



import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


__lowerCamelCase  				=					logging.get_logger(__name__)  # pylint: disable=invalid-name




def     UpperCAmelCase__ (							self						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> int:
  _A           =    image.to(device=__snake_case							)
  _A           =    image  # take image as image_embeddings
  _A           =    image_embeddings.unsqueeze(1							)

  # duplicate image embeddings for each generation per prompt, using mps friendly method
  _A    ,   _A    ,   _A           =    image_embeddings.shape
  _A           =    image_embeddings.repeat(1						,				__snake_case						,				1							)
  _A           =    image_embeddings.view(bs_embed * num_images_per_prompt						,				__snake_case						,				-1							)

  if do_classifier_free_guidance:
    _A           =    torch.zeros_like(__snake_case							)

    # For classifier free guidance, we need to do two forward passes.
    # Here we concatenate the unconditional and text embeddings into a single batch
    # to avoid doing two forward passes
    _A           =    torch.cat([uncond_embeddings, image_embeddings]							)

  return image_embeddings



class 		_snake_case      (			lowerCamelCase				):





 """simple docstring"""



 def __init__(						self	,    a	,    a	,    a	,    a	,    a = None	,    )	->							int:




   """simple docstring"""







   super().__init__()

   _A           =    {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

   _A           =    decoder_pipe_kwargs.get('''torch_dtype'''	,    a					) or prior.dtype

   _A           =    StableDiffusionImageVariationPipeline.from_pretrained(
       '''lambdalabs/sd-image-variations-diffusers'''	,    **a					)

   # replace `_encode_image` method
   _A           =    types.MethodType(_encode_image	,    self.decoder_pipe					)

   self.register_modules(
       prior=a	,    tokenizer=a	,    text_encoder=a	,    prior_scheduler=a	,    )
 def lowercase_		(						self	,    a	,    a	,    a	,    a	,    a = None	,    a = None	,    )	->							Tuple:




   """simple docstring"""







   if text_model_output is None:
     _A           =    len(a					) if isinstance(a	,    a					) else 1
     # get prompt text embeddings
     _A           =    self.tokenizer(
         a	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    return_tensors='''pt'''	,    )
     _A           =    text_inputs.input_ids
     _A           =    text_inputs.attention_mask.bool().to(a					)

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
       _A           =    self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
       logger.warning(
           '''The following part of your input was truncated because CLIP can only handle sequences up to'''
           f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''					)
       _A           =    text_input_ids[:, : self.tokenizer.model_max_length]

     _A           =    self.text_encoder(text_input_ids.to(a					)					)

     _A           =    text_encoder_output.text_embeds
     _A           =    text_encoder_output.last_hidden_state

   else:
     _A           =    text_model_output[0].shape[0]
     _A    ,   _A           =    text_model_output[0], text_model_output[1]
     _A           =    text_attention_mask

   _A           =    text_embeddings.repeat_interleave(a	,    dim=0					)
   _A           =    text_encoder_hidden_states.repeat_interleave(a	,    dim=0					)
   _A           =    text_mask.repeat_interleave(a	,    dim=0					)

   if do_classifier_free_guidance:
     _A           =    [''''''] * batch_size

     _A           =    self.tokenizer(
         a	,    padding='''max_length'''	,    max_length=self.tokenizer.model_max_length	,    truncation=a	,    return_tensors='''pt'''	,    )
     _A           =    uncond_input.attention_mask.bool().to(a					)
     _A           =    self.text_encoder(uncond_input.input_ids.to(a					)					)

     _A           =    uncond_embeddings_text_encoder_output.text_embeds
     _A           =    uncond_embeddings_text_encoder_output.last_hidden_state

     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

     _A           =    uncond_embeddings.shape[1]
     _A           =    uncond_embeddings.repeat(1	,    a					)
     _A           =    uncond_embeddings.view(batch_size * num_images_per_prompt	,    a					)

     _A           =    uncond_text_encoder_hidden_states.shape[1]
     _A           =    uncond_text_encoder_hidden_states.repeat(1	,    a	,    1					)
     _A           =    uncond_text_encoder_hidden_states.view(
         batch_size * num_images_per_prompt	,    a	,    -1					)
     _A           =    uncond_text_mask.repeat_interleave(a	,    dim=0					)

     # done duplicates

     # For classifier free guidance, we need to do two forward passes.
     # Here we concatenate the unconditional and text embeddings into a single batch
     # to avoid doing two forward passes
     _A           =    torch.cat([uncond_embeddings, text_embeddings]					)
     _A           =    torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)

     _A           =    torch.cat([uncond_text_mask, text_mask]					)

   return text_embeddings, text_encoder_hidden_states, text_mask
 @property
 def lowercase_		(						self					)	->							str:




   """simple docstring"""







   if self.device != torch.device('''meta'''					) or not hasattr(self.prior	,    '''_hf_hook'''					):
     return self.device
   for module in self.prior.modules():
     if (
         hasattr(a	,    '''_hf_hook'''					)
         and hasattr(module._hf_hook	,    '''execution_device'''					)
         and module._hf_hook.execution_device is not None
     ):
       return torch.device(module._hf_hook.execution_device					)
   return self.device
 def lowercase_		(						self	,    a	,    a	,    a	,    a	,    a	,    a					)	->							Union[str, Any]:




   """simple docstring"""







   if latents is None:
     _A           =    randn_tensor(a	,    generator=a	,    device=a	,    dtype=a					)
   else:
     if latents.shape != shape:
       raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
     _A           =    latents.to(a					)

   _A           =    latents * scheduler.init_noise_sigma
   return latents
 def lowercase_		(						self	,    a = None					)	->							List[str]:




   """simple docstring"""







   self.decoder_pipe.to(a					)
   super().to(a					)



 @torch.no_grad()
 def __call__(						self	,    a = None	,    a = None	,    a = None	,    a = 1	,    a = 2_5	,    a = None	,    a = None	,    a = None	,    a = None	,    a = 4.0	,    a = 8.0	,    a = 5_0	,    a = 1	,    a = 0.0	,    a = "pil"	,    a = True	,    )	->							List[str]:




   """simple docstring"""







   if prompt is not None:
     if isinstance(a	,    a					):
       _A           =    1
     elif isinstance(a	,    a					):
       _A           =    len(a					)
     else:
       raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(a					)}'''					)
   else:
     _A           =    text_model_output[0].shape[0]

   _A           =    self._execution_device

   _A           =    batch_size * num_images_per_prompt

   _A           =    prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

   _A    ,   _A    ,   _A           =    self._encode_prompt(
       a	,    a	,    a	,    a	,    a	,    a					)

   # prior

   self.prior_scheduler.set_timesteps(a	,    device=a					)
   _A           =    self.prior_scheduler.timesteps

   _A           =    self.prior.config.embedding_dim

   _A           =    self.prepare_latents(
       (batch_size, embedding_dim)	,    text_embeddings.dtype	,    a	,    a	,    a	,    self.prior_scheduler	,    )

   for i, t in enumerate(self.progress_bar(a					)					):
     # expand the latents if we are doing classifier free guidance
     _A           =    torch.cat([prior_latents] * 2					) if do_classifier_free_guidance else prior_latents

     _A           =    self.prior(
         a	,    timestep=a	,    proj_embedding=a	,    encoder_hidden_states=a	,    attention_mask=a	,    ).predicted_image_embedding

     if do_classifier_free_guidance:
       _A    ,   _A           =    predicted_image_embedding.chunk(2					)
       _A           =    predicted_image_embedding_uncond + prior_guidance_scale * (
           predicted_image_embedding_text - predicted_image_embedding_uncond
       )

     if i + 1 == prior_timesteps_tensor.shape[0]:
       _A           =    None
     else:
       _A           =    prior_timesteps_tensor[i + 1]

     _A           =    self.prior_scheduler.step(
         a	,    timestep=a	,    sample=a	,    generator=a	,    prev_timestep=a	,    ).prev_sample

   _A           =    self.prior.post_process_latents(a					)

   _A           =    prior_latents

   _A           =    self.decoder_pipe(
       image=a	,    height=a	,    width=a	,    num_inference_steps=a	,    guidance_scale=a	,    generator=a	,    output_type=a	,    return_dict=a	,    num_images_per_prompt=a	,    eta=a	,    )
   return output




import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()




def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Tuple:
  # Initialise PyTorch model
  _A           =    ElectraConfig.from_json_file(__snake_case							)
  print(F'''Building PyTorch model from configuration: {config}'''							)

  if discriminator_or_generator == "discriminator":
    _A           =    ElectraForPreTraining(__snake_case							)
  elif discriminator_or_generator == "generator":
    _A           =    ElectraForMaskedLM(__snake_case							)
  else:
    raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''							)

  # Load weights from tf checkpoint
  load_tf_weights_in_electra(
      __snake_case						,				__snake_case						,				__snake_case						,				discriminator_or_generator=__snake_case							)

  # Save pytorch-model
  print(F'''Save PyTorch model to {pytorch_dump_path}'''							)
  torch.save(model.state_dict()						,				__snake_case							)


if __name__ == "__main__":
    __lowerCamelCase  				=					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(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--discriminator_or_generator""",
        default=None,
        type=str,
        required=True,
        help=(
            """Whether to export the generator or the discriminator. Should be a string, either 'discriminator' or """
            """'generator'."""
        ),
    )
    __lowerCamelCase  				=					parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
    )