



'''simple docstring'''




from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream





class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				lowercase	,				lowercase = None	,				lowercase = None	,				lowercase = False	,				lowercase = False	,				lowercase = None	,				lowercase = None	,				**lowercase	,				):
				super().__init__(
				    features=_lowerCAmelCase	,				cache_dir=_lowerCAmelCase	,				keep_in_memory=_lowerCAmelCase	,				streaming=_lowerCAmelCase	,				num_proc=_lowerCAmelCase	,				**_lowerCAmelCase	,				)
				A_     :						str     =					Generator(
				    cache_dir=_lowerCAmelCase	,				features=_lowerCAmelCase	,				generator=_lowerCAmelCase	,				gen_kwargs=_lowerCAmelCase	,				**_lowerCAmelCase	,				)


	def    _a     (self							):

				if self.streaming:
							A_     :						List[str]     =					self.builder.as_streaming_dataset(split="""train"""							)
				# Build regular (map-style) dataset
				else:
							A_     :						str     =					None
							A_     :						Tuple     =					None
							A_     :						Any     =					None
							A_     :						Dict     =					None

							self.builder.download_and_prepare(
							    download_config=_lowerCAmelCase	,				download_mode=_lowerCAmelCase	,				verification_mode=_lowerCAmelCase	,				base_path=_lowerCAmelCase	,				num_proc=self.num_proc	,				)
							A_     :						Tuple     =					self.builder.as_dataset(
							    split="""train"""	,				verification_mode=_lowerCAmelCase	,				in_memory=self.keep_in_memory							)
				return dataset




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase							:Any       = logging.get_logger(__name__)

lowerCamelCase							:List[Any]       = {
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Optional[int]		      = 'camembert'


	def __init__(self	,				lowercase=30522	,				lowercase=768	,				lowercase=12	,				lowercase=12	,				lowercase=3072	,				lowercase="gelu"	,				lowercase=0.1	,				lowercase=0.1	,				lowercase=512	,				lowercase=2	,				lowercase=0.02	,				lowercase=1E-12	,				lowercase=1	,				lowercase=0	,				lowercase=2	,				lowercase="absolute"	,				lowercase=True	,				lowercase=None	,				**lowercase	,				):
				super().__init__(pad_token_id=lowercase	,				bos_token_id=lowercase	,				eos_token_id=lowercase	,				**lowercase							)

				A_     :						List[Any]     =					vocab_size
				A_     :						int     =					hidden_size
				A_     :						Dict     =					num_hidden_layers
				A_     :						Dict     =					num_attention_heads
				A_     :						Optional[Any]     =					hidden_act
				A_     :						str     =					intermediate_size
				A_     :						int     =					hidden_dropout_prob
				A_     :						List[Any]     =					attention_probs_dropout_prob
				A_     :						Optional[Any]     =					max_position_embeddings
				A_     :						Optional[int]     =					type_vocab_size
				A_     :						int     =					initializer_range
				A_     :						str     =					layer_norm_eps
				A_     :						int     =					position_embedding_type
				A_     :						Dict     =					use_cache
				A_     :						Any     =					classifier_dropout







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	@property
	def    _a     (self							):
				if self.task == "multiple-choice":
							A_     :						Optional[Any]     =					{0: """batch""", 1: """choice""", 2: """sequence"""}
				else:
							A_     :						int     =					{0: """batch""", 1: """sequence"""}
				return OrderedDict(
				    [
				        ("""input_ids""", dynamic_axis),
				        ("""attention_mask""", dynamic_axis),
				    ]							)