



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


SCREAMING_SNAKE_CASE  					=    logging.get_logger(__name__)


SCREAMING_SNAKE_CASE  					=    TypeVar("DatasetType", Dataset, IterableDataset)







def snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE = None      ,  __SCREAMING_SNAKE_CASE = None      ,  __SCREAMING_SNAKE_CASE = None      ,  __SCREAMING_SNAKE_CASE = None      ,  __SCREAMING_SNAKE_CASE = "first_exhausted"      ,  )		->				DatasetType:
		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(__SCREAMING_SNAKE_CASE			):
				if not isinstance(__SCREAMING_SNAKE_CASE      ,  (Dataset, IterableDataset)			):
						if isinstance(__SCREAMING_SNAKE_CASE      ,  (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(__SCREAMING_SNAKE_CASE			)}\n'''
								    f'''Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(__SCREAMING_SNAKE_CASE			)			)}\']'''			)
						raise ValueError(
						    f'''Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__SCREAMING_SNAKE_CASE			).__name__}.'''			)
				if i == 0:
						UpperCAmelCase_       ,    UpperCAmelCase_						       =					(
						    (Dataset, IterableDataset) if isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			) else (IterableDataset, Dataset)
						)
				elif not isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			):
						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(
				    __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  info=__SCREAMING_SNAKE_CASE      ,  split=__SCREAMING_SNAKE_CASE      ,  stopping_strategy=__SCREAMING_SNAKE_CASE			)
		else:
				return _interleave_iterable_datasets(
				    __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  info=__SCREAMING_SNAKE_CASE      ,  split=__SCREAMING_SNAKE_CASE      ,  stopping_strategy=__SCREAMING_SNAKE_CASE			)







def snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE = None      ,  __SCREAMING_SNAKE_CASE = None      ,  __SCREAMING_SNAKE_CASE = 0      ,  )		->				DatasetType:

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







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Tuple									= ['''RoFormerTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Any									= [
						    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RoFormerForCausalLM''',
						    '''RoFormerForMaskedLM''',
						    '''RoFormerForMultipleChoice''',
						    '''RoFormerForQuestionAnswering''',
						    '''RoFormerForSequenceClassification''',
						    '''RoFormerForTokenClassification''',
						    '''RoFormerLayer''',
						    '''RoFormerModel''',
						    '''RoFormerPreTrainedModel''',
						    '''load_tf_weights_in_roformer''',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     Dict									= [
						    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRoFormerForCausalLM''',
						    '''TFRoFormerForMaskedLM''',
						    '''TFRoFormerForMultipleChoice''',
						    '''TFRoFormerForQuestionAnswering''',
						    '''TFRoFormerForSequenceClassification''',
						    '''TFRoFormerForTokenClassification''',
						    '''TFRoFormerLayer''',
						    '''TFRoFormerModel''',
						    '''TFRoFormerPreTrainedModel''',
						]


try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE:     str									= [
						    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxRoFormerForMaskedLM''',
						    '''FlaxRoFormerForMultipleChoice''',
						    '''FlaxRoFormerForQuestionAnswering''',
						    '''FlaxRoFormerForSequenceClassification''',
						    '''FlaxRoFormerForTokenClassification''',
						    '''FlaxRoFormerModel''',
						    '''FlaxRoFormerPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
						from .tokenization_roformer import RoFormerTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roformer_fast import RoFormerTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roformer import (
												    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RoFormerForCausalLM,
												    RoFormerForMaskedLM,
												    RoFormerForMultipleChoice,
												    RoFormerForQuestionAnswering,
												    RoFormerForSequenceClassification,
												    RoFormerForTokenClassification,
												    RoFormerLayer,
												    RoFormerModel,
												    RoFormerPreTrainedModel,
												    load_tf_weights_in_roformer,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roformer import (
												    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRoFormerForCausalLM,
												    TFRoFormerForMaskedLM,
												    TFRoFormerForMultipleChoice,
												    TFRoFormerForQuestionAnswering,
												    TFRoFormerForSequenceClassification,
												    TFRoFormerForTokenClassification,
												    TFRoFormerLayer,
												    TFRoFormerModel,
												    TFRoFormerPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roformer import (
												    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxRoFormerForMaskedLM,
												    FlaxRoFormerForMultipleChoice,
												    FlaxRoFormerForQuestionAnswering,
												    FlaxRoFormerForSequenceClassification,
												    FlaxRoFormerForTokenClassification,
												    FlaxRoFormerModel,
												    FlaxRoFormerPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE:     Any									= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)