




'''simple docstring'''



import torch
from torch import nn

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






class 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       @register_to_config
       def __init__(      self,							*,
           A = 4,							A = 768,							A,							A,							):







             '''simple docstring'''







             super().__init__()

             SCREAMING_SNAKE_CASE   :      List[str]    					=						nn.Parameter(torch.zeros(A  )  )

             # parameters for additional clip time embeddings
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						nn.Linear(A,							A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						nn.Linear(A,							A  )

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







       def        UpperCamelCase_							(      self,							*, A,							A,							A,							A  ):







             '''simple docstring'''







             if do_classifier_free_guidance:
                   # Add the classifier free guidance embeddings to the image embeddings
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						image_embeddings.shape[0]
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.learned_classifier_free_guidance_embeddings.unsqueeze(0  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						classifier_free_guidance_embeddings.expand(
                       A,							-1  )
                   SCREAMING_SNAKE_CASE   :      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]

             SCREAMING_SNAKE_CASE   :      Union[str, 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, ...
             SCREAMING_SNAKE_CASE   :      Any    					=						self.embedding_proj(A  )
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.clip_image_embeddings_project_to_time_embeddings(A  )
             SCREAMING_SNAKE_CASE   :      List[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"
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.clip_extra_context_tokens_proj(A  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						clip_extra_context_tokens.reshape(A,							-1,							self.clip_extra_context_tokens  )
             SCREAMING_SNAKE_CASE   :      int    					=						clip_extra_context_tokens.permute(0,							2,							1  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						self.encoder_hidden_states_proj(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.text_encoder_hidden_states_norm(A  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.cat([clip_extra_context_tokens, text_encoder_hidden_states],							dim=1  )

             return text_encoder_hidden_states, additive_clip_time_embeddings








'''simple docstring'''



import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version




def   lowercase__(			*__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Optional[Union[Dict, Any]] = None      ,__UpperCamelCase:					Dict=True      ,__UpperCamelCase:					List[Any]=2    ):

      """simple docstring"""






      from .. import __version__

      SCREAMING_SNAKE_CASE   :      int    					=						take_from
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						()
      if not isinstance(args[0]      ,__UpperCamelCase    ):
            SCREAMING_SNAKE_CASE   :      List[str]    					=						(args,)

      for attribute, version_name, message in args:
            if version.parse(version.parse(__UpperCamelCase    ).base_version    ) >= version.parse(__UpperCamelCase    ):
                  raise ValueError(
                      f"The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers'"
                      f" version {__version__} is >= {version_name}"    )

            SCREAMING_SNAKE_CASE   :      Tuple    					=						None
            if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) and attribute in deprecated_kwargs:
                  values += (deprecated_kwargs.pop(__UpperCamelCase    ),)
                  SCREAMING_SNAKE_CASE   :      Dict    					=						f"The `{attribute}` argument is deprecated and will be removed in version {version_name}."
            elif hasattr(__UpperCamelCase      ,__UpperCamelCase    ):
                  values += (getattr(__UpperCamelCase      ,__UpperCamelCase    ),)
                  SCREAMING_SNAKE_CASE   :      Optional[int]    					=						f"The `{attribute}` attribute is deprecated and will be removed in version {version_name}."
            elif deprecated_kwargs is None:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						f"`{attribute}` is deprecated and will be removed in version {version_name}."

            if warning is not None:
                  SCREAMING_SNAKE_CASE   :      Dict    					=						warning + ' ' if standard_warn else ''
                  warnings.warn(warning + message      ,__UpperCamelCase      ,stacklevel=__UpperCamelCase    )

      if isinstance(__UpperCamelCase      ,__UpperCamelCase    ) and len(__UpperCamelCase    ) > 0:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						inspect.getouterframes(inspect.currentframe()    )[1]
            SCREAMING_SNAKE_CASE   :      Any    					=						call_frame.filename
            SCREAMING_SNAKE_CASE   :      Tuple    					=						call_frame.lineno
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						call_frame.function
            SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Tuple    					=						next(iter(deprecated_kwargs.items()    )    )
            raise TypeError(f"{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`"    )

      if len(__UpperCamelCase    ) == 0:
            return
      elif len(__UpperCamelCase    ) == 1:
            return values[0]
      return values


