




"""simple docstring"""

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class 				lowercase_       (     nn.Module							):



 '''simple docstring'''

 def __init__( self       :       Dict    ,							_UpperCAmelCase       :       Tuple = 16    ,							_UpperCAmelCase       :       Union[str, Any] = 88    ,							_UpperCAmelCase       :       str = None    ,							_UpperCAmelCase       :       int = 1    ,							_UpperCAmelCase       :       int = 0.0    ,							_UpperCAmelCase       :       int = 32    ,							_UpperCAmelCase       :       str = None    ,							_UpperCAmelCase       :       str = False    ,							_UpperCAmelCase       :       int = None    ,							_UpperCAmelCase       :       Optional[Any] = None    ,							_UpperCAmelCase       :       Union[str, Any] = "geglu"    ,							_UpperCAmelCase       :       Any = None    ,							):
       super().__init__()
       _A					   =   nn.ModuleList(
           [
               TransformeraDModel(
                   num_attention_heads=_a    ,							attention_head_dim=_a    ,							in_channels=_a    ,							num_layers=_a    ,							dropout=_a    ,							norm_num_groups=_a    ,							cross_attention_dim=_a    ,							attention_bias=_a    ,							sample_size=_a    ,							num_vector_embeds=_a    ,							activation_fn=_a    ,							num_embeds_ada_norm=_a    ,							)
               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 			lowerCAmelCase_							( self       :       List[str]    ,							_UpperCAmelCase       :       Dict    ,							_UpperCAmelCase       :       Tuple    ,							_UpperCAmelCase       :       int=None    ,							_UpperCAmelCase       :       Dict=None    ,							_UpperCAmelCase       :       Optional[int]=None    ,							_UpperCAmelCase       :       Any = True    ,							):
       _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](
                 _a    ,							encoder_hidden_states=_a    ,							timestep=_a    ,							cross_attention_kwargs=_a    ,							return_dict=_a    ,							)[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=_a		)




import requests

a__				=		'''YOUR API KEY'''



def    __UpperCAmelCase (	__a      :			str		,__a      :			str = giphy_api_key	)			->     list:

					"""simple docstring"""

					_a	:    Optional[Any]         =		'''+'''.join(query.split()	)
					_a	:    Union[str, Any]         =		F"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
					_a	:    List[Any]         =		requests.get(__a	).json()['''data''']
					return [gif["url"] for gif in gifs]


if __name__ == "__main__":
			print('''\n'''.join(get_gifs('''space ship''')))



