




import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


snake_case_  :      str		=		os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"{bindir}/../../examples/pytorch/translation"):
	from run_translation import main  # noqa


set_seed(42)
snake_case_  :      str		=		"sshleifer/student_marian_en_ro_6_1"
snake_case_  :      str		=		"sshleifer/tiny-mbart"





@require_torch
class 					snake_case_   (     __A	):



				'''simple docstring'''





				def    __SCREAMING_SNAKE_CASE     (      self							:   Any      ,							__magic_name__							:   Any=False      ,							__magic_name__							:   List[Any]=None      ,							__magic_name__							:   Optional[int]=True      ,							__magic_name__							:   Optional[Any]=True      ,							__magic_name__							:   Union[str, Any]=True      ,							__magic_name__							:   Tuple=True      ,							)							->			Union[str, Any]:
											lowerCamelCase_			:    List[str]   						=      self.run_trainer(
											    eval_steps=1      ,							max_len=12      ,							model_name=__a      ,							num_train_epochs=1      ,							distributed=__a      ,							extra_args_str=__a      ,							predict_with_generate=__a      ,							do_train=__a      ,							do_eval=__a      ,							do_predict=__a      ,							)
											lowerCamelCase_			:    str   						=      TrainerState.load_from_json(os.path.join(__a      ,							"trainer_state.json"							)							).log_history

											if not do_eval:
																		return

											lowerCamelCase_			:    Union[str, Any]   						=      [log for log in logs if "eval_loss" in log.keys()]

											lowerCamelCase_			:    Union[str, Any]   						=      eval_metrics[0]
											if predict_with_generate:
																		assert "eval_bleu" in first_step_stats

																		lowerCamelCase_			:    Union[str, Any]   						=      eval_metrics[-1]
																		assert isinstance(last_step_stats["eval_bleu"]      ,							__a							)
																		assert not math.isnan(float(last_step_stats["eval_loss"]							)							), "eval_loss must not be `nan`"





				@require_torch_non_multi_gpu
				def    __SCREAMING_SNAKE_CASE     (      self							:   Dict							)							->			Optional[Any]:
											self.run_seqaseq_quick()





				@require_torch_multi_gpu
				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]							)							->			List[Any]:
											self.run_seqaseq_quick(distributed=__a							)





				@require_torch_multi_gpu
				def    __SCREAMING_SNAKE_CASE     (      self							:   Union[str, Any]							)							->			str:
											self.run_seqaseq_quick(distributed=__a							)





				@unittest.skip("Requires an update of the env running those tests"							)
				@require_torch_multi_gpu
				@require_fairscale
				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]							)							->			Optional[Any]:
											self.run_seqaseq_quick(distributed=__a      ,							extra_args_str="--sharded_ddp simple"							)





				@unittest.skip("Requires an update of the env running those tests"							)
				@require_torch_multi_gpu
				@require_fairscale
				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]							)							->			Union[str, Any]:
											self.run_seqaseq_quick(distributed=__a      ,							extra_args_str="--sharded_ddp simple --fp16"							)





				@unittest.skip("Requires an update of the env running those tests"							)
				@require_torch_multi_gpu
				@require_fairscale
				def    __SCREAMING_SNAKE_CASE     (      self							:   int							)							->			str:
											self.run_seqaseq_quick(distributed=__a      ,							extra_args_str="--sharded_ddp zero_dp_2"      ,							predict_with_generate=__a							)





				@unittest.skip("Requires an update of the env running those tests"							)
				@require_torch_multi_gpu
				@require_fairscale
				def    __SCREAMING_SNAKE_CASE     (      self							:   Any							)							->			Optional[int]:
											self.run_seqaseq_quick(
											    distributed=__a      ,							extra_args_str="--sharded_ddp zero_dp_2 --fp16"      ,							predict_with_generate=__a							)





				@require_apex
				@require_torch_gpu
				def    __SCREAMING_SNAKE_CASE     (      self							:   int							)							->			Optional[Any]:
											# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
											# program and it breaks other tests that run from the same pytest worker, therefore until this is
											# sorted out it must be run only in an external program, that is distributed=True in this
											# test and only under one or more gpus - if we want cpu will need to make a special test
											#
											# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
											# 2nd main() call it botches the future eval.
											#
											self.run_seqaseq_quick(distributed=__a      ,							extra_args_str="--fp16 --fp16_backend=apex"							)
											# test 2nd time - was getting eval_loss': nan'
											# to reproduce the problem set distributed=False
											self.run_seqaseq_quick(distributed=__a      ,							extra_args_str="--fp16 --fp16_backend=apex"							)





				@parameterized.expand(["base", "low", "high", "mixed"]							)
				@require_torch_multi_gpu
				def    __SCREAMING_SNAKE_CASE     (      self							:   Dict      ,							__magic_name__							:   str							)							->			Optional[Any]:
											# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
											lowerCamelCase_			:    List[str]   						=      {
											    # test with the default log_level - should be info and thus log info once
											    "base": {"extra_args_str": "", "n_matches": 1},
											    # test with low log_level and log_level_replica - should be noisy on all processes
											    # now the info string should appear twice on 2 processes
											    "low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
											    # test with high log_level and low log_level_replica
											    # now the info string should appear once only on the replica
											    "high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
											    # test with high log_level and log_level_replica - should be quiet on all processes
											    "mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
											}

											lowerCamelCase_			:    Tuple   						=      experiments[experiment_id]
											lowerCamelCase_			:    Any   						=      {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
											lowerCamelCase_			:    List[Any]   						=      "Running training"
											with CaptureStderr() as cl:
																		self.run_seqaseq_quick(**__a      ,							extra_args_str=data["extra_args_str"]							)
											lowerCamelCase_			:    str   						=      len(re.findall(__a      ,							cl.err							)							)
											self.assertEqual(__a      ,							data["n_matches"]							)





				@slow
				def    __SCREAMING_SNAKE_CASE     (      self							:   List[Any]							)							->			int:
											lowerCamelCase_			:    str   						=      self.run_trainer(
											    eval_steps=2      ,							max_len=128      ,							model_name=__a      ,							learning_rate=3e-4      ,							num_train_epochs=10      ,							distributed=__a      ,							)

											# Check metrics
											lowerCamelCase_			:    Any   						=      TrainerState.load_from_json(os.path.join(__a      ,							"trainer_state.json"							)							).log_history
											lowerCamelCase_			:    Tuple   						=      [log for log in logs if "eval_loss" in log.keys()]
											lowerCamelCase_			:    Union[str, Any]   						=      eval_metrics[0]
											lowerCamelCase_			:    List[Any]   						=      eval_metrics[-1]

											assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
											assert isinstance(last_step_stats["eval_bleu"]      ,							__a							)

											# test if do_predict saves generations and metrics
											lowerCamelCase_			:    Optional[Any]   						=      os.listdir(__a							)
											lowerCamelCase_			:    Tuple   						=      {os.path.basename(__a							) for p in contents}
											assert "generated_predictions.txt" in contents
											assert "predict_results.json" in contents





				@slow
				@require_bitsandbytes
				def    __SCREAMING_SNAKE_CASE     (      self							:   Tuple							)							->			Optional[Any]:
											from transformers.training_args import OptimizerNames

											def train_and_return_metrics(__magic_name__							:   str							) -> Tuple[int, float]:
																		lowerCamelCase_			:    str   						=      "--skip_memory_metrics 0"

																		lowerCamelCase_			:    Dict   						=      self.run_trainer(
																		    max_len=128      ,							model_name=__a      ,							learning_rate=3e-4      ,							num_train_epochs=1      ,							optim=__a      ,							distributed=__a      ,							extra_args_str=__a      ,							do_eval=__a      ,							do_predict=__a      ,							n_gpus_to_use=1      ,							)

																		# Check metrics
																		lowerCamelCase_			:    Tuple   						=      TrainerState.load_from_json(Path(__a      ,							"trainer_state.json"							)							).log_history
																		lowerCamelCase_			:    Dict   						=      int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20							)
																		lowerCamelCase_			:    str   						=      int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20							)

																		lowerCamelCase_			:    Tuple   						=      logs[0]["train_loss"]
																		return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

											lowerCamelCase_						,						lowerCamelCase_						,						lowerCamelCase_			:    Tuple   						=      train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value							)
											lowerCamelCase_						,						lowerCamelCase_						,						lowerCamelCase_			:    Optional[Any]   						=      train_and_return_metrics(OptimizerNames.ADAMW_BNB.value							)

											lowerCamelCase_			:    str   						=      gpu_alloc_mem_orig - gpu_alloc_mem_bnb

											lowerCamelCase_			:    int   						=      gpu_peak_mem_orig + gpu_alloc_mem_orig
											lowerCamelCase_			:    str   						=      gpu_peak_mem_bnb + gpu_alloc_mem_bnb
											lowerCamelCase_			:    Tuple   						=      gpu_total_mem_orig - gpu_total_mem_bnb

											# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
											# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
											# in 2 bytes and the diff in optim memory usage is derived as so:
											#
											# - normal 25*8=~200MB (8 bytes per param)
											# - bnb    25*2= ~50MB (2 bytes per param)
											#
											# Thus we should expect ~150MB total memory saved.
											#
											# Peak memory should be the same - the total should be different by about that same margin
											#
											# After leaving a small margin to accommodate for differences between gpus let's check
											# that we have at least 120MB in savings
											lowerCamelCase_			:    Dict   						=      120

											# uncomment the following if this test starts failing - requires py38 for a new print feature
											# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
											# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
											# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
											# print(f"{gpu_alloc_mem_diff=}MB")
											# print(f"{gpu_peak_mem_diff=}MB")
											# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
											# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

											self.assertGreater(
											    __a      ,							__a      ,							"should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
											    F" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"
											    F" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB"      ,							)

											self.assertGreater(
											    __a      ,							__a      ,							"should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
											    F" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"
											    F" gpu_total_mem_bnb={gpu_total_mem_bnb}MB"      ,							)

											self.assertEqual(
											    __a      ,							__a      ,							F"loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   Dict      ,							__magic_name__							:   int      ,							__magic_name__							:   str      ,							__magic_name__							:   int      ,							__magic_name__							:   float = 3e-3      ,							__magic_name__							:   str = "adafactor"      ,							__magic_name__							:   bool = False      ,							__magic_name__							:   str = None      ,							__magic_name__							:   int = 0      ,							__magic_name__							:   bool = True      ,							__magic_name__							:   bool = True      ,							__magic_name__							:   bool = True      ,							__magic_name__							:   bool = True      ,							__magic_name__							:   int = None      ,							)							->			List[str]:
											lowerCamelCase_			:    Union[str, Any]   						=      self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
											lowerCamelCase_			:    Any   						=      self.get_auto_remove_tmp_dir()
											lowerCamelCase_			:    List[Any]   						=      F"\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(__a							)}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(__a							)}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

											lowerCamelCase_			:    Dict   						=      F"\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(__a							)}\n        ".split()

											lowerCamelCase_			:    int   						=      "\n            --do_predict\n        ".split()

											lowerCamelCase_			:    int   						=      []
											if do_train:
																		args += args_train

											if do_eval:
																		args += args_eval

											if do_predict:
																		args += args_predict

											if predict_with_generate:
																		args += "--predict_with_generate".split()

											if do_train:
																		if optim == "adafactor":
																									args += "--adafactor".split()
																		else:
																									args += F"--optim {optim}".split()

											if extra_args_str is not None:
																		args += extra_args_str.split()

											if distributed:
																		if n_gpus_to_use is None:
																									lowerCamelCase_			:    List[str]   						=      get_gpu_count()
																		lowerCamelCase_			:    List[str]   						=      get_torch_dist_unique_port()
																		lowerCamelCase_			:    Optional[int]   						=      F"\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            ".split()
																		lowerCamelCase_			:    Dict   						=      [sys.executable] + distributed_args + args
																		# keep for quick debug
																		# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
																		execute_subprocess_async(__a      ,							env=self.get_env()							)
											else:
																		lowerCamelCase_			:    int   						=      ["run_translation.py"] + args
																		with patch.object(__a      ,							"argv"      ,							__a							):
																									main()

											return output_dir


'''simple docstring'''


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def    lowerCAmelCase       (		UpperCamelCase__					: dict							):


	"""simple docstring"""
	return (data["data"], data["target"])





def    lowerCAmelCase       (		UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray							):


	"""simple docstring"""
	__UpperCAmelCase      					=      XGBRegressor(verbosity=0     ,       random_state=4_2							)
	xgb.fit(UpperCamelCase__     ,       UpperCamelCase__							)
	# Predict target for test data
	__UpperCAmelCase      					=      xgb.predict(UpperCamelCase__							)
	__UpperCAmelCase      					=      predictions.reshape(len(UpperCamelCase__							)     ,       1							)
	return predictions





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      fetch_california_housing()
	__UpperCAmelCase					,      __UpperCAmelCase      					=      data_handling(UpperCamelCase__							)
	__UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase      					=      train_test_split(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       test_size=0.25     ,       random_state=1							)
	__UpperCAmelCase      					=      xgboost(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
	# Error printing
	print(f"""Mean Absolute Error : {mean_absolute_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)
	print(f"""Mean Square Error  : {mean_squared_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()
