




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


__lowerCAmelCase	:       List[Any]     =	[
    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)

__lowerCAmelCase	:       int     =	logging.getLogger()




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    argparse.ArgumentParser()
			parser.add_argument("-f"     )
			a					=    parser.parse_args()
			return args.f




def     __magic_name__       (				A	:      Union[str, Any],						A	:      Union[str, Any]="eval"     ):






			'''simple docstring'''







			a					=    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}"""     )


__lowerCAmelCase	:       str     =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  str:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_flax_glue.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertGreaterEqual(result["eval_accuracy"]      ,   0.75							)





				@slow
				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Dict:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_clm_flax.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertLess(result["eval_perplexity"]      ,   1_00							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Dict:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_summarization_flax.main()
										a					=    get_results(__lowerCamelCase      ,   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 							__UpperCAmelCase							(   self       :  Any							)      ->  Tuple:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_mlm_flax.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertLess(result["eval_perplexity"]      ,   42							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Optional[Any]:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_ta_mlm_flax.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertGreaterEqual(result["eval_accuracy"]      ,   0.42							)





				@slow
				def 							__UpperCAmelCase							(   self       :  str							)      ->  List[Any]:
							# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
							a					=    7 if get_gpu_count() > 1 else 2

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

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_flax_ner.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertGreaterEqual(result["eval_accuracy"]      ,   0.75							)
										self.assertGreaterEqual(result["eval_f1"]      ,   0.3							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  str:
							a					=    self.get_auto_remove_tmp_dir()
							a					=    f"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

							with patch.object(__lowerCamelCase      ,   "argv"      ,   __lowerCamelCase							):
										run_qa.main()
										a					=    get_results(__lowerCamelCase							)
										self.assertGreaterEqual(result["eval_f1"]      ,   30							)
										self.assertGreaterEqual(result["eval_exact"]      ,   30							)












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






