
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 ,       lowercase_):
     snake_case_ :    Dict     			=						parent





 def        snake_case__		(    self):
     return {}


def 						UpperCamelCase_     (	):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						"<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>"

    snake_case_ :    Optional[int]     			=						"\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_				(	snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						MarkupLMFeatureExtractor if is_bsa_available() else None





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						MarkupLMFeatureExtractionTester(self)





 @property
 def        snake_case__		(    self):
     return self.feature_extract_tester.prepare_feat_extract_dict()





 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						self.feature_extraction_class()

     # Test not batched input
     snake_case_ :    Optional[int]     			=						get_html_strings()[0]
     snake_case_ :    int     			=						feature_extractor(lowercase_)

     # fmt: off
     snake_case_ :    Tuple     			=						[["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"]]
     snake_case_ :    Any     			=						[["/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 ,       lowercase_)
     self.assertEqual(encoding.xpaths ,       lowercase_)

     # Test batched
     snake_case_ :    Dict     			=						get_html_strings()
     snake_case_ :    Optional[int]     			=						feature_extractor(lowercase_)

     # fmt: off
     snake_case_ :    str     			=						expected_nodes + [["My First Heading", "My first paragraph."]]
     snake_case_ :    List[Any]     			=						expected_xpaths + [["/html/body/h1", "/html/body/p"]]

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

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






'''simple docstring'''
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


a_											=						logging.get_logger(__name__)

a_											=						{
    "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    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""longformer"""





 def __init__(    self ,       lowercase_ = 5_12 ,       lowercase_ = 2 ,       lowercase_ = 1 ,       lowercase_ = 0 ,       lowercase_ = 2 ,       lowercase_ = 3_05_22 ,       lowercase_ = 7_68 ,       lowercase_ = 12 ,       lowercase_ = 12 ,       lowercase_ = 30_72 ,       lowercase_ = "gelu" ,       lowercase_ = 0.1 ,       lowercase_ = 0.1 ,       lowercase_ = 5_12 ,       lowercase_ = 2 ,       lowercase_ = 0.02 ,       lowercase_ = 1E-12 ,       lowercase_ = False ,       **lowercase_ ,       ):
     super().__init__(pad_token_id=lowercase_ ,       **lowercase_)

     snake_case_ :    Dict     			=						attention_window
     snake_case_ :    Tuple     			=						sep_token_id
     snake_case_ :    Optional[Any]     			=						bos_token_id
     snake_case_ :    str     			=						eos_token_id
     snake_case_ :    Optional[int]     			=						vocab_size
     snake_case_ :    Dict     			=						hidden_size
     snake_case_ :    Optional[Any]     			=						num_hidden_layers
     snake_case_ :    int     			=						num_attention_heads
     snake_case_ :    Union[str, Any]     			=						hidden_act
     snake_case_ :    Union[str, Any]     			=						intermediate_size
     snake_case_ :    Union[str, Any]     			=						hidden_dropout_prob
     snake_case_ :    Optional[Any]     			=						attention_probs_dropout_prob
     snake_case_ :    List[str]     			=						max_position_embeddings
     snake_case_ :    str     			=						type_vocab_size
     snake_case_ :    Tuple     			=						initializer_range
     snake_case_ :    List[str]     			=						layer_norm_eps
     snake_case_ :    Tuple     			=						onnx_export






class    UpperCAmelCase_				(	snake_case__					):





 def __init__(    self ,       lowercase_ ,       lowercase_ = "default" ,       lowercase_ = None):
     super().__init__(lowercase_ ,       lowercase_ ,       lowercase_)
     snake_case_ :    Dict     			=						True





 @property
 def        snake_case__		(    self):
     if self.task == "multiple-choice":
         snake_case_ :    Tuple     			=						{0: "batch", 1: "choice", 2: "sequence"}
     else:
         snake_case_ :    int     			=						{0: "batch", 1: "sequence"}
     return OrderedDict(
         [
             ("input_ids", dynamic_axis),
             ("attention_mask", dynamic_axis),
             ("global_attention_mask", dynamic_axis),
         ])





 @property
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						super().outputs
     if self.task == "default":
         snake_case_ :    str     			=						{0: "batch"}
     return outputs





 @property
 def        snake_case__		(    self):
     return 1E-4





 @property
 def        snake_case__		(    self):
     # needs to be >= 14 to support tril operator
     return max(super().default_onnx_opset ,       14)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = -1 ,       lowercase_ = -1 ,       lowercase_ = False ,       lowercase_ = None ,       ):
     snake_case_ :    Optional[Any]     			=						super().generate_dummy_inputs(
         preprocessor=lowercase_ ,       batch_size=lowercase_ ,       seq_length=lowercase_ ,       is_pair=lowercase_ ,       framework=lowercase_)
     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
     snake_case_ :    Any     			=						torch.zeros_like(inputs["input_ids"])
     # make every second token global
     snake_case_ :    Tuple     			=						1

     return inputs




