


'''simple docstring'''



import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_lowercase    :    List[str]			     =  get_tests_dir("""fixtures/test_sentencepiece.model""")
_lowercase    :    Optional[int]			     =  get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")

_lowercase    :    Union[str, Any]			     =  """pt""" if is_torch_available() else """tf"""






@require_sentencepiece
@require_tokenizers
class  UpperCamelCase__(     lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Optional[int]							= CamembertTokenizer
				__magic_name__       :   Dict							= CamembertTokenizerFast
				__magic_name__       :   int							= True
				__magic_name__       :   Optional[int]							= True




				def   a__(	self    :				Optional[Any]   )->  Any:

										"""simple docstring"""




										super().setUp()

										# We have a SentencePiece fixture for testing
										UpperCAmelCase         =			CamembertTokenizer(lowerCAmelCase   )
										tokenizer.save_pretrained(self.tmpdirname   )




				def   a__(	self    :				Dict   )->  Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase         =			'''<pad>'''
										UpperCAmelCase         =			1

										self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase   )   ,					lowerCAmelCase   )
										self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase   )   ,					lowerCAmelCase   )




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

										"""simple docstring"""




										UpperCAmelCase         =			list(self.get_tokenizer().get_vocab().keys()   )

										self.assertEqual(vocab_keys[0]   ,					'''<s>NOTUSED'''   )
										self.assertEqual(vocab_keys[1]   ,					'''<pad>'''   )
										self.assertEqual(vocab_keys[-1]   ,					'''<mask>'''   )
										self.assertEqual(len(lowerCAmelCase   )   ,					1004   )




				def   a__(	self    :				Optional[Any]   )->  Optional[Any]:

										"""simple docstring"""




										self.assertEqual(self.get_tokenizer().vocab_size   ,					1005   )




				def   a__(	self    :				List[str]   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			CamembertTokenizer(lowerCAmelCase   )
										tokenizer.save_pretrained(self.tmpdirname   )
										UpperCAmelCase         =			CamembertTokenizerFast.from_pretrained(self.tmpdirname   )

										UpperCAmelCase         =			'''I was born in 92000, and this is falsé.'''

										UpperCAmelCase         =			tokenizer.encode(lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.encode(lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )

										UpperCAmelCase         =			tokenizer.encode(lowerCAmelCase   ,					add_special_tokens=lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.encode(lowerCAmelCase   ,					add_special_tokens=lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )

										# <unk> tokens are not the same for `rust` than for `slow`.
										# Because spm gives back raw token instead of `unk` in EncodeAsPieces
										# tokens = tokenizer.tokenize(sequence)
										UpperCAmelCase         =			tokenizer.convert_ids_to_tokens(lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.tokenize(lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )




				def   a__(	self    :				Union[str, Any]   )->  List[Any]:

										"""simple docstring"""




										if not self.test_rust_tokenizer:
																return

										UpperCAmelCase         =			self.get_tokenizer()
										UpperCAmelCase         =			self.get_rust_tokenizer()

										UpperCAmelCase         =			'''I was born in 92000, and this is falsé.'''

										UpperCAmelCase         =			tokenizer.tokenize(lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.tokenize(lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )

										UpperCAmelCase         =			tokenizer.encode(lowerCAmelCase   ,					add_special_tokens=lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.encode(lowerCAmelCase   ,					add_special_tokens=lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )

										UpperCAmelCase         =			self.get_rust_tokenizer()
										UpperCAmelCase         =			tokenizer.encode(lowerCAmelCase   )
										UpperCAmelCase         =			rust_tokenizer.encode(lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )





				@slow
				def   a__(	self    :				int   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			{'''input_ids''': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
										# fmt: on

										# camembert is a french model. So we also use french texts.
										UpperCAmelCase         =			[
										    '''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
										    '''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
										    '''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
										    '''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
										    '''telles que la traduction et la synthèse de texte.''',
										]

										self.tokenizer_integration_test_util(
										    expected_encoding=lowerCAmelCase   ,					model_name='''camembert-base'''   ,					revision='''3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'''   ,					sequences=lowerCAmelCase   ,					)









'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase    :    Any			     =  {
    """configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
    """configuration_maskformer_swin""": ["""MaskFormerSwinConfig"""],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  ["""MaskFormerFeatureExtractor"""]
	_lowercase    :    Dict			     =  ["""MaskFormerImageProcessor"""]


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Any			     =  [
	    """MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """MaskFormerForInstanceSegmentation""",
	    """MaskFormerModel""",
	    """MaskFormerPreTrainedModel""",
	]
	_lowercase    :    List[Any]			     =  [
	    """MaskFormerSwinBackbone""",
	    """MaskFormerSwinModel""",
	    """MaskFormerSwinPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
	from .configuration_maskformer_swin import MaskFormerSwinConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_maskformer import MaskFormerFeatureExtractor
		from .image_processing_maskformer import MaskFormerImageProcessor
	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_maskformer import (
		    MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MaskFormerForInstanceSegmentation,
		    MaskFormerModel,
		    MaskFormerPreTrainedModel,
		)
		from .modeling_maskformer_swin import (
		    MaskFormerSwinBackbone,
		    MaskFormerSwinModel,
		    MaskFormerSwinPreTrainedModel,
		)


else:
	import sys

	_lowercase    :    List[Any]			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure)





