import os
import unittest

from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin


class 						a__							(       __snake_case				,						unittest.TestCase		):
			A__    :       List[str]        =      ProphetNetTokenizer
			A__    :       Any        =      False



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								super().setUp()

								__a		 =       [
								    '[UNK]',
								    '[CLS]',
								    '[SEP]',
								    '[PAD]',
								    '[MASK]',
								    'want',
								    '##want',
								    '##ed',
								    'wa',
								    'un',
								    'runn',
								    '##ing',
								    ',',
								    'low',
								    'lowest',
								]
								__a		 =       os.path.join(self.tmpdirname				,						VOCAB_FILES_NAMES['vocab_file']   )
								with open(self.vocab_file				,						'w'				,						encoding='utf-8'   ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   int:
								__a		 =       'UNwant\u00E9d,running'
								__a		 =       'unwanted, running'
								return input_text, output_text



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								__a		 =       self.tokenizer_class(self.vocab_file   )

								__a		 =       tokenizer.tokenize('UNwant\u00E9d,running'   )
								self.assertListEqual(UpperCAmelCase				,						['un', '##want', '##ed', ',', 'runn', '##ing']   )
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase   )				,						[9, 6, 7, 1_2, 1_0, 1_1]   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								__a		 =       BasicTokenizer()

								self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'   )				,						['ah', '\u535A', '\u63A8', 'zz']   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       BasicTokenizer(do_lower_case=UpperCAmelCase   )

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '   )				,						['hello', '!', 'how', 'are', 'you', '?']   )
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )				,						['hello']   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								__a		 =       BasicTokenizer(do_lower_case=UpperCAmelCase				,						strip_accents=UpperCAmelCase   )

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )				,						['hällo', '!', 'how', 'are', 'you', '?']   )
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )				,						['h\u00E9llo']   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       BasicTokenizer(do_lower_case=UpperCAmelCase				,						strip_accents=UpperCAmelCase   )

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )				,						['hallo', '!', 'how', 'are', 'you', '?']   )
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )				,						['hello']   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       BasicTokenizer(do_lower_case=UpperCAmelCase   )

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )				,						['hallo', '!', 'how', 'are', 'you', '?']   )
								self.assertListEqual(tokenizer.tokenize('H\u00E9llo'   )				,						['hello']   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Dict:
								__a		 =       BasicTokenizer(do_lower_case=UpperCAmelCase   )

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '   )				,						['HeLLo', '!', 'how', 'Are', 'yoU', '?']   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       BasicTokenizer(do_lower_case=UpperCAmelCase				,						strip_accents=UpperCAmelCase   )

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )				,						['HäLLo', '!', 'how', 'Are', 'yoU', '?']   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       BasicTokenizer(do_lower_case=UpperCAmelCase				,						strip_accents=UpperCAmelCase   )

								self.assertListEqual(
								    tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '   )				,						['HaLLo', '!', 'how', 'Are', 'yoU', '?']   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Union[str, Any]:
								__a		 =       BasicTokenizer(do_lower_case=UpperCAmelCase				,						never_split=['[UNK]']   )

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'   )				,						['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a		 =       ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

								__a		 =       {}
								for i, token in enumerate(UpperCAmelCase   ):
													__a		 =       i
								__a		 =       WordpieceTokenizer(vocab=UpperCAmelCase				,						unk_token='[UNK]'   )

								self.assertListEqual(tokenizer.tokenize(''   )				,						[]   )

								self.assertListEqual(tokenizer.tokenize('unwanted running'   )				,						['un', '##want', '##ed', 'runn', '##ing']   )

								self.assertListEqual(tokenizer.tokenize('unwantedX running'   )				,						['[UNK]', 'runn', '##ing']   )



			@require_torch
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								__a		 =       self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased'   )

								__a		 =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
								__a		 =       [1_0_3_7, 2_1_4_6, 2_0_4_2_3, 2_0_0_5, 7_6_8_0, 7_8_4_9, 3_9_8_9, 1_0_1_2, 1_0_2]
								__a		 =       tokenizer(UpperCAmelCase				,						padding=UpperCAmelCase				,						return_tensors='pt'   )
								self.assertIsInstance(UpperCAmelCase				,						UpperCAmelCase   )
								__a		 =       list(batch.input_ids.numpy()[0]   )
								self.assertListEqual(UpperCAmelCase				,						UpperCAmelCase   )

								self.assertEqual((2, 9)				,						batch.input_ids.shape   )
								self.assertEqual((2, 9)				,						batch.attention_mask.shape   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								self.assertTrue(_is_whitespace(' '   )   )
								self.assertTrue(_is_whitespace('\t'   )   )
								self.assertTrue(_is_whitespace('\r'   )   )
								self.assertTrue(_is_whitespace('\n'   )   )
								self.assertTrue(_is_whitespace('\u00A0'   )   )

								self.assertFalse(_is_whitespace('A'   )   )
								self.assertFalse(_is_whitespace('-'   )   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Any:
								self.assertTrue(_is_control('\u0005'   )   )

								self.assertFalse(_is_control('A'   )   )
								self.assertFalse(_is_control(' '   )   )
								self.assertFalse(_is_control('\t'   )   )
								self.assertFalse(_is_control('\r'   )   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[Any]:
								self.assertTrue(_is_punctuation('-'   )   )
								self.assertTrue(_is_punctuation('$'   )   )
								self.assertTrue(_is_punctuation('`'   )   )
								self.assertTrue(_is_punctuation('.'   )   )

								self.assertFalse(_is_punctuation('A'   )   )
								self.assertFalse(_is_punctuation(' '   )   )







			@slow
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								__a		 =       self.tokenizer_class.from_pretrained('microsoft/prophetnet-large-uncased'   )

								__a		 =       tokenizer.encode('sequence builders'				,						add_special_tokens=UpperCAmelCase   )
								__a		 =       tokenizer.encode('multi-sequence build'				,						add_special_tokens=UpperCAmelCase   )

								__a		 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase   )
								__a		 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase				,						UpperCAmelCase   )

								assert encoded_sentence == text + [1_0_2]
								assert encoded_pair == text + [1_0_2] + text_a + [1_0_2]





from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Optional[Any]							=	{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	[
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




