

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_							:					Optional[Any]		 =   os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(f"""{bindir}/../../examples/pytorch/translation"""):
			from run_translation import main  # noqa


set_seed(42)
UpperCAmelCase_							:					Any		 =   '''sshleifer/student_marian_en_ro_6_1'''
UpperCAmelCase_							:					Union[str, Any]		 =   '''sshleifer/tiny-mbart'''






@require_torch
class      lowerCamelCase_      (	_lowercase	):




	def        lowerCAmelCase_       (     self						:     Optional[int]					,      __A						:     Union[str, Any]=False					,      __A						:     str=None					,      __A						:     List[Any]=True					,      __A						:     Union[str, Any]=True					,      __A						:     Union[str, Any]=True					,      __A						:     Union[str, Any]=True					,      ):
		__A :			List[Any]		    =			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					,      )
		__A :			int		    =			TrainerState.load_from_json(os.path.join(__A					,      """trainer_state.json"""							)							).log_history

		if not do_eval:
			return

		__A :			Tuple		    =			[log for log in logs if """eval_loss""" in log.keys()]

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

			__A :			Tuple		    =			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        lowerCAmelCase_       (     self						:     Optional[int]							):
		self.run_seqaseq_quick()




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




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




	@unittest.skip("""Requires an update of the env running those tests"""							)
	@require_torch_multi_gpu
	@require_fairscale
	def        lowerCAmelCase_       (     self						:     Tuple							):
		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        lowerCAmelCase_       (     self						:     Dict							):
		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        lowerCAmelCase_       (     self						:     Tuple							):
		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        lowerCAmelCase_       (     self						:     Optional[Any]							):
		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        lowerCAmelCase_       (     self						:     Dict							):
		# 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        lowerCAmelCase_       (     self						:     str					,      __A						:     List[str]							):
		# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
		__A :			Optional[Any]		    =			{
		    # 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 :			Union[str, Any]		    =			{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
		__A :			Optional[int]		    =			"""Running training"""
		with CaptureStderr() as cl:
			self.run_seqaseq_quick(**__A					,      extra_args_str=data["""extra_args_str"""]							)
		__A :			List[Any]		    =			len(re.findall(__A					,      cl.err							)							)
		self.assertEqual(__A					,      data["""n_matches"""]							)




	@slow
	def        lowerCAmelCase_       (     self						:     str							):
		__A :			List[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
		__A :			str		    =			TrainerState.load_from_json(os.path.join(__A					,      """trainer_state.json"""							)							).log_history
		__A :			List[str]		    =			[log for log in logs if """eval_loss""" in log.keys()]
		__A :			int		    =			eval_metrics[0]
		__A :			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
		__A :			Any		    =			os.listdir(__A							)
		__A :			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        lowerCAmelCase_       (     self						:     int							):
		from transformers.training_args import OptimizerNames

		def train_and_return_metrics(__A						:     str							) -> Tuple[int, float]:
			__A :			List[Any]		    =			"""--skip_memory_metrics 0"""

			__A :			Any		    =			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
			__A :			List[Any]		    =			TrainerState.load_from_json(Path(__A					,      """trainer_state.json"""							)							).log_history
			__A :			List[Any]		    =			int(logs[0]["""train_mem_gpu_peaked_delta"""] / 2**20							)
			__A :			Any		    =			int(logs[0]["""train_mem_gpu_alloc_delta"""] / 2**20							)

			__A :			Union[str, Any]		    =			logs[0]["""train_loss"""]
			return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

		__A    ,				__A    ,				__A :			str		    =			train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value							)
		__A    ,				__A    ,				__A :			Any		    =			train_and_return_metrics(OptimizerNames.ADAMW_BNB.value							)

		__A :			Optional[Any]		    =			gpu_alloc_mem_orig - gpu_alloc_mem_bnb

		__A :			Dict		    =			gpu_peak_mem_orig + gpu_alloc_mem_orig
		__A :			List[Any]		    =			gpu_peak_mem_bnb + gpu_alloc_mem_bnb
		__A :			Union[str, Any]		    =			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 :			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(
		    __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        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     int					,      __A						:     str					,      __A						:     int					,      __A						:     float = 3e-3					,      __A						:     str = "adafactor"					,      __A						:     bool = False					,      __A						:     str = None					,      __A						:     int = 0					,      __A						:     bool = True					,      __A						:     bool = True					,      __A						:     bool = True					,      __A						:     bool = True					,      __A						:     int = None					,      ):
		__A :			List[Any]		    =			self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
		__A :			Optional[int]		    =			self.get_auto_remove_tmp_dir()
		__A :			str		    =			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(__A							)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(__A							)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        """.split()

		__A :			Optional[int]		    =			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(__A							)}
        """.split()

		__A :			str		    =			"""
            --do_predict
        """.split()

		__A :			Union[str, Any]		    =			[]
		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 :			Union[str, Any]		    =			get_gpu_count()
			__A :			Tuple		    =			get_torch_dist_unique_port()
			__A :			int		    =			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()
			__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(__A					,      env=self.get_env()							)
		else:
			__A :			Any		    =			["""run_translation.py"""] + args
			with patch.object(__A					,      """argv"""					,      __A							):
				main()

		return output_dir












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_							=				{
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				["""MaskFormerFeatureExtractor"""]
							UpperCAmelCase_							=				["""MaskFormerImageProcessor"""]


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase_							=				[
							    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MaskFormerForInstanceSegmentation""",
							    """MaskFormerModel""",
							    """MaskFormerPreTrainedModel""",
							]
							UpperCAmelCase_							=				[
							    """MaskFormerSwinBackbone""",
							    """MaskFormerSwinModel""",
							    """MaskFormerSwinPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
							from .configuration_maskformer_swin import MaskFormerSwinConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_maskformer import MaskFormerFeatureExtractor
														from .image_processing_maskformer import MaskFormerImageProcessor
							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskformer import (
														    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskFormerForInstanceSegmentation,
														    MaskFormerModel,
														    MaskFormerPreTrainedModel,
														)
														from .modeling_maskformer_swin import (
														    MaskFormerSwinBackbone,
														    MaskFormerSwinModel,
														    MaskFormerSwinPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase_							=				_LazyModule(__name__, globals()["""__file__"""], _import_structure)