code: 
  
  
  
  
  """simple docstring"""
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  __A 		=			logging.get_logger(__name__)
  
  __A 		=			{
      'nghuyong/ernie-1.0-base-zh': 'https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json',
      'nghuyong/ernie-2.0-base-en': 'https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json',
      'nghuyong/ernie-2.0-large-en': 'https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json',
      'nghuyong/ernie-3.0-base-zh': 'https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json',
      'nghuyong/ernie-3.0-medium-zh': 'https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json',
      'nghuyong/ernie-3.0-mini-zh': 'https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json',
      'nghuyong/ernie-3.0-micro-zh': 'https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json',
      'nghuyong/ernie-3.0-nano-zh': 'https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json',
      'nghuyong/ernie-gram-zh': 'https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json',
      'nghuyong/ernie-health-zh': 'https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json',
  }
  
  class 			lowerCamelCase__    (  lowerCAmelCase_			):
  			a__ :      Any								=     """ernie"""
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE=30_522				, SCREAMING_SNAKE_CASE=768				, SCREAMING_SNAKE_CASE=12				, SCREAMING_SNAKE_CASE=12				, SCREAMING_SNAKE_CASE=3_072				, SCREAMING_SNAKE_CASE="gelu"				, SCREAMING_SNAKE_CASE=0.1				, SCREAMING_SNAKE_CASE=0.1				, SCREAMING_SNAKE_CASE=512				, SCREAMING_SNAKE_CASE=2				, SCREAMING_SNAKE_CASE=3				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=0.02				, SCREAMING_SNAKE_CASE=1E-12				, SCREAMING_SNAKE_CASE=0				, SCREAMING_SNAKE_CASE="absolute"				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().__init__(pad_token_id=__a				, **__a							)
  
  					snake_case		:						Dict								=				vocab_size
  					snake_case		:						int								=				hidden_size
  					snake_case		:						int								=				num_hidden_layers
  					snake_case		:						List[Any]								=				num_attention_heads
  					snake_case		:						Optional[int]								=				hidden_act
  					snake_case		:						Optional[Any]								=				intermediate_size
  					snake_case		:						List[str]								=				hidden_dropout_prob
  					snake_case		:						Union[str, Any]								=				attention_probs_dropout_prob
  					snake_case		:						str								=				max_position_embeddings
  					snake_case		:						Optional[Any]								=				type_vocab_size
  					snake_case		:						Tuple								=				task_type_vocab_size
  					snake_case		:						int								=				use_task_id
  					snake_case		:						int								=				initializer_range
  					snake_case		:						Optional[int]								=				layer_norm_eps
  					snake_case		:						List[str]								=				position_embedding_type
  					snake_case		:						List[str]								=				use_cache
  					snake_case		:						str								=				classifier_dropout
  
  
  
  
  
  
  
  class 			lowerCamelCase__    (  lowerCAmelCase_			):
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if self.task == "multiple-choice":
  							snake_case		:						int								=				{0: 'batch', 1: 'choice', 2: 'sequence'}
  					else:
  							snake_case		:						Dict								=				{0: 'batch', 1: 'sequence'}
  					return OrderedDict(
  					    [
  					        ("input_ids", dynamic_axis),
  					        ("attention_mask", dynamic_axis),
  					        ("token_type_ids", dynamic_axis),
  					        ("task_type_ids", dynamic_axis),
  					    ]							)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import textwrap
  
  import pyarrow as pa
  import pytest
  
  from datasets import Features, Image
  from datasets.packaged_modules.text.text import Text
  
  from ..utils import require_pil
  
  
  
  @pytest.fixture
  def   UpperCamelCase__     (		lowercase__  :     str   ):
  		snake_case		:						List[str]								=				tmp_path / "text.txt"
  		snake_case		:						Tuple								=				textwrap.dedent(
  		    "\\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n\n        Second paragraph:\n        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.\n        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.\n        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.\n        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.\n        "   )
  		with open(lowercase__       ,     "w"       ,     encoding="utf-8"   ) as f:
  				f.write(lowercase__   )
  		return str(lowercase__   )
  
  
  
  @pytest.fixture
  def   UpperCamelCase__     (		lowercase__  :     str       ,     lowercase__  :     Dict   ):
  		snake_case		:						Any								=				tmp_path / "text_with_image.txt"
  		with open(lowercase__       ,     "w"       ,     encoding="utf-8"   ) as f:
  				f.write(lowercase__   )
  		return str(lowercase__   )
  
  
  
  @pytest.mark.parametrize("keep_linebreaks"       ,     [True, False]   )
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     Tuple   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						List[Any]								=				f.read().splitlines(keepends=lowercase__   )
  		snake_case		:						List[Any]								=				Text(keep_linebreaks=lowercase__       ,     encoding="utf-8"   )
  		snake_case		:						Optional[int]								=				text._generate_tables([[text_file]]   )
  		snake_case		:						int								=				pa.concat_tables([table for _, table in generator]   ).to_pydict()["text"]
  		assert generated_content == expected_content
  
  
  
  @require_pil
  def   UpperCamelCase__     (		lowercase__  :     Optional[Any]   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						Union[str, Any]								=				f.read().splitlines()[0]
  		snake_case		:						List[Any]								=				Text(encoding="utf-8"       ,     features=Features({"image": Image()}   )   )
  		snake_case		:						Tuple								=				text._generate_tables([[text_file_with_image]]   )
  		snake_case		:						List[str]								=				pa.concat_tables([table for _, table in generator]   )
  		assert pa_table.schema.field("image"   ).type == Image()()
  		snake_case		:						Optional[Any]								=				pa_table.to_pydict()["image"]
  		assert generated_content == [{"path": image_file, "bytes": None}]
  
  
  
  @pytest.mark.parametrize("sample_by"       ,     ["line", "paragraph", "document"]   )
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]       ,     lowercase__  :     List[str]   ):
  		with open(lowercase__       ,     encoding="utf-8"   ) as f:
  				snake_case		:						Tuple								=				f.read()
  		if sample_by == "line":
  				snake_case		:						Optional[int]								=				expected_content.splitlines()
  		elif sample_by == "paragraph":
  				snake_case		:						str								=				expected_content.split("\n\n"   )
  		elif sample_by == "document":
  				snake_case		:						List[Any]								=				[expected_content]
  		snake_case		:						str								=				Text(sample_by=lowercase__       ,     encoding="utf-8"       ,     chunksize=100   )
  		snake_case		:						Optional[int]								=				text._generate_tables([[text_file]]   )
  		snake_case		:						Optional[Any]								=				pa.concat_tables([table for _, table in generator]   ).to_pydict()["text"]
  		assert generated_content == expected_content
  
  