


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    (   _a					,				unittest.TestCase ):
							UpperCamelCase_   :							Optional[int]       =None
							UpperCamelCase_   :							int       =BloomTokenizerFast
							UpperCamelCase_   :							List[str]       =BloomTokenizerFast
							UpperCamelCase_   :							List[Any]       =True
							UpperCamelCase_   :							str       =False
							UpperCamelCase_   :							Tuple       ='''tokenizer_file'''
							UpperCamelCase_   :							Optional[Any]       ={'''bos_token''': '''<s>''', '''eos_token''': '''</s>''', '''unk_token''': '''<unk>''', '''pad_token''': '''<pad>'''}


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


							def 			_A       (self      ,				**lowerCAmelCase					):
									kwargs.update(self.special_tokens_map					)
									return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,				**UpperCamelCase__					)


							def 			_A       (self					):
									__lowercase=			self.get_rust_tokenizer()

									__lowercase=			['The quick brown fox</s>', 'jumps over the lazy dog</s>']
									__lowercase=			[[2_1_7_5, 2_3_7_1_4, 7_3_1_7_3, 1_4_4_2_5_2, 2], [7_7, 1_3_2_6_1_9, 3_4_7_8, 3_6_8, 1_0_9_5_8_6, 3_5_4_3_3, 2]]

									__lowercase=			tokenizer.batch_encode_plus(UpperCamelCase__					)['input_ids']
									self.assertListEqual(UpperCamelCase__      ,				UpperCamelCase__					)

									__lowercase=			tokenizer.batch_decode(UpperCamelCase__					)
									self.assertListEqual(UpperCamelCase__      ,				UpperCamelCase__					)


							def 			_A       (self      ,				lowerCAmelCase=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(UpperCamelCase__      ,				**UpperCamelCase__					)
													# 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(UpperCamelCase__      ,				max_length=UpperCamelCase__					)
															tokenizer_r.encode_plus(UpperCamelCase__      ,				max_length=UpperCamelCase__					)

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

													__lowercase=			None  # Hotfixing padding = None
													self.assertRaises(UpperCamelCase__      ,				tokenizer_r.encode      ,				UpperCamelCase__      ,				max_length=UpperCamelCase__      ,				padding='max_length'					)

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

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

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

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

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


							def 			_A       (self					):
									__lowercase=			self.get_rust_tokenizer()
									__lowercase=			load_dataset('xnli'      ,				'all_languages'      ,				split='test'      ,				streaming=UpperCamelCase__					)

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

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







							def 			_A       (self					):
									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					)











'''simple docstring'''


_lowerCamelCase	:       int      	=	"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
   from .utils import rich

