





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


__UpperCamelCase:	List[Any] = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
			from run_translation import main  # noqa


set_seed(4_2)
__UpperCamelCase:	Optional[int] = """sshleifer/student_marian_en_ro_6_1"""
__UpperCamelCase:	str = """sshleifer/tiny-mbart"""


@require_torch
class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	def    snake_case__(						self:						int,					lowerCamelCase_:						List[Any]=False,					lowerCamelCase_:						int=None,					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						Optional[Any]=True,					lowerCamelCase_:						Tuple=True,					):
			lowercase__					:      Optional[Any]      =		self.run_trainer(
			    eval_steps=1,					max_len=12,					model_name=lowerCamelCase_,					num_train_epochs=1,					distributed=lowerCamelCase_,					extra_args_str=lowerCamelCase_,					predict_with_generate=lowerCamelCase_,					do_train=lowerCamelCase_,					do_eval=lowerCamelCase_,					do_predict=lowerCamelCase_,					)
			lowercase__					:      Dict      =		TrainerState.load_from_json(os.path.join(lowerCamelCase_,					'trainer_state.json'			)			).log_history

			if not do_eval:
					return

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

			lowercase__					:      List[Any]      =		eval_metrics[0]
			if predict_with_generate:
					assert "eval_bleu" in first_step_stats

					lowercase__					:      int      =		eval_metrics[-1]
					assert isinstance(last_step_stats['eval_bleu'],					lowerCamelCase_			)
					assert not math.isnan(float(last_step_stats['eval_loss']			)			), "eval_loss must not be `nan`"

	@require_torch_non_multi_gpu
	def    snake_case__(						self:						Dict			):
			self.run_seqaseq_quick()

	@require_torch_multi_gpu
	def    snake_case__(						self:						Union[str, Any]			):
			self.run_seqaseq_quick(distributed=lowerCamelCase_			)

	@require_torch_multi_gpu
	def    snake_case__(						self:						List[Any]			):
			self.run_seqaseq_quick(distributed=lowerCamelCase_			)

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

	@unittest.skip('Requires an update of the env running those tests'			)
	@require_torch_multi_gpu
	@require_fairscale
	def    snake_case__(						self:						str			):
			self.run_seqaseq_quick(distributed=lowerCamelCase_,					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    snake_case__(						self:						List[str]			):
			self.run_seqaseq_quick(distributed=lowerCamelCase_,					extra_args_str='--sharded_ddp zero_dp_2',					predict_with_generate=lowerCamelCase_			)

	@unittest.skip('Requires an update of the env running those tests'			)
	@require_torch_multi_gpu
	@require_fairscale
	def    snake_case__(						self:						List[str]			):
			self.run_seqaseq_quick(
			    distributed=lowerCamelCase_,					extra_args_str='--sharded_ddp zero_dp_2 --fp16',					predict_with_generate=lowerCamelCase_			)

	@require_apex
	@require_torch_gpu
	def    snake_case__(						self:						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=lowerCamelCase_,					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=lowerCamelCase_,					extra_args_str='--fp16 --fp16_backend=apex'			)

	@parameterized.expand(['base', 'low', 'high', 'mixed']			)
	@require_torch_multi_gpu
	def    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						Any			):
			# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
			lowercase__					:      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},
			}

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

	@slow
	def    snake_case__(						self:						str			):
			lowercase__					:      Union[str, Any]      =		self.run_trainer(
			    eval_steps=2,					max_len=128,					model_name=lowerCamelCase_,					learning_rate=3E-4,					num_train_epochs=10,					distributed=lowerCamelCase_,					)

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

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

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

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

			def train_and_return_metrics(lowerCamelCase_:						str			) -> Tuple[int, float]:
					lowercase__					:      Any      =		'--skip_memory_metrics 0'

					lowercase__					:      List[Any]      =		self.run_trainer(
					    max_len=128,					model_name=lowerCamelCase_,					learning_rate=3E-4,					num_train_epochs=1,					optim=lowerCamelCase_,					distributed=lowerCamelCase_,					extra_args_str=lowerCamelCase_,					do_eval=lowerCamelCase_,					do_predict=lowerCamelCase_,					n_gpus_to_use=1,					)

					# Check metrics
					lowercase__					:      Optional[Any]      =		TrainerState.load_from_json(Path(lowerCamelCase_,					'trainer_state.json'			)			).log_history
					lowercase__					:      int      =		int(logs[0]['train_mem_gpu_peaked_delta'] / 2**20			)
					lowercase__					:      Tuple      =		int(logs[0]['train_mem_gpu_alloc_delta'] / 2**20			)

					lowercase__					:      Optional[int]      =		logs[0]['train_loss']
					return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

			lowercase__   ,				lowercase__   ,				lowercase__					:      Tuple      =		train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value			)
			lowercase__   ,				lowercase__   ,				lowercase__					:      Union[str, Any]      =		train_and_return_metrics(OptimizerNames.ADAMW_BNB.value			)

			lowercase__					:      List[str]      =		gpu_alloc_mem_orig - gpu_alloc_mem_bnb

			lowercase__					:      Optional[Any]      =		gpu_peak_mem_orig + gpu_alloc_mem_orig
			lowercase__					:      int      =		gpu_peak_mem_bnb + gpu_alloc_mem_bnb
			lowercase__					:      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
			lowercase__					:      List[Any]      =		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(
			    lowerCamelCase_,					lowerCamelCase_,					'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(
			    lowerCamelCase_,					lowerCamelCase_,					'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(
			    lowerCamelCase_,					lowerCamelCase_,					F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}"""			)





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

			lowercase__					:      Optional[Any]      =		F"""
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(lowerCamelCase_			)}
        """.split()

			lowercase__					:      Optional[Any]      =		'\n            --do_predict\n        '.split()

			lowercase__					:      Dict      =		[]
			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:
							lowercase__					:      Optional[int]      =		get_gpu_count()
					lowercase__					:      Optional[int]      =		get_torch_dist_unique_port()
					lowercase__					:      List[Any]      =		F"""
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            """.split()
					lowercase__					:      int      =		[sys.executable] + distributed_args + args
					# keep for quick debug
					# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
					execute_subprocess_async(lowerCamelCase_,					env=self.get_env()			)
			else:
					lowercase__					:      Union[str, Any]      =		['run_translation.py'] + args
					with patch.object(lowerCamelCase_,					'argv',					lowerCamelCase_			):
							main()

			return output_dir









import argparse
import os
import re


__UpperCamelCase:	Any = """src/transformers/models/auto"""


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__UpperCamelCase:	Dict = re.compile(r"""[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict""")
# re pattern that matches identifiers in mappings
__UpperCamelCase:	List[str] = re.compile(r"""\s*\(\s*\"(\S[^\"]+)\"""")


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : bool = False			)   ->							List[str]:




		'''simple docstring'''
		with open(_lowercase   ,    'r'   ,    encoding='utf-8'			) as f:
				lowercase__					:      Union[str, Any]      =		f.read()

		lowercase__					:      Optional[Any]      =		content.split('\n'			)
		lowercase__					:      Optional[int]      =		[]
		lowercase__					:      int      =		0
		while line_idx < len(_lowercase			):
				if _re_intro_mapping.search(lines[line_idx]			) is not None:
						lowercase__					:      Tuple      =		len(re.search(r'^(\s*)\S'   ,    lines[line_idx]			).groups()[0]			) + 8
						# Start of a new mapping!
						while not lines[line_idx].startswith(' ' * indent + '('			):
								new_lines.append(lines[line_idx]			)
								line_idx += 1

						lowercase__					:      Tuple      =		[]
						while lines[line_idx].strip() != "]":
								# Blocks either fit in one line or not
								if lines[line_idx].strip() == "(":
										lowercase__					:      Any      =		line_idx
										while not lines[line_idx].startswith(' ' * indent + ')'			):
												line_idx += 1
										blocks.append('\n'.join(lines[start_idx : line_idx + 1]			)			)
								else:
										blocks.append(lines[line_idx]			)
								line_idx += 1

						# Sort blocks by their identifiers
						lowercase__					:      List[str]      =		sorted(_lowercase   ,    key=lambda _lowercase			: _re_identifier.search(_lowercase			).groups()[0]			)
						new_lines += blocks
				else:
						new_lines.append(lines[line_idx]			)
						line_idx += 1

		if overwrite:
				with open(_lowercase   ,    'w'   ,    encoding='utf-8'			) as f:
						f.write('\n'.join(_lowercase			)			)
		elif "\n".join(_lowercase			) != content:
				return True


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : bool = False			)   ->							List[Any]:




		'''simple docstring'''
		lowercase__					:      List[Any]      =		[os.path.join(_lowercase   ,    _lowercase			) for f in os.listdir(_lowercase			) if f.endswith('.py'			)]
		lowercase__					:      str      =		[sort_auto_mapping(_lowercase   ,    overwrite=_lowercase			) for fname in fnames]

		if not overwrite and any(_lowercase			):
				lowercase__					:      List[Any]      =		[f for f, d in zip(_lowercase   ,    _lowercase			) if d]
				raise ValueError(
				    f"""The following files have auto mappings that need sorting: {", ".join(_lowercase			)}. Run `make style` to fix"""
				    ' this.'			)


if __name__ == "__main__":
			__UpperCamelCase:	Tuple = argparse.ArgumentParser()
			parser.add_argument("""--check_only""", action="""store_true""", help="""Whether to only check or fix style.""")
			__UpperCamelCase:	List[Any] = parser.parse_args()

			sort_all_auto_mappings(not args.check_only)


