'''simple docstring'''




import torch
from torch import nn

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


class       UpperCAmelCase__  (       _snake_case					,     _snake_case			):






   """simple docstring"""
   @register_to_config
   def __init__(self       ,				*,
       _a = 4       ,				_a = 768       ,				_a       ,				_a       ,				)    ->     Any:
        super().__init__()

        lowercase_					:       List[Any]     				=					nn.Parameter(torch.zeros(_a				)				)

        # parameters for additional clip time embeddings
        lowercase_					:       Optional[int]     				=					nn.Linear(_a       ,				_a				)
        lowercase_					:       Dict     				=					nn.Linear(_a       ,				_a				)

        # parameters for encoder hidden states
        lowercase_					:       str     				=					clip_extra_context_tokens
        lowercase_					:       Union[str, Any]     				=					nn.Linear(
            _a       ,				self.clip_extra_context_tokens * cross_attention_dim				)
        lowercase_					:       Any     				=					nn.Linear(_a       ,				_a				)
        lowercase_					:       List[str]     				=					nn.LayerNorm(_a				)





   def 		_lowerCamelCase (self       ,				*, _a       ,				_a       ,				_a       ,				_a				)    ->     str:
        if do_classifier_free_guidance:
             # Add the classifier free guidance embeddings to the image embeddings
             lowercase_					:       List[str]     				=					image_embeddings.shape[0]
             lowercase_					:       int     				=					self.learned_classifier_free_guidance_embeddings.unsqueeze(0				)
             lowercase_					:       Optional[int]     				=					classifier_free_guidance_embeddings.expand(
                 _a       ,				-1				)
             lowercase_					:       List[str]     				=					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]

        lowercase_					:       Any     				=					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, ...
        lowercase_					:       Tuple     				=					self.embedding_proj(_a				)
        lowercase_					:       List[str]     				=					self.clip_image_embeddings_project_to_time_embeddings(_a				)
        lowercase_					:       Union[str, Any]     				=					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"
        lowercase_					:       Union[str, Any]     				=					self.clip_extra_context_tokens_proj(_a				)
        lowercase_					:       Any     				=					clip_extra_context_tokens.reshape(_a       ,				-1       ,				self.clip_extra_context_tokens				)
        lowercase_					:       Dict     				=					clip_extra_context_tokens.permute(0       ,				2       ,				1				)

        lowercase_					:       Dict     				=					self.encoder_hidden_states_proj(_a				)
        lowercase_					:       str     				=					self.text_encoder_hidden_states_norm(_a				)
        lowercase_					:       Union[str, Any]     				=					torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]       ,				dim=1				)

        return text_encoder_hidden_states, additive_clip_time_embeddings





'''simple docstring'''




class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   pass



class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   pass



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self				)    ->     List[str]:
        lowercase_					:       Optional[int]     				=					[
            [],
            [],
            [],
        ]
   def 		_lowerCamelCase (self       ,				_a       ,				_a				)    ->     None:
        try:
             if len(self.queues[priority]				) >= 100:
                  raise OverflowError('Maximum queue size is 100'				)
             self.queues[priority].append(_a				)
        except IndexError:
             raise ValueError('Valid priorities are 0, 1, and 2'				)
   def 		_lowerCamelCase (self				)    ->     int:
        for queue in self.queues:
             if queue:
                  return queue.pop(0				)
        raise UnderFlowError('All queues are empty'				)





   def __str__(self				)    ->     str:
        return "\n".join(f'''Priority {i}: {q}''' for i, q in enumerate(self.queues				)				)



class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self				)    ->     str:
        lowercase_					:       List[Any]     				=					[]
   def 		_lowerCamelCase (self       ,				_a				)    ->     None:
        if len(self.queue				) == 100:
             raise OverFlowError('Maximum queue size is 100'				)
        self.queue.append(_a				)
   def 		_lowerCamelCase (self				)    ->     int:
        if not self.queue:
             raise UnderFlowError('The queue is empty'				)
        else:
             lowercase_					:       Tuple     				=					min(self.queue				)
             self.queue.remove(_a				)
             return data





   def __str__(self				)    ->     str:
        return str(self.queue				)



def _UpperCamelCase   (       ):
     lowercase_					:       Any     				=					FixedPriorityQueue()
     fpq.enqueue(0  ,       10							)
     fpq.enqueue(1  ,       70							)
     fpq.enqueue(0  ,       100							)
     fpq.enqueue(2  ,       1							)
     fpq.enqueue(2  ,       5							)
     fpq.enqueue(1  ,       7							)
     fpq.enqueue(2  ,       4							)
     fpq.enqueue(1  ,       64							)
     fpq.enqueue(0  ,       128							)
     print(SCREAMING_SNAKE_CASE_							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(SCREAMING_SNAKE_CASE_							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)
     print(fpq.dequeue()							)

def _UpperCamelCase   (       ):
     lowercase_					:       Optional[int]     				=					ElementPriorityQueue()
     epq.enqueue(10							)
     epq.enqueue(70							)
     epq.enqueue(100							)
     epq.enqueue(1							)
     epq.enqueue(5							)
     epq.enqueue(7							)
     epq.enqueue(4							)
     epq.enqueue(64							)
     epq.enqueue(128							)
     print(SCREAMING_SNAKE_CASE_							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(SCREAMING_SNAKE_CASE_							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)
     print(epq.dequeue()							)


if __name__ == "__main__":
 fixed_priority_queue()
 element_priority_queue()




