


"""simple docstring"""






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class 	lowerCamelCase__  (	nn.Module							):







					"""simple docstring"""


					def __init__(			self       :					Tuple					,			UpperCamelCase       :					str = 16					,			UpperCamelCase       :					Optional[int] = 88					,			UpperCamelCase       :					Optional[Any] = None					,			UpperCamelCase       :					List[str] = 1					,			UpperCamelCase       :					int = 0.0					,			UpperCamelCase       :					int = 32					,			UpperCamelCase       :					Tuple = None					,			UpperCamelCase       :					Optional[Any] = False					,			UpperCamelCase       :					Optional[Any] = None					,			UpperCamelCase       :					Optional[Any] = None					,			UpperCamelCase       :					int = "geglu"					,			UpperCamelCase       :					List[Any] = None					,			):
												'''simple docstring'''






												super().__init__()
												__UpperCAmelCase  :		Optional[int] 				=   nn.ModuleList(
												    [
												        TransformeraDModel(
												            num_attention_heads=UpperCamelCase_					,			attention_head_dim=UpperCamelCase_					,			in_channels=UpperCamelCase_					,			num_layers=UpperCamelCase_					,			dropout=UpperCamelCase_					,			norm_num_groups=UpperCamelCase_					,			cross_attention_dim=UpperCamelCase_					,			attention_bias=UpperCamelCase_					,			sample_size=UpperCamelCase_					,			num_vector_embeds=UpperCamelCase_					,			activation_fn=UpperCamelCase_					,			num_embeds_ada_norm=UpperCamelCase_					,			)
												        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  :		Dict 				=   0.5

												# The shape of `encoder_hidden_states` is expected to be
												# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
												__UpperCAmelCase  :		int 				=   [77, 257]

												# 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  :		Union[str, Any] 				=   [1, 0]






					def lowerCamelCase__				(			self       :					str					,			UpperCamelCase       :					Optional[Any]					,			UpperCamelCase       :					Optional[int]					,			UpperCamelCase       :					Optional[Any]=None					,			UpperCamelCase       :					Any=None					,			UpperCamelCase       :					Optional[Any]=None					,			UpperCamelCase       :					Optional[int] = True					,			):
												'''simple docstring'''






												__UpperCAmelCase  :		Tuple 				=   hidden_states

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

												__UpperCAmelCase  :		Union[str, Any] 				=   encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
												__UpperCAmelCase  :		List[str] 				=   output_states + input_states

												if not return_dict:
																			return (output_states,)

												return TransformeraDModelOutput(sample=UpperCamelCase_    )







'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__					:			Dict          =     {
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a__					:			Union[str, Any]          =     [
			    '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''CanineForMultipleChoice''',
			    '''CanineForQuestionAnswering''',
			    '''CanineForSequenceClassification''',
			    '''CanineForTokenClassification''',
			    '''CanineLayer''',
			    '''CanineModel''',
			    '''CaninePreTrainedModel''',
			    '''load_tf_weights_in_canine''',
			]


if TYPE_CHECKING:
			from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
			from .tokenization_canine import CanineTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_canine import (
						    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CanineForMultipleChoice,
						    CanineForQuestionAnswering,
						    CanineForSequenceClassification,
						    CanineForTokenClassification,
						    CanineLayer,
						    CanineModel,
						    CaninePreTrainedModel,
						    load_tf_weights_in_canine,
						)


else:
			import sys

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



