
'''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_	(   lowerCAmelCase__	:  List[str] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Union[str, Any]										=     tmp_path / 'text.txt'
			__UpperCAmelCase   :      Optional[Any]										=     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   lowercase_	(   lowerCAmelCase__	:  Any			,							lowerCAmelCase__	:  Union[str, Any] ):

			"""simple docstring"""





			__UpperCAmelCase   :      Optional[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   lowercase_	(   lowerCAmelCase__	:  List[str]			,							lowerCAmelCase__	:  Optional[int] ):

			"""simple docstring"""





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


@require_pil
def   lowercase_	(   lowerCAmelCase__	:  Dict ):

			"""simple docstring"""





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






@pytest.mark.parametrize("""sample_by"""			,							["""line""", """paragraph""", """document"""] )
def   lowercase_	(   lowerCAmelCase__	:  List[Any]			,							lowerCAmelCase__	:  Optional[Any] ):

			"""simple docstring"""





			with open(lowercase_			,							encoding="""utf-8""" ) as f:
						__UpperCAmelCase   :      Optional[int]										=     f.read()
			if sample_by == "line":
						__UpperCAmelCase   :      Union[str, Any]										=     expected_content.splitlines()
			elif sample_by == "paragraph":
						__UpperCAmelCase   :      str										=     expected_content.split("""\n\n""" )
			elif sample_by == "document":
						__UpperCAmelCase   :      Tuple										=     [expected_content]
			__UpperCAmelCase   :      int										=     Text(sample_by=lowercase_			,							encoding="""utf-8"""			,							chunksize=100 )
			__UpperCAmelCase   :      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









'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


_UpperCamelCase											=			logging.get_logger(__name__)

_UpperCamelCase											=			{
    '''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 _A  (    __SCREAMING_SNAKE_CASE    ):
		_SCREAMING_SNAKE_CASE					:    Optional[int]						      =			"ernie"





		def __init__(     self ,       __UpperCAmelCase=30_522 ,       __UpperCAmelCase=768 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=12 ,       __UpperCAmelCase=3_072 ,       __UpperCAmelCase="gelu" ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=0.1 ,       __UpperCAmelCase=512 ,       __UpperCAmelCase=2 ,       __UpperCAmelCase=3 ,       __UpperCAmelCase=False ,       __UpperCAmelCase=0.02 ,       __UpperCAmelCase=1E-12 ,       __UpperCAmelCase=0 ,       __UpperCAmelCase="absolute" ,       __UpperCAmelCase=True ,       __UpperCAmelCase=None ,       **__UpperCAmelCase ,       )       ->     List[Any]:

					'''simple docstring'''
					super().__init__(pad_token_id=__UpperCAmelCase ,       **__UpperCAmelCase				)

					__UpperCAmelCase   :      List[str]										=     vocab_size
					__UpperCAmelCase   :      Optional[int]										=     hidden_size
					__UpperCAmelCase   :      Optional[Any]										=     num_hidden_layers
					__UpperCAmelCase   :      int										=     num_attention_heads
					__UpperCAmelCase   :      int										=     hidden_act
					__UpperCAmelCase   :      List[Any]										=     intermediate_size
					__UpperCAmelCase   :      Optional[Any]										=     hidden_dropout_prob
					__UpperCAmelCase   :      Dict										=     attention_probs_dropout_prob
					__UpperCAmelCase   :      int										=     max_position_embeddings
					__UpperCAmelCase   :      str										=     type_vocab_size
					__UpperCAmelCase   :      int										=     task_type_vocab_size
					__UpperCAmelCase   :      List[str]										=     use_task_id
					__UpperCAmelCase   :      Union[str, Any]										=     initializer_range
					__UpperCAmelCase   :      Tuple										=     layer_norm_eps
					__UpperCAmelCase   :      List[str]										=     position_embedding_type
					__UpperCAmelCase   :      Union[str, Any]										=     use_cache
					__UpperCAmelCase   :      Optional[int]										=     classifier_dropout



class _A  (    __SCREAMING_SNAKE_CASE    ):





		@property
		def 							__A       (     self				)       ->     Mapping[str, Mapping[int, str]]:

					'''simple docstring'''
					if self.task == "multiple-choice":
								__UpperCAmelCase   :      Union[str, Any]										=     {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
								__UpperCAmelCase   :      Tuple										=     {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					        ("""token_type_ids""", dynamic_axis),
					        ("""task_type_ids""", dynamic_axis),
					    ]				)




