





'''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 lowerCamelCase						(					__lowerCamelCase   )						->str:
    _SCREAMING_SNAKE_CASE     =      tmp_path / """text.txt"""
    _SCREAMING_SNAKE_CASE     =      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(lowerCamelCase_ ,	"""w""" ,	encoding="""utf-8"""   ) as f:
        f.write(lowerCamelCase_   )
    return str(lowerCamelCase_   )





@pytest.fixture
def lowerCamelCase						(					__lowerCamelCase ,	__lowerCamelCase   )						->int:
    _SCREAMING_SNAKE_CASE     =      tmp_path / """text_with_image.txt"""
    with open(lowerCamelCase_ ,	"""w""" ,	encoding="""utf-8"""   ) as f:
        f.write(lowerCamelCase_   )
    return str(lowerCamelCase_   )





@pytest.mark.parametrize("""keep_linebreaks""" ,	[True, False]   )
def lowerCamelCase						(					__lowerCamelCase ,	__lowerCamelCase   )						->Optional[int]:
    with open(lowerCamelCase_ ,	encoding="""utf-8"""   ) as f:
        _SCREAMING_SNAKE_CASE     =      f.read().splitlines(keepends=lowerCamelCase_   )
    _SCREAMING_SNAKE_CASE     =      Text(keep_linebreaks=lowerCamelCase_ ,	encoding="""utf-8"""   )
    _SCREAMING_SNAKE_CASE     =      text._generate_tables([[text_file]]   )
    _SCREAMING_SNAKE_CASE     =      pa.concat_tables([table for _, table in generator]   ).to_pydict()["""text"""]
    assert generated_content == expected_content





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






@pytest.mark.parametrize("""sample_by""" ,	["""line""", """paragraph""", """document"""]   )
def lowerCamelCase						(					__lowerCamelCase ,	__lowerCamelCase   )						->List[str]:
    with open(lowerCamelCase_ ,	encoding="""utf-8"""   ) as f:
        _SCREAMING_SNAKE_CASE     =      f.read()
    if sample_by == "line":
        _SCREAMING_SNAKE_CASE     =      expected_content.splitlines()
    elif sample_by == "paragraph":
        _SCREAMING_SNAKE_CASE     =      expected_content.split("""\n\n"""   )
    elif sample_by == "document":
        _SCREAMING_SNAKE_CASE     =      [expected_content]
    _SCREAMING_SNAKE_CASE     =      Text(sample_by=lowerCamelCase_ ,	encoding="""utf-8""" ,	chunksize=100   )
    _SCREAMING_SNAKE_CASE     =      text._generate_tables([[text_file]]   )
    _SCREAMING_SNAKE_CASE     =      pa.concat_tables([table for _, table in generator]   ).to_pydict()["""text"""]
    assert generated_content == expected_content










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowercase_         =			logging.get_logger(__name__)

lowercase_         =			{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			'''rembert'''


    def __init__(		self			,		A=25_0300			,		A=1152			,		A=32			,		A=18			,		A=256			,		A=1664			,		A=4608			,		A="gelu"			,		A=0.0			,		A=0.0			,		A=0.1			,		A=512			,		A=2			,		A=0.02			,		A=1e-12			,		A=True			,		A=0			,		A=312			,		A=313			,		**A			,		)		->       Dict:
        super().__init__(pad_token_id=A			,		bos_token_id=A			,		eos_token_id=A			,		**A	)

        _SCREAMING_SNAKE_CASE     =      vocab_size
        _SCREAMING_SNAKE_CASE     =      input_embedding_size
        _SCREAMING_SNAKE_CASE     =      output_embedding_size
        _SCREAMING_SNAKE_CASE     =      max_position_embeddings
        _SCREAMING_SNAKE_CASE     =      hidden_size
        _SCREAMING_SNAKE_CASE     =      num_hidden_layers
        _SCREAMING_SNAKE_CASE     =      num_attention_heads
        _SCREAMING_SNAKE_CASE     =      intermediate_size
        _SCREAMING_SNAKE_CASE     =      hidden_act
        _SCREAMING_SNAKE_CASE     =      hidden_dropout_prob
        _SCREAMING_SNAKE_CASE     =      attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE     =      classifier_dropout_prob
        _SCREAMING_SNAKE_CASE     =      initializer_range
        _SCREAMING_SNAKE_CASE     =      type_vocab_size
        _SCREAMING_SNAKE_CASE     =      layer_norm_eps
        _SCREAMING_SNAKE_CASE     =      use_cache
        _SCREAMING_SNAKE_CASE     =      False




class  a_					(    snake_case_	):

    '''simple docstring'''


    @property
    def       snake_case_(		self	)		->       Mapping[str, Mapping[int, str]]:
        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),
                ("""token_type_ids""", dynamic_axis),
            ]	)





    @property
    def       snake_case_(		self	)		->       float:
        return 1e-4



