


import torch
from torch import nn

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


class     lowercase__  (					__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE		):






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




         """simple docstring"""







         super().__init__()

         UpperCAmelCase__										=     nn.Parameter(torch.zeros(_lowercase ) )

         # parameters for additional clip time embeddings
         UpperCAmelCase__										=     nn.Linear(_lowercase		,						_lowercase )
         UpperCAmelCase__										=     nn.Linear(_lowercase		,						_lowercase )

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






  def 			_UpperCAmelCase  (					self      :					Tuple		,						*, _lowercase      :					Optional[Any]		,						_lowercase      :					List[str]		,						_lowercase      :					Any		,						_lowercase      :					Union[str, Any] ):




         """simple docstring"""







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

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

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

         return text_encoder_hidden_states, additive_clip_time_embeddings









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





