




"""simple docstring"""

from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_lowerCamelCase  :			Dict        =       logging.get_logger(__name__)


_lowerCamelCase  :			int        =       TypeVar('DatasetType', Dataset, IterableDataset)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = "first_exhausted"  ,      ):




	"""simple docstring"""






	from .arrow_dataset import Dataset
	from .iterable_dataset import IterableDataset

	if not datasets:
		raise ValueError('''Unable to interleave an empty list of datasets.'''	)
	for i, dataset in enumerate(_UpperCAmelCase	):
		if not isinstance(_UpperCAmelCase  ,      (Dataset, IterableDataset)	):
			if isinstance(_UpperCAmelCase  ,      (DatasetDict, IterableDatasetDict)	):
				if not dataset:
					raise ValueError(
					    f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
					    '''is an empty dataset dictionary.'''	)
				raise ValueError(
				    f"""Dataset at position {i} has at least one split: {list(_UpperCAmelCase	)}\n"""
				    f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_UpperCAmelCase	)	)}']"""	)
			raise ValueError(
			    f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_UpperCAmelCase	).__name__}."""	)
		if i == 0:
			A_   , A_  :				str	   =		(
			    (Dataset, IterableDataset) if isinstance(_UpperCAmelCase  ,      _UpperCAmelCase	) else (IterableDataset, Dataset)
			)
		elif not isinstance(_UpperCAmelCase  ,      _UpperCAmelCase	):
			raise ValueError(
			    f"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""	)
	if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
		raise ValueError(f"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""	)
	if dataset_type is Dataset:
		return _interleave_map_style_datasets(
		    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      info=_UpperCAmelCase  ,      split=_UpperCAmelCase  ,      stopping_strategy=_UpperCAmelCase	)
	else:
		return _interleave_iterable_datasets(
		    _UpperCAmelCase  ,      _UpperCAmelCase  ,      _UpperCAmelCase  ,      info=_UpperCAmelCase  ,      split=_UpperCAmelCase  ,      stopping_strategy=_UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = None  ,      _UpperCAmelCase = 0  ,      ):




	"""simple docstring"""






	if not dsets:
		raise ValueError('''Unable to concatenate an empty list of datasets.'''	)
	for i, dataset in enumerate(_UpperCAmelCase	):
		if not isinstance(_UpperCAmelCase  ,      (Dataset, IterableDataset)	):
			if isinstance(_UpperCAmelCase  ,      (DatasetDict, IterableDatasetDict)	):
				if not dataset:
					raise ValueError(
					    f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
					    '''is an empty dataset dictionary.'''	)
				raise ValueError(
				    f"""Dataset at position {i} has at least one split: {list(_UpperCAmelCase	)}\n"""
				    f"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(_UpperCAmelCase	)	)}']"""	)
			raise ValueError(
			    f"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(_UpperCAmelCase	).__name__}."""	)
		if i == 0:
			A_   , A_  :				Any	   =		(
			    (Dataset, IterableDataset) if isinstance(_UpperCAmelCase  ,      _UpperCAmelCase	) else (IterableDataset, Dataset)
			)
		elif not isinstance(_UpperCAmelCase  ,      _UpperCAmelCase	):
			raise ValueError(
			    f"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""	)
	if dataset_type is Dataset:
		return _concatenate_map_style_datasets(_UpperCAmelCase  ,      info=_UpperCAmelCase  ,      split=_UpperCAmelCase  ,      axis=_UpperCAmelCase	)
	else:
		return _concatenate_iterable_datasets(_UpperCAmelCase  ,      info=_UpperCAmelCase  ,      split=_UpperCAmelCase  ,      axis=_UpperCAmelCase	)











"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase  :			List[Any]        =       logging.get_logger(__name__)

_lowerCamelCase  :			Tuple        =       {
    '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        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    Optional[Any]         =   """camembert"""







				def __init__(							self   :					List[Any]      ,     _lowerCamelCase   :					Optional[Any]=3_05_22      ,     _lowerCamelCase   :					List[str]=7_68      ,     _lowerCamelCase   :					Optional[Any]=12      ,     _lowerCamelCase   :					List[str]=12      ,     _lowerCamelCase   :					Any=30_72      ,     _lowerCamelCase   :					List[str]="gelu"      ,     _lowerCamelCase   :					Optional[Any]=0.1      ,     _lowerCamelCase   :					Optional[int]=0.1      ,     _lowerCamelCase   :					Union[str, Any]=5_12      ,     _lowerCamelCase   :					str=2      ,     _lowerCamelCase   :					Optional[Any]=0.02      ,     _lowerCamelCase   :					int=1E-12      ,     _lowerCamelCase   :					str=1      ,     _lowerCamelCase   :					Union[str, Any]=0      ,     _lowerCamelCase   :					Optional[int]=2      ,     _lowerCamelCase   :					List[str]="absolute"      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					List[Any]=None      ,     **_lowerCamelCase   :					Union[str, Any]      ,     ):



					"""simple docstring"""

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

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

class        lowercase      (    __UpperCAmelCase):







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

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





