"""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_     =	logging.get_logger(__name__)


lowerCAmelCase_     =	TypeVar('''DatasetType''', Dataset, IterableDataset)



def      lowerCamelCase_(__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:
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (
																					    (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      lowerCamelCase_(__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:
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    (
																					    (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  )



"""simple docstring"""





import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"M-CLIP"





				def __init__(      self							:							Optional[Any]      ,    _A							:							List[str]=1_0_2_4      ,    _A							:							Union[str, Any]=7_6_8      ,    **_A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    transformerDimSize
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    imageDimSize
											super().__init__(**_A)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						MCLIPConfig





				def __init__(      self							:							Dict      ,    _A							:							Optional[Any]      ,    *_A							:							Any      ,    **_A							:							Dict):



											"""simple docstring"""







											super().__init__(_A      ,    *_A      ,    **_A)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    XLMRobertaModel(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    torch.nn.Linear(
											    in_features=config.transformerDimensions      ,    out_features=config.numDims)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    _A							:							str      ,    _A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.transformer(input_ids=_A      ,    attention_mask=_A)[0]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    (embs * attention_mask.unsqueeze(2)).sum(dim=1) / attention_mask.sum(dim=1)[:, None]
											return self.LinearTransformation(_A), embs


