"""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


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


set_seed(42)
_a				:		Optional[Any]	   =       "sshleifer/student_marian_en_ro_6_1"
_a				:		Union[str, Any]	   =       "sshleifer/tiny-mbart"
@require_torch
class 						__A							(			SCREAMING_SNAKE_CASE__		):






     def    __A  (		self    ,					a__=False    ,					a__=None    ,					a__=True    ,					a__=True    ,					a__=True    ,					a__=True    ,					):
       _lowerCAmelCase						:	Optional[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_    ,					)
       _lowerCAmelCase						:	Union[str, Any]        =					TrainerState.load_from_json(os.path.join(a_    ,					"""trainer_state.json"""  )  ).log_history

       if not do_eval:
         return

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

       _lowerCAmelCase						:	List[Any]        =					eval_metrics[0]
       if predict_with_generate:
         assert "eval_bleu" in first_step_stats

         _lowerCAmelCase						:	List[str]        =					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    __A  (		self  ):
       self.run_seqaseq_quick()






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






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






     @unittest.skip("""Requires an update of the env running those tests"""  )
     @require_torch_multi_gpu
     @require_fairscale
     def    __A  (		self  ):
       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    __A  (		self  ):
       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    __A  (		self  ):
       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    __A  (		self  ):
       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    __A  (		self  ):
       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    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	str        =					{
           # 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						:	Dict        =					experiments[experiment_id]
       _lowerCAmelCase						:	int        =					{"""distributed""": True, """predict_with_generate""": False, """do_eval""": False, """do_predict""": False}
       _lowerCAmelCase						:	Union[str, Any]        =					"""Running training"""
       with CaptureStderr() as cl:
         self.run_seqaseq_quick(**a_    ,					extra_args_str=data["""extra_args_str"""]  )
       _lowerCAmelCase						:	Dict        =					len(re.findall(a_    ,					cl.err  )  )
       self.assertEqual(a_    ,					data["""n_matches"""]  )






     @slow
     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					self.run_trainer(
           eval_steps=2    ,					max_len=128    ,					model_name=a_    ,					learning_rate=3e-4    ,					num_train_epochs=10    ,					distributed=a_    ,					)

       # Check metrics
       _lowerCAmelCase						:	str        =					TrainerState.load_from_json(os.path.join(a_    ,					"""trainer_state.json"""  )  ).log_history
       _lowerCAmelCase						:	str        =					[log for log in logs if """eval_loss""" in log.keys()]
       _lowerCAmelCase						:	int        =					eval_metrics[0]
       _lowerCAmelCase						:	Union[str, 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
       _lowerCAmelCase						:	int        =					os.listdir(a_  )
       _lowerCAmelCase						:	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    __A  (		self  ):
       from transformers.training_args import OptimizerNames

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

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

         _lowerCAmelCase						:	Any        =					logs[0]["""train_loss"""]
         return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Tuple        =					train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value  )
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Any        =					train_and_return_metrics(OptimizerNames.ADAMW_BNB.value  )

       _lowerCAmelCase						:	Optional[Any]        =					gpu_alloc_mem_orig - gpu_alloc_mem_bnb

       _lowerCAmelCase						:	int        =					gpu_peak_mem_orig + gpu_alloc_mem_orig
       _lowerCAmelCase						:	Any        =					gpu_peak_mem_bnb + gpu_alloc_mem_bnb
       _lowerCAmelCase						:	Optional[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
       _lowerCAmelCase						:	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(
           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    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__ = 3e-3    ,					a__ = "adafactor"    ,					a__ = False    ,					a__ = None    ,					a__ = 0    ,					a__ = True    ,					a__ = True    ,					a__ = True    ,					a__ = True    ,					a__ = None    ,					):
       _lowerCAmelCase						:	Dict        =					self.test_file_dir / """../fixtures/tests_samples/wmt_en_ro"""
       _lowerCAmelCase						:	List[str]        =					self.get_auto_remove_tmp_dir()
       _lowerCAmelCase						:	Union[str, Any]        =					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(a_  )}\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(a_  )}\n            --group_by_length\n            --label_smoothing_factor 0.1\n            --target_lang ro_RO\n            --source_lang en_XX\n        ".split()

       _lowerCAmelCase						:	List[str]        =					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(a_  )}\n        ".split()

       _lowerCAmelCase						:	List[Any]        =					"""\n            --do_predict\n        """.split()

       _lowerCAmelCase						:	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:
           _lowerCAmelCase						:	Union[str, Any]        =					get_gpu_count()
         _lowerCAmelCase						:	Any        =					get_torch_dist_unique_port()
         _lowerCAmelCase						:	Dict        =					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()
         _lowerCAmelCase						:	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:
         _lowerCAmelCase						:	Optional[Any]        =					["""run_translation.py"""] + args
         with patch.object(a_    ,					"""argv"""    ,					a_  ):
           main()

       return output_dir





"""simple docstring"""




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor
class 						__A							(			unittest.TestCase		):






     def __init__(		self    ,					a__    ,					a__=7    ,					a__=3    ,					a__=30    ,					a__=400    ,					a__=True    ,					a__=None    ,					a__=0.9    ,					a__=None    ,					a__=True    ,					a__=[0.5, 0.5, 0.5]    ,					a__=[0.5, 0.5, 0.5]    ,					):
       _lowerCAmelCase						:	int        =					size if size is not None else {"""shortest_edge""": 30}
       _lowerCAmelCase						:	Dict        =					crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
       _lowerCAmelCase						:	List[Any]        =					parent
       _lowerCAmelCase						:	Optional[int]        =					batch_size
       _lowerCAmelCase						:	Any        =					num_channels
       _lowerCAmelCase						:	str        =					min_resolution
       _lowerCAmelCase						:	Dict        =					max_resolution
       _lowerCAmelCase						:	str        =					do_resize_and_center_crop
       _lowerCAmelCase						:	List[str]        =					size
       _lowerCAmelCase						:	int        =					crop_pct
       _lowerCAmelCase						:	int        =					crop_size
       _lowerCAmelCase						:	Union[str, Any]        =					do_normalize
       _lowerCAmelCase						:	Tuple        =					image_mean
       _lowerCAmelCase						:	Optional[Any]        =					image_std






     def    __A  (		self  ):
       return {
           "size": self.size,
           "do_resize_and_center_crop": self.do_resize_and_center_crop,
           "crop_pct": self.crop_pct,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
       }







@require_torch
@require_vision
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[str]		 =		PoolFormerImageProcessor if is_vision_available() else None






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					PoolFormerImageProcessingTester(self  )






     @property
     def    __A  (		self  ):
       return self.image_processor_tester.prepare_image_processor_dict()






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__    ,					"""do_resize_and_center_crop"""  )  )
       self.assertTrue(hasattr(a__    ,					"""size"""  )  )
       self.assertTrue(hasattr(a__    ,					"""crop_pct"""  )  )
       self.assertTrue(hasattr(a__    ,					"""do_normalize"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_mean"""  )  )
       self.assertTrue(hasattr(a__    ,					"""image_std"""  )  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 30}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 30, """width""": 30}  )

       _lowerCAmelCase						:	List[Any]        =					self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42    ,					crop_size=84  )
       self.assertEqual(image_processor.size    ,					{"""shortest_edge""": 42}  )
       self.assertEqual(image_processor.crop_size    ,					{"""height""": 84, """width""": 84}  )






     def    __A  (		self  ):
       pass






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	List[str]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:	Tuple        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					Image.Image  )

       # Test not batched input
       _lowerCAmelCase						:	Tuple        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values

       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:	Union[str, Any]        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					numpify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					np.ndarray  )

       # Test not batched input
       _lowerCAmelCase						:	Dict        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	int        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)






     def    __A  (		self  ):
       # Initialize image_processing
       _lowerCAmelCase						:	Any        =					self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:	Any        =					prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=a__    ,					torchify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__    ,					torch.Tensor  )

       # Test not batched input
       _lowerCAmelCase						:	str        =					image_processing(image_inputs[0]    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)

       # Test batched
       _lowerCAmelCase						:	List[str]        =					image_processing(a__    ,					return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape    ,					(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size["""height"""],
               self.image_processor_tester.crop_size["""width"""],
           )    ,					)




