_batch_size': 2,
    'eval_batch_size': 2,
    'max_source_length': 12,
    'max_target_length': 12,
    'val_max_target_length': 12,
    'test_max_target_length': 12,
    'fast_dev_run': False,
    'no_cache': False,
    'n_train': -1,
    'n_val': -1,
    'n_test': -1,
    'student_encoder_layers': 1,
    'freeze_encoder': False,
    'auto_scale_batch_size': False,
    'overwrite_output_dir': False,
    'student': None,
}
def     UpperCAmelCase__							(       __magic_name__						:     Path							,  __magic_name__						:     list  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[Any]  	=     '''\n'''.join(__magic_name__  )
					Path(__magic_name__  ).open('''w'''  ).writelines(__magic_name__  )


__SCREAMING_SNAKE_CASE			:   str         =     [' Sam ate lunch today.', 'Sams lunch ingredients.']
__SCREAMING_SNAKE_CASE			:   List[Any]         =     ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     'patrickvonplaten/t5-tiny-random'
__SCREAMING_SNAKE_CASE			:   str         =     'sshleifer/t5-tinier-random'
__SCREAMING_SNAKE_CASE			:   int         =     'sshleifer/bart-tiny-random'
__SCREAMING_SNAKE_CASE			:   Dict         =     'sshleifer/tiny-mbart'
__SCREAMING_SNAKE_CASE			:   Dict         =     'sshleifer/tiny-marian-en-de'
__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     'stas/tiny-wmt19-en-de'


__SCREAMING_SNAKE_CASE			:   Union[str, Any]         =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def     UpperCAmelCase__							(       __magic_name__						:     List[Any]  ):


					'''simple docstring'''

					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(__magic_name__							,  f'''{split}.source'''  )							,  __magic_name__  )
										_dump_articles(os.path.join(__magic_name__							,  f'''{split}.target'''  )							,  __magic_name__  )
					return tmp_dir


class 		__magic_name__   (						snake_case    ):





		@classmethod
		def       _A							(							cls    :	Union[str, Any]				):
							logging.disable(logging.CRITICAL				)  # remove noisy download output from tracebacks
							return cls





		@slow
		@require_torch_gpu
		def       _A							(							self    :	int				):
							lowerCAmelCase		:					List[str]  	=     list_models()
							lowerCAmelCase		:					int  	=     '''sshleifer'''
							lowerCAmelCase		:					Tuple  	=     [x.modelId for x in model_list if x.modelId.startswith(lowerCamelCase__				)]
							lowerCAmelCase		:					List[str]  	=     ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
							lowerCAmelCase		:					Any  	=     []
							for m in model_ids:
												if m in allowed_to_be_broken:
																	continue
												try:
																	AutoConfig.from_pretrained(lowerCamelCase__				)
												except Exception:
																	failures.append(lowerCamelCase__				)
							assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''





		def       _A							(							self    :	List[Any]				):
							lowerCAmelCase		:					Tuple  	=     {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
							self._test_distiller_cli(lowerCamelCase__				)





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					Dict  	=     {
							    '''student_encoder_layers''': 2,
							    '''student_decoder_layers''': 1,
							    '''max_epochs''': 4,
							    '''val_check_interval''': 0.2_5,
							    '''alpha_hid''': 2.0,
							    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
							}
							lowerCAmelCase		:					int  	=     self._test_distiller_cli(lowerCamelCase__						,  check_contents=lowerCamelCase__				)

							lowerCAmelCase		:					List[Any]  	=     list(Path(model.output_dir				).glob('''*.ckpt'''				)				)
							self.assertEqual(1						,  len(lowerCamelCase__				)				)
							lowerCAmelCase		:					Dict  	=     list(Path(model.output_dir				).glob('''**/*.bin'''				)				)
							self.assertEqual(len(lowerCamelCase__				)						,  2				)
							lowerCAmelCase		:					Optional[Any]  	=     lmap(str.strip						,  Path(model.hparams.data_dir				).joinpath('''test.source'''				).open().readlines()				)
							lowerCAmelCase		:					Optional[Any]  	=     tempfile.mktemp()  # XXX: not being cleaned up
							generate_summaries_or_translations(lowerCamelCase__						,  lowerCamelCase__						,  str(model.output_dir / '''best_tfmr'''				)				)
							self.assertTrue(Path(lowerCamelCase__				).exists()				)

							lowerCAmelCase		:					Tuple  	=     self.get_auto_remove_tmp_dir()
							convert_pl_to_hf(ckpts[0]						,  transformer_ckpts[0].parent						,  lowerCamelCase__				)
							assert os.path.exists(os.path.join(lowerCamelCase__						,  '''pytorch_model.bin'''				)				)





		def       _A							(							self    :	Any				):
							lowerCAmelCase		:					List[Any]  	=     AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase__				)
							lowerCAmelCase	,							lowerCAmelCase		:					List[str]  	=     model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
							lowerCAmelCase		:					Tuple  	=     torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]						,  dtype=torch.long						,  device=model.device				)
							lowerCAmelCase		:					List[str]  	=     target_ids[:, :-1].contiguous()  # Why this line?
							lowerCAmelCase		:					Union[str, Any]  	=     target_ids[:, 1:].clone()  # why clone?
							lowerCAmelCase		:					List[str]  	=     model(
							    lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  decoder_input_ids=lowerCamelCase__						,  labels=lowerCamelCase__						,  use_cache=lowerCamelCase__				).loss

							lowerCAmelCase		:					Union[str, Any]  	=     model(lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  decoder_input_ids=lowerCamelCase__						,  use_cache=lowerCamelCase__				).logits

							lowerCAmelCase		:					Any  	=     nn.functional.log_softmax(lowerCamelCase__						,  dim=-1				)
							lowerCAmelCase	,							lowerCAmelCase		:					str  	=     label_smoothed_nll_loss(
							    lowerCamelCase__						,  lowerCamelCase__						,  0.1						,  ignore_index=model.config.pad_token_id				)
							with self.assertRaises(lowerCamelCase__				):
												# TODO: understand why this breaks
												self.assertEqual(lowerCamelCase__						,  lowerCamelCase__				)





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					Union[str, Any]  	=     {
							    '''student_encoder_layers''': 2,
							    '''student_decoder_layers''': 1,
							    '''num_train_epochs''': 4,
							    '''val_check_interval''': 0.2_5,
							    '''alpha_hid''': 2.0,
							    '''task''': '''translation''',
							    '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
							    '''tokenizer_name''': MBART_TINY,
							    '''teacher''': MBART_TINY,
							    '''src_lang''': '''en_XX''',
							    '''tgt_lang''': '''ro_RO''',
							}
							lowerCAmelCase		:					Optional[int]  	=     self._test_distiller_cli(lowerCamelCase__						,  check_contents=lowerCamelCase__				)
							assert model.model.config.model_type == "mbart"

							lowerCAmelCase		:					int  	=     list(Path(model.output_dir				).glob('''*.ckpt'''				)				)
							self.assertEqual(1						,  len(lowerCamelCase__				)				)
							lowerCAmelCase		:					int  	=     list(Path(model.output_dir				).glob('''**/*.bin'''				)				)
							lowerCAmelCase		:					Dict  	=     list(Path(model.output_dir				).glob('''best_tfmr/*'''				)				)
							assert len(lowerCamelCase__				) > 2
							self.assertEqual(len(lowerCamelCase__				)						,  2				)





		def       _A							(							self    :	Union[str, Any]				):
							lowerCAmelCase		:					Union[str, Any]  	=     {
							    '''student_encoder_layers''': 1,
							    '''student_decoder_layers''': 1,
							    '''alpha_hid''': 2.0,
							    '''teacher''': T5_TINY,
							    '''model_name_or_path''': T5_TINY,
							    '''tokenizer_name''': T5_TINY,
							}
							self._test_distiller_cli(lowerCamelCase__				)





		def       _A							(							self    :	Optional[Any]				):
							lowerCAmelCase		:					int  	=     {
							    '''teacher''': T5_TINY,
							    '''student''': T5_TINIER,
							    '''model_name_or_path''': T5_TINIER,
							    '''tokenizer_name''': T5_TINIER,
							}
							self._test_distiller_cli(lowerCamelCase__				)





		def       _A							(							self    :	Optional[int]						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Any=True				):
							lowerCAmelCase		:					int  	=     {
							    '''label_smoothing''': 0.0,
							    '''early_stopping_patience''': -1,
							    '''train_batch_size''': 1,
							    '''eval_batch_size''': 2,
							    '''max_epochs''': 2,
							    '''alpha_mlm''': 0.2,
							    '''alpha_ce''': 0.8,
							    '''do_predict''': True,
							    '''model_name_or_path''': '''sshleifer/tinier_bart''',
							    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
							    '''val_check_interval''': 0.5,
							}
							default_updates.update(lowerCamelCase__				)
							lowerCAmelCase		:					dict  	=     CHEAP_ARGS.copy()
							lowerCAmelCase		:					List[str]  	=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
							lowerCAmelCase		:					List[str]  	=     self.get_auto_remove_tmp_dir()

							args_d.update(data_dir=lowerCamelCase__						,  output_dir=lowerCamelCase__						,  **lowerCamelCase__				)
							lowerCAmelCase		:					Union[str, Any]  	=     distill_main(argparse.Namespace(**lowerCamelCase__				)				)
							if not check_contents:
												return model
							lowerCAmelCase		:					int  	=     os.listdir(lowerCamelCase__				)
							lowerCAmelCase		:					List[Any]  	=     {os.path.basename(lowerCamelCase__				) for p in contents}
							lowerCAmelCase		:					List[Any]  	=     [p for p in contents if p.endswith('''ckpt'''				)]
							assert len(lowerCamelCase__				) > 0

							self.assertIn('''test_generations.txt'''						,  lowerCamelCase__				)
							self.assertIn('''test_results.txt'''						,  lowerCamelCase__				)

							lowerCAmelCase		:					Optional[int]  	=     load_json(model.metrics_save_path				)
							lowerCAmelCase		:					Optional[int]  	=     metrics['''val'''][-1]
							self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']						,  0.0_1				)
							self.assertGreaterEqual(1.0						,  last_step_stats['''val_avg_gen_time''']				)
							self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']						,  lowerCamelCase__				)
							lowerCAmelCase		:					Optional[Any]  	=     int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1				)
							self.assertEqual(len(metrics['''val''']				)						,  lowerCamelCase__				)
							self.assertEqual(len(metrics['''test''']				)						,  1				)
							return model



class 		__magic_name__   (						snake_case    ):





		@parameterized.expand(
		    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]						,  )
		def       _A							(							self    :	List[str]						,  lowerCamelCase__    :	List[Any]				):
							lowerCAmelCase		:					dict  	=     CHEAP_ARGS.copy()
							lowerCAmelCase		:					str  	=     '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
							lowerCAmelCase		:					int  	=     0.1 if task == '''translation''' else 0

							lowerCAmelCase		:					Optional[int]  	=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
							lowerCAmelCase		:					Any  	=     self.get_auto_remove_tmp_dir()
							args_d.update(
							    data_dir=lowerCamelCase__						,  model_name_or_path=lowerCamelCase__						,  tokenizer_name=lowerCamelCase__						,  train_batch_size=2						,  eval_batch_size=2						,  output_dir=lowerCamelCase__						,  do_predict=lowerCamelCase__						,  task=lowerCamelCase__						,  src_lang='''en_XX'''						,  tgt_lang='''ro_RO'''						,  freeze_encoder=lowerCamelCase__						,  freeze_embeds=lowerCamelCase__						,  )
							assert "n_train" in args_d
							lowerCAmelCase		:					Any  	=     argparse.Namespace(**lowerCamelCase__				)
							lowerCAmelCase		:					str  	=     main(lowerCamelCase__				)

							lowerCAmelCase		:					Any  	=     module.model.get_input_embeddings()
							assert not input_embeds.weight.requires_grad
							if model == T5_TINY:
												lowerCAmelCase		:					int  	=     module.model.lm_head
												assert not lm_head.weight.requires_grad
												assert (lm_head.weight == input_embeds.weight).all().item()
							elif model == FSMT_TINY:
												lowerCAmelCase		:					int  	=     module.model.model
												lowerCAmelCase		:					Optional[Any]  	=     fsmt.decoder.embed_positions
												assert not embed_pos.weight.requires_grad
												assert not fsmt.decoder.embed_tokens.weight.requires_grad
												# check that embeds are not the same
												assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
							else:
												lowerCAmelCase		:					Tuple  	=     module.model.model
												lowerCAmelCase		:					Tuple  	=     bart.decoder.embed_positions
												assert not embed_pos.weight.requires_grad
												assert not bart.shared.weight.requires_grad
												# check that embeds are the same
												assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
												assert bart.decoder.embed_tokens == bart.shared

							lowerCAmelCase		:					str  	=     load_json(module.output_dir / '''text_batch.json'''				)
							assert isinstance(lowerCamelCase__						,  lowerCamelCase__				)
							assert len(lowerCamelCase__				) >= 4





		def       _A							(							self    :	List[str]				):
							lowerCAmelCase		:					dict  	=     CHEAP_ARGS.copy()

							lowerCAmelCase		:					Optional[int]  	=     '''summarization'''
							lowerCAmelCase		:					Union[str, Any]  	=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)

							args_d.update(
							    data_dir=lowerCamelCase__						,  tokenizer_name=lowerCamelCase__						,  train_batch_size=2						,  eval_batch_size=2						,  do_predict=lowerCamelCase__						,  task=lowerCamelCase__						,  src_lang='''en_XX'''						,  tgt_lang='''ro_RO'''						,  freeze_encoder=lowerCamelCase__						,  freeze_embeds=lowerCamelCase__						,  )

							# test models whose config includes the extra_model_args
							lowerCAmelCase		:					Optional[Any]  	=     BART_TINY
							lowerCAmelCase		:					List[str]  	=     self.get_auto_remove_tmp_dir()
							lowerCAmelCase		:					str  	=     args_d.copy()
							args_da.update(
							    model_name_or_path=lowerCamelCase__						,  output_dir=lowerCamelCase__						,  )
							lowerCAmelCase		:					Dict  	=     ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
							for p in extra_model_params:
												lowerCAmelCase		:					int  	=     0.5
							lowerCAmelCase		:					List[Any]  	=     argparse.Namespace(**lowerCamelCase__				)
							lowerCAmelCase		:					Union[str, Any]  	=     main(lowerCamelCase__				)
							for p in extra_model_params:
												assert getattr(model.config						,  lowerCamelCase__				) == 0.5, f'''failed to override the model config for param {p}'''

							# test models whose config doesn't include the extra_model_args
							lowerCAmelCase		:					int  	=     T5_TINY
							lowerCAmelCase		:					Optional[int]  	=     self.get_auto_remove_tmp_dir()
							lowerCAmelCase		:					List[str]  	=     args_d.copy()
							args_da.update(
							    model_name_or_path=lowerCamelCase__						,  output_dir=lowerCamelCase__						,  )
							lowerCAmelCase		:					List[str]  	=     '''encoder_layerdrop'''
							lowerCAmelCase		:					List[Any]  	=     0.5
							lowerCAmelCase		:					int  	=     argparse.Namespace(**lowerCamelCase__				)
							with pytest.raises(lowerCamelCase__				) as excinfo:
												lowerCAmelCase		:					int  	=     main(lowerCamelCase__				)
							assert str(excinfo.value				) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''





		def       _A							(							self    :	int				):
							lowerCAmelCase		:					dict  	=     CHEAP_ARGS.copy()

							lowerCAmelCase		:					int  	=     '''summarization'''
							lowerCAmelCase		:					List[str]  	=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)

							lowerCAmelCase		:					Union[str, Any]  	=     BART_TINY
							lowerCAmelCase		:					Optional[Any]  	=     self.get_auto_remove_tmp_dir()

							args_d.update(
							    data_dir=lowerCamelCase__						,  model_name_or_path=lowerCamelCase__						,  output_dir=lowerCamelCase__						,  tokenizer_name=lowerCamelCase__						,  train_batch_size=2						,  eval_batch_size=2						,  do_predict=lowerCamelCase__						,  task=lowerCamelCase__						,  src_lang='''en_XX'''						,  tgt_lang='''ro_RO'''						,  freeze_encoder=lowerCamelCase__						,  freeze_embeds=lowerCamelCase__						,  )

							# emulate finetune.py
							lowerCAmelCase		:					Dict  	=     argparse.ArgumentParser()
							lowerCAmelCase		:					Optional[Any]  	=     pl.Trainer.add_argparse_args(lowerCamelCase__				)
							lowerCAmelCase		:					str  	=     SummarizationModule.add_model_specific_args(lowerCamelCase__						,  os.getcwd()				)
							lowerCAmelCase		:					Tuple  	=     {'''--help''': True}

							# --help test
							with pytest.raises(lowerCamelCase__				) as excinfo:
												with CaptureStdout() as cs:
																	lowerCAmelCase		:					Dict  	=     parser.parse_args(lowerCamelCase__				)
												assert False, "--help is expected to sys.exit"
							assert excinfo.type == SystemExit
							lowerCAmelCase		:					Any  	=     lightning_base.arg_to_scheduler_metavar
							assert expected in cs.out, "--help is expected to list the supported schedulers"

							# --lr_scheduler=non_existing_scheduler test
							lowerCAmelCase		:					Tuple  	=     '''non_existing_scheduler'''
							lowerCAmelCase		:					Dict  	=     {f'''--lr_scheduler={unsupported_param}'''}
							with pytest.raises(lowerCamelCase__				) as excinfo:
												with CaptureStderr() as cs:
																	lowerCAmelCase		:					List[str]  	=     parser.parse_args(lowerCamelCase__				)
												assert False, "invalid argument is expected to sys.exit"
							assert excinfo.type == SystemExit
							lowerCAmelCase		:					Tuple  	=     f'''invalid choice: \'{unsupported_param}\''''
							assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

							# --lr_scheduler=existing_scheduler test
							lowerCAmelCase		:					Dict  	=     '''cosine'''
							lowerCAmelCase		:					Any  	=     args_d.copy()
							lowerCAmelCase		:					Dict  	=     supported_param
							lowerCAmelCase		:					Union[str, Any]  	=     argparse.Namespace(**lowerCamelCase__				)
							lowerCAmelCase		:					List[Any]  	=     main(lowerCamelCase__				)
							assert (
							    getattr(model.hparams						,  '''lr_scheduler'''				) == supported_param
							), f'''lr_scheduler={supported_param} shouldn\'t fail'''


import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


__SCREAMING_SNAKE_CASE			:   List[Any]         =     10_00_00_00_00_00
__SCREAMING_SNAKE_CASE			:   Optional[Any]         =     1_00_00

__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE			:   Any         =     os.path.split(__file__)
__SCREAMING_SNAKE_CASE			:   List[str]         =     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int  ):


					'''simple docstring'''

					lowerCAmelCase		:					Optional[int]  	=     pa.Table.from_pydict({'''col''': [0] * chunk_size}  )
					lowerCAmelCase		:					int  	=     pa.concat_tables([table] * (num_examples // chunk_size)  )
					return datasets.Dataset(__magic_name__							,  fingerprint='''table_100B'''  )


@dataclass
class 		__magic_name__   :
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42
		_lowerCAmelCase		=	42





		def       _A							(							self    :	Optional[int]				):
							lowerCAmelCase		:					Union[str, Any]  	=     np.random.default_rng(self.seed				)
							lowerCAmelCase		:					Tuple  	=     rng.integers(low=self.low						,  high=self.high						,  size=self.size				).tolist()





		def __iter__(							self    :	Dict				):
							return iter(self._sampled_values				)





		def __len__(							self    :	Tuple				):
							return self.size



@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     dataset[0]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					Any  	=     dataset[-1]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					int  	=     dataset[range(len(__magic_name__  ) // 2							,  len(__magic_name__  ) // 2 + 10_24  )]
@get_duration
def     UpperCAmelCase__							(       __magic_name__						:     datasets.Dataset  ):


					'''simple docstring'''

					lowerCAmelCase		:					List[Any]  	=     dataset[RandIter(0							,  len(__magic_name__  )							,  10_24							,  seed=42  )]
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					Dict  	=     {'''num examples''': SPEED_TEST_N_EXAMPLES}
					lowerCAmelCase		:					Tuple  	=     (get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print('''generating dataset'''  )
					lowerCAmelCase		:					Dict  	=     generate_100B_dataset(num_examples=__magic_name__							,  chunk_size=__magic_name__  )
					print('''Functions'''  )
					for func in functions:
										print(func.__name__  )
										lowerCAmelCase		:					List[Any]  	=     func(__magic_name__  )

					with open(__magic_name__							,  '''wb'''  ) as f:
										f.write(json.dumps(__magic_name__  ).encode('''utf-8'''  )  )


if __name__ == "__main__":  # useful to run the profiler
		benchmark_table_100B()
