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


set_seed(42)
lowercase			:				Dict    		=			'sshleifer/student_marian_en_ro_6_1'
lowercase			:				Optional[int]    		=			'sshleifer/tiny-mbart'





@require_torch
class  A				(							__snake_case						):





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=True						,			) ->				str:






         """simple docstring"""






         A       :   str					=						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						,			)
         A       :   List[Any]					=						TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE						,			'''trainer_state.json'''	)	).log_history

         if not do_eval:
             return

         A       :   Dict					=						[log for log in logs if '''eval_loss''' in log.keys()]

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

             A       :   Tuple					=						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 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         self.run_seqaseq_quick()





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






         """simple docstring"""






         self.run_seqaseq_quick(distributed=SCREAMING_SNAKE_CASE	)





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






         """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 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """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 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """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 				__lowerCAmelCase							(							self	) ->				int:






         """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 				__lowerCAmelCase							(							self	) ->				Union[str, 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 				__lowerCAmelCase							(							self	) ->				int:






         """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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				str:






         """simple docstring"""






         A       :   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},
         }

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





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         A       :   Tuple					=						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
         A       :   Union[str, Any]					=						TrainerState.load_from_json(os.path.join(SCREAMING_SNAKE_CASE						,			'''trainer_state.json'''	)	).log_history
         A       :   Union[str, Any]					=						[log for log in logs if '''eval_loss''' in log.keys()]
         A       :   List[str]					=						eval_metrics[0]
         A       :   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''']						,			SCREAMING_SNAKE_CASE	)

         # test if do_predict saves generations and metrics
         A       :   int					=						os.listdir(SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						{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 				__lowerCAmelCase							(							self	) ->				Union[str, Any]:






         """simple docstring"""






         from transformers.training_args import OptimizerNames

         def train_and_return_metrics(SCREAMING_SNAKE_CASE	) -> Tuple[int, float]:
             A       :   Optional[int]					=						'''--skip_memory_metrics 0'''

             A       :   List[Any]					=						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
             A       :   str					=						TrainerState.load_from_json(Path(SCREAMING_SNAKE_CASE						,			'''trainer_state.json'''	)	).log_history
             A       :   Union[str, Any]					=						int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20	)
             A       :   int					=						int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20	)

             A       :   List[Any]					=						logs[0]['''train_loss''']
             return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

         A,  A,  A       :   List[Any]					=						train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value	)
         A,  A,  A       :   Optional[Any]					=						train_and_return_metrics(OptimizerNames.ADAMW_BNB.value	)

         A       :   Dict					=						gpu_alloc_mem_orig - gpu_alloc_mem_bnb

         A       :   Union[str, Any]					=						gpu_peak_mem_orig + gpu_alloc_mem_orig
         A       :   Optional[Any]					=						gpu_peak_mem_bnb + gpu_alloc_mem_bnb
         A       :   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
         A       :   List[str]					=						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 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = 3e-3						,			SCREAMING_SNAKE_CASE = "adafactor"						,			SCREAMING_SNAKE_CASE = False						,			SCREAMING_SNAKE_CASE = None						,			SCREAMING_SNAKE_CASE = 0						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = True						,			SCREAMING_SNAKE_CASE = None						,			) ->				Tuple:






         """simple docstring"""






         A       :   Tuple					=						self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
         A       :   Dict					=						self.get_auto_remove_tmp_dir()
         A       :   int					=						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()

         A       :   Any					=						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()

         A       :   Optional[Any]					=						'''
            --do_predict
        '''.split()

         A       :   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:
                 A       :   Dict					=						get_gpu_count()
             A       :   Tuple					=						get_torch_dist_unique_port()
             A       :   str					=						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()
             A       :   str					=						[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:
             A       :   List[str]					=						['''run_translation.py'''] + args
             with patch.object(SCREAMING_SNAKE_CASE						,			'''argv'''						,			SCREAMING_SNAKE_CASE	):
                 main()

         return output_dir






'''simple docstring'''




import requests
from bsa import BeautifulSoup







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   str					=						BeautifulSoup(requests.get(snake_case__		,				params=snake_case__						).content		,				'''html.parser'''						)
    A       :   Dict					=						soup.find('''div'''		,				attrs={'''class''': '''gs_ri'''}						)
    A       :   Optional[int]					=						div.find('''div'''		,				attrs={'''class''': '''gs_fl'''}						).find_all('''a'''						)
    return anchors[2].get_text()


if __name__ == "__main__":
      lowercase			:				str    		=			{
          'title': (
              'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
              'capacitance, volumetric capacitance, and energy density'
          ),
          'journal': 'Chem. Mater.',
          'volume': 30,
          'pages': '3979-3990',
          'year': 20_18,
          'hl': 'en',
      }
      print(get_citation('https://scholar.google.com/scholar_lookup', params=params))




