
from collections import OrderedDict
from typing import Mapping

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


__a							  =						logging.get_logger(__name__)

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

class        UpperCamelCase__(    snake_case_	):

	"""simple docstring"""

	_A  =		"rembert"







	def __init__(      self				:			Tuple							,			snake_case__				:			List[Any]=25_03_00							,			snake_case__				:			Union[str, Any]=11_52							,			snake_case__				:			List[Any]=32							,			snake_case__				:			int=18							,			snake_case__				:			Dict=2_56							,			snake_case__				:			List[Any]=16_64							,			snake_case__				:			Optional[int]=46_08							,			snake_case__				:			Optional[Any]="gelu"							,			snake_case__				:			Optional[int]=0.0							,			snake_case__				:			Tuple=0.0							,			snake_case__				:			Union[str, Any]=0.1							,			snake_case__				:			str=5_12							,			snake_case__				:			Optional[int]=2							,			snake_case__				:			List[str]=0.02							,			snake_case__				:			Dict=1E-12							,			snake_case__				:			Optional[Any]=True							,			snake_case__				:			Optional[Any]=0							,			snake_case__				:			List[str]=3_12							,			snake_case__				:			Any=3_13							,			**snake_case__				:			List[Any]							,			):
			"""simple docstring"""





			super().__init__(pad_token_id=UpperCAmelCase__							,			bos_token_id=UpperCAmelCase__							,			eos_token_id=UpperCAmelCase__							,			**UpperCAmelCase__ )

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


class        UpperCamelCase__(    snake_case_	):

	"""simple docstring"""

	@property
	def        _a					(      self				:			str ):
			"""simple docstring"""





			if self.task == "multiple-choice":
					A				={0: '''batch''', 1: '''choice''', 2: '''sequence'''}
			else:
					A				={0: '''batch''', 1: '''sequence'''}
			return OrderedDict(
			    [
			        ("input_ids", dynamic_axis),
			        ("attention_mask", dynamic_axis),
			        ("token_type_ids", dynamic_axis),
			    ] )







	@property
	def        _a					(      self				:			Dict ):
			"""simple docstring"""





			return 1E-4











'''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 :						Union[str, Any]):



				"""simple docstring"""


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





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				str				=  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      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


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





@pytest.mark.parametrize('''sample_by'''   , ['''line''', '''paragraph''', '''document'''])
def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


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