'''simple docstring'''

from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
							import importlib_metadata
else:
							import importlib.metadata as importlib_metadata


lowercase_     		= ''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):
							class   SCREAMING_SNAKE_CASE			(						tr.AbstractTransform):







									"""simple docstring"""






									def __init__(   self		,						__A = " "				) ->     str:
															_lowerCAmelCase										=sentence_delimiter






									def 		UpperCamelCase__       (   self		,						__A				) ->     Optional[Any]:
															return list(__A				)






									def 		UpperCamelCase__       (   self		,						__A				) ->     Any:
															_lowerCAmelCase										=[]
															for sent_idx, sentence in enumerate(__A				):
																					chars.extend(self.process_string(__A				)				)
																					if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__A				) - 1:
																											chars.append(self.sentence_delimiter				)
															return chars

							lowercase_     		= tr.Compose(
							    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
							)
else:
							lowercase_     		= tr.Compose(
							    [
							        tr.RemoveMultipleSpaces(),
							        tr.Strip(),
							        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
							        tr.ReduceToListOfListOfChars(),
							    ]
							)


lowercase_     		= '''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

lowercase_     		= '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

lowercase_     		= '''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,			_KWARGS_DESCRIPTION)
class   SCREAMING_SNAKE_CASE			(						datasets.Metric):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								return datasets.MetricInfo(
								    description=_DESCRIPTION		,						citation=_CITATION		,						inputs_description=_KWARGS_DESCRIPTION		,						features=datasets.Features(
								        {
								            'predictions': datasets.Value('string'		,						id='sequence'				),
								            'references': datasets.Value('string'		,						id='sequence'				),
								        }				)		,						codebase_urls=['https://github.com/jitsi/jiwer/']		,						reference_urls=[
								        'https://en.wikipedia.org/wiki/Word_error_rate',
								        'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
								    ]		,						)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A=False				) ->     str:
								if concatenate_texts:
														return jiwer.compute_measures(
														    __A		,						__A		,						truth_transform=__A		,						hypothesis_transform=__A		,						)["wer"]

								_lowerCAmelCase										=0
								_lowerCAmelCase										=0
								for prediction, reference in zip(__A		,						__A				):
														_lowerCAmelCase										=jiwer.compute_measures(
														    __A		,						__A		,						truth_transform=__A		,						hypothesis_transform=__A		,						)
														incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
														total += measures["substitutions"] + measures["deletions"] + measures["hits"]

								return incorrect / total






'''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


lowercase_     		= '''sshleifer/mar_enro_6_3_student'''
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






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

								_lowerCAmelCase										=cached_path(
								    'https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'		,						extract_compressed_file=__A		,						)
								_lowerCAmelCase										=F'''{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'''






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								MarianMTModel.from_pretrained(__A				)






		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										={
								    '$MAX_LEN': 64,
								    '$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
								_lowerCAmelCase										=(self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
								_lowerCAmelCase										=bash_script.replace('\\\n'		,						''				).strip().replace('"$@"'		,						''				)
								for k, v in env_vars_to_replace.items():
														_lowerCAmelCase										=bash_script.replace(__A		,						str(__A				)				)
								_lowerCAmelCase										=self.get_auto_remove_tmp_dir()

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

								_lowerCAmelCase										=['finetune.py'] + bash_script.split() + args
								with patch.object(__A		,						'argv'		,						__A				):
														_lowerCAmelCase										=argparse.ArgumentParser()
														_lowerCAmelCase										=pl.Trainer.add_argparse_args(__A				)
														_lowerCAmelCase										=SummarizationModule.add_model_specific_args(__A		,						os.getcwd()				)
														_lowerCAmelCase										=parser.parse_args()
														_lowerCAmelCase										=main(__A				)

								# Check metrics
								_lowerCAmelCase										=load_json(model.metrics_save_path				)
								_lowerCAmelCase										=metrics['val'][0]
								_lowerCAmelCase										=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}''']		,						__A				)

								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
								_lowerCAmelCase										=os.listdir(__A				)
								_lowerCAmelCase										=[x for x in contents if x.endswith('.ckpt'				)][0]
								_lowerCAmelCase										=os.path.join(args.output_dir		,						__A				)
								_lowerCAmelCase										=torch.load(__A		,						map_location='cpu'				)
								_lowerCAmelCase										='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:
														_lowerCAmelCase										={os.path.basename(__A				) 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   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@timeout_decorator.timeout(600				)
		@slow
		@require_torch_gpu
		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=F'''{self.test_file_dir_str}/test_data/wmt_en_ro'''
								_lowerCAmelCase										={
								    '--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
								_lowerCAmelCase										=(
								    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
								)
								_lowerCAmelCase										=bash_script.replace('\\\n'		,						''				).strip().replace('"$@"'		,						''				)
								_lowerCAmelCase										=bash_script.replace('--fp16 '		,						' '				)

								for k, v in env_vars_to_replace.items():
														_lowerCAmelCase										=bash_script.replace(__A		,						str(__A				)				)
								_lowerCAmelCase										=self.get_auto_remove_tmp_dir()
								_lowerCAmelCase										=bash_script.replace('--fp16'		,						''				)
								_lowerCAmelCase										=6
								_lowerCAmelCase										=(
								    ['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(__A		,						'argv'		,						__A				):
														_lowerCAmelCase										=argparse.ArgumentParser()
														_lowerCAmelCase										=pl.Trainer.add_argparse_args(__A				)
														_lowerCAmelCase										=SummarizationDistiller.add_model_specific_args(__A		,						os.getcwd()				)
														_lowerCAmelCase										=parser.parse_args()
														# assert args.gpus == gpus THIS BREAKS for multi_gpu

														_lowerCAmelCase										=distill_main(__A				)

								# Check metrics
								_lowerCAmelCase										=load_json(model.metrics_save_path				)
								_lowerCAmelCase										=metrics['val'][0]
								_lowerCAmelCase										=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}''']		,						__A				)

								# check lightning ckpt can be loaded and has a reasonable statedict
								_lowerCAmelCase										=os.listdir(__A				)
								_lowerCAmelCase										=[x for x in contents if x.endswith('.ckpt'				)][0]
								_lowerCAmelCase										=os.path.join(args.output_dir		,						__A				)
								_lowerCAmelCase										=torch.load(__A		,						map_location='cpu'				)
								_lowerCAmelCase										='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:
														_lowerCAmelCase										={os.path.basename(__A				) 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





