






'''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							="sshleifer/mar_enro_6_3_student"




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       super().setUp()

       UpperCamelCase_				:   List[Any]					    =							cached_path(
           """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""			,     extract_compressed_file=__lowerCAmelCase			,     )
       UpperCamelCase_				:   int					    =							F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"
 @slow
 @require_torch_gpu
 def   _UpperCAmelCase       (							self ):
       MarianMTModel.from_pretrained(__lowerCAmelCase )





 @slow
 @require_torch_gpu
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							{
           """$MAX_LEN""": 64,
           """$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_				:   Optional[Any]					    =							(self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py""" )[1].strip()
       UpperCamelCase_				:   List[str]					    =							bash_script.replace("""\\\n"""			,     """""" ).strip().replace("""\"$@\""""			,     """""" )
       for k, v in env_vars_to_replace.items():
             UpperCamelCase_				:   Any					    =							bash_script.replace(__lowerCAmelCase			,     str(__lowerCAmelCase ) )
       UpperCamelCase_				:   Optional[Any]					    =							self.get_auto_remove_tmp_dir()

       # bash_script = bash_script.replace("--fp16 ", "")
       UpperCamelCase_				:   str					    =							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_				:   Tuple					    =							["""finetune.py"""] + bash_script.split() + args
       with patch.object(__lowerCAmelCase			,     """argv"""			,     __lowerCAmelCase ):
             UpperCamelCase_				:   List[str]					    =							argparse.ArgumentParser()
             UpperCamelCase_				:   Dict					    =							pl.Trainer.add_argparse_args(__lowerCAmelCase )
             UpperCamelCase_				:   Tuple					    =							SummarizationModule.add_model_specific_args(__lowerCAmelCase			,     os.getcwd() )
             UpperCamelCase_				:   Optional[Any]					    =							parser.parse_args()
             UpperCamelCase_				:   Optional[int]					    =							main(__lowerCAmelCase )

       # Check metrics
       UpperCamelCase_				:   Dict					    =							load_json(model.metrics_save_path )
       UpperCamelCase_				:   Union[str, Any]					    =							metrics["""val"""][0]
       UpperCamelCase_				:   Tuple					    =							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}"]			,     __lowerCAmelCase )

       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_				:   int					    =							os.listdir(__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							[x for x in contents if x.endswith(""".ckpt""" )][0]
       UpperCamelCase_				:   Optional[Any]					    =							os.path.join(args.output_dir			,     __lowerCAmelCase )
       UpperCamelCase_				:   Union[str, Any]					    =							torch.load(__lowerCAmelCase			,     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_				:   str					    =							{os.path.basename(__lowerCAmelCase ) 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    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 @timeout_decorator.timeout(6_00 )
 @slow
 @require_torch_gpu
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   str					    =							F"{self.test_file_dir_str}/test_data/wmt_en_ro"
       UpperCamelCase_				:   Any					    =							{
           """--fp16_opt_level=O1""": """""",
           """$MAX_LEN""": 1_28,
           """$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_				:   List[str]					    =							(
           (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py""" )[1].strip()
       )
       UpperCamelCase_				:   List[Any]					    =							bash_script.replace("""\\\n"""			,     """""" ).strip().replace("""\"$@\""""			,     """""" )
       UpperCamelCase_				:   Dict					    =							bash_script.replace("""--fp16 """			,     """ """ )

       for k, v in env_vars_to_replace.items():
             UpperCamelCase_				:   Optional[int]					    =							bash_script.replace(__lowerCAmelCase			,     str(__lowerCAmelCase ) )
       UpperCamelCase_				:   Union[str, Any]					    =							self.get_auto_remove_tmp_dir()
       UpperCamelCase_				:   List[Any]					    =							bash_script.replace("""--fp16"""			,     """""" )
       UpperCamelCase_				:   Any					    =							6
       UpperCamelCase_				:   List[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(__lowerCAmelCase			,     """argv"""			,     __lowerCAmelCase ):
             UpperCamelCase_				:   Union[str, Any]					    =							argparse.ArgumentParser()
             UpperCamelCase_				:   Optional[int]					    =							pl.Trainer.add_argparse_args(__lowerCAmelCase )
             UpperCamelCase_				:   Dict					    =							SummarizationDistiller.add_model_specific_args(__lowerCAmelCase			,     os.getcwd() )
             UpperCamelCase_				:   Any					    =							parser.parse_args()
             # assert args.gpus == gpus THIS BREAKS for multi_gpu

             UpperCamelCase_				:   Union[str, Any]					    =							distill_main(__lowerCAmelCase )

       # Check metrics
       UpperCamelCase_				:   Optional[Any]					    =							load_json(model.metrics_save_path )
       UpperCamelCase_				:   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}"]			,     __lowerCAmelCase )

       # check lightning ckpt can be loaded and has a reasonable statedict
       UpperCamelCase_				:   Optional[int]					    =							os.listdir(__lowerCAmelCase )
       UpperCamelCase_				:   int					    =							[x for x in contents if x.endswith(""".ckpt""" )][0]
       UpperCamelCase_				:   List[Any]					    =							os.path.join(args.output_dir			,     __lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							torch.load(__lowerCAmelCase			,     map_location="""cpu""" )
       UpperCamelCase_				:   Any					    =							"""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_				:   Optional[Any]					    =							{os.path.basename(__lowerCAmelCase ) 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 typing import Dict, List

from nltk.translate import gleu_score

import datasets
from datasets import MetricInfo


UpperCamelCase							="\\n@misc{wu2016googles,\n      title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n      author={Yonghui Wu and Mike Schuster and Zhifeng Chen and Quoc V. Le and Mohammad Norouzi and Wolfgang Macherey\n              and Maxim Krikun and Yuan Cao and Qin Gao and Klaus Macherey and Jeff Klingner and Apurva Shah and Melvin\n              Johnson and Xiaobing Liu and Łukasz Kaiser and Stephan Gouws and Yoshikiyo Kato and Taku Kudo and Hideto\n              Kazawa and Keith Stevens and George Kurian and Nishant Patil and Wei Wang and Cliff Young and\n              Jason Smith and Jason Riesa and Alex Rudnick and Oriol Vinyals and Greg Corrado and Macduff Hughes\n              and Jeffrey Dean},\n      year={2016},\n      eprint={1609.08144},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL}\n}\n"

UpperCamelCase							="\\nThe BLEU score has some undesirable properties when used for single\nsentences, as it was designed to be a corpus measure. We therefore\nuse a slightly different score for our RL experiments which we call\nthe 'GLEU score'. For the GLEU score, we record all sub-sequences of\n1, 2, 3 or 4 tokens in output and target sequence (n-grams). We then\ncompute a recall, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the target (ground truth) sequence,\nand a precision, which is the ratio of the number of matching n-grams\nto the number of total n-grams in the generated output sequence. Then\nGLEU score is simply the minimum of recall and precision. This GLEU\nscore's range is always between 0 (no matches) and 1 (all match) and\nit is symmetrical when switching output and target. According to\nour experiments, GLEU score correlates quite well with the BLEU\nmetric on a corpus level but does not have its drawbacks for our per\nsentence reward objective.\n"

UpperCamelCase							="\\nComputes corpus-level Google BLEU (GLEU) score of translated segments against one or more references.\nInstead of averaging the sentence level GLEU scores (i.e. macro-average precision), Wu et al. (2016) sum up the matching\ntokens and the max of hypothesis and reference tokens for each sentence, then compute using the aggregate values.\n\nArgs:\n    predictions (list of str): list of translations to score.\n        Each translation should be tokenized into a list of tokens.\n    references (list of list of str): list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\n    min_len (int): The minimum order of n-gram this function should extract. Defaults to 1.\n    max_len (int): The maximum order of n-gram this function should extract. Defaults to 4.\n\nReturns:\n    'google_bleu': google_bleu score\n\nExamples:\n    Example 1:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.44\n\n    Example 2:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.61\n\n    Example 3:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses, references=list_of_references, min_len=2)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.53\n\n    Example 4:\n        >>> hyp1 = ['It', 'is', 'a', 'guide', 'to', 'action', 'which',\n        ...         'ensures', 'that', 'the', 'rubber', 'duck', 'always',\n        ...         'disobeys', 'the', 'commands', 'of', 'the', 'cat']\n        >>> ref1a = ['It', 'is', 'the', 'guiding', 'principle', 'which',\n        ...          'guarantees', 'the', 'rubber', 'duck', 'forces', 'never',\n        ...          'being', 'under', 'the', 'command', 'of', 'the', 'cat']\n        >>> ref1b = ['It', 'is', 'a', 'guide', 'to', 'action', 'that',\n        ...          'ensures', 'that', 'the', 'rubber', 'duck', 'will', 'never',\n        ...          'heed', 'the', 'cat', 'commands']\n        >>> ref1c = ['It', 'is', 'the', 'practical', 'guide', 'for', 'the',\n        ...          'rubber', 'duck', 'army', 'never', 'to', 'heed', 'the', 'directions',\n        ...          'of', 'the', 'cat']\n\n        >>> hyp2 = ['he', 'read', 'the', 'book', 'because', 'he', 'was',\n        ...         'interested', 'in', 'world', 'history']\n        >>> ref2a = ['he', 'was', 'interested', 'in', 'world', 'history',\n        ...          'because', 'he', 'read', 'the', 'book']\n\n        >>> list_of_references = [[ref1a, ref1b, ref1c], [ref2a]]\n        >>> hypotheses = [hyp1, hyp2]\n        >>> google_bleu = datasets.load_metric(\"google_bleu\")\n        >>> results = google_bleu.compute(predictions=hypotheses,references=list_of_references, min_len=2, max_len=6)\n        >>> print(round(results[\"google_bleu\"], 2))\n        0.4\n"




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






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       return datasets.MetricInfo(
           description=_DESCRIPTION			,     citation=_CITATION			,     inputs_description=_KWARGS_DESCRIPTION			,     features=datasets.Features(
               {
                   """predictions""": datasets.Sequence(datasets.Value("""string"""			,     id="""token""" )			,     id="""sequence""" ),
                   """references""": datasets.Sequence(
                       datasets.Sequence(datasets.Value("""string"""			,     id="""token""" )			,     id="""sequence""" )			,     id="""references""" ),
               } )			,     )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase = 1			,     __lowerCAmelCase = 4			,     ):
       return {
           "google_bleu": gleu_score.corpus_gleu(
               list_of_references=__lowerCAmelCase			,     hypotheses=__lowerCAmelCase			,     min_len=__lowerCAmelCase			,     max_len=__lowerCAmelCase )
       }






