



"""simple docstring"""
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 					a	(  UpperCAmelCase__	,      UpperCAmelCase__       ):



  @register_to_config
  def __init__(						self :					Dict       ,		*,
      lowerCAmelCase :					int = 4       ,		lowerCAmelCase :					int = 768       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					Optional[int]       ,		)     ->					Any:


     '''simple docstring'''




     super().__init__()

     SCREAMING_SNAKE_CASE_: str     			=nn.Parameter(torch.zeros(lowerCAmelCase		)		)

     # parameters for additional clip time embeddings
     SCREAMING_SNAKE_CASE_: List[str]     			=nn.Linear(lowerCAmelCase       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=nn.Linear(lowerCAmelCase       ,		lowerCAmelCase		)

     # parameters for encoder hidden states
     SCREAMING_SNAKE_CASE_: Tuple     			=clip_extra_context_tokens
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=nn.Linear(
         lowerCAmelCase       ,		self.clip_extra_context_tokens * cross_attention_dim		)
     SCREAMING_SNAKE_CASE_: List[str]     			=nn.Linear(lowerCAmelCase       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=nn.LayerNorm(lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		*, lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					List[str]       ,		lowerCAmelCase :					Dict       ,		lowerCAmelCase :					Dict		)     ->					Optional[Any]:


     '''simple docstring'''




     if do_classifier_free_guidance:
        # Add the classifier free guidance embeddings to the image embeddings
        SCREAMING_SNAKE_CASE_: Optional[int]     			=image_embeddings.shape[0]
        SCREAMING_SNAKE_CASE_: Any     			=self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=classifier_free_guidance_embeddings.expand(
            lowerCAmelCase       ,		-1		)
        SCREAMING_SNAKE_CASE_: List[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]

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=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_: Dict     			=self.embedding_proj(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=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_: Optional[Any]     			=self.clip_extra_context_tokens_proj(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=clip_extra_context_tokens.reshape(lowerCAmelCase       ,		-1       ,		self.clip_extra_context_tokens		)
     SCREAMING_SNAKE_CASE_: str     			=clip_extra_context_tokens.permute(0       ,		2       ,		1		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.encoder_hidden_states_proj(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=self.text_encoder_hidden_states_norm(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]       ,		dim=1		)

     return text_encoder_hidden_states, additive_clip_time_embeddings








"""simple docstring"""
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser(
       description=(
           """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
           """ Distillation"""
       )
   )
   parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
   parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
   parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
   parser.add_argument("""--vocab_transform""", action="""store_true""")
   _UpperCAmelCase     =			parser.parse_args()

   if args.model_type == "bert":
      _UpperCAmelCase     =			BertForMaskedLM.from_pretrained(args.model_name)
      _UpperCAmelCase     =			"""bert"""
   else:
      raise ValueError("""args.model_type should be \"bert\".""")

   _UpperCAmelCase     =			model.state_dict()
   _UpperCAmelCase     =			{}

   for w in ["word_embeddings", "position_embeddings"]:
      _UpperCAmelCase     =			state_dict[f"""{prefix}.embeddings.{w}.weight"""]
   for w in ["weight", "bias"]:
      _UpperCAmelCase     =			state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

   _UpperCAmelCase     =			0
   for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
         ]
      std_idx += 1

   _UpperCAmelCase     =			state_dict["""cls.predictions.decoder.weight"""]
   _UpperCAmelCase     =			state_dict["""cls.predictions.bias"""]
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.dense.{w}"""]
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

   print(f"""N layers selected for distillation: {std_idx}""")
   print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)



