


'''simple docstring'''



import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


__UpperCAmelCase    :Any				       =     "sshleifer/mar_enro_6_3_student"



class 			a       (	_a ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	int    )		->							Optional[Any]:
          super().setUp()

          __UpperCAmelCase			:				Optional[Any]													=    cached_path(
              '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''							,				extract_compressed_file=snake_case							,				)
          __UpperCAmelCase			:				str													=    f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'

     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Optional[Any]:
          MarianMTModel.from_pretrained(snake_case    )






     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							int:
          __UpperCAmelCase			:				Optional[Any]													=    {
              '''$MAX_LEN''': 64,
              '''$BS''': 64,
              '''$GAS''': 1,
              '''$ENRO_DIR''': self.data_dir,
              '''facebook/mbart-large-cc25''': MARIAN_MODEL,
              # "val_check_interval=0.25": "val_check_interval=1.0",
              '''--learning_rate=3e-5''': '''--learning_rate 3e-4''',
              '''--num_train_epochs 6''': '''--num_train_epochs 1''',
          }

          # Clean up bash script
          __UpperCAmelCase			:				Tuple													=    (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''    )[1].strip()
          __UpperCAmelCase			:				List[Any]													=    bash_script.replace('''\\\n'''							,				''''''    ).strip().replace('''"$@"'''							,				''''''    )
          for k, v in env_vars_to_replace.items():
               __UpperCAmelCase			:				Any													=    bash_script.replace(snake_case							,				str(snake_case    )    )
          __UpperCAmelCase			:				str													=    self.get_auto_remove_tmp_dir()

          # bash_script = bash_script.replace("--fp16 ", "")
          __UpperCAmelCase			:				Optional[Any]													=    f'\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        '.split()
          # XXX: args.gpus > 1 : handle multi_gpu in the future

          __UpperCAmelCase			:				Optional[int]													=    ['''finetune.py'''] + bash_script.split() + args
          with patch.object(snake_case							,				'''argv'''							,				snake_case    ):
               __UpperCAmelCase			:				int													=    argparse.ArgumentParser()
               __UpperCAmelCase			:				List[str]													=    pl.Trainer.add_argparse_args(snake_case    )
               __UpperCAmelCase			:				Union[str, Any]													=    SummarizationModule.add_model_specific_args(snake_case							,				os.getcwd()    )
               __UpperCAmelCase			:				Dict													=    parser.parse_args()
               __UpperCAmelCase			:				List[str]													=    main(snake_case    )

          # Check metrics
          __UpperCAmelCase			:				Optional[int]													=    load_json(model.metrics_save_path    )
          __UpperCAmelCase			:				int													=    metrics['''val'''][0]
          __UpperCAmelCase			:				List[Any]													=    metrics['''val'''][-1]
          self.assertEqual(len(metrics['''val''']    )							,				(args.max_epochs / args.val_check_interval)    )
          assert isinstance(last_step_stats[f'val_avg_{model.val_metric}']							,				snake_case    )

          self.assertGreater(last_step_stats['''val_avg_gen_time''']							,				0.01    )
          # model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
          self.assertLessEqual(last_step_stats['''val_avg_gen_time''']							,				1.0    )

          # test learning requirements:

          # 1. BLEU improves over the course of training by more than 2 pts
          self.assertGreater(last_step_stats['''val_avg_bleu'''] - first_step_stats['''val_avg_bleu''']							,				2    )

          # 2. BLEU finishes above 17
          self.assertGreater(last_step_stats['''val_avg_bleu''']							,				17    )

          # 3. test BLEU and val BLEU within ~1.1 pt.
          self.assertLess(abs(metrics['''val'''][-1]['''val_avg_bleu'''] - metrics['''test'''][-1]['''test_avg_bleu''']    )							,				1.1    )

          # check lightning ckpt can be loaded and has a reasonable statedict
          __UpperCAmelCase			:				Any													=    os.listdir(snake_case    )
          __UpperCAmelCase			:				Tuple													=    [x for x in contents if x.endswith('''.ckpt'''    )][0]
          __UpperCAmelCase			:				Optional[int]													=    os.path.join(args.output_dir							,				snake_case    )
          __UpperCAmelCase			:				Optional[Any]													=    torch.load(snake_case							,				map_location='''cpu'''    )
          __UpperCAmelCase			:				str													=    '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
          assert expected_key in ckpt["state_dict"]
          assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

          # TODO: turn on args.do_predict when PL bug fixed.
          if args.do_predict:
               __UpperCAmelCase			:				Union[str, Any]													=    {os.path.basename(snake_case    ) for p in contents}
               assert "test_generations.txt" in contents
               assert "test_results.txt" in contents
               # assert len(metrics["val"]) ==  desired_n_evals
               assert len(metrics['''test''']    ) == 1



class 			a       (	_a ):


     """simple docstring"""






     @timeout_decorator.timeout(600    )
     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Optional[Any]:
          __UpperCAmelCase			:				Any													=    f'{self.test_file_dir_str}/test_data/wmt_en_ro'
          __UpperCAmelCase			:				List[str]													=    {
              '''--fp16_opt_level=O1''': '''''',
              '''$MAX_LEN''': 128,
              '''$BS''': 16,
              '''$GAS''': 1,
              '''$ENRO_DIR''': data_dir,
              '''$m''': '''sshleifer/student_marian_en_ro_6_1''',
              '''val_check_interval=0.25''': '''val_check_interval=1.0''',
          }

          # Clean up bash script
          __UpperCAmelCase			:				str													=    (
              (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''    )[1].strip()
          )
          __UpperCAmelCase			:				Dict													=    bash_script.replace('''\\\n'''							,				''''''    ).strip().replace('''"$@"'''							,				''''''    )
          __UpperCAmelCase			:				Tuple													=    bash_script.replace('''--fp16 '''							,				''' '''    )

          for k, v in env_vars_to_replace.items():
               __UpperCAmelCase			:				Tuple													=    bash_script.replace(snake_case							,				str(snake_case    )    )
          __UpperCAmelCase			:				Tuple													=    self.get_auto_remove_tmp_dir()
          __UpperCAmelCase			:				Dict													=    bash_script.replace('''--fp16'''							,				''''''    )
          __UpperCAmelCase			:				Tuple													=    6
          __UpperCAmelCase			:				Any													=    (
              ['''distillation.py''']
              + bash_script.split()
              + [
                  f'--output_dir={output_dir}',
                  '''--gpus=1''',
                  '''--learning_rate=1e-3''',
                  f'--num_train_epochs={epochs}',
                  '''--warmup_steps=10''',
                  '''--val_check_interval=1.0''',
                  '''--do_predict''',
              ]
          )
          with patch.object(snake_case							,				'''argv'''							,				snake_case    ):
               __UpperCAmelCase			:				int													=    argparse.ArgumentParser()
               __UpperCAmelCase			:				Union[str, Any]													=    pl.Trainer.add_argparse_args(snake_case    )
               __UpperCAmelCase			:				str													=    SummarizationDistiller.add_model_specific_args(snake_case							,				os.getcwd()    )
               __UpperCAmelCase			:				Union[str, Any]													=    parser.parse_args()
               # assert args.gpus == gpus THIS BREAKS for multi_gpu

               __UpperCAmelCase			:				str													=    distill_main(snake_case    )

          # Check metrics
          __UpperCAmelCase			:				int													=    load_json(model.metrics_save_path    )
          __UpperCAmelCase			:				List[str]													=    metrics['''val'''][0]
          __UpperCAmelCase			:				Tuple													=    metrics['''val'''][-1]
          assert len(metrics['''val''']    ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

          assert last_step_stats["val_avg_gen_time"] >= 0.01

          assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
          assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
          assert isinstance(last_step_stats[f'val_avg_{model.val_metric}']							,				snake_case    )

          # check lightning ckpt can be loaded and has a reasonable statedict
          __UpperCAmelCase			:				Tuple													=    os.listdir(snake_case    )
          __UpperCAmelCase			:				Tuple													=    [x for x in contents if x.endswith('''.ckpt'''    )][0]
          __UpperCAmelCase			:				int													=    os.path.join(args.output_dir							,				snake_case    )
          __UpperCAmelCase			:				Dict													=    torch.load(snake_case							,				map_location='''cpu'''    )
          __UpperCAmelCase			:				Dict													=    '''model.model.decoder.layers.0.encoder_attn_layer_norm.weight'''
          assert expected_key in ckpt["state_dict"]
          assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

          # TODO: turn on args.do_predict when PL bug fixed.
          if args.do_predict:
               __UpperCAmelCase			:				int													=    {os.path.basename(snake_case    ) for p in contents}
               assert "test_generations.txt" in contents
               assert "test_results.txt" in contents
               # assert len(metrics["val"]) ==  desired_n_evals
               assert len(metrics['''test''']    ) == 1



'''simple docstring'''



from scipy.stats import pearsonr

import datasets


__UpperCAmelCase    :Tuple				       =     "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"


__UpperCAmelCase    :List[str]				       =     "\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        ['p-value', 'pearsonr']\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n        >>> print(round(results['p-value'], 2))\n        0.15\n"


__UpperCAmelCase    :List[Any]				       =     "\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,						_KWARGS_DESCRIPTION )
class 			a       (	datasets.Metric ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	List[Any]    )		->							Union[str, Any]:
          return datasets.MetricInfo(
              description=_DESCRIPTION							,				citation=_CITATION							,				inputs_description=_KWARGS_DESCRIPTION							,				features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''float'''    ),
                      '''references''': datasets.Value('''float'''    ),
                  }    )							,				reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html''']							,				)






     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	Union[str, Any]							,				snake_case  :	int							,				snake_case  :	Dict=False    )		->							List[Any]:
          if return_pvalue:
               __UpperCAmelCase			:				Tuple													=    pearsonr(snake_case							,				snake_case    )
               return {"pearsonr": results[0], "p-value": results[1]}
          else:
               return {"pearsonr": float(pearsonr(snake_case							,				snake_case    )[0]    )}