#
# Sort large text files in a minimum amount of memory
#
import argparse
import os
class 			lowercase__:



	"""simple docstring"""




	a      :Optional[Any]   					=     'block_{0}.dat'
	def __init__(	self  :      str	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					Optional[Any]:
					lowercase_					  = filename
					lowercase_					  = []
	def 						_lowercase  (	self  :      List[Any]	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Dict	)	->					Optional[Any]:
					lowercase_					  = self.BLOCK_FILENAME_FORMAT.format(SCREAMING_SNAKE_CASE_	)
					with open(SCREAMING_SNAKE_CASE_	,							'''w'''	) as file:
									file.write(SCREAMING_SNAKE_CASE_	)
					self.block_filenames.append(SCREAMING_SNAKE_CASE_	)
	def 						_lowercase  (	self  :      List[str]	)	->					str:
					return self.block_filenames
	def 						_lowercase  (	self  :      int	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      List[Any]=None	)	->					Union[str, Any]:
					lowercase_					  = 0
					with open(self.filename	) as file:
									while True:
													lowercase_					  = file.readlines(SCREAMING_SNAKE_CASE_	)

													if lines == []:
																	break

													if sort_key is None:
																	lines.sort()
													else:
																	lines.sort(key=SCREAMING_SNAKE_CASE_	)

													self.write_block(''''''.join(SCREAMING_SNAKE_CASE_	)	,							SCREAMING_SNAKE_CASE_	)
													i += 1




	def 						_lowercase  (	self  :      Union[str, Any]	)	->					List[str]:
					map(os.remove	,							self.block_filenames	)

class 			lowercase__:



	"""simple docstring"""




	def 						_lowercase  (	self  :      Optional[Any]	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					Tuple:
					lowercase_					  = -1
					lowercase_					  = None

					for i in range(len(SCREAMING_SNAKE_CASE_	)	):
									if min_str is None or choices[i] < min_str:
													lowercase_					  = i

					return min_index

class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      str	)	->					Any:
					lowercase_					  = files
					lowercase_					  = set()
					lowercase_					  = len(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = {i: None for i in range(self.num_buffers	)}
	def 						_lowercase  (	self  :      Dict	)	->					int:
					return {
					    i: self.buffers[i] for i in range(self.num_buffers	) if i not in self.empty
					}
	def 						_lowercase  (	self  :      List[str]	)	->					Optional[int]:
					for i in range(self.num_buffers	):
									if self.buffers[i] is None and i not in self.empty:
													lowercase_					  = self.files[i].readline()

													if self.buffers[i] == "":
																	self.empty.add(SCREAMING_SNAKE_CASE_	)
																	self.files[i].close()

					if len(self.empty	) == self.num_buffers:
									return False

					return True




	def 						_lowercase  (	self  :      Optional[int]	,							SCREAMING_SNAKE_CASE_  :      List[Any]	)	->					Any:
					lowercase_					  = self.buffers[index]
					lowercase_					  = None

					return value

class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      int	)	->					int:
					lowercase_					  = merge_strategy
	def 						_lowercase  (	self  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      List[str]	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      int	)	->					List[Any]:
					lowercase_					  = FilesArray(self.get_file_handles(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)	)
					with open(SCREAMING_SNAKE_CASE_	,							'''w'''	,							SCREAMING_SNAKE_CASE_	) as outfile:
									while buffers.refresh():
													lowercase_					  = self.merge_strategy.select(buffers.get_dict()	)
													outfile.write(buffers.unshift(SCREAMING_SNAKE_CASE_	)	)




	def 						_lowercase  (	self  :      Tuple	,							SCREAMING_SNAKE_CASE_  :      int	,							SCREAMING_SNAKE_CASE_  :      Tuple	)	->					int:
					lowercase_					  = {}

					for i in range(len(SCREAMING_SNAKE_CASE_	)	):
									lowercase_					  = open(filenames[i]	,							'''r'''	,							SCREAMING_SNAKE_CASE_	)  # noqa: UP015

					return files

class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      Any	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					Dict:
					lowercase_					  = block_size
	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      Any	,							SCREAMING_SNAKE_CASE_  :      str=None	)	->					str:
					lowercase_					  = self.get_number_blocks(SCREAMING_SNAKE_CASE_	,							self.block_size	)
					lowercase_					  = FileSplitter(SCREAMING_SNAKE_CASE_	)
					splitter.split(self.block_size	,							SCREAMING_SNAKE_CASE_	)

					lowercase_					  = FileMerger(NWayMerge()	)
					lowercase_					  = self.block_size / (num_blocks + 1)
					merger.merge(splitter.get_block_filenames()	,							filename + '''.out'''	,							SCREAMING_SNAKE_CASE_	)

					splitter.cleanup()




	def 						_lowercase  (	self  :      int	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Dict	)	->					List[str]:
					return (os.stat(SCREAMING_SNAKE_CASE_	).st_size / block_size) + 1




def     a						(							snake_case__: Union[str, Any] ):


				'''simple docstring'''
				if string[-1].lower() == "k":
								return int(string[:-1] ) * 1_024
				elif string[-1].lower() == "m":
								return int(string[:-1] ) * 1_024 * 1_024
				elif string[-1].lower() == "g":
								return int(string[:-1] ) * 1_024 * 1_024 * 1_024
				else:
								return int(snake_case__ )




def     a						(							):


				'''simple docstring'''
				lowercase_					  = argparse.ArgumentParser()
				parser.add_argument(
				    '''-m'''						,       '''--mem'''						,       help='''amount of memory to use for sorting'''						,       default='''100M''' )
				parser.add_argument(
				    '''filename'''						,       metavar='''<filename>'''						,       nargs=1						,       help='''name of file to sort''' )
				lowercase_					  = parser.parse_args()

				lowercase_					  = ExternalSort(parse_memory(args.mem ) )
				sorter.sort(args.filename[0] )


if __name__ == "__main__":
						main()






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

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__a        = [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-generation',
        'text-classification',
        'token-classification',
        'language-modeling',
        'multiple-choice',
        'question-answering',
        'summarization',
        'translation',
        'image-classification',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
						import run_clm
						import run_image_classification
						import run_mlm
						import run_ner
						import run_qa as run_squad
						import run_summarization
						import run_swag
						import run_text_classification
						import run_translation


logging.basicConfig(level=logging.DEBUG)

__a        = logging.getLogger()




def     a						(							):


				'''simple docstring'''
				lowercase_					  = argparse.ArgumentParser()
				parser.add_argument('''-f''' )
				lowercase_					  = parser.parse_args()
				return args.f




def     a						(							snake_case__: List[Any] ):


				'''simple docstring'''
				lowercase_					  = {}
				lowercase_					  = os.path.join(snake_case__						,       '''all_results.json''' )
				if os.path.exists(snake_case__ ):
								with open(snake_case__						,       '''r''' ) as f:
												lowercase_					  = json.load(snake_case__ )
				else:
								raise ValueError(F'''can\'t find {path}''' )
				return results




def     a						(							):


				'''simple docstring'''
				return bool(tf.config.list_physical_devices('''GPU''' ) )


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


class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''	)
	def 						_lowercase  (	self  :      Tuple	)	->					List[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

					if is_cuda_available():
									testargs.append('''--fp16'''	)

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_text_classification.main()
									# Reset the mixed precision policy so we don't break other tests
									tf.keras.mixed_precision.set_global_policy('''float32'''	)
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''eval_accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Optional[Any]	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_clm.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 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

					if len(tf.config.list_physical_devices('''GPU'''	)	) > 1:
									# Skipping because there are not enough batches to train the model + would need a drop_last to work.
									return

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_clm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							1_0_0	)
	def 						_lowercase  (	self  :      Any	)	->					Union[str, Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_mlm.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertLess(result['''eval_perplexity''']	,							4_2	)
	def 						_lowercase  (	self  :      Any	)	->					str:
					# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
					lowercase_					  = 7 if get_gpu_count() > 1 else 2

					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_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
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_ner.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.75	)
	def 						_lowercase  (	self  :      Any	)	->					Optional[Any]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_squad.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''f1''']	,							3_0	)
									self.assertGreaterEqual(result['''exact''']	,							3_0	)
	def 						_lowercase  (	self  :      str	)	->					Dict:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_swag.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''val_accuracy''']	,							0.8	)
	@slow
	def 						_lowercase  (	self  :      int	)	->					int:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = 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
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_summarization.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''rouge1''']	,							1_0	)
									self.assertGreaterEqual(result['''rouge2''']	,							2	)
									self.assertGreaterEqual(result['''rougeL''']	,							7	)
									self.assertGreaterEqual(result['''rougeLsum''']	,							7	)
	@slow
	def 						_lowercase  (	self  :      Dict	)	->					Any:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_translation.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''bleu''']	,							3_0	)




	def 						_lowercase  (	self  :      int	)	->					List[str]:
					lowercase_					  = self.get_auto_remove_tmp_dir()
					lowercase_					  = f'''
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            '''.split()

					with patch.object(SCREAMING_SNAKE_CASE_	,							'''argv'''	,							SCREAMING_SNAKE_CASE_	):
									run_image_classification.main()
									lowercase_					  = get_results(SCREAMING_SNAKE_CASE_	)
									self.assertGreaterEqual(result['''accuracy''']	,							0.7	)




