

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


A_ :				Union[str, Any]						 =    os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f'''{bindir}/../../examples/pytorch/translation'''):
				from run_translation import main  # noqa


set_seed(42)
A_ :				List[Any]						 =    'sshleifer/student_marian_en_ro_6_1'
A_ :				str						 =    'sshleifer/tiny-mbart'



@require_torch
class 					_a		(__magic_name__       ):


					'''simple docstring'''






					def 						__A    (				self					, A__=False					, A__=None					, A__=True					, A__=True					, A__=True					, A__=True					, ):
						A__							:					Optional[int]       =	self.run_trainer(
						    eval_steps=1					, max_len=12					, model_name=_SCREAMING_SNAKE_CASE					, num_train_epochs=1					, distributed=_SCREAMING_SNAKE_CASE					, extra_args_str=_SCREAMING_SNAKE_CASE					, predict_with_generate=_SCREAMING_SNAKE_CASE					, do_train=_SCREAMING_SNAKE_CASE					, do_eval=_SCREAMING_SNAKE_CASE					, do_predict=_SCREAMING_SNAKE_CASE					, )
						A__							:					str       =	TrainerState.load_from_json(os.path.join(_SCREAMING_SNAKE_CASE					, """trainer_state.json"""    )    ).log_history

						if not do_eval:
							return

						A__							:					Optional[int]       =	[log for log in logs if """eval_loss""" in log.keys()]

						A__							:					Tuple       =	eval_metrics[0]
						if predict_with_generate:
							assert "eval_bleu" in first_step_stats

							A__							:					Dict       =	eval_metrics[-1]
							assert isinstance(last_step_stats["""eval_bleu"""]					, _SCREAMING_SNAKE_CASE    )
							assert not math.isnan(float(last_step_stats["""eval_loss"""]    )    ), "eval_loss must not be `nan`"






					@require_torch_non_multi_gpu
					def 						__A    (				self    ):
						self.run_seqaseq_quick()






					@require_torch_multi_gpu
					def 						__A    (				self    ):
						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE    )






					@require_torch_multi_gpu
					def 						__A    (				self    ):
						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE    )






					@unittest.skip("""Requires an update of the env running those tests"""    )
					@require_torch_multi_gpu
					@require_fairscale
					def 						__A    (				self    ):
						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE					, extra_args_str="""--sharded_ddp simple"""    )






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






					@unittest.skip("""Requires an update of the env running those tests"""    )
					@require_torch_multi_gpu
					@require_fairscale
					def 						__A    (				self    ):
						self.run_seqaseq_quick(
						    distributed=_SCREAMING_SNAKE_CASE					, extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""					, predict_with_generate=_SCREAMING_SNAKE_CASE    )






					@require_apex
					@require_torch_gpu
					def 						__A    (				self    ):
						self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE					, 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=_SCREAMING_SNAKE_CASE					, extra_args_str="""--fp16 --fp16_backend=apex"""    )






					@parameterized.expand(["""base""", """low""", """high""", """mixed"""]    )
					@require_torch_multi_gpu
					def 						__A    (				self					, A__    ):
						A__							:					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},
						}

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






					@slow
					def 						__A    (				self    ):
						A__							:					int       =	self.run_trainer(
						    eval_steps=2					, max_len=128					, model_name=_SCREAMING_SNAKE_CASE					, learning_rate=3e-4					, num_train_epochs=10					, distributed=_SCREAMING_SNAKE_CASE					, )

						# Check metrics
						A__							:					Tuple       =	TrainerState.load_from_json(os.path.join(_SCREAMING_SNAKE_CASE					, """trainer_state.json"""    )    ).log_history
						A__							:					Dict       =	[log for log in logs if """eval_loss""" in log.keys()]
						A__							:					Tuple       =	eval_metrics[0]
						A__							:					Union[str, 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"""]					, _SCREAMING_SNAKE_CASE    )

						# test if do_predict saves generations and metrics
						A__							:					int       =	os.listdir(_SCREAMING_SNAKE_CASE    )
						A__							:					List[str]       =	{os.path.basename(_SCREAMING_SNAKE_CASE    ) for p in contents}
						assert "generated_predictions.txt" in contents
						assert "predict_results.json" in contents






					@slow
					@require_bitsandbytes
					def 						__A    (				self    ):
						from transformers.training_args import OptimizerNames

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

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

							# Check metrics
							A__							:					List[Any]       =	TrainerState.load_from_json(Path(_SCREAMING_SNAKE_CASE					, """trainer_state.json"""    )    ).log_history
							A__							:					Union[str, Any]       =	int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20    )
							A__							:					List[Any]       =	int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20    )

							A__							:					int       =	logs[0]["""train_loss"""]
							return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

						A__     ,     A__     ,     A__							:					Tuple       =	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value    )
						A__     ,     A__     ,     A__							:					Tuple       =	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value    )

						A__							:					Any       =	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

						A__							:					Union[str, Any]       =	gpu_peak_mem_orig + gpu_alloc_mem_orig
						A__							:					Dict       =	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
						A__							:					int       =	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
						A__							:					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(
						    _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, """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(
						    _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, """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(
						    _SCREAMING_SNAKE_CASE					, _SCREAMING_SNAKE_CASE					, F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""    )






					def 						__A    (				self					, A__					, A__					, A__					, A__ = 3e-3					, A__ = "adafactor"					, A__ = False					, A__ = None					, A__ = 0					, A__ = True					, A__ = True					, A__ = True					, A__ = True					, A__ = None					, ):
						A__							:					int       =	self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
						A__							:					List[str]       =	self.get_auto_remove_tmp_dir()
						A__							:					Tuple       =	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(_SCREAMING_SNAKE_CASE    )}\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(_SCREAMING_SNAKE_CASE    )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        """.split()

						A__							:					List[str]       =	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(_SCREAMING_SNAKE_CASE    )}\n        """.split()

						A__							:					Optional[Any]       =	"""
            --do_predict
        """.split()

						A__							:					List[str]       =	[]
						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:
								A__							:					Optional[Any]       =	get_gpu_count()
							A__							:					List[Any]       =	get_torch_dist_unique_port()
							A__							:					str       =	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()
							A__							:					Union[str, Any]       =	[sys.executable] + distributed_args + args
							# keep for quick debug
							# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
							execute_subprocess_async(_SCREAMING_SNAKE_CASE					, env=self.get_env()    )
						else:
							A__							:					Tuple       =	["""run_translation.py"""] + args
							with patch.object(_SCREAMING_SNAKE_CASE					, """argv"""					, _SCREAMING_SNAKE_CASE    ):
								main()

						return output_dir






from __future__ import annotations

from collections.abc import Callable

A_ :				List[Any]						 =    list[list[float | int]]

def 		UpperCamelCase      (lowercase_:				Matrix					,   lowercase_:				Matrix      )  ->   Matrix:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(size + 1      )] for _ in range(lowercase_      )]
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					int
	A__							:					float

	for row in range(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					List[str]       =	matrix[row][col]

		A__							:					int       =	vector[row][0]

	A__							:					Optional[int]       =	0
	A__							:					str       =	0
	while row < size and col < size:
		# pivoting
		A__							:					int       =	max((abs(augmented[rowa][col]      ), rowa) for rowa in range(lowercase_					,   lowercase_      )      )[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			A__     ,     A__							:					Union[str, Any]       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1					,   lowercase_      ):
			A__							:					List[Any]       =	augmented[rowa][col] / augmented[row][col]
			A__							:					Dict       =	0
			for cola in range(col + 1					,   size + 1      ):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1					,   lowercase_      ):
		for row in range(lowercase_      ):
			A__							:					List[str]       =	augmented[row][col] / augmented[col][col]
			for cola in range(lowercase_					,   size + 1      ):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]					,   10      )] for row in range(lowercase_      )
	]

def 		UpperCamelCase      (lowercase_:				list[int]      )  ->   Callable[[int], int]:
	A__							:					int       =	len(lowercase_      )
	A__							:					Matrix       =	[[0 for _ in range(lowercase_      )] for _ in range(lowercase_      )]
	A__							:					Matrix       =	[[0] for _ in range(lowercase_      )]
	A__							:					Matrix
	A__							:					int
	A__							:					int
	A__							:					int

	for x_val, y_val in enumerate(lowercase_      ):
		for col in range(lowercase_      ):
			A__							:					Dict       =	(x_val + 1) ** (size - col - 1)
		A__							:					Any       =	y_val

	A__							:					Union[str, Any]       =	solve(lowercase_					,   lowercase_      )

	def interpolated_func(lowercase_:				int      ) -> int:
		return sum(
		    round(coeffs[x_val][0]      ) * (var ** (size - x_val - 1))
		    for x_val in range(lowercase_      )      )

	return interpolated_func

def 		UpperCamelCase      (lowercase_:				int      )  ->   int:
	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)

def 		UpperCamelCase      (lowercase_:				Callable[[int], int] = question_function					,   lowercase_:				int = 10      )  ->   int:
	A__							:					list[int]       =	[func(lowercase_      ) for x_val in range(1					,   order + 1      )]

	A__							:					list[Callable[[int], int]]       =	[
	    interpolate(data_points[:max_coeff]      ) for max_coeff in range(1					,   order + 1      )
	]

	A__							:					int       =	0
	A__							:					Callable[[int], int]
	A__							:					int

	for poly in polynomials:
		A__							:					List[str]       =	1
		while func(lowercase_      ) == poly(lowercase_      ):
			x_val += 1

		ret += poly(lowercase_      )

	return ret


if __name__ == "__main__":
	print(f'''{solution() = }''')



