"""simple docstring"""






import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase=1_0_2_4       ):
				UpperCAmelCase__      ,	UpperCAmelCase__             =							[], []

				UpperCAmelCase__             =							list(zip(lowerCamelCase					,			lowerCamelCase       )       )
				UpperCAmelCase__      ,	UpperCAmelCase__             =							sorted_examples[0]

				def is_too_big(lowerCamelCase       ):
								return tok(lowerCamelCase					,			return_tensors='pt'       ).input_ids.shape[1] > max_tokens

				for src, tgt in tqdm(sorted_examples[1:]       ):
								UpperCAmelCase__             =							new_src + ' ' + src
								UpperCAmelCase__             =							new_tgt + ' ' + tgt
								if is_too_big(lowerCamelCase       ) or is_too_big(lowerCamelCase       ):  # cant fit, finalize example
												finished_src.append(lowerCamelCase       )
												finished_tgt.append(lowerCamelCase       )
												UpperCAmelCase__      ,	UpperCAmelCase__             =							src, tgt
								else:  # can fit, keep adding
												UpperCAmelCase__      ,	UpperCAmelCase__             =							cand_src, cand_tgt

    # cleanup
				if new_src:
								assert new_tgt
								finished_src.append(lowerCamelCase       )
								finished_tgt.append(lowerCamelCase       )
				return finished_src, finished_tgt



def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__             =							Path(lowerCamelCase       )
				save_path.mkdir(exist_ok=lowerCamelCase       )
				for split in ["train"]:
								UpperCAmelCase__      ,	UpperCAmelCase__             =							data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
								UpperCAmelCase__             =							[x.rstrip() for x in Path(lowerCamelCase       ).open().readlines()]
								UpperCAmelCase__             =							[x.rstrip() for x in Path(lowerCamelCase       ).open().readlines()]
								UpperCAmelCase__      ,	UpperCAmelCase__             =							pack_examples(lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       )
								print(f'''packed {split} split from {len(lowerCamelCase       )} examples -> {len(lowerCamelCase       )}.'''       )
								Path(save_path / f'''{split}.source'''       ).open('w'       ).write('\n'.join(lowerCamelCase       )       )
								Path(save_path / f'''{split}.target'''       ).open('w'       ).write('\n'.join(lowerCamelCase       )       )
				for split in ["val", "test"]:
								UpperCAmelCase__      ,	UpperCAmelCase__             =							data_dir / f'''{split}.source''', data_dir / f'''{split}.target'''
								shutil.copyfile(lowerCamelCase					,			save_path / f'''{split}.source'''       )
								shutil.copyfile(lowerCamelCase					,			save_path / f'''{split}.target'''       )



def   a_						(					):
				UpperCAmelCase__             =							argparse.ArgumentParser()
				parser.add_argument('--tok_name'					,			type=lowerCamelCase					,			help='like facebook/bart-large-cnn,t5-base, etc.'       )
				parser.add_argument('--max_seq_len'					,			type=lowerCamelCase					,			default=1_2_8       )
				parser.add_argument('--data_dir'					,			type=lowerCamelCase       )
				parser.add_argument('--save_path'					,			type=lowerCamelCase       )
				UpperCAmelCase__             =							parser.parse_args()
				UpperCAmelCase__             =							AutoTokenizer.from_pretrained(args.tok_name       )
				return pack_data_dir(lowerCamelCase					,			Path(args.data_dir       )					,			args.max_seq_len					,			args.save_path       )


if __name__ == "__main__":
						packer_cli()









'''simple docstring'''







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

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


A     =[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

A     =logging.getLogger()





def 						snake_case_							():
    UpperCAmelCase			       =		argparse.ArgumentParser()
    parser.add_argument('''-f'''     )
    UpperCAmelCase			       =		parser.parse_args()
    return args.f





def 						snake_case_							(_a	:							List[str]    ,   _a	:							Union[str, Any]="eval"     ):
    UpperCAmelCase			       =		os.path.join(_a    ,   F"{split}_results.json"     )
    if os.path.exists(_a     ):
        with open(_a    ,   '''r'''     ) as f:
            return json.load(_a     )
    raise ValueError(F"can't find {path}"     )


A     =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 			_a  (	__a ):
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_flax_glue.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.75				)
       @slow
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_clm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertLess(result['''eval_perplexity''']   ,				100				)
       @slow
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_summarization_flax.main()
               UpperCAmelCase			       =		get_results(lowercase   ,				split='''test'''				)
               self.assertGreaterEqual(result['''test_rouge1''']   ,				10				)
               self.assertGreaterEqual(result['''test_rouge2''']   ,				2				)
               self.assertGreaterEqual(result['''test_rougeL''']   ,				7				)
               self.assertGreaterEqual(result['''test_rougeLsum''']   ,				7				)
       @slow
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_mlm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertLess(result['''eval_perplexity''']   ,				42				)
       @slow
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_ta_mlm_flax.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.42				)
       @slow
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		7 if get_gpu_count() > 1 else 2

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_flax_ner.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_accuracy''']   ,				0.75				)
               self.assertGreaterEqual(result['''eval_f1''']   ,				0.3				)







       @slow
       def     A      (	self						:	Union[str, Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.get_auto_remove_tmp_dir()
           UpperCAmelCase			       =		f"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

           with patch.object(lowercase   ,				'''argv'''   ,				lowercase				):
               run_qa.main()
               UpperCAmelCase			       =		get_results(lowercase				)
               self.assertGreaterEqual(result['''eval_f1''']   ,				30				)
               self.assertGreaterEqual(result['''eval_exact''']   ,				30				)




