


'''simple docstring'''
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				 =	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				 =	'''sshleifer/student_marian_en_ro_6_1'''
__snake_case				 =	'''sshleifer/tiny-mbart'''
@require_torch
class   lowercase      (				A__   ):

		"""simple docstring"""

		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_=False       ,  UpperCamelCase_=None       ,  UpperCamelCase_=True       ,  UpperCamelCase_=True       ,  UpperCamelCase_=True       ,  UpperCamelCase_=True       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Tuple				   =					self.run_trainer(
							    eval_steps=1       ,  max_len=12       ,  model_name=UpperCamelCase_       ,  num_train_epochs=1       ,  distributed=UpperCamelCase_       ,  extra_args_str=UpperCamelCase_       ,  predict_with_generate=UpperCamelCase_       ,  do_train=UpperCamelCase_       ,  do_eval=UpperCamelCase_       ,  do_predict=UpperCamelCase_       ,  )
							UpperCamelCase__  :Tuple				   =					TrainerState.load_from_json(os.path.join(UpperCamelCase_       ,  '''trainer_state.json'''				)				).log_history

							if not do_eval:
												return

							UpperCamelCase__  :int				   =					[log for log in logs if '''eval_loss''' in log.keys()]

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

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




		@require_torch_non_multi_gpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.run_seqaseq_quick()




		@require_torch_multi_gpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_				)




		@require_torch_multi_gpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_				)




		@unittest.skip('''Requires an update of the env running those tests'''				)
		@require_torch_multi_gpu
		@require_fairscale
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_       ,  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				):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_       ,  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				):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_       ,  extra_args_str='''--sharded_ddp zero_dp_2'''       ,  predict_with_generate=UpperCamelCase_				)




		@unittest.skip('''Requires an update of the env running those tests'''				)
		@require_torch_multi_gpu
		@require_fairscale
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.run_seqaseq_quick(
							    distributed=UpperCamelCase_       ,  extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''       ,  predict_with_generate=UpperCamelCase_				)




		@require_apex
		@require_torch_gpu
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_       ,  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=UpperCamelCase_       ,  extra_args_str='''--fp16 --fp16_backend=apex'''				)




		@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']				)
		@require_torch_multi_gpu
		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


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




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[str]				   =					self.run_trainer(
							    eval_steps=2       ,  max_len=128       ,  model_name=UpperCamelCase_       ,  learning_rate=3e-4       ,  num_train_epochs=10       ,  distributed=UpperCamelCase_       ,  )

							# Check metrics
							UpperCamelCase__  :Any				   =					TrainerState.load_from_json(os.path.join(UpperCamelCase_       ,  '''trainer_state.json'''				)				).log_history
							UpperCamelCase__  :List[Any]				   =					[log for log in logs if '''eval_loss''' in log.keys()]
							UpperCamelCase__  :Dict				   =					eval_metrics[0]
							UpperCamelCase__  :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''']       ,  UpperCamelCase_				)

							# test if do_predict saves generations and metrics
							UpperCamelCase__  :Tuple				   =					os.listdir(UpperCamelCase_				)
							UpperCamelCase__  :Dict				   =					{os.path.basename(UpperCamelCase_				) for p in contents}
							assert "generated_predictions.txt" in contents
							assert "predict_results.json" in contents




		@slow
		@require_bitsandbytes
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							from transformers.training_args import OptimizerNames

							def train_and_return_metrics(UpperCamelCase_				) -> Tuple[int, float]:
												UpperCamelCase__  :List[str]				   =					'''--skip_memory_metrics 0'''

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

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

												UpperCamelCase__  :int				   =					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[str]				   =					train_and_return_metrics(OptimizerNames.ADAMW_BNB.value				)

							UpperCamelCase__  :Any				   =					gpu_alloc_mem_orig - gpu_alloc_mem_bnb

							UpperCamelCase__  :Any				   =					gpu_peak_mem_orig + gpu_alloc_mem_orig
							UpperCamelCase__  :int				   =					gpu_peak_mem_bnb + gpu_alloc_mem_bnb
							UpperCamelCase__  :Dict				   =					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__  :Optional[int]				   =					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(
							    UpperCamelCase_       ,  UpperCamelCase_       ,  '''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(
							    UpperCamelCase_       ,  UpperCamelCase_       ,  '''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(
							    UpperCamelCase_       ,  UpperCamelCase_       ,  F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_       ,  UpperCamelCase_ = 3e-3       ,  UpperCamelCase_ = "adafactor"       ,  UpperCamelCase_ = False       ,  UpperCamelCase_ = None       ,  UpperCamelCase_ = 0       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = True       ,  UpperCamelCase_ = None       ,  ):

							'''simple docstring'''


							UpperCamelCase__  :Dict				   =					self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
							UpperCamelCase__  :List[Any]				   =					self.get_auto_remove_tmp_dir()
							UpperCamelCase__  :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(UpperCamelCase_				)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase_				)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

							UpperCamelCase__  :Tuple				   =					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(UpperCamelCase_				)}
        '''.split()

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

							return output_dir



'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case				 =	{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	['''ConvBertTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ConvBertForMaskedLM''',
		    '''ConvBertForMultipleChoice''',
		    '''ConvBertForQuestionAnswering''',
		    '''ConvBertForSequenceClassification''',
		    '''ConvBertForTokenClassification''',
		    '''ConvBertLayer''',
		    '''ConvBertModel''',
		    '''ConvBertPreTrainedModel''',
		    '''load_tf_weights_in_convbert''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFConvBertForMaskedLM''',
		    '''TFConvBertForMultipleChoice''',
		    '''TFConvBertForQuestionAnswering''',
		    '''TFConvBertForSequenceClassification''',
		    '''TFConvBertForTokenClassification''',
		    '''TFConvBertLayer''',
		    '''TFConvBertModel''',
		    '''TFConvBertPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
		from .tokenization_convbert import ConvBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_convbert_fast import ConvBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_convbert import (
				    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConvBertForMaskedLM,
				    ConvBertForMultipleChoice,
				    ConvBertForQuestionAnswering,
				    ConvBertForSequenceClassification,
				    ConvBertForTokenClassification,
				    ConvBertLayer,
				    ConvBertModel,
				    ConvBertPreTrainedModel,
				    load_tf_weights_in_convbert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_convbert import (
				    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFConvBertForMaskedLM,
				    TFConvBertForMultipleChoice,
				    TFConvBertForQuestionAnswering,
				    TFConvBertForSequenceClassification,
				    TFConvBertForTokenClassification,
				    TFConvBertLayer,
				    TFConvBertModel,
				    TFConvBertPreTrainedModel,
				)


else:
		import sys

		__snake_case				 =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)