
import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class        A_   (			__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Tuple							 =       None
	_UpperCamelCase     :     Dict							 =       BloomTokenizerFast
	_UpperCamelCase     :     int							 =       BloomTokenizerFast
	_UpperCamelCase     :     List[Any]							 =       True
	_UpperCamelCase     :     List[Any]							 =       False
	_UpperCamelCase     :     Tuple							 =       """tokenizer_file"""
	_UpperCamelCase     :     int							 =       {"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		super().setUp()
		lowercase       =    BloomTokenizerFast.from_pretrained('bigscience/tokenizer'       )
		tokenizer.save_pretrained(self.tmpdirname       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		kwargs.update(self.special_tokens_map       )
		return BloomTokenizerFast.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_rust_tokenizer()

		lowercase       =    ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
		lowercase       =    [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

		lowercase       =    tokenizer.batch_encode_plus(snake_case       )['input_ids']
		self.assertListEqual(snake_case		,    snake_case       )

		lowercase       =    tokenizer.batch_decode(snake_case       )
		self.assertListEqual(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case=6       ):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
				lowercase       =    self.rust_tokenizer_class.from_pretrained(snake_case		,    **snake_case       )
				# tokenizer_r.pad_token = None # Hotfixing padding = None
				# Simple input
				lowercase       =    'This is a simple input'
				lowercase       =    ['This is a simple input 1', 'This is a simple input 2']
				lowercase       =    ('This is a simple input', 'This is a pair')
				lowercase       =    [
				    ('This is a simple input 1', 'This is a simple input 2'),
				    ('This is a simple pair 1', 'This is a simple pair 2'),
				]

				# Simple input tests
				try:
					tokenizer_r.encode(snake_case		,    max_length=snake_case       )
					tokenizer_r.encode_plus(snake_case		,    max_length=snake_case       )

					tokenizer_r.batch_encode_plus(snake_case		,    max_length=snake_case       )
					tokenizer_r.encode(snake_case		,    max_length=snake_case       )
					tokenizer_r.batch_encode_plus(snake_case		,    max_length=snake_case       )
				except ValueError:
					self.fail('Bloom Tokenizer should be able to deal with padding'       )

				lowercase       =    None  # Hotfixing padding = None
				self.assertRaises(snake_case		,    tokenizer_r.encode		,    snake_case		,    max_length=snake_case		,    padding='max_length'       )

				# Simple input
				self.assertRaises(snake_case		,    tokenizer_r.encode_plus		,    snake_case		,    max_length=snake_case		,    padding='max_length'       )

				# Simple input
				self.assertRaises(
				    snake_case		,    tokenizer_r.batch_encode_plus		,    snake_case		,    max_length=snake_case		,    padding='max_length'		,    )

				# Pair input
				self.assertRaises(snake_case		,    tokenizer_r.encode		,    snake_case		,    max_length=snake_case		,    padding='max_length'       )

				# Pair input
				self.assertRaises(snake_case		,    tokenizer_r.encode_plus		,    snake_case		,    max_length=snake_case		,    padding='max_length'       )

				# Pair input
				self.assertRaises(
				    snake_case		,    tokenizer_r.batch_encode_plus		,    snake_case		,    max_length=snake_case		,    padding='max_length'		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_rust_tokenizer()
		lowercase       =    load_dataset('xnli'		,    'all_languages'		,    split='test'		,    streaming=snake_case       )

		lowercase       =    next(iter(snake_case       )       )['premise']  # pick up one data
		lowercase       =    list(sample_data.values()       )

		lowercase       =    list(map(tokenizer.encode		,    snake_case       )       )
		lowercase       =    [tokenizer.decode(snake_case		,    clean_up_tokenization_spaces=snake_case       ) for x in output_tokens]
		self.assertListEqual(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
		# any sequence length constraints. This test of the parent class will fail since it relies on the
		# maximum sequence length of the positoonal embeddings.
		self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map       )		,    1       )
		self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()       )[0]       )		,    1       )



import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp






def 							UpperCAmelCase_		(							):
	lowercase       =    ArgumentParser(
	    description=(
	        'PyTorch TPU distributed training launch '
	        'helper utility that will spawn up '
	        'multiple distributed processes'
	    )							)

	# Optional arguments for the launch helper
	parser.add_argument('--num_cores' ,							type=__SCREAMING_SNAKE_CASE ,							default=1 ,							help='Number of TPU cores to use (1 or 8).'							)

	# positional
	parser.add_argument(
	    'training_script' ,							type=__SCREAMING_SNAKE_CASE ,							help=(
	        'The full path to the single TPU training '
	        'program/script to be launched in parallel, '
	        'followed by all the arguments for the '
	        'training script'
	    ) ,							)

	# rest from the training program
	parser.add_argument('training_script_args' ,							nargs=__SCREAMING_SNAKE_CASE							)

	return parser.parse_args()






def 							UpperCAmelCase_		(							):
	lowercase       =    parse_args()

	# Import training_script as a module.
	lowercase       =    Path(args.training_script							)
	sys.path.append(str(script_fpath.parent.resolve()							)							)
	lowercase       =    script_fpath.stem
	lowercase       =    importlib.import_module(__SCREAMING_SNAKE_CASE							)

	# Patch sys.argv
	lowercase       =    [args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores							)]
	xmp.spawn(mod._mp_fn ,							args=() ,							nprocs=args.num_cores							)


if __name__ == "__main__":
	main()

