


'''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


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


set_seed(42)
_lowercase    :    int			     =  'sshleifer/student_marian_en_ro_6_1'
_lowercase    :    Dict			     =  'sshleifer/tiny-mbart'






@require_torch
class  UpperCamelCase__(     lowerCAmelCase__ ):




				def   a__(	self    :				str   ,					lowerCAmelCase    :				Dict=False   ,					lowerCAmelCase    :				Any=None   ,					lowerCAmelCase    :				List[str]=True   ,					lowerCAmelCase    :				Any=True   ,					lowerCAmelCase    :				Optional[Any]=True   ,					lowerCAmelCase    :				str=True   ,					)->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			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   ,					)
										UpperCAmelCase         =			TrainerState.load_from_json(os.path.join(_SCREAMING_SNAKE_CASE   ,					'''trainer_state.json'''   )   ).log_history

										if not do_eval:
																return

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

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

																UpperCAmelCase         =			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    :				Dict   )->  Optional[int]:

										"""simple docstring"""




										self.run_seqaseq_quick()




				@require_torch_multi_gpu
				def   a__(	self    :				Optional[Any]   )->  Optional[Any]:

										"""simple docstring"""




										self.run_seqaseq_quick(distributed=_SCREAMING_SNAKE_CASE   )




				@require_torch_multi_gpu
				def   a__(	self    :				List[Any]   )->  List[str]:

										"""simple docstring"""




										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    :				Optional[int]   )->  Dict:

										"""simple docstring"""




										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    :				List[str]   )->  List[str]:

										"""simple docstring"""




										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    :				str   )->  List[Any]:

										"""simple docstring"""




										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    :				Optional[Any]   )->  Optional[Any]:

										"""simple docstring"""




										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    :				str   )->  List[str]:

										"""simple docstring"""




										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    :				List[str]   ,					lowerCAmelCase    :				List[str]   )->  Dict:

										"""simple docstring"""




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




				@slow
				def   a__(	self    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			self.run_trainer(
										    eval_steps=2   ,					max_len=128   ,					model_name=_SCREAMING_SNAKE_CASE   ,					learning_rate=3E-4   ,					num_train_epochs=10   ,					distributed=_SCREAMING_SNAKE_CASE   ,					)

										# Check metrics
										UpperCAmelCase         =			TrainerState.load_from_json(os.path.join(_SCREAMING_SNAKE_CASE   ,					'''trainer_state.json'''   )   ).log_history
										UpperCAmelCase         =			[log for log in logs if '''eval_loss''' in log.keys()]
										UpperCAmelCase         =			eval_metrics[0]
										UpperCAmelCase         =			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
										UpperCAmelCase         =			os.listdir(_SCREAMING_SNAKE_CASE   )
										UpperCAmelCase         =			{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[Any]   )->  Optional[Any]:

										"""simple docstring"""




										from transformers.training_args import OptimizerNames

										def train_and_return_metrics(lowerCAmelCase    :				Union[str, Any]   ) -> Tuple[int, float]:
																UpperCAmelCase         =			'''--skip_memory_metrics 0'''

																UpperCAmelCase         =			self.run_trainer(
																    max_len=128   ,					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
																UpperCAmelCase         =			TrainerState.load_from_json(Path(_SCREAMING_SNAKE_CASE   ,					'''trainer_state.json'''   )   ).log_history
																UpperCAmelCase         =			int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20   )
																UpperCAmelCase         =			int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20   )

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

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

										UpperCAmelCase         =			gpu_alloc_mem_orig - gpu_alloc_mem_bnb

										UpperCAmelCase         =			gpu_peak_mem_orig + gpu_alloc_mem_orig
										UpperCAmelCase         =			gpu_peak_mem_bnb + gpu_alloc_mem_bnb
										UpperCAmelCase         =			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         =			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(
										    _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    :				List[Any]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				int = 3E-3   ,					lowerCAmelCase    :				List[Any] = "adafactor"   ,					lowerCAmelCase    :				Tuple = False   ,					lowerCAmelCase    :				List[Any] = None   ,					lowerCAmelCase    :				int = 0   ,					lowerCAmelCase    :				List[str] = True   ,					lowerCAmelCase    :				Dict = True   ,					lowerCAmelCase    :				int = True   ,					lowerCAmelCase    :				Any = True   ,					lowerCAmelCase    :				Union[str, Any] = None   ,					)->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
										UpperCAmelCase         =			self.get_auto_remove_tmp_dir()
										UpperCAmelCase         =			F"""\n            --model_name_or_path {model_name}\n            --train_file {data_dir}/train.json\n            --validation_file {data_dir}/val.json\n            --test_file {data_dir}/test.json\n            --output_dir {output_dir}\n            --overwrite_output_dir\n            --max_train_samples 8\n            --max_source_length {max_len}\n            --max_target_length {max_len}\n            --do_train\n            --num_train_epochs {str(_SCREAMING_SNAKE_CASE   )}\n            --per_device_train_batch_size 4\n            --learning_rate {learning_rate}\n            --warmup_steps 8\n            --logging_steps 0\n            --logging_strategy no\n            --save_steps {str(_SCREAMING_SNAKE_CASE   )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        """.split()

										UpperCAmelCase         =			F"""\n            --do_eval\n            --per_device_eval_batch_size 4\n            --max_eval_samples 8\n            --val_max_target_length {max_len}\n            --evaluation_strategy steps\n            --eval_steps {str(_SCREAMING_SNAKE_CASE   )}\n        """.split()

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

										UpperCAmelCase         =			[]
										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         =			get_gpu_count()
																UpperCAmelCase         =			get_torch_dist_unique_port()
																UpperCAmelCase         =			F"""\n                -m torch.distributed.run\n                --nproc_per_node={n_gpus_to_use}\n                --master_port={master_port}\n                {self.examples_dir_str}/pytorch/translation/run_translation.py\n            """.split()
																UpperCAmelCase         =			[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:
																UpperCAmelCase         =			['''run_translation.py'''] + args
																with patch.object(_SCREAMING_SNAKE_CASE   ,					'''argv'''   ,					_SCREAMING_SNAKE_CASE   ):
																						main()

										return output_dir









'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''   ,					lowerCAmelCase   ,					)
																UpperCAmelCase         =			kwargs.pop('''feature_extractor'''   )

										UpperCAmelCase         =			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You need to specify an `image_processor`.'''   )
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''   )

										super().__init__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''   )

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

										if text is not None:
																UpperCAmelCase         =			self.tokenizer(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None and images is not None:
																UpperCAmelCase         =			{
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase         =			image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase         =			{
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase   )   ,					tensor_type=lowerCAmelCase   )




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				@property
				def   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor_class





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor





