
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class 			__A( nn.Module			):




				def __init__(     self		,     _snake_case = 16		,     _snake_case = 88		,     _snake_case = None		,     _snake_case = 1		,     _snake_case = 0.0		,     _snake_case = 32		,     _snake_case = None		,     _snake_case = False		,     _snake_case = None		,     _snake_case = None		,     _snake_case = "geglu"		,     _snake_case = None		,     )    ->      Any:


								'''simple docstring'''




								super().__init__()
								__a										=    nn.ModuleList(
								    [
								        TransformeraDModel(
								            num_attention_heads=_snake_case		,     attention_head_dim=_snake_case		,     in_channels=_snake_case		,     num_layers=_snake_case		,     dropout=_snake_case		,     norm_num_groups=_snake_case		,     cross_attention_dim=_snake_case		,     attention_bias=_snake_case		,     sample_size=_snake_case		,     num_vector_embeds=_snake_case		,     activation_fn=_snake_case		,     num_embeds_ada_norm=_snake_case		,     )
								        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
								__a										=    0.5

								# The shape of `encoder_hidden_states` is expected to be
								# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
								__a										=    [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])`
								__a										=    [1, 0]




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     _snake_case = True		,     )    ->      Tuple:


								'''simple docstring'''




								__a										=    hidden_states

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

								__a										=    encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
								__a										=    output_states + input_states

								if not return_dict:
												return (output_states,)

								return TransformeraDModelOutput(sample=_snake_case				)

from typing import Dict

from .base import GenericTensor, Pipeline







class 			__A( a			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if tokenize_kwargs is None:
												__a										=    {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''				)
												__a										=    truncation

								__a										=    tokenize_kwargs

								__a										=    {}
								if return_tensors is not None:
												__a										=    return_tensors

								return preprocess_params, {}, postprocess_params




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     **_snake_case				)    ->      Dict[str, GenericTensor]:


								'''simple docstring'''




								__a										=    self.framework
								__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)
								return model_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.model(**_snake_case				)
								return model_outputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False				)    ->      Optional[int]:


								'''simple docstring'''




								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(     self		,     *_snake_case		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								return super().__call__(*_snake_case		,     **_snake_case				)