




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    lowercase_		(unittest.TestCase ):



						def __init__(			self	:   Optional[int]					,							snake_case__	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							parent






						def 	__a					(			self	:   Tuple  ):

								"""simple docstring"""

								return {}


def 					_a							(					)->	List[Any]:
		SCREAMING_SNAKE_CASE_					      =							'<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>'

		SCREAMING_SNAKE_CASE_					      =							'\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    lowercase_		(SCREAMING_SNAKE_CASE__							,	unittest.TestCase ):
						lowerCAmelCase__			      =MarkupLMFeatureExtractor if is_bsa_available() else None



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							MarkupLMFeatureExtractionTester(self  )



						@property
						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								return self.feature_extract_tester.prepare_feat_extract_dict()






						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.feature_extraction_class()

								# Test not batched input
								SCREAMING_SNAKE_CASE_					      =							get_html_strings()[0]
								SCREAMING_SNAKE_CASE_					      =							feature_extractor(snake_case__  )

								# fmt: off
								SCREAMING_SNAKE_CASE_					      =							[['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']]
								SCREAMING_SNAKE_CASE_					      =							[['/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					,							snake_case__  )
								self.assertEqual(encoding.xpaths					,							snake_case__  )

								# Test batched
								SCREAMING_SNAKE_CASE_					      =							get_html_strings()
								SCREAMING_SNAKE_CASE_					      =							feature_extractor(snake_case__  )

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

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

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





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
						from ...onnx.config import PatchingSpec
						from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE:     str									= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      ="longformer"



						def __init__(			self	:   Optional[Any]					,							snake_case__	:   Union[List[int], int] = 5_12					,							snake_case__	:   int = 2					,							snake_case__	:   int = 1					,							snake_case__	:   int = 0					,							snake_case__	:   int = 2					,							snake_case__	:   int = 3_05_22					,							snake_case__	:   int = 7_68					,							snake_case__	:   int = 12					,							snake_case__	:   int = 12					,							snake_case__	:   int = 30_72					,							snake_case__	:   str = "gelu"					,							snake_case__	:   float = 0.1					,							snake_case__	:   float = 0.1					,							snake_case__	:   int = 5_12					,							snake_case__	:   int = 2					,							snake_case__	:   float = 0.02					,							snake_case__	:   float = 1e-12					,							snake_case__	:   bool = False					,							**snake_case__	:   Tuple					,							):

								"""simple docstring"""

								super().__init__(pad_token_id=snake_case__					,							**snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							attention_window
								SCREAMING_SNAKE_CASE_					      =							sep_token_id
								SCREAMING_SNAKE_CASE_					      =							bos_token_id
								SCREAMING_SNAKE_CASE_					      =							eos_token_id
								SCREAMING_SNAKE_CASE_					      =							vocab_size
								SCREAMING_SNAKE_CASE_					      =							hidden_size
								SCREAMING_SNAKE_CASE_					      =							num_hidden_layers
								SCREAMING_SNAKE_CASE_					      =							num_attention_heads
								SCREAMING_SNAKE_CASE_					      =							hidden_act
								SCREAMING_SNAKE_CASE_					      =							intermediate_size
								SCREAMING_SNAKE_CASE_					      =							hidden_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE_					      =							max_position_embeddings
								SCREAMING_SNAKE_CASE_					      =							type_vocab_size
								SCREAMING_SNAKE_CASE_					      =							initializer_range
								SCREAMING_SNAKE_CASE_					      =							layer_norm_eps
								SCREAMING_SNAKE_CASE_					      =							onnx_export



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):



						def __init__(			self	:   Dict					,							snake_case__	:   "PretrainedConfig"					,							snake_case__	:   str = "default"					,							snake_case__	:   "List[PatchingSpec]" = None  ):

								"""simple docstring"""

								super().__init__(snake_case__					,							snake_case__					,							snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							True



						@property
						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								if self.task == "multiple-choice":
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								        ('global_attention_mask', dynamic_axis),
								    ]  )



						@property
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							super().outputs
								if self.task == "default":
										SCREAMING_SNAKE_CASE_					      =							{0: 'batch'}
								return outputs



						@property
						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								return 1e-4



						@property
						def 	__a					(			self	:   List[str]  ):

								"""simple docstring"""

								return max(super().default_onnx_opset					,							14  )






						def 	__a					(			self	:   List[str]					,							snake_case__	:   "PreTrainedTokenizerBase"					,							snake_case__	:   int = -1					,							snake_case__	:   int = -1					,							snake_case__	:   bool = False					,							snake_case__	:   Optional[TensorType] = None					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							super().generate_dummy_inputs(
								    preprocessor=snake_case__					,							batch_size=snake_case__					,							seq_length=snake_case__					,							is_pair=snake_case__					,							framework=snake_case__  )
								import torch

								# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
								# makes the export fail randomly
								SCREAMING_SNAKE_CASE_					      =							torch.zeros_like(inputs['input_ids']  )
								# make every second token global
								SCREAMING_SNAKE_CASE_					      =							1

								return inputs