


from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE :   int							      =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :   Any							      =				{
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _lowerCamelCase(		_a     ):
       lowercase_				:			str									=    """roformer"""
       def __init__(      self,   lowerCamelCase=5_00_00,   lowerCamelCase=None,   lowerCamelCase=7_68,   lowerCamelCase=12,   lowerCamelCase=12,   lowerCamelCase=30_72,   lowerCamelCase="gelu",   lowerCamelCase=0.1,   lowerCamelCase=0.1,   lowerCamelCase=15_36,   lowerCamelCase=2,   lowerCamelCase=0.0_2,   lowerCamelCase=1E-12,   lowerCamelCase=0,   lowerCamelCase=False,   lowerCamelCase=True,   **lowerCamelCase,   ) ->     List[str]:




           """simple docstring"""

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

           _lowercase :							Optional[int]   	=	vocab_size
           _lowercase :							Optional[Any]   	=	hidden_size if embedding_size is None else embedding_size
           _lowercase :							Dict   	=	hidden_size
           _lowercase :							int   	=	num_hidden_layers
           _lowercase :							List[Any]   	=	num_attention_heads
           _lowercase :							Tuple   	=	hidden_act
           _lowercase :							str   	=	intermediate_size
           _lowercase :							Dict   	=	hidden_dropout_prob
           _lowercase :							List[Any]   	=	attention_probs_dropout_prob
           _lowercase :							Union[str, Any]   	=	max_position_embeddings
           _lowercase :							Optional[int]   	=	type_vocab_size
           _lowercase :							int   	=	initializer_range
           _lowercase :							List[Any]   	=	layer_norm_eps
           _lowercase :							str   	=	rotary_value
           _lowercase :							List[str]   	=	use_cache






class _lowerCamelCase(		_a     ):
       @property
       def     UpperCamelCase  (      self) ->     Mapping[str, Mapping[int, str]]:




           """simple docstring"""

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




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 _lowerCamelCase(		unittest.TestCase     ):
       def __init__(      self,   lowerCamelCase) ->     List[str]:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	parent




       def     UpperCamelCase  (      self) ->     str:




           """simple docstring"""

           return {}



def        UpperCamelCase_(   )					->		List[str]:
    _lowercase :							int   	=	'<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>'

    _lowercase :							List[Any]   	=	'\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 _lowerCamelCase(		_a,				unittest.TestCase     ):
       lowercase_				:			Tuple									=    MarkupLMFeatureExtractor if is_bsa_available() else None
       def     UpperCamelCase  (      self) ->     Optional[int]:




           """simple docstring"""

           _lowercase :							Tuple   	=	MarkupLMFeatureExtractionTester(self)
       @property
       def     UpperCamelCase  (      self) ->     List[Any]:




           """simple docstring"""

           return self.feature_extract_tester.prepare_feat_extract_dict()




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




           """simple docstring"""

           _lowercase :							int   	=	self.feature_extraction_class()

           # Test not batched input
           _lowercase :							List[Any]   	=	get_html_strings()[0]
           _lowercase :							Optional[Any]   	=	feature_extractor(lowerCamelCase)

           # fmt: off
           _lowercase :							str   	=	[['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']]
           _lowercase :							Dict   	=	[['/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,   lowerCamelCase)
           self.assertEqual(encoding.xpaths,   lowerCamelCase)

           # Test batched
           _lowercase :							Optional[int]   	=	get_html_strings()
           _lowercase :							Tuple   	=	feature_extractor(lowerCamelCase)

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

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

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


