"""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       (						UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				tmp_path / 'text.txt'
							A__						  =				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(_SCREAMING_SNAKE_CASE							,     'w'							,     encoding='utf-8'					) as f:
														f.write(_SCREAMING_SNAKE_CASE					)
							return str(_SCREAMING_SNAKE_CASE					)






@pytest.fixture
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				tmp_path / 'text_with_image.txt'
							with open(_SCREAMING_SNAKE_CASE							,     'w'							,     encoding='utf-8'					) as f:
														f.write(_SCREAMING_SNAKE_CASE					)
							return str(_SCREAMING_SNAKE_CASE					)






@pytest.mark.parametrize('keep_linebreaks'							,     [True, False]					)
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





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






@require_pil
def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





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






@pytest.mark.parametrize('sample_by'							,     ['line', 'paragraph', 'document']					)
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





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






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {
    'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/config.json',
    'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json',
    'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/config.json',
    'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""splinter"""



      def __init__(		self       ,     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=True       ,     lowercase=0       ,     lowercase=1_04       ,     **lowercase       ,     )      ->     Union[str, Any]:





        '''simple docstring'''





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

        a__:	Dict     				=      vocab_size
        a__:	Dict     				=      max_position_embeddings
        a__:	Dict     				=      hidden_size
        a__:	int     				=      num_hidden_layers
        a__:	str     				=      num_attention_heads
        a__:	Dict     				=      intermediate_size
        a__:	Tuple     				=      hidden_act
        a__:	Optional[Any]     				=      hidden_dropout_prob
        a__:	List[Any]     				=      attention_probs_dropout_prob
        a__:	Tuple     				=      initializer_range
        a__:	str     				=      type_vocab_size
        a__:	Optional[Any]     				=      layer_norm_eps
        a__:	Any     				=      use_cache
        a__:	List[Any]     				=      question_token_id


