





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       :   Any							,     *,
			    __lowerCAmelCase       :   int = 4							,     __lowerCAmelCase       :   int = 7_68							,     __lowerCAmelCase       :   int							,     __lowerCAmelCase       :   Optional[int]							,     )     ->     Dict:







					"""simple docstring"""






					super().__init__()

					A__  				=       nn.Parameter(torch.zeros(__lowerCAmelCase       )       )

					# parameters for additional clip time embeddings
					A__  				=       nn.Linear(__lowerCAmelCase							,     __lowerCAmelCase       )
					A__  				=       nn.Linear(__lowerCAmelCase							,     __lowerCAmelCase       )

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







			def        a_	(      self       :   Tuple							,     *, __lowerCAmelCase       :   str							,     __lowerCAmelCase       :   Union[str, Any]							,     __lowerCAmelCase       :   List[str]							,     __lowerCAmelCase       :   Tuple       )     ->     Optional[Any]:







					"""simple docstring"""






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

					A__  				=       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, ...
					A__  				=       self.embedding_proj(__lowerCAmelCase       )
					A__  				=       self.clip_image_embeddings_project_to_time_embeddings(__lowerCAmelCase       )
					A__  				=       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"
					A__  				=       self.clip_extra_context_tokens_proj(__lowerCAmelCase       )
					A__  				=       clip_extra_context_tokens.reshape(__lowerCAmelCase							,     -1							,     self.clip_extra_context_tokens       )
					A__  				=       clip_extra_context_tokens.permute(0							,     2							,     1       )

					A__  				=       self.encoder_hidden_states_proj(__lowerCAmelCase       )
					A__  				=       self.text_encoder_hidden_states_norm(__lowerCAmelCase       )
					A__  				=       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_flax_available,
    is_tf_available,
    is_torch_available,
)


A   : Optional[Any]			=				{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : List[str]			=				[
					    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''ViTMAEForPreTraining''',
					    '''ViTMAELayer''',
					    '''ViTMAEModel''',
					    '''ViTMAEPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A   : Any			=				[
					    '''TFViTMAEForPreTraining''',
					    '''TFViTMAEModel''',
					    '''TFViTMAEPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_vit_mae import (
										    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ViTMAEForPreTraining,
										    ViTMAELayer,
										    ViTMAEModel,
										    ViTMAEPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
					import sys

					A   : str			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



