
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__							=    datasets.utils.logging.get_logger(__name__)
@dataclass
class      A__       (						datasets.BuilderConfig    ):
							lowerCAmelCase__		:						Optional[datasets.Features]         =							None






class      A__       (						datasets.ArrowBasedBuilder    ):
							lowerCAmelCase__		:						Union[str, Any]         =							ArrowConfig





							def       a__			(			self  :       int    )						->		Any:



											"""simple docstring"""


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





							def       a__			(			self  :       Optional[int]					,			_UpperCAmelCase  :       Union[str, Any]    )						->		List[Any]:



											"""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}"""    )
											__lowercase								= dl_manager.download_and_extract(self.config.data_files    )
											if isinstance(_UpperCAmelCase					,			(str, list, tuple)    ):
															__lowercase								= data_files
															if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
																			__lowercase								= [files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															__lowercase								= [dl_manager.iter_files(_UpperCAmelCase    ) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,			gen_kwargs={'files': files}    )]
											__lowercase								= []
											for split_name, files in data_files.items():
															if isinstance(_UpperCAmelCase					,			_UpperCAmelCase    ):
																			__lowercase								= [files]
															# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
															__lowercase								= [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:
																											__lowercase								= 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  :       Any					,			_UpperCAmelCase  :       pa.Table    )						->		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
															__lowercase								= table_cast(_UpperCAmelCase					,			self.info.features.arrow_schema    )
											return pa_table





							def       a__			(			self  :       Any					,			_UpperCAmelCase  :       Any    )						->		List[str]:



											"""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    )    ):
																											__lowercase								= 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







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase__		:						Optional[int]         =							"squeezebert"





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       int=3_05_22					,			_UpperCAmelCase  :       Any=7_68					,			_UpperCAmelCase  :       Tuple=12					,			_UpperCAmelCase  :       Union[str, Any]=12					,			_UpperCAmelCase  :       Union[str, Any]=30_72					,			_UpperCAmelCase  :       Optional[Any]="gelu"					,			_UpperCAmelCase  :       Optional[int]=0.1					,			_UpperCAmelCase  :       Any=0.1					,			_UpperCAmelCase  :       Union[str, Any]=5_12					,			_UpperCAmelCase  :       int=2					,			_UpperCAmelCase  :       Dict=0.02					,			_UpperCAmelCase  :       str=1e-1_2					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       str=7_68					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Union[str, Any]=4					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       List[Any]=4					,			_UpperCAmelCase  :       List[str]=4					,			**_UpperCAmelCase  :       Dict					,			)						->		Any:



											"""simple docstring"""


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

											__lowercase								= vocab_size
											__lowercase								= hidden_size
											__lowercase								= num_hidden_layers
											__lowercase								= num_attention_heads
											__lowercase								= hidden_act
											__lowercase								= intermediate_size
											__lowercase								= hidden_dropout_prob
											__lowercase								= attention_probs_dropout_prob
											__lowercase								= max_position_embeddings
											__lowercase								= type_vocab_size
											__lowercase								= initializer_range
											__lowercase								= layer_norm_eps
											__lowercase								= embedding_size
											__lowercase								= q_groups
											__lowercase								= k_groups
											__lowercase								= v_groups
											__lowercase								= post_attention_groups
											__lowercase								= intermediate_groups
											__lowercase								= output_groups






class      A__       (						lowerCAmelCase__    ):





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



											"""simple docstring"""


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





