
'''simple docstring'''





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






 def 		lowercase_  (	self     :Optional[int]	) ->	List[Any]:







       '''simple docstring'''



       super().setUp()
       __A						=	BloomTokenizerFast.from_pretrained('bigscience/tokenizer'	)
       tokenizer.save_pretrained(self.tmpdirname	)






 def 		lowercase_  (	self     :Optional[Any]		, **_A     :Union[str, Any]	) ->	Union[str, Any]:







       '''simple docstring'''



       kwargs.update(self.special_tokens_map	)
       return BloomTokenizerFast.from_pretrained(self.tmpdirname		, **UpperCamelCase__	)






 def 		lowercase_  (	self     :Any	) ->	int:







       '''simple docstring'''



       __A						=	self.get_rust_tokenizer()

       __A						=	['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
       __A						=	[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

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

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






 def 		lowercase_  (	self     :List[str]		, _A     :Optional[Any]=6	) ->	Dict:







       '''simple docstring'''



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

                   __A						=	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 		lowercase_  (	self     :List[Any]	) ->	int:







       '''simple docstring'''



       __A						=	self.get_rust_tokenizer()
       __A						=	load_dataset('xnli'		, 'all_languages'		, split='test'		, streaming=UpperCamelCase__	)

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

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






 def 		lowercase_  (	self     :List[str]	) ->	Optional[Any]:







       '''simple docstring'''



       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	)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase		:       Any									=     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     ['MBartTokenizer']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       Dict									=     ['MBartTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'MBartForCausalLM',
					    'MBartForConditionalGeneration',
					    'MBartForQuestionAnswering',
					    'MBartForSequenceClassification',
					    'MBartModel',
					    'MBartPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'TFMBartForConditionalGeneration',
					    'TFMBartModel',
					    'TFMBartPreTrainedModel',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       List[str]									=     [
					    'FlaxMBartForConditionalGeneration',
					    'FlaxMBartForQuestionAnswering',
					    'FlaxMBartForSequenceClassification',
					    'FlaxMBartModel',
					    'FlaxMBartPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart import MBartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mbart_fast import MBartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mbart import (
										    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MBartForCausalLM,
										    MBartForConditionalGeneration,
										    MBartForQuestionAnswering,
										    MBartForSequenceClassification,
										    MBartModel,
										    MBartPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_mbart import (
										    FlaxMBartForConditionalGeneration,
										    FlaxMBartForQuestionAnswering,
										    FlaxMBartForSequenceClassification,
										    FlaxMBartModel,
										    FlaxMBartPreTrainedModel,
										)

else:
					import sys

					__UpperCamelCase		:       int									=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





