

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput






class 	__magic_name__    (			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					,  )   -> List[str]:




						'''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 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case=None					,  __snake_case=None					,  __snake_case=None					,  __snake_case = True					,  )   -> Optional[int]:




						'''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					)









import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_lowerCAmelCase					:			str  				=			logging.get_logger(__name__)






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  *__snake_case					,  **__snake_case					)   -> None:




						'''simple docstring'''
						warnings.warn(
						    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
						    ' use CLIPImageProcessor instead.'					,  __snake_case					,  )
						super().__init__(*__snake_case					,  **__snake_case					)






