



'''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 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		str        =				None
							UpperCAmelCase			:		Tuple        =				BloomTokenizerFast
							UpperCAmelCase			:		Dict        =				BloomTokenizerFast
							UpperCAmelCase			:		Optional[int]        =				True
							UpperCAmelCase			:		Union[str, Any]        =				False
							UpperCAmelCase			:		Dict        =				"""tokenizer_file"""
							UpperCAmelCase			:		Dict        =				{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}


							def    UpperCamelCase     (				self   :						int			)			->					int:
										super().setUp()
										snake_case:    int      							=BloomTokenizerFast.from_pretrained('bigscience/tokenizer'			)
										tokenizer.save_pretrained(self.tmpdirname			)


							def    UpperCamelCase     (				self   :						int      ,						**a_   :						Any			)			->					Union[str, Any]:
										kwargs.update(self.special_tokens_map			)
										return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						Union[str, Any]			)			->					Any:
										snake_case:    Any      							=self.get_rust_tokenizer()

										snake_case:    Optional[int]      							=['The quick brown fox</s>', 'jumps over the lazy dog</s>']
										snake_case:    List[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]]

										snake_case:    List[str]      							=tokenizer.batch_encode_plus(a_			)['input_ids']
										self.assertListEqual(a_      ,						a_			)

										snake_case:    Any      							=tokenizer.batch_decode(a_			)
										self.assertListEqual(a_      ,						a_			)


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

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

																snake_case:    Union[str, Any]      							=None  # Hotfixing padding = None
																self.assertRaises(a_      ,						tokenizer_r.encode      ,						a_      ,						max_length=a_      ,						padding='max_length'			)

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

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

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

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

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


							def    UpperCamelCase     (				self   :						List[str]			)			->					Optional[Any]:
										snake_case:    Dict      							=self.get_rust_tokenizer()
										snake_case:    Union[str, Any]      							=load_dataset('xnli'      ,						'all_languages'      ,						split='test'      ,						streaming=a_			)

										snake_case:    Optional[Any]      							=next(iter(a_			)			)['premise']  # pick up one data
										snake_case:    Tuple      							=list(sample_data.values()			)

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




							def    UpperCamelCase     (				self   :						str			)			->					Optional[Any]:
										# 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			)









'''simple docstring'''

from typing import TYPE_CHECKING

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


a							   =				{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				['OwlViTFeatureExtractor']
	a							   =				['OwlViTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a							   =				[
	    'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'OwlViTModel',
	    'OwlViTPreTrainedModel',
	    'OwlViTTextModel',
	    'OwlViTVisionModel',
	    'OwlViTForObjectDetection',
	]

if TYPE_CHECKING:
	from .configuration_owlvit import (
	    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    OwlViTConfig,
	    OwlViTOnnxConfig,
	    OwlViTTextConfig,
	    OwlViTVisionConfig,
	)
	from .processing_owlvit import OwlViTProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_owlvit import OwlViTFeatureExtractor
		from .image_processing_owlvit import OwlViTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_owlvit import (
		    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OwlViTForObjectDetection,
		    OwlViTModel,
		    OwlViTPreTrainedModel,
		    OwlViTTextModel,
		    OwlViTVisionModel,
		)

else:
	import sys

	a							   =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




