


import torch
from torch import nn

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






class      UpperCamelCase					(		lowercase__						,      lowercase__     ):


			'''simple docstring'''







			@register_to_config
			def __init__(     self   ,							*,
			    UpperCamelCase_ = 4   ,							UpperCamelCase_ = 768   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							):
								super().__init__()

								lowercase_       :Union[str, Any]     =       nn.Parameter(torch.zeros(UpperCamelCase_		)		)

								# parameters for additional clip time embeddings
								lowercase_       :List[Any]     =       nn.Linear(UpperCamelCase_   ,							UpperCamelCase_		)
								lowercase_       :Optional[Any]     =       nn.Linear(UpperCamelCase_   ,							UpperCamelCase_		)

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




			def 		UpperCamelCase  (     self   ,							*, UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_   ,							UpperCamelCase_		):
								if do_classifier_free_guidance:
													# Add the classifier free guidance embeddings to the image embeddings
													lowercase_       :Dict     =       image_embeddings.shape[0]
													lowercase_       :Optional[Any]     =       self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
													lowercase_       :List[Any]     =       classifier_free_guidance_embeddings.expand(
													    UpperCamelCase_   ,							-1		)
													lowercase_       :Optional[int]     =       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_       :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, ...
								lowercase_       :Union[str, Any]     =       self.embedding_proj(UpperCamelCase_		)
								lowercase_       :Tuple     =       self.clip_image_embeddings_project_to_time_embeddings(UpperCamelCase_		)
								lowercase_       :Optional[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_       :int     =       self.clip_extra_context_tokens_proj(UpperCamelCase_		)
								lowercase_       :Dict     =       clip_extra_context_tokens.reshape(UpperCamelCase_   ,							-1   ,							self.clip_extra_context_tokens		)
								lowercase_       :List[Any]     =       clip_extra_context_tokens.permute(0   ,							2   ,							1		)

								lowercase_       :Tuple     =       self.encoder_hidden_states_proj(UpperCamelCase_		)
								lowercase_       :List[Any]     =       self.text_encoder_hidden_states_norm(UpperCamelCase_		)
								lowercase_       :Optional[int]     =       torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]   ,							dim=1		)

								return text_encoder_hidden_states, additive_clip_time_embeddings





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE       :		str         =				{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		Dict         =				[
       "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TapasForMaskedLM",
       "TapasForQuestionAnswering",
       "TapasForSequenceClassification",
       "TapasModel",
       "TapasPreTrainedModel",
       "load_tf_weights_in_tapas",
   ]
try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE       :		str         =				[
       "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
       "TFTapasForMaskedLM",
       "TFTapasForQuestionAnswering",
       "TFTapasForSequenceClassification",
       "TFTapasModel",
       "TFTapasPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
   from .tokenization_tapas import TapasTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tapas import (
          TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TapasForMaskedLM,
          TapasForQuestionAnswering,
          TapasForSequenceClassification,
          TapasModel,
          TapasPreTrainedModel,
          load_tf_weights_in_tapas,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_tapas import (
          TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFTapasForMaskedLM,
          TFTapasForQuestionAnswering,
          TFTapasForSequenceClassification,
          TFTapasModel,
          TFTapasPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE       :		Union[str, Any]         =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

