
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   :			List[str]												=    {
    "configuration_blip": [
        "BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlipConfig",
        "BlipTextConfig",
        "BlipVisionConfig",
    ],
    "processing_blip": ["BlipProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			str												=    ["BlipImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "BlipModel",
					    "BlipPreTrainedModel",
					    "BlipForConditionalGeneration",
					    "BlipForQuestionAnswering",
					    "BlipVisionModel",
					    "BlipTextModel",
					    "BlipForImageTextRetrieval",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Any												=    [
					    "TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFBlipModel",
					    "TFBlipPreTrainedModel",
					    "TFBlipForConditionalGeneration",
					    "TFBlipForQuestionAnswering",
					    "TFBlipVisionModel",
					    "TFBlipTextModel",
					    "TFBlipForImageTextRetrieval",
					]

if TYPE_CHECKING:
					from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
					from .processing_blip import BlipProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_blip import BlipImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_blip import (
										    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BlipForConditionalGeneration,
										    BlipForImageTextRetrieval,
										    BlipForQuestionAnswering,
										    BlipModel,
										    BlipPreTrainedModel,
										    BlipTextModel,
										    BlipVisionModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_blip import (
										    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFBlipForConditionalGeneration,
										    TFBlipForImageTextRetrieval,
										    TFBlipForQuestionAnswering,
										    TFBlipModel,
										    TFBlipPreTrainedModel,
										    TFBlipTextModel,
										    TFBlipVisionModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   :			Tuple												=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





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


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


set_seed(42)
SCREAMING_SNAKE_CASE__   :			Any												=    "sshleifer/student_marian_en_ro_6_1"
SCREAMING_SNAKE_CASE__   :			Tuple												=    "sshleifer/tiny-mbart"

@require_torch
class    lowerCAmelCase__     (	__lowercase			):






		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Tuple=False							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=None							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=True							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=True							,			)     ->							Optional[int]:
						__lowerCamelCase    					=	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__							,			)
						__lowerCamelCase    					=	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history

						if not do_eval:
										return

						__lowerCamelCase    					=	[log for log in logs if '''eval_loss''' in log.keys()]

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

										__lowerCamelCase    					=	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		:							Optional[int]  )     ->							int:
						self.run_seqaseq_quick()






		@require_torch_multi_gpu
		def  __A       (		self		:							int  )     ->							List[str]:
						self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE__  )






		@require_torch_multi_gpu
		def  __A       (		self		:							Optional[Any]  )     ->							Tuple:
						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		:							Dict  )     ->							Tuple:
						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		:							Optional[int]  )     ->							List[str]:
						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		:							Tuple  )     ->							Any:
						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		:							Dict  )     ->							Tuple:
						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		:							Union[str, Any]  )     ->							List[str]:
						# 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=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		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							Optional[Any]:
						# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
						__lowerCamelCase    					=	{
						    # 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},
						}

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






		@slow
		def  __A       (		self		:							Any  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.run_trainer(
						    eval_steps=2							,			max_len=1_28							,			model_name=SCREAMING_SNAKE_CASE__							,			learning_rate=3e-4							,			num_train_epochs=10							,			distributed=SCREAMING_SNAKE_CASE__							,			)

						# Check metrics
						__lowerCamelCase    					=	TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history
						__lowerCamelCase    					=	[log for log in logs if '''eval_loss''' in log.keys()]
						__lowerCamelCase    					=	eval_metrics[0]
						__lowerCamelCase    					=	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
						__lowerCamelCase    					=	os.listdir(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	{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		:							Optional[int]  )     ->							str:
						from transformers.training_args import OptimizerNames

						def train_and_return_metrics(SCREAMING_SNAKE_CASE__		:							str  ) -> Tuple[int, float]:
										__lowerCamelCase    					=	'''--skip_memory_metrics 0'''

										__lowerCamelCase    					=	self.run_trainer(
										    max_len=1_28							,			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
										__lowerCamelCase    					=	TrainerState.load_from_json(Path(SCREAMING_SNAKE_CASE__							,			'''trainer_state.json'''  )  ).log_history
										__lowerCamelCase    					=	int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20  )
										__lowerCamelCase    					=	int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20  )

										__lowerCamelCase    					=	logs[0]['''train_loss''']
										return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
						__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase    					=	train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

						__lowerCamelCase    					=	gpu_alloc_mem_orig - gpu_alloc_mem_bnb

						__lowerCamelCase    					=	gpu_peak_mem_orig + gpu_alloc_mem_orig
						__lowerCamelCase    					=	gpu_peak_mem_bnb + gpu_alloc_mem_bnb
						__lowerCamelCase    					=	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
						__lowerCamelCase    					=	1_20

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

						__lowerCamelCase    					=	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(SCREAMING_SNAKE_CASE__  )}
        '''.split()

						__lowerCamelCase    					=	'''
            --do_predict
        '''.split()

						__lowerCamelCase    					=	[]
						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:
														__lowerCamelCase    					=	get_gpu_count()
										__lowerCamelCase    					=	get_torch_dist_unique_port()
										__lowerCamelCase    					=	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()
										__lowerCamelCase    					=	[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:
										__lowerCamelCase    					=	['''run_translation.py'''] + args
										with patch.object(SCREAMING_SNAKE_CASE__							,			'''argv'''							,			SCREAMING_SNAKE_CASE__  ):
														main()

						return output_dir



