





"""simple docstring"""




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a						: List[str]     	=			logging.get_logger(__name__)

a						: int     	=			{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Optional[Any]      					="""t5"""
							lowerCamelCase   :					Dict      					=["""past_key_values"""]
							lowerCamelCase   :					Optional[int]      					={"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






							def __init__(   self      ,					lowerCAmelCase__=3_2128      ,					lowerCAmelCase__=512      ,					lowerCAmelCase__=64      ,					lowerCAmelCase__=2048      ,					lowerCAmelCase__=6      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=8      ,					lowerCAmelCase__=32      ,					lowerCAmelCase__=128      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=1E-6      ,					lowerCAmelCase__=1.0      ,					lowerCAmelCase__="relu"      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=0      ,					lowerCAmelCase__=1      ,					**lowerCAmelCase__      ,					)	-> str:
								a      :		List[Any]	=				vocab_size
								a      :		Any	=				d_model
								a      :		Optional[int]	=				d_kv
								a      :		Optional[int]	=				d_ff
								a      :		Any	=				num_layers
								a      :		Any	=				(
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								a      :		Optional[Any]	=				num_heads
								a      :		List[str]	=				relative_attention_num_buckets
								a      :		int	=				relative_attention_max_distance
								a      :		List[str]	=				dropout_rate
								a      :		Optional[Any]	=				layer_norm_epsilon
								a      :		Optional[Any]	=				initializer_factor
								a      :		Union[str, Any]	=				feed_forward_proj
								a      :		List[Any]	=				use_cache

								a      :		Dict	=				self.feed_forward_proj.split("-"      )
								a      :		Tuple	=				act_info[-1]
								a      :		Optional[Any]	=				act_info[0] == "gated"

								if len(lowerCAmelCase__      ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__      ) > 2:
									raise ValueError(
									    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
									    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
									    "'gated-gelu' or 'relu'"      )

								# for backwards compatibility
								if feed_forward_proj == "gated-gelu":
									a      :		Union[str, Any]	=				"gelu_new"

								super().__init__(
								    pad_token_id=lowerCAmelCase__      ,					eos_token_id=lowerCAmelCase__      ,					is_encoder_decoder=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)


class        __UpperCamelCase				(						a__						):






							@property
							def     __a   (   self      )	-> Mapping[str, Mapping[int, str]]:
								a      :		Optional[Any]	=				{
								    "input_ids": {0: "batch", 1: "encoder_sequence"},
								    "attention_mask": {0: "batch", 1: "encoder_sequence"},
								}
								if self.use_past:
									a      :		str	=				"past_encoder_sequence + sequence"
									a      :		Optional[Any]	=				{0: "batch"}
									a      :		Optional[Any]	=				{0: "batch", 1: "past_decoder_sequence + sequence"}
								else:
									a      :		Tuple	=				{0: "batch", 1: "decoder_sequence"}
									a      :		Any	=				{0: "batch", 1: "decoder_sequence"}

								if self.use_past:
									self.fill_with_past_key_values_(lowerCAmelCase__      ,					direction="inputs"      )

								return common_inputs






							@property
							def     __a   (   self      )	-> int:
								return 13









"""simple docstring"""




import torch
from torch import nn

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


class        __UpperCamelCase				(						a__		,       a__						):






							@register_to_config
							def __init__(   self      ,					*,
							    lowerCAmelCase__ = 4      ,					lowerCAmelCase__ = 768      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Optional[Any]:
								super().__init__()

								a      :		Tuple	=				nn.Parameter(torch.zeros(lowerCAmelCase__      )      )

								# parameters for additional clip time embeddings
								a      :		str	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )

								# parameters for encoder hidden states
								a      :		int	=				clip_extra_context_tokens
								a      :		int	=				nn.Linear(
								    lowerCAmelCase__      ,					self.clip_extra_context_tokens * cross_attention_dim      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		str	=				nn.LayerNorm(lowerCAmelCase__      )






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

								a      :		Optional[int]	=				self.encoder_hidden_states_proj(lowerCAmelCase__      )
								a      :		str	=				self.text_encoder_hidden_states_norm(lowerCAmelCase__      )
								a      :		List[str]	=				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]      ,					dim=1      )

								return text_encoder_hidden_states, additive_clip_time_embeddings


