


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
	from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


SCREAMING_SNAKE_CASE : Union[str, Any]          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : List[str]          =    {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							Dict         = """deberta"""





	def __init__(  self				,    UpperCamelCase_=50265				,    UpperCamelCase_=768				,    UpperCamelCase_=12				,    UpperCamelCase_=12				,    UpperCamelCase_=3072				,    UpperCamelCase_="gelu"				,    UpperCamelCase_=0.1				,    UpperCamelCase_=0.1				,    UpperCamelCase_=512				,    UpperCamelCase_=0				,    UpperCamelCase_=0.02				,    UpperCamelCase_=1e-7				,    UpperCamelCase_=False				,    UpperCamelCase_=-1				,    UpperCamelCase_=0				,    UpperCamelCase_=True				,    UpperCamelCase_=None				,    UpperCamelCase_=0				,    UpperCamelCase_="gelu"				,    **UpperCamelCase_				,    )		->			int:
								"""simple docstring"""






								super().__init__(**UpperCamelCase_							)

								a_       :   Optional[int]			=	hidden_size
								a_       :   str			=	num_hidden_layers
								a_       :   Tuple			=	num_attention_heads
								a_       :   List[Any]			=	intermediate_size
								a_       :   Union[str, Any]			=	hidden_act
								a_       :   str			=	hidden_dropout_prob
								a_       :   str			=	attention_probs_dropout_prob
								a_       :   Optional[int]			=	max_position_embeddings
								a_       :   Dict			=	type_vocab_size
								a_       :   str			=	initializer_range
								a_       :   Dict			=	relative_attention
								a_       :   Dict			=	max_relative_positions
								a_       :   Union[str, Any]			=	pad_token_id
								a_       :   List[str]			=	position_biased_input

								# Backwards compatibility
								if type(UpperCamelCase_							) == str:
															a_       :   Tuple			=	[x.strip() for x in pos_att_type.lower().split("""|"""							)]

								a_       :   Any			=	pos_att_type
								a_       :   List[str]			=	vocab_size
								a_       :   Tuple			=	layer_norm_eps

								a_       :   List[Any]			=	kwargs.get("""pooler_hidden_size"""				,    UpperCamelCase_							)
								a_       :   Any			=	pooler_dropout
								a_       :   Union[str, Any]			=	pooler_hidden_act





class 					snake_case__  ( __A       ):





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






								if self.task == "multiple-choice":
															a_       :   List[str]			=	{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
															a_       :   Optional[int]			=	{0: """batch""", 1: """sequence"""}
								if self._config.type_vocab_size > 0:
															return OrderedDict(
															    [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)]							)
								else:
															return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)]							)





	@property
	def     A				(  self							)		->			int:
								"""simple docstring"""






								return 12





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = -1				,    UpperCamelCase_ = -1				,    UpperCamelCase_ = -1				,    UpperCamelCase_ = False				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 3				,    UpperCamelCase_ = 40				,    UpperCamelCase_ = 40				,    UpperCamelCase_ = None				,    )		->			Mapping[str, Any]:
								"""simple docstring"""






								a_       :   Union[str, Any]			=	super().generate_dummy_inputs(preprocessor=UpperCamelCase_				,    framework=UpperCamelCase_							)
								if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
															del dummy_inputs["token_type_ids"]
								return dummy_inputs






import itertools
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa

import datasets
from datasets.table import table_cast


SCREAMING_SNAKE_CASE : int          =    datasets.utils.logging.get_logger(__name__)





@dataclass
class 					snake_case__  ( datasets.BuilderConfig       ):
	UpperCAmelCase   :							Optional[datasets.Features]         = None





class 					snake_case__  ( datasets.ArrowBasedBuilder       ):
	UpperCAmelCase   :							Optional[int]         = ArrowConfig





	def     A				(  self							)		->			str:
								"""simple docstring"""






								return datasets.DatasetInfo(features=self.config.features							)





	def     A				(  self				,    UpperCamelCase_							)		->			str:
								"""simple docstring"""






								if not self.config.data_files:
															raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
								a_       :   List[str]			=	dl_manager.download_and_extract(self.config.data_files							)
								if isinstance(UpperCamelCase_				,    (str, list, tuple)							):
															a_       :   Dict			=	data_files
															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   Optional[Any]			=	[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={"""files""": files}							)]
								a_       :   Any			=	[]
								for split_name, files in data_files.items():
															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   Dict			=	[files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															a_       :   List[Any]			=	[dl_manager.iter_files(UpperCamelCase_							) for file in files]
															# Infer features is they are stoed in the arrow schema
															if self.info.features is None:
																						for file in itertools.chain.from_iterable(UpperCamelCase_							):
																													with open(UpperCamelCase_				,    """rb"""							) as f:
																																				a_       :   List[str]			=	datasets.Features.from_arrow_schema(pa.ipc.open_stream(UpperCamelCase_							).schema							)
																													break
															splits.append(datasets.SplitGenerator(name=UpperCamelCase_				,    gen_kwargs={"""files""": files}							)							)
								return splits





	def     A				(  self				,    UpperCamelCase_							)		->			pa.Table:
								"""simple docstring"""






								if self.info.features is not None:
															# more expensive cast to support nested features with keys in a different order
															# allows str <-> int/float or str to Audio for example
															a_       :   int			=	table_cast(UpperCamelCase_				,    self.info.features.arrow_schema							)
								return pa_table





	def     A				(  self				,    UpperCamelCase_							)		->			Optional[Any]:
								"""simple docstring"""






								for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCamelCase_							)							):
															with open(UpperCamelCase_				,    """rb"""							) as f:
																						try:
																													for batch_idx, record_batch in enumerate(pa.ipc.open_stream(UpperCamelCase_							)							):
																																				a_       :   Tuple			=	pa.Table.from_batches([record_batch]							)
																																				# Uncomment for debugging (will print the Arrow table size and elements)
																																				# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																																				# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																				yield f"""{file_idx}_{batch_idx}""", self._cast_table(UpperCamelCase_							)
																						except ValueError as e:
																													logger.error(f"""Failed to read file '{file}' with error {type(UpperCamelCase_							)}: {e}"""							)
																													raise


