


import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class 	__lowerCamelCase			(_a				,      _a						):







       @register_to_config
       def __init__(			self: List[Any],*,
           A_: int = 4,A_: int = 768,A_: int,A_: Tuple,):






            '''simple docstring'''




            super().__init__()

            __UpperCamelCase =			nn.Parameter(torch.zeros(A_					)					)

            # parameters for additional clip time embeddings
            __UpperCamelCase =			nn.Linear(A_,A_					)
            __UpperCamelCase =			nn.Linear(A_,A_					)

            # parameters for encoder hidden states
            __UpperCamelCase =			clip_extra_context_tokens
            __UpperCamelCase =			nn.Linear(
                A_,self.clip_extra_context_tokens * cross_attention_dim					)
            __UpperCamelCase =			nn.Linear(A_,A_					)
            __UpperCamelCase =			nn.LayerNorm(A_					)







       def 		snake_case_	(			self: int,*, A_: Optional[int],A_: Tuple,A_: str,A_: int					):






            '''simple docstring'''




            if do_classifier_free_guidance:
                 # Add the classifier free guidance embeddings to the image embeddings
                 __UpperCamelCase =			image_embeddings.shape[0]
                 __UpperCamelCase =			self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
                 __UpperCamelCase =			classifier_free_guidance_embeddings.expand(
                     A_,-1					)
                 __UpperCamelCase =			torch.cat([classifier_free_guidance_embeddings, image_embeddings],dim=0					)

            # The image embeddings batch size and the text embeddings batch size are equal
            assert image_embeddings.shape[0] == prompt_embeds.shape[0]

            __UpperCamelCase =			prompt_embeds.shape[0]

            # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
            # adding CLIP embeddings to the existing timestep embedding, ...
            __UpperCamelCase =			self.embedding_proj(A_					)
            __UpperCamelCase =			self.clip_image_embeddings_project_to_time_embeddings(A_					)
            __UpperCamelCase =			time_projected_image_embeddings + time_projected_prompt_embeds

            # ... and by projecting CLIP embeddings into four
            # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
            __UpperCamelCase =			self.clip_extra_context_tokens_proj(A_					)
            __UpperCamelCase =			clip_extra_context_tokens.reshape(A_,-1,self.clip_extra_context_tokens					)
            __UpperCamelCase =			clip_extra_context_tokens.permute(0,2,1					)

            __UpperCamelCase =			self.encoder_hidden_states_proj(A_					)
            __UpperCamelCase =			self.text_encoder_hidden_states_norm(A_					)
            __UpperCamelCase =			torch.cat([clip_extra_context_tokens, text_encoder_hidden_states],dim=1					)

            return text_encoder_hidden_states, additive_clip_time_embeddings






__snake_case   							=						{
    '''a''': '''AAAAA''',
    '''b''': '''AAAAB''',
    '''c''': '''AAABA''',
    '''d''': '''AAABB''',
    '''e''': '''AABAA''',
    '''f''': '''AABAB''',
    '''g''': '''AABBA''',
    '''h''': '''AABBB''',
    '''i''': '''ABAAA''',
    '''j''': '''BBBAA''',
    '''k''': '''ABAAB''',
    '''l''': '''ABABA''',
    '''m''': '''ABABB''',
    '''n''': '''ABBAA''',
    '''o''': '''ABBAB''',
    '''p''': '''ABBBA''',
    '''q''': '''ABBBB''',
    '''r''': '''BAAAA''',
    '''s''': '''BAAAB''',
    '''t''': '''BAABA''',
    '''u''': '''BAABB''',
    '''v''': '''BBBAB''',
    '''w''': '''BABAA''',
    '''x''': '''BABAB''',
    '''y''': '''BABBA''',
    '''z''': '''BABBB''',
    ''' ''': ''' ''',
}


__snake_case   							=						{value: key for key, value in encode_dict.items()}






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     __UpperCamelCase =			''
     for letter in word.lower():
          if letter.isalpha() or letter == " ":
               encoded += encode_dict[letter]
          else:
               raise Exception('encode() accepts only letters of the alphabet and spaces' )
     return encoded






def 		_A		(		_lowercase )					-> str:


     """simple docstring"""


     if set(_lowercase ) - {"A", "B", " "} != set():
          raise Exception('decode() accepts only \'A\', \'B\' and spaces' )
     __UpperCamelCase =			''
     for word in coded.split():
          while len(_lowercase ) != 0:
               decoded += decode_dict[word[:5]]
               __UpperCamelCase =			word[5:]
          decoded += " "
     return decoded.strip()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


