




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


UpperCamelCase__ :   Optional[Any]						=							logging.get_logger(__name__)


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





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[DatasetType]  ,							lowerCamelCase_ :     Optional[List[float]] = None  ,							lowerCamelCase_ :     Optional[int] = None  ,							lowerCamelCase_ :     Optional[DatasetInfo] = None  ,							lowerCamelCase_ :     Optional[NamedSplit] = None  ,							lowerCamelCase_ :     Literal["first_exhausted", "all_exhausted"] = "first_exhausted"  ,							)       ->  DatasetType:







			"""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(lowerCamelCase_  ):
						if not isinstance(lowerCamelCase_  ,							(Dataset, IterableDataset)  ):
									if isinstance(lowerCamelCase_  ,							(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(lowerCamelCase_  )}\n'
												    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(lowerCamelCase_  )  )}\']'  )
									raise ValueError(
									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(lowerCamelCase_  ).__name__}.'  )
						if i == 0:
									SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Dict					  =      (
									    (Dataset, IterableDataset) if isinstance(lowerCamelCase_  ,							lowerCamelCase_  ) else (IterableDataset, Dataset)
									)
						elif not isinstance(lowerCamelCase_  ,							lowerCamelCase_  ):
									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(
						    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							info=lowerCamelCase_  ,							split=lowerCamelCase_  ,							stopping_strategy=lowerCamelCase_  )
			else:
						return _interleave_iterable_datasets(
						    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							info=lowerCamelCase_  ,							split=lowerCamelCase_  ,							stopping_strategy=lowerCamelCase_  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[DatasetType]  ,							lowerCamelCase_ :     Optional[DatasetInfo] = None  ,							lowerCamelCase_ :     Optional[NamedSplit] = None  ,							lowerCamelCase_ :     int = 0  ,							)       ->  DatasetType:







			"""simple docstring"""





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







'''simple docstring'''





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	int = 1_6			,       __lowerCamelCase							:	int = 8_8			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "geglu"			,       __lowerCamelCase							:	Optional[int] = None			,       ):
    super().__init__()
    UpperCAmelCase__	:Optional[int]      =      nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=__lowerCamelCase			,       attention_head_dim=__lowerCamelCase			,       in_channels=__lowerCamelCase			,       num_layers=__lowerCamelCase			,       dropout=__lowerCamelCase			,       norm_num_groups=__lowerCamelCase			,       cross_attention_dim=__lowerCamelCase			,       attention_bias=__lowerCamelCase			,       sample_size=__lowerCamelCase			,       num_vector_embeds=__lowerCamelCase			,       activation_fn=__lowerCamelCase			,       num_embeds_ada_norm=__lowerCamelCase			,       )
            for _ in range(2 )
        ] )

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    UpperCAmelCase__	:Tuple      =      0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase__	:int      =      [7_7, 2_5_7]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    UpperCAmelCase__	:Any      =      [1, 0]





 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	bool = True			,       ):
    UpperCAmelCase__	:Optional[int]      =      hidden_states

    UpperCAmelCase__	:Dict      =      []
    UpperCAmelCase__	:Tuple      =      0
    # attention_mask is not used yet
    for i in range(2 ):
       # for each of the two transformers, pass the corresponding condition tokens
       UpperCAmelCase__	:List[Any]      =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       UpperCAmelCase__	:Dict      =      self.transformer_index_for_condition[i]
       UpperCAmelCase__	:Tuple      =      self.transformers[transformer_index](
           __lowerCamelCase			,       encoder_hidden_states=__lowerCamelCase			,       timestep=__lowerCamelCase			,       cross_attention_kwargs=__lowerCamelCase			,       return_dict=__lowerCamelCase			,       )[0]
       encoded_states.append(encoded_state - input_states )
       tokens_start += self.condition_lengths[i]

    UpperCAmelCase__	:Optional[int]      =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase__	:Optional[Any]      =      output_states + input_states

    if not return_dict:
       return (output_states,)

    return TransformeraDModelOutput(sample=__lowerCamelCase )



