






import torch
from torch import nn

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


class     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__     ):






  """simple docstring"""





  @register_to_config
  def __init__(  self		,		*,
      _UpperCAmelCase = 4		,		_UpperCAmelCase = 768		,		_UpperCAmelCase		,		_UpperCAmelCase		,		)      ->			Any:
     super().__init__()

     UpperCamelCase_						=  nn.Parameter(torch.zeros(_UpperCAmelCase			)			)

     # parameters for additional clip time embeddings
     UpperCamelCase_						=  nn.Linear(_UpperCAmelCase		,		_UpperCAmelCase			)
     UpperCamelCase_						=  nn.Linear(_UpperCAmelCase		,		_UpperCAmelCase			)

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




  def  _UpperCAmelCase  (  self		,		*, _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Dict:
     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(
            _UpperCAmelCase		,		-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(_UpperCAmelCase			)
     UpperCamelCase_						=  self.clip_image_embeddings_project_to_time_embeddings(_UpperCAmelCase			)
     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(_UpperCAmelCase			)
     UpperCamelCase_						=  clip_extra_context_tokens.reshape(_UpperCAmelCase		,		-1		,		self.clip_extra_context_tokens			)
     UpperCamelCase_						=  clip_extra_context_tokens.permute(0		,		2		,		1			)

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

     return text_encoder_hidden_states, additive_clip_time_embeddings













import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  MobileBertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  MobileBertForPreTraining(__lowercase)
   # Load weights from tf checkpoint
   UpperCamelCase_						=  load_tf_weights_in_mobilebert(__lowercase     ,   __lowercase     ,   __lowercase)
   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict()     ,   __lowercase)


if __name__ == "__main__":
 snake_case__   :						int													=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--mobilebert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained MobileBERT model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 snake_case__   :						Optional[Any]													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





