
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


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


set_seed(42)
lowerCamelCase_						: List[str]         =  """sshleifer/student_marian_en_ro_6_1"""
lowerCamelCase_						: Union[str, Any]         =  """sshleifer/tiny-mbart"""

@require_torch
class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

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

							if not do_eval:
											return

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

							UpperCamelCase_:							List[str]	=						eval_metrics[0]
							if predict_with_generate:
											assert "eval_bleu" in first_step_stats

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

			@require_torch_non_multi_gpu
			def       lowerCAmelCase__	(       self :       str      ):
							self.run_seqaseq_quick()

			@require_torch_multi_gpu
			def       lowerCAmelCase__	(       self :       Dict      ):
							self.run_seqaseq_quick(distributed=snake_case_      )

			@require_torch_multi_gpu
			def       lowerCAmelCase__	(       self :       str      ):
							self.run_seqaseq_quick(distributed=snake_case_      )

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

			@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=snake_case_						,	extra_args_str="""--sharded_ddp zero_dp_2 --fp16"""						,	predict_with_generate=snake_case_      )

			@require_apex
			@require_torch_gpu
			def       lowerCAmelCase__	(       self :       List[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=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=snake_case_						,	extra_args_str="""--fp16 --fp16_backend=apex"""      )

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

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

			@slow
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Tuple	=						self.run_trainer(
							    eval_steps=2						,	max_len=128						,	model_name=snake_case_						,	learning_rate=3e-4						,	num_train_epochs=10						,	distributed=snake_case_						,	)

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

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

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

							def train_and_return_metrics(snake_case_ :       str      ) -> Tuple[int, float]:
											UpperCamelCase_:							Optional[int]	=						"""--skip_memory_metrics 0"""

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

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

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

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

							UpperCamelCase_:							Tuple	=						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

							UpperCamelCase_:							List[str]	=						gpu_peak_mem_orig + gpu_alloc_mem_orig
							UpperCamelCase_:							Tuple	=						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
							UpperCamelCase_:							List[str]	=						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
							UpperCamelCase_:							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(
							    snake_case_						,	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(
							    snake_case_						,	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(
							    snake_case_						,	snake_case_						,	f'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''      )




			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       int						,	snake_case_ :       str						,	snake_case_ :       int						,	snake_case_ :       float = 3e-3						,	snake_case_ :       str = "adafactor"						,	snake_case_ :       bool = False						,	snake_case_ :       str = None						,	snake_case_ :       int = 0						,	snake_case_ :       bool = True						,	snake_case_ :       bool = True						,	snake_case_ :       bool = True						,	snake_case_ :       bool = True						,	snake_case_ :       int = None						,	):
							UpperCamelCase_:							Union[str, Any]	=						self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
							UpperCamelCase_:							List[str]	=						self.get_auto_remove_tmp_dir()
							UpperCamelCase_:							Dict	=						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(snake_case_      )}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(snake_case_      )}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

							UpperCamelCase_:							List[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(snake_case_      )}
        '''.split()

							UpperCamelCase_:							int	=						"""
            --do_predict
        """.split()

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

							return output_dir








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_						: Optional[Any]         =  {
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[Any]         =  ["""DistilBertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[int]         =  [
							    """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DistilBertForMaskedLM""",
							    """DistilBertForMultipleChoice""",
							    """DistilBertForQuestionAnswering""",
							    """DistilBertForSequenceClassification""",
							    """DistilBertForTokenClassification""",
							    """DistilBertModel""",
							    """DistilBertPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[str]         =  [
							    """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDistilBertForMaskedLM""",
							    """TFDistilBertForMultipleChoice""",
							    """TFDistilBertForQuestionAnswering""",
							    """TFDistilBertForSequenceClassification""",
							    """TFDistilBertForTokenClassification""",
							    """TFDistilBertMainLayer""",
							    """TFDistilBertModel""",
							    """TFDistilBertPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """FlaxDistilBertForMaskedLM""",
							    """FlaxDistilBertForMultipleChoice""",
							    """FlaxDistilBertForQuestionAnswering""",
							    """FlaxDistilBertForSequenceClassification""",
							    """FlaxDistilBertForTokenClassification""",
							    """FlaxDistilBertModel""",
							    """FlaxDistilBertPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_distilbert import (
							    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DistilBertConfig,
							    DistilBertOnnxConfig,
							)
							from .tokenization_distilbert import DistilBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_distilbert_fast import DistilBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_distilbert import (
														    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DistilBertForMaskedLM,
														    DistilBertForMultipleChoice,
														    DistilBertForQuestionAnswering,
														    DistilBertForSequenceClassification,
														    DistilBertForTokenClassification,
														    DistilBertModel,
														    DistilBertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_distilbert import (
														    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDistilBertForMaskedLM,
														    TFDistilBertForMultipleChoice,
														    TFDistilBertForQuestionAnswering,
														    TFDistilBertForSequenceClassification,
														    TFDistilBertForTokenClassification,
														    TFDistilBertMainLayer,
														    TFDistilBertModel,
														    TFDistilBertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_distilbert import (
														    FlaxDistilBertForMaskedLM,
														    FlaxDistilBertForMultipleChoice,
														    FlaxDistilBertForQuestionAnswering,
														    FlaxDistilBertForSequenceClassification,
														    FlaxDistilBertForTokenClassification,
														    FlaxDistilBertModel,
														    FlaxDistilBertPreTrainedModel,
														)

else:
							import sys

							lowerCamelCase_						: Optional[Any]         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






