

import torch
from torch import nn

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



class       __snake_case  (    lowerCAmelCase__     , lowerCAmelCase__       ):

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

      SCREAMING_SNAKE_CASE_									=						nn.Parameter(torch.zeros(_A))

      # parameters for additional clip time embeddings
      SCREAMING_SNAKE_CASE_									=						nn.Linear(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						nn.Linear(_A						,		_A)

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


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

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

      SCREAMING_SNAKE_CASE_									=						self.encoder_hidden_states_proj(_A)
      SCREAMING_SNAKE_CASE_									=						self.text_encoder_hidden_states_norm(_A)
      SCREAMING_SNAKE_CASE_									=						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_tokenizers_available, is_torch_available


UpperCamelCase__  :      Any			=    {
    "configuration_mvp": ["MVP_PRETRAINED_CONFIG_ARCHIVE_MAP", "MvpConfig", "MvpOnnxConfig"],
    "tokenization_mvp": ["MvpTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      Optional[int]			=    ["MvpTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__  :      str			=    [
           "MVP_PRETRAINED_MODEL_ARCHIVE_LIST",
           "MvpForCausalLM",
           "MvpForConditionalGeneration",
           "MvpForQuestionAnswering",
           "MvpForSequenceClassification",
           "MvpModel",
           "MvpPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
       from .tokenization_mvp import MvpTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mvp_fast import MvpTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mvp import (
                  MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MvpForCausalLM,
                  MvpForConditionalGeneration,
                  MvpForQuestionAnswering,
                  MvpForSequenceClassification,
                  MvpModel,
                  MvpPreTrainedModel,
              )

else:
       import sys

       UpperCamelCase__  :      Dict			=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


