

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__						=							{
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							["BlenderbotSmallTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							[
						    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BlenderbotSmallForCausalLM",
						    "BlenderbotSmallForConditionalGeneration",
						    "BlenderbotSmallModel",
						    "BlenderbotSmallPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							[
						    "TFBlenderbotSmallForConditionalGeneration",
						    "TFBlenderbotSmallModel",
						    "TFBlenderbotSmallPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						=							[
						    "FlaxBlenderbotSmallForConditionalGeneration",
						    "FlaxBlenderbotSmallModel",
						    "FlaxBlenderbotSmallPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_blenderbot_small import (
						    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    BlenderbotSmallConfig,
						    BlenderbotSmallOnnxConfig,
						)
						from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_blenderbot_small import (
												    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BlenderbotSmallForCausalLM,
												    BlenderbotSmallForConditionalGeneration,
												    BlenderbotSmallModel,
												    BlenderbotSmallPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_blenderbot_small import (
												    TFBlenderbotSmallForConditionalGeneration,
												    TFBlenderbotSmallModel,
												    TFBlenderbotSmallPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_blenderbot_small import (
												    FlaxBlenderbotSmallForConditionalGeneration,
												    FlaxBlenderbotSmallModel,
												    FlaxBlenderbotSmallPreTrainedModel,
												)

else:
						import sys

						A__						=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







import torch
from torch import nn

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

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			):







	@register_to_config
	def __init__(   self: Optional[Any]  , *,
	    __UpperCamelCase: int = 4  , __UpperCamelCase: int = 7_68  , __UpperCamelCase: int  , __UpperCamelCase: Any  , ):

						'''simple docstring'''





						super().__init__()

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

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

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







	def 	_SCREAMING_SNAKE_CASE							(   self: List[str]  , *, __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





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

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

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

						return text_encoder_hidden_states, additive_clip_time_embeddings




