

from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput





class 				__lowerCAmelCase  (      nn.Module							):




  def __init__(       self							,     lowerCAmelCase = 16							,     lowerCAmelCase = 88							,     lowerCAmelCase = None							,     lowerCAmelCase = 1							,     lowerCAmelCase = 0.0							,     lowerCAmelCase = 32							,     lowerCAmelCase = None							,     lowerCAmelCase = False							,     lowerCAmelCase = None							,     lowerCAmelCase = None							,     lowerCAmelCase = "geglu"							,     lowerCAmelCase = None							,     )						->				Optional[int]:
        '''simple docstring'''






        super().__init__()
        _lowercase			 =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
        _lowercase			 =0.5

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




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None							,     lowerCAmelCase=None							,     lowerCAmelCase=None							,     lowerCAmelCase = True							,     )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =hidden_states

        _lowercase			 =[]
        _lowercase			 =0
        # attention_mask is not used yet
        for i in range(2      ):
              # for each of the two transformers, pass the corresponding condition tokens
              _lowercase			 =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
              _lowercase			 =self.transformer_index_for_condition[i]
              _lowercase			 =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]

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

        if not return_dict:
              return (output_states,)

        return TransformeraDModelOutput(sample=lowerCAmelCase      )





import requests

lowercase_             =     'YOUR API KEY'
def   a   (    A__			:     str   ,     A__			:     str = giphy_api_key  )					->   list:





      """simple docstring"""



      _lowercase			 ='+'.join(query.split()  )
      _lowercase			 =F'''https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}'''
      _lowercase			 =requests.get(A__  ).json()['data']
      return [gif["url"] for gif in gifs]


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


