






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 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       Dict							       = None
				_UpperCamelCase					:       str							       = BloomTokenizerFast
				_UpperCamelCase					:       int							       = BloomTokenizerFast
				_UpperCamelCase					:       List[str]							       = True
				_UpperCamelCase					:       Union[str, Any]							       = False
				_UpperCamelCase					:       int							       = "tokenizer_file"
				_UpperCamelCase					:       str							       = {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}


				def 						__a					(					self      ):
											super().setUp()
											_lowercase							:   List[str] 							=   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 ,						**_lowerCAmelCase      )


				def 						__a					(					self      ):
											_lowercase							:   int 							=   self.get_rust_tokenizer()

											_lowercase							:   Any 							=   ['The quick brown fox</s>', 'jumps over the lazy dog</s>']
											_lowercase							:   str 							=   [[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							:   Dict 							=   tokenizer.batch_encode_plus(_lowerCAmelCase      )['input_ids']
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )

											_lowercase							:   Optional[int] 							=   tokenizer.batch_decode(_lowerCAmelCase      )
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )


				def 						__a					(					self ,						_lowerCAmelCase=6      ):
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																									_lowercase							:   Any 							=   self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase ,						**_lowerCAmelCase      )
																									# tokenizer_r.pad_token = None # Hotfixing padding = None
																									# Simple input
																									_lowercase							:   int 							=   'This is a simple input'
																									_lowercase							:   Union[str, Any] 							=   ['This is a simple input 1', 'This is a simple input 2']
																									_lowercase							:   Union[str, Any] 							=   ('This is a simple input', 'This is a pair')
																									_lowercase							:   Optional[int] 							=   [
																									    ('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(_lowerCAmelCase ,						max_length=_lowerCAmelCase      )
																																tokenizer_r.encode_plus(_lowerCAmelCase ,						max_length=_lowerCAmelCase      )

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

																									_lowercase							:   Any 							=   None  # Hotfixing padding = None
																									self.assertRaises(_lowerCAmelCase ,						tokenizer_r.encode ,						_lowerCAmelCase ,						max_length=_lowerCAmelCase ,						padding='max_length'      )

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

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

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

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

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


				def 						__a					(					self      ):
											_lowercase							:   List[str] 							=   self.get_rust_tokenizer()
											_lowercase							:   List[Any] 							=   load_dataset('xnli' ,						'all_languages' ,						split='test' ,						streaming=_lowerCAmelCase      )

											_lowercase							:   Union[str, Any] 							=   next(iter(_lowerCAmelCase      )      )['premise']  # pick up one data
											_lowercase							:   Optional[int] 							=   list(sample_data.values()      )

											_lowercase							:   str 							=   list(map(tokenizer.encode ,						_lowerCAmelCase      )      )
											_lowercase							:   Union[str, Any] 							=   [tokenizer.decode(_lowerCAmelCase ,						clean_up_tokenization_spaces=_lowerCAmelCase      ) for x in output_tokens]
											self.assertListEqual(_lowerCAmelCase ,						_lowerCAmelCase      )


				def 						__a					(					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      )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCAmelCase__    :Tuple       =				{'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[Any]       =				['''DeiTFeatureExtractor''']
		lowerCAmelCase__    :Union[str, Any]       =				['''DeiTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Tuple       =				[
		    '''DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''DeiTForImageClassification''',
		    '''DeiTForImageClassificationWithTeacher''',
		    '''DeiTForMaskedImageModeling''',
		    '''DeiTModel''',
		    '''DeiTPreTrainedModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Union[str, Any]       =				[
		    '''TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFDeiTForImageClassification''',
		    '''TFDeiTForImageClassificationWithTeacher''',
		    '''TFDeiTForMaskedImageModeling''',
		    '''TFDeiTModel''',
		    '''TFDeiTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__    :List[str]       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
