





'''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_   (	SCREAMING_SNAKE_CASE__   :      List[Any] )			->						str:
						UpperCAmelCase_      :   Tuple      =	tmp_path / '''text.txt'''
						UpperCAmelCase_      :   Optional[int]      =	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(SCREAMING_SNAKE_CASE__,      '''w''',      encoding='''utf-8''' ) as f:
												f.write(SCREAMING_SNAKE_CASE__ )
						return str(SCREAMING_SNAKE_CASE__ )





@pytest.fixture
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      List[str] )			->						Any:
						UpperCAmelCase_      :   Tuple      =	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 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      str )			->						int:
						with open(SCREAMING_SNAKE_CASE__,      encoding='''utf-8''' ) as f:
												UpperCAmelCase_      :   Tuple      =	f.read().splitlines(keepends=SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	Text(keep_linebreaks=SCREAMING_SNAKE_CASE__,      encoding='''utf-8''' )
						UpperCAmelCase_      :   Optional[Any]      =	text._generate_tables([[text_file]] )
						UpperCAmelCase_      :   Optional[Any]      =	pa.concat_tables([table for _, table in generator] ).to_pydict()['''text''']
						assert generated_content == expected_content





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





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












from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

