



from collections import OrderedDict
from typing import Mapping

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


a__						:			str										=			logging.get_logger(__name__)

a__						:			Dict										=			{
    '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        UpperCAmelCase__  (    UpperCAmelCase_):
 __SCREAMING_SNAKE_CASE					      =				'''ernie'''



 def __init__(  self		,	lowercase=3_0_5_2_2		,	lowercase=7_6_8		,	lowercase=1_2		,	lowercase=1_2		,	lowercase=3_0_7_2		,	lowercase="gelu"		,	lowercase=0.1		,	lowercase=0.1		,	lowercase=5_1_2		,	lowercase=2		,	lowercase=3		,	lowercase=False		,	lowercase=0.02		,	lowercase=1E-12		,	lowercase=0		,	lowercase="absolute"		,	lowercase=True		,	lowercase=None		,	**lowercase		,	)  ->							Optional[Any]:
  super().__init__(pad_token_id=lowercase		,	**lowercase			)

  __UpperCamelCase				      =  vocab_size
  __UpperCamelCase				      =  hidden_size
  __UpperCamelCase				      =  num_hidden_layers
  __UpperCamelCase				      =  num_attention_heads
  __UpperCamelCase				      =  hidden_act
  __UpperCamelCase				      =  intermediate_size
  __UpperCamelCase				      =  hidden_dropout_prob
  __UpperCamelCase				      =  attention_probs_dropout_prob
  __UpperCamelCase				      =  max_position_embeddings
  __UpperCamelCase				      =  type_vocab_size
  __UpperCamelCase				      =  task_type_vocab_size
  __UpperCamelCase				      =  use_task_id
  __UpperCamelCase				      =  initializer_range
  __UpperCamelCase				      =  layer_norm_eps
  __UpperCamelCase				      =  position_embedding_type
  __UpperCamelCase				      =  use_cache
  __UpperCamelCase				      =  classifier_dropout


class        UpperCAmelCase__  (    UpperCAmelCase_):



 @property
 def    __lowerCamelCase				(  self			)  ->							Mapping[str, Mapping[int, str]]:
  if self.task == "multiple-choice":
   __UpperCamelCase				      =  {0: """batch""", 1: """choice""", 2: """sequence"""}
  else:
   __UpperCamelCase				      =  {0: """batch""", 1: """sequence"""}
  return OrderedDict(
      [
          ("""input_ids""", dynamic_axis),
          ("""attention_mask""", dynamic_axis),
          ("""token_type_ids""", dynamic_axis),
          ("""task_type_ids""", dynamic_axis),
      ]			)










'''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 _lowercase   (  __A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  tmp_path / """text.txt"""
 __UpperCamelCase				      =  textwrap.dedent(
     """\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        """       )
 with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
  f.write(__A       )
 return str(__A       )







@pytest.fixture
def _lowercase   (  __A							,__A       ):



 '''simple docstring'''



 __UpperCamelCase				      =  tmp_path / """text_with_image.txt"""
 with open(__A							,"""w"""							,encoding="""utf-8"""       ) as f:
  f.write(__A       )
 return str(__A       )







@pytest.mark.parametrize("""keep_linebreaks"""							,[True, False]       )
def _lowercase   (  __A							,__A       ):



 '''simple docstring'''



 with open(__A							,encoding="""utf-8"""       ) as f:
  __UpperCamelCase				      =  f.read().splitlines(keepends=__A       )
 __UpperCamelCase				      =  Text(keep_linebreaks=__A							,encoding="""utf-8"""       )
 __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
 __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
 assert generated_content == expected_content







@require_pil
def _lowercase   (  __A       ):



 '''simple docstring'''



 with open(__A							,encoding="""utf-8"""       ) as f:
  __UpperCamelCase				      =  f.read().splitlines()[0]
 __UpperCamelCase				      =  Text(encoding="""utf-8"""							,features=Features({"""image""": Image()}       )       )
 __UpperCamelCase				      =  text._generate_tables([[text_file_with_image]]       )
 __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       )
 assert pa_table.schema.field("""image"""       ).type == Image()()
 __UpperCamelCase				      =  pa_table.to_pydict()["""image"""]
 assert generated_content == [{"path": image_file, "bytes": None}]







@pytest.mark.parametrize("""sample_by"""							,["""line""", """paragraph""", """document"""]       )
def _lowercase   (  __A							,__A       ):



 '''simple docstring'''



 with open(__A							,encoding="""utf-8"""       ) as f:
  __UpperCamelCase				      =  f.read()
 if sample_by == "line":
  __UpperCamelCase				      =  expected_content.splitlines()
 elif sample_by == "paragraph":
  __UpperCamelCase				      =  expected_content.split("""\n\n"""       )
 elif sample_by == "document":
  __UpperCamelCase				      =  [expected_content]
 __UpperCamelCase				      =  Text(sample_by=__A							,encoding="""utf-8"""							,chunksize=100       )
 __UpperCamelCase				      =  text._generate_tables([[text_file]]       )
 __UpperCamelCase				      =  pa.concat_tables([table for _, table in generator]       ).to_pydict()["""text"""]
 assert generated_content == expected_content





