"""simple docstring"""




from typing import TYPE_CHECKING

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


__lowerCamelCase											=  {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FNetConfig"]}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCamelCase											=  ["FNetTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCamelCase											=  ["FNetTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowerCamelCase											=  [
					    "FNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "FNetForMaskedLM",
					    "FNetForMultipleChoice",
					    "FNetForNextSentencePrediction",
					    "FNetForPreTraining",
					    "FNetForQuestionAnswering",
					    "FNetForSequenceClassification",
					    "FNetForTokenClassification",
					    "FNetLayer",
					    "FNetModel",
					    "FNetPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_fnet import FNetTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_fnet_fast import FNetTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_fnet import (
										    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FNetForMaskedLM,
										    FNetForMultipleChoice,
										    FNetForNextSentencePrediction,
										    FNetForPreTraining,
										    FNetForQuestionAnswering,
										    FNetForSequenceClassification,
										    FNetForTokenClassification,
										    FNetLayer,
										    FNetModel,
										    FNetPreTrainedModel,
										)


else:
					import sys

					__lowerCamelCase											=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






"""simple docstring"""




import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
				from transformers import MarkupLMFeatureExtractor
class     UpperCamelCase__(			unittest.TestCase ):





						def __init__(       self		,__UpperCAmelCase     )					->				str:
													A__	     =							parent





						def 				snake_case__	(       self     )					->				int:
													return {}






def   UpperCAmelCase					(	):






							"""simple docstring"""





							A__	     =							'<HTML>\n\n    <HEAD>\n    <TITLE>sample document</TITLE>\n    </HEAD>\n\n    <BODY BGCOLOR="FFFFFF">\n    <HR>\n    <a href="http://google.com">Goog</a>\n    <H1>This is one header</H1>\n    <H2>This is a another Header</H2>\n    <P>Travel from\n        <P>\n        <B>SFO to JFK</B>\n        <BR>\n        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n        <HR>\n        <div style="color:#0000FF">\n            <h3>Traveler <b> name </b> is\n            <p> John Doe </p>\n        </div>'

							A__	     =							'\n    <!DOCTYPE html>\n    <html>\n    <body>\n\n    <h1>My First Heading</h1>\n    <p>My first paragraph.</p>\n\n    </body>\n    </html>\n    '

							return [html_string_a, html_string_a]


@require_bsa
class     UpperCamelCase__(			__A		,						unittest.TestCase ):
						lowerCAmelCase__  :						Dict													=		MarkupLMFeatureExtractor if is_bsa_available() else None





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							MarkupLMFeatureExtractionTester(self     )





						@property
						def 				snake_case__	(       self     )					->				Optional[Any]:
													return self.feature_extract_tester.prepare_feat_extract_dict()





						def 				snake_case__	(       self     )					->				Any:
													# Initialize feature_extractor
													A__	     =							self.feature_extraction_class()

													# Test not batched input
													A__	     =							get_html_strings()[0]
													A__	     =							feature_extractor(__UpperCAmelCase     )

													# fmt: off
													A__	     =							[['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
													A__	     =							[['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
													# fmt: on

													self.assertEqual(encoding.nodes		,__UpperCAmelCase     )
													self.assertEqual(encoding.xpaths		,__UpperCAmelCase     )

													# Test batched
													A__	     =							get_html_strings()
													A__	     =							feature_extractor(__UpperCAmelCase     )

													# fmt: off
													A__	     =							expected_nodes + [['My First Heading', 'My first paragraph.']]
													A__	     =							expected_xpaths + [['/html/body/h1', '/html/body/p']]

													self.assertEqual(len(encoding.nodes     )		,2     )
													self.assertEqual(len(encoding.xpaths     )		,2     )

													self.assertEqual(encoding.nodes		,__UpperCAmelCase     )
													self.assertEqual(encoding.xpaths		,__UpperCAmelCase     )





